System

Abstract

To provide a system that can suggest crops or varieties taking an environment condition of a soil and an emotion of a worker into account.SOLUTION: A system includes: a sensor that acquires environment data consisting of a humidity, temperature, sunshine quantity, and a soil moisture content of a soil in which crops are to be cultivated; means of analyzing an expression or behavior of a worker by an emotion engine, and thereby recognizing an emotion state of the worker; means of generating a prompt sentence instructing a generation of a suggestion of crops or varieties suitable for a state of the soil and the emotion state of the worker on the basis of the environment data acquired by the sensor, and the recognized emotion state of the worker; means of generating a suggestion of crops or varieties suitable for a state of the soil and the emotion state of the worker using the generated prompt sentence and a generative AI; and means of notifying the suggestion of the crop or variety suitable for the state of the soil and the emotion state of the worker outputted from the generative AI.SELECTED DRAWING: Figure 1

Description

[Technical Field]

[0001] The technology of the present disclosure relates to a system. [Background technology]

[0002] Patent document 1 discloses a persona chatbot control method performed by at least one processor, the method including the steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to a description of the chatbot character, encoding the prompt, and inputting the encoded prompt into a language model to generate a chatbot utterance in response to the user utterance. [Prior art documents] [Patent documents]

[0003] [Patent Document 1] Japanese Patent Publication No. 2022-180282 Summary of the Invention [Problem to be solved by the invention]

[0004] The aging and declining agricultural workforce has raised concerns about the stable supply of agricultural products. Furthermore, the environment for maximizing agricultural profits, such as selecting optimal crops and varieties, managing watering and fertilizer, and adjusting shipping timing to match market prices, is not yet in place. [Means for solving the problem]

[0005] Based on data obtained from various sensors, the company will provide a system that uses generative AI to suggest optimal crops and varieties to cultivate, automates optimal watering for crops, suggests the type of fertilizer and timing of application, automates sunlight and temperature adjustments to allow shipments at times when market prices are high, and provides sales support, thereby creating an environment that maximizes profits for farmers. [Brief explanation of the drawings]

[0006] [Figure 1] 1 is a conceptual diagram showing an example of the configuration of a data processing system according to a first embodiment. [Figure 2] 1 is a conceptual diagram showing an example of main functions of a data processing device and a smart device according to a first embodiment. [Figure 3] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a second embodiment. [Figure 4] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and smart glasses according to a second embodiment. [Figure 5] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a third embodiment. [Figure 6] FIG. 11 is a conceptual diagram showing an example of main functions of a data processing device and a headset-type terminal according to a third embodiment. [Figure 7] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a fourth embodiment. [Figure 8] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and a robot according to a fourth embodiment. [Figure 9] 1 shows an emotion map onto which multiple emotions are mapped. [Figure 10] 1 shows an emotion map onto which multiple emotions are mapped. [Figure 11] FIG. 2 is a sequence diagram showing a flow of processing in the data processing system according to the first embodiment of the first form example. [Figure 12] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in Application Example 1 of Embodiment 1. [Figure 13] FIG. 10 is a sequence diagram showing a processing flow of a data processing system in a second embodiment of the second form example. [Figure 14] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in Application Example 2 of Embodiment Example 2. [Figure 15] FIG. 10 is a sequence diagram showing the flow of processing in a data processing system according to a third embodiment of the third embodiment. [Figure 16] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in Application Example 3 of Embodiment 3. [Figure 17] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in the first embodiment of the first form example when an emotion engine is combined. [Figure 18] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in Application Example 1 of Embodiment 1 when an emotion engine is combined. [Figure 19] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in the second embodiment of the second form example when an emotion engine is combined. [Figure 20] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in Application Example 2 of Form Example 2 when an emotion engine is combined. [Figure 21] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in the third embodiment of the third form example when an emotion engine is combined. [Figure 22] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in Application Example 3 of Form Example 3 when an emotion engine is combined. DETAILED DESCRIPTION OF THE INVENTION

[0007] An example of an embodiment of a system according to the technology of the present disclosure will be described below with reference to the accompanying drawings.

[0008] First, the terms used in the following description will be explained.

[0009] In the following embodiments, a coded processor (hereinafter simply referred to as a "processor") may be a single arithmetic device or a combination of multiple arithmetic devices. Furthermore, the processor may be a single type of arithmetic device or a combination of multiple types of arithmetic devices. Examples of arithmetic devices include a CPU (Central Processing Unit), a GPU (Graphics Processing Unit), a GPGPU (General-Purpose computing on Graphics Processing Units), an APU (Accelerated Processing Unit), or a TPU (TENSOR PROCESSING UNIT (registered trademark)).

[0010] In the following embodiments, a coded RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a working memory by a processor.

[0011] In the following embodiments, the coded storage is one or more non-volatile storage devices that store various programs, various parameters, etc. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), and magnetic tapes.

[0012] In the following embodiments, a communication I / F (Interface) with a symbol is an interface including a communication processor, an antenna, etc. The communication I / F controls communication between multiple computers. Examples of communication standards applied to the communication I / F include wireless communication standards including 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), Bluetooth (registered trademark), etc.

[0013] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." In other words, "A and / or B" means that it may be only A, only B, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" is also applied when three or more things are expressed connected by "and / or."

[0014] [First embodiment]

[0015] FIG. 1 shows an example of the configuration of a data processing system 10 according to the first embodiment.

[0016] 1, a data processing system 10 includes a data processing device 12 and a smart device 14. An example of the data processing device 12 is a server.

[0017] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0018] The smart device 14 includes a computer 36, a reception device 38, an output device 40, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The reception device 38, the output device 40, and the camera 42 are also connected to the bus 52.

[0019] The reception device 38 includes a touch panel 38A, a microphone 38B, and the like, and receives user input. The touch panel 38A detects contact with an indicator (for example, a pen or a finger) to receive user input by the touch of the indicator. The microphone 38B detects the user's voice to receive user input by voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the data indicating the user input.

[0020] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user 20 by outputting the data in a form of expression that the user 20 can perceive (for example, audio and / or text). The display 40A displays visible information such as text and images in accordance with instructions from the processor 46. The speaker 40B outputs audio in accordance with instructions from the processor 46. The camera 42 is a compact digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0021] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 control the exchange of various information between the processor 46 and the processor 28 via the network 54.

[0022] FIG. 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0023] 2, in the data processing device 12, a specific process is performed by the processor 28. A specific processing program 56 is stored in the storage 32. The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific process is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0024] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0025] In the smart device 14, the processor 46 performs the reception output process. The storage 50 stores a reception output program 60. The reception output program 60 is used in conjunction with the specific processing program 56 by the data processing system 10. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0026] Next, the specific processing by the specific processing unit 290 of the data processing device 12 will be described.

[0027] "Example 1"

[0028] This invention is a system that uses a generative AI to propose optimal crops and varieties for cultivation based on data acquired from various sensors. Specifically, data such as soil humidity, temperature, and sunshine hours are acquired, and the generative AI proposes optimal crops and varieties based on this data. For example, if the soil humidity is high and the sunshine hours are long, the generative AI will propose rice cultivation.

[0029] "Example 2"

[0030] Another aspect of the present invention is a system that automates optimal watering for crops. Specifically, the AI ​​automatically adjusts the timing and amount of watering based on soil humidity data acquired from sensors. For example, if the soil humidity drops, the AI ​​automatically waters the crops.

[0031] "Example 3"

[0032] Furthermore, one aspect of the present invention is a system that automates the adjustment of sunlight and temperature so that crops can be shipped at a time when market prices are high. Specifically, the generation AI automatically adjusts sunlight and temperature based on market price data and the maturity of the crops, allowing crops to be harvested at a time when market prices are high. For example, the generation AI adjusts sunlight and temperature to coincide with the period when the market price of tomatoes is high, allowing tomatoes to be harvested at that time.

[0033] The processing flow of each embodiment will be described below.

[0034] "Example 1"

[0035] Step 1: Obtain data such as soil moisture, temperature, and sunlight hours from various sensors.

[0036] Step 2: Input the acquired data into the generation AI.

[0037] Step 3: The AI ​​analyzes the data and suggests the best crop and variety. For example, if the soil moisture is high and the sunshine hours are long, the AI ​​will suggest growing rice.

[0038] "Example 2"

[0039] Step 1: Obtain soil moisture data from the sensor.

[0040] Step 2: Input the acquired humidity data into the generation AI.

[0041] Step 3: The AI ​​analyzes the humidity data and automatically adjusts the timing and amount of watering. For example, if the soil humidity drops, the AI ​​will automatically water the plants.

[0042] "Example 3"

[0043] Step 1: Generative AI automatically adjusts the amount of sunlight and temperature based on market price data and crop maturity.

[0044] Step 2: The AI ​​adjusts the amount of sunlight and temperature so that crops can be harvested at a time when market prices are high. For example, when the market price of tomatoes is high, the AI ​​adjusts the amount of sunlight and temperature so that tomatoes can be harvested at that time.

[0045] Example 1

[0046] Next, a description will be given of Example 1 of Form Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0047] With conventional agricultural systems, it was difficult to effectively utilize data acquired from various sensors to propose optimal crops and varieties and manage cultivation.It was also difficult to adjust shipping timing in response to fluctuations in market prices, which made it difficult to maximize profits.

[0048] The identification processing by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means. In this invention, the server includes a means for proposing optimal crops and varieties to cultivate based on data acquired from various sensors, a means for preprocessing the collected data, a means for analyzing the preprocessed data, a means for inputting a prompt statement to the generative AI model based on the analysis results, a means for acquiring a proposal from the generative AI model, and a means for displaying the acquired proposal to the user. This makes it possible to effectively utilize data acquired from various sensors to propose optimal crops and varieties and manage cultivation. In addition, it is possible to adjust shipping timing in response to fluctuations in market prices, thereby maximizing profits.

[0049] "Various sensors" are devices used to acquire environmental data on agricultural land, such as soil moisture sensors, temperature sensors, and sunlight sensors.

[0050] The "means for preprocessing data" is a function for performing processing to remove outliers from collected data and normalize the data.

[0051] "Means for analyzing data" refers to a function that performs statistical analysis and calculates average values ​​based on preprocessed data.

[0052] A "generative AI model" is an artificial intelligence model that generates optimal crop and variety recommendations based on input prompt statements.

[0053] A "prompt sentence" is an instruction sentence to be input into a generative AI model, and is a sentence that contains the analysis results.

[0054] The "means for obtaining proposals" is a function for receiving proposals generated from a generative AI model.

[0055] The "means for displaying proposals to the user" is a function for displaying the acquired proposals on the user's terminal.

[0056] "Automated sunlight and temperature adjustment" is a function that automatically adjusts sunlight and temperature based on data from environmental sensors.

[0057] This invention is a system that uses a generative AI model to propose optimal crops and varieties for cultivation based on data acquired from various sensors. Specifically, data such as soil humidity, temperature, and sunlight hours are acquired, and the generative AI model proposes optimal crops and varieties based on this data.

[0058] Hardware and software used

[0059] The server collects data from various sensors (e.g., soil moisture sensors, temperature sensors, and sunlight sensors). These sensors are installed in the farmland and provide data in real time. The server uses Python libraries such as Pandas and NumPy to preprocess and analyze the data. The generative AI model used is, for example, GPT-4 (registered trademark) from OpenAI (registered trademark).

[0060] The terminal is a device for displaying suggestions to the user, such as a smartphone or tablet.

[0061] Data collection and preprocessing

[0062] The server collects data from various sensors every day at 9:00 AM. For example, the soil humidity sensor gives data such as "Humidity: 70%," the temperature sensor gives data such as "Temperature: 25°C," and the sunshine sensor gives data such as "Sunshine hours: 8 hours." The collected data is then normalized and outliers are removed. For example, if an outlier such as "Humidity: 150%" is obtained from the soil humidity sensor, that data is removed. Temperature data is also normalized by converting it from Celsius to Fahrenheit.

[0063] Data analysis and the use of generative AI models

[0064] The server performs statistical analysis based on the preprocessed data. For example, if the soil humidity data for the past week was "65%, 70%, 75%, 70%, 68%, 72%, and 71%, the average value is calculated and set to "humidity: 70%." Based on the analysis results, a prompt is input to the generative AI model. The prompt includes the analysis results. For example, it could be "Please suggest the optimal crop and variety if the soil humidity is 70%, the temperature is 25 degrees, and there are 8 hours of sunshine."

[0065] Generate and view suggestions

[0066] The generative AI model generates a suggestion, "Rice cultivation is optimal," based on the prompt text. This suggestion is returned to the server. The server then sends the generated suggestion to the device. The device then displays "Rice cultivation is optimal" to the user. The user can view this suggestion on the screen of their smartphone or tablet.

[0067] Specific examples

[0068] Prompt Sentence Examples

[0069] "Please suggest the best crop and variety for a soil with 70% moisture, 25°C temperature, and 8 hours of sunshine."

[0070] Example proposal

[0071] "Rice cultivation is the best."

[0072] In this way, users can receive real-time recommendations on the best crops and varieties to suit the conditions of their farmland. They can also adjust shipping timing in response to fluctuations in market prices, maximizing profits.

[0073] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0074] Step 1: Data collection

[0075] The server collects data from various sensors. Specifically, soil humidity sensors, temperature sensors, sunlight sensors, etc. are installed on the farmland, and data is obtained from these sensors in real time. For example, the soil humidity sensor obtains data such as "humidity: 70%," the temperature sensor obtains data such as "temperature: 25 degrees," and the sunlight sensor obtains data such as "sunshine hours: 8 hours." The input is data from the various sensors, and the output is the collected raw data.

[0076] Step 2: Preprocessing the data

[0077] The server preprocesses the collected data. Specifically, it removes outliers and normalizes the data. For example, if an abnormal value such as "Humidity: 150%" is obtained from a soil humidity sensor, the data is removed. It also performs normalization such as converting temperature data from Celsius to Fahrenheit. The input is the collected raw data, and the output is the preprocessed data.

[0078] Step 3: Analyze the data

[0079] The server analyzes the preprocessed data. For the analysis, it uses Python libraries such as Pandas and NumPy. Specifically, it calculates the average and standard deviation of soil humidity, temperature, and sunshine hours. For example, it calculates the average soil humidity based on data from the past week. The input is the preprocessed data, and the output is the analysis results.

[0080] Step 4: Using the generative AI model

[0081] The server inputs a prompt statement to a generative AI model (for example, OpenAI's GPT-4) based on the analysis results. The prompt statement includes the analysis results. For example, it could say, "Please suggest the optimal crop and variety when the soil humidity is 70%, the temperature is 25 degrees, and the sunshine hours are 8 hours." The input is the analysis result and the prompt statement, and the output is the input to the generative AI model.

[0082] Step 5: Generate proposals

[0083] The generative AI model suggests the best crop and variety based on the input prompt. For example, it generates a suggestion such as "Rice cultivation is best." The generated suggestion is returned to the server. The input is the prompt, and the output is the generated suggestion.

[0084] Step 6: Viewing Proposals

[0085] The server sends the generated proposal to the terminal. The terminal displays the proposal to the user. For example, "Rice cultivation is optimal" may be displayed on the screen of a smartphone or tablet. The input is the generated proposal, and the output is the proposal displayed to the user.

[0086] (Application example 1)

[0087] Next, a description will be given of Application Example 1 of Embodiment Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0088] Conventional plant cultivation systems can suggest optimal crops and varieties based on data obtained from various sensors, but in automated plant cultivation systems within factories, it is difficult to integrate the suggestion of optimal crops and varieties with the automation of the cultivation process. In addition, it is not possible to issue instructions to maintain optimal environmental conditions based on the suggested crops, making efficient cultivation difficult.

[0089] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[0090] In this invention, the server includes: means for proposing the optimal crop and variety to cultivate based on data acquired from various sensors; means for automating optimal watering for the crops; means for proposing the type of fertilizer to apply to the soil and the timing of fertilizer application based on the environmental condition of the soil; means for automating the amount of sunlight and temperature adjustment so that products can be shipped at a time when market prices are high; means for providing sales support; means for collecting data from various sensors installed in the plant cultivation area within the factory and proposing the optimal crop and variety using generation AI; and means for automating the cultivation process based on the proposed crop and issuing instructions for maintaining optimal environmental conditions. This enables the automated plant cultivation system within the factory to comprehensively propose the optimal crop and variety and automate the cultivation process.

[0091] "Various sensors" are devices for acquiring environmental data, such as humidity sensors, temperature sensors, and light sensors.

[0092] "Data" refers to information such as humidity, temperature, and hours of sunlight obtained from various sensors.

[0093] "Generative AI" is a system that uses artificial intelligence technology to suggest optimal crops and varieties based on input data.

[0094] "Crop and variety proposal" refers to the generative AI analyzing data obtained from various sensors and selecting the most suitable crop and its variety.

[0095] "Automated watering" is a system that automatically supplies the necessary water to crops.

[0096] "Fertilizer type" refers to the type of nutrients used to promote crop growth.

[0097] "Application timing" refers to the optimal time or period to apply fertilizer or water to crops.

[0098] "Automated sunlight and temperature adjustment" is a system that automatically adjusts the amount of sunlight and temperature to suit crop growth.

[0099] "Sales support" is a system that supports sales strategies based on market prices and demand for crops.

[0100] A "plant cultivation area" is a specific area within a factory set aside for growing plants.

[0101] "Cultivation process automation" refers to a system that automatically performs a series of tasks related to crop cultivation.

[0102] "Environmental conditions" are factors such as humidity, temperature, and hours of sunlight that affect crop growth.

[0103] "Instruct" refers to issuing instructions for the system to take any action or adjustment necessary to maintain optimal environmental conditions.

[0104] The system for implementing this invention collects data from various sensors installed in the plant cultivation area within the factory, uses generative AI to propose optimal crops and varieties, and automates the cultivation process. A specific embodiment of this system is shown below.

[0105] The server collects data from various sensors, including humidity sensors, temperature sensors, and light sensors, which are installed in the plant cultivation area and obtain environmental data such as soil humidity, temperature, and sunlight hours in real time.

[0106] The collected data is sent to a server and input into a generative AI model. The generative AI model analyzes this data and suggests the optimal crop and variety. For example, if the soil humidity is 70%, the temperature is 25 degrees, and the sunshine hours are 10 hours, the generative AI model will suggest "rice" as the optimal crop.

[0107] Based on the proposed crop, the server issues instructions to automate the cultivation process, such as determining the optimal timing and amount of watering, the type of fertilizer and when to apply it, and adjusting the amount of sunlight and temperature, thereby maintaining optimal environmental conditions and enabling efficient cultivation.

[0108] The server also collects market price data and suggests optimal shipping times, adjusting the amount of sunlight and temperature to control crop growth so that products can be shipped at a time when market prices are high.

[0109] For example, by inputting the following prompt into the generative AI model, you can receive suggestions for the optimal crop and variety:

[0110] "What is the best crop / variety to grow if the soil moisture is 70%, the temperature is 25°C, and there are 10 hours of sunshine?"

[0111] In this way, the server uses a generative AI model to suggest optimal crops and varieties based on data from various sensors, automating the cultivation process and achieving efficient plant cultivation.

[0112] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0113] Step 1:

[0114] The server collects data from various sensors, such as humidity sensors, temperature sensors, and light sensors. These sensors are installed in the plant cultivation area and obtain environmental data such as soil humidity, temperature, and sunlight hours in real time. The input is the environmental data from the various sensors, and the output is the collected data.

[0115] Step 2:

[0116] The server inputs the collected data into the generative AI model, which analyzes this data and proposes the optimal crop and variety. The input is the collected environmental data, and the output is the optimal crop and variety proposed by the generative AI model. Specifically, the server generates a prompt sentence and sends it to the generative AI model.

[0117] Step 3:

[0118] The server receives suggestions from the generative AI model and issues instructions to automate the cultivation process based on the optimal crop and variety. The input is the suggestions from the generative AI model, and the output is instructions for the automated cultivation process. Specifically, the server automatically sets the timing and amount of watering, the type of fertilizer and when to apply it, and adjusts the amount of sunlight and temperature.

[0119] Step 4:

[0120] The server collects market price data and proposes the optimal shipping timing. The input is market price data, and the output is a proposal for the optimal shipping timing. Specifically, the server accesses a market price database and obtains the current market price.

[0121] Step 5:

[0122] The server adjusts the amount of sunlight and temperature based on the optimal shipping timing to control crop growth. The input is the proposal for the optimal shipping timing, and the output is the adjusted amount of sunlight and temperature. Specifically, the server sends control signals to adjust the amount of sunlight and temperature.

[0123] Step 6:

[0124] The user checks the suggestions and instructions from the server and makes manual adjustments as necessary. The input is the suggestions and instructions from the server, and the output is the result of manual adjustments made by the user. Specifically, the user checks the server settings through the management screen and makes changes as necessary.

[0125] Example 2

[0126] Next, a description will be given of Example 2 of Form Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0127] Conventional agricultural systems require manual operations such as watering crops, spreading fertilizer, and adjusting sunlight and temperature, which is inefficient and often results in missing the optimal timing. It is also difficult to plan shipments at times when market prices are high. This leads to problems such as reduced crop quality and yields, and a decline in agricultural management efficiency.

[0128] The specific processing by the specific processing unit 290 of the data processing device 12 in Example 2 is realized by the following means. In this invention, the server includes a means for proposing the optimal crop and variety to cultivate based on data acquired from various sensors, a means for automatically adjusting the timing and amount of watering using a generative AI model based on humidity data acquired from a soil humidity sensor, a means for proposing the type of fertilizer and the timing of spraying, a means for automating the adjustment of sunlight and temperature so that products can be shipped at a time when market prices are high, and a means for providing sales support. This automates the entire process from crop cultivation to shipping, enabling efficient and optimal agricultural management.

[0129] "Various sensors" are devices used to acquire data on the crop cultivation environment, such as soil moisture sensors, temperature sensors, and sunlight sensors.

[0130] A "soil moisture sensor" is a device for measuring soil moisture and acquiring that data.

[0131] A "generative AI model" is an algorithm that uses artificial intelligence technology to analyze data and calculate the optimal timing and amount of watering.

[0132] The "means for automatically adjusting the timing and amount of watering" is a system that uses a generative AI model to determine the start time of watering and the amount of water to supply based on data obtained from a soil moisture sensor, and then automatically executes the process.

[0133] The "means for proposing the type of fertilizer and the timing of application" is a system that suggests the optimal type of fertilizer and the timing of its application based on the growth stage of the crop and environmental data.

[0134] The "means for automating sunlight and temperature adjustment" is a system that uses a generative AI model to optimally adjust sunlight and temperature based on data obtained from sunlight and temperature sensors.

[0135] "Sales support means" refers to a system that proposes optimal shipping times and sales strategies based on market prices and demand forecast data.

[0136] This invention is a system that automates optimal watering for crops. Specifically, a generative AI model automatically adjusts the timing and amount of watering based on soil moisture data obtained from sensors.

[0137] Hardware and software used

[0138] The server uses the following hardware and software to collect and analyze data:

[0139] Soil moisture sensor (e.g. DHT11, DHT22)

[0140] Temperature sensor, sunlight sensor

[0141] Generative AI models (e.g., OpenAI's GPT-4)

[0142] Communication modules (e.g. Wi-Fi, LoRa)

[0143] Watering systems (e.g. solenoid valves connected to computer control circuits)

[0144] The terminal uses the following hardware and software to collect data from the sensors and send it to the server:

[0145] soil moisture sensor

[0146] Communication Module

[0147] Computer Control Circuit

[0148] Data processing and calculation

[0149] The device collects data from the soil moisture sensor in real time and sends it to the server using wireless communication technologies such as Wi-Fi or LoRa. Specifically, the device converts the humidity data into JSON format and sends it to the server using an HTTP POST request.

[0150] The server inputs the received soil moisture data into the generative AI model. The generative AI model analyzes the soil moisture data and calculates the optimal timing and amount of watering. Specifically, the server converts the soil moisture data into a prompt sentence and sends it to the generative AI model. For example, it generates a prompt sentence such as, "The current soil moisture level is 30%. Please tell me the optimal timing and amount of watering."

[0151] The server generates watering instructions based on the output from the generative AI model. The instructions include when to start watering and how much water to supply. Specifically, it analyzes the output from the generative AI model and generates the instruction "Start watering and supply 500ml of water."

[0152] The server sends the generated watering instructions to the device using wireless communication technologies such as Wi-Fi or LoRa. Specifically, the server converts the watering instructions into JSON format and sends them to the device using an HTTP POST request.

[0153] The terminal carries out watering based on the received watering instructions. Specifically, it controls a solenoid valve connected to a computer control circuit to supply the required amount of water to the crops. Specifically, the terminal opens the solenoid valve and supplies 500 ml of water. When watering is complete, it closes the solenoid valve.

[0154] Specific examples

[0155] For example, if the soil moisture content drops below 30%, the generative AI model generates the instruction to "start watering and supply 500 ml of water." The server receives this instruction and sends it to the terminal, which then opens the solenoid valve and supplies 500 ml of water to the crops.

[0156] Example prompts to input to the generative AI model

[0157] "The current soil moisture level is 30%. What is the best time and amount to water?"

[0158] The system allows users to automate the watering of their crops and maintain an optimal environment.

[0159] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0160] Step 1:

[0161] The terminal collects soil moisture data using a soil moisture sensor. The sensor is inserted into the soil and measures the soil moisture at regular intervals (for example, every hour). The input is soil moisture data, and the output is the collected soil moisture data. Specifically, the sensor acquires the soil moisture data as an analog signal, which the terminal converts into digital data.

[0162] Step 2:

[0163] The terminal sends the collected soil moisture data to a server. Communication is performed using wireless communication technologies such as Wi-Fi or LoRa. The input is the collected humidity data, and the output is the humidity data sent to the server. Specifically, the terminal converts the humidity data into JSON format and sends it to the server using an HTTP POST request.

[0164] Step 3:

[0165] The server inputs the received soil moisture data into the generative AI model. The generative AI model analyzes the soil moisture data and calculates the optimal timing and amount of watering. The input is the humidity data sent to the server, and the output is the analysis result from the generative AI model. Specifically, the server converts the soil moisture data into a prompt sentence and sends it to the generative AI model. For example, it generates a prompt sentence such as, "The current soil moisture level is 30%. Please tell me the optimal timing and amount of watering."

[0166] Step 4:

[0167] The server generates watering instructions based on the output from the generative AI model. The instructions include when to start watering and the amount of water to supply. The input is the analysis result from the generative AI model, and the output is the generated watering instructions. Specifically, the server analyzes the output from the generative AI model and generates the instruction to "start watering and supply 500ml of water."

[0168] Step 5:

[0169] The server sends the generated watering instructions to the device. Communication is performed using wireless communication technologies such as Wi-Fi or LoRa. The input is the generated watering instructions, and the output is the watering instructions sent to the device. Specifically, the server converts the watering instructions into JSON format and sends them to the device using an HTTP POST request.

[0170] Step 6:

[0171] The terminal carries out watering based on the received watering instructions. Specifically, it controls a solenoid valve connected to a computer control circuit to supply the required amount of water to the crops. The input is the watering instruction sent to the terminal, and the output is the executed watering. Specifically, the terminal opens the solenoid valve and supplies 500 ml of water. When watering is complete, it closes the solenoid valve.

[0172] (Application example 2)

[0173] Next, a description will be given of Application Example 2 of Form Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0174] Conventional plant cultivation systems require manual management of soil moisture and watering timing, making efficient management difficult. Furthermore, because environmental conditions vary when cultivating plants within factories, a system that automatically adjusts the appropriate watering timing and amount was required.

[0175] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[0176] In this invention, the server includes a means for proposing the optimal crop and variety to cultivate based on data obtained from various sensors, a means for automating the optimal watering for the crops, a means for proposing the type of fertilizer and the timing of application, a means for automating the amount of sunlight and temperature adjustment so that the crops can be shipped at a time when the market price is high, a means for providing sales support, and a means installed on a robot that grows plants in a factory, where a generating AI automatically adjusts the timing and amount of watering based on soil moisture data obtained from the sensors.This enables efficient and automatic plant growth management.

[0177] "Various sensors" are devices used to acquire environmental data such as soil humidity, temperature, amount of sunlight, and soil moisture.

[0178] The "means for proposing the optimal crops and varieties to cultivate" is a function that analyzes the acquired data and selects and proposes the optimal crops and varieties.

[0179] "Automated watering method optimal for crops" is a function that automatically waters crops at the appropriate time and in the appropriate amount based on soil moisture data.

[0180] The "means for suggesting the type of fertilizer and the timing of application" is a function for suggesting the type of fertilizer required for crop growth and the timing of application.

[0181] "Automated means for adjusting sunlight and temperature" is a function for automatically adjusting the amount of sunlight and temperature to suit crop growth.

[0182] "Sales support means" is a function for supporting sales strategies based on the market price and demand of crops.

[0183] A "factory plant growing robot" is an automated device designed to grow plants in a factory.

[0184] "Generative AI" is an artificial intelligence technology that analyzes acquired data and predicts and executes optimal actions.

[0185] "Soil moisture content data" is data that indicates the amount of moisture in the soil.

[0186] The "means for automatically adjusting the timing and amount of watering" is a function for watering at the appropriate timing and amount based on soil moisture data.

[0187] The system for implementing this invention includes various sensors, a generative AI model, and a plant growing robot. The specific configuration and operation of the system are described below.

[0188] System configuration

[0189] 1. Various sensors

[0190] The various sensors are devices for acquiring environmental data such as soil moisture content, temperature, and amount of sunlight. Specifically, they include soil moisture sensors, temperature sensors, and light sensors.

[0191] 2. Generative AI Models

[0192] A generative AI model is an artificial intelligence technology that analyzes acquired data and predicts and executes optimal actions. For example, a model trained using a machine learning library such as TENSORFLOW® is used.

[0193] 3. Plant-growing robot

[0194] A plant-growing robot is an automated device designed to grow plants in a factory. The robot receives data from sensors and follows instructions from a generative AI model to water and fertilize the plants.

[0195] System Operation

[0196] 1. Data Acquisition

[0197] Various sensors collect real-time environmental data, such as soil moisture, temperature, and sunlight, which is then sent to the plant-growing robot's control system.

[0198] 2. Data Analysis

[0199] The plant-growing robot's control system inputs the acquired data into a generative AI model to predict the optimal timing and amount of watering, which then determines the optimal behavior based on past data and current environmental data.

[0200] 3. Watering

[0201] The plant-growing robot automatically waters plants based on the timing and amount of watering predicted by the generative AI model. Specifically, the robot's onboard water pump delivers the appropriate amount of water.

[0202] Hardware and software used

[0203] Hardware

[0204] Soil moisture sensor (e.g., Arduino Soil Moisture Sensor)

[0205] Temperature sensor (e.g. DHT22)

[0206] Optical sensor (e.g. BH1750)

[0207] Water pump (e.g. Peristaltic Liquid Pump)

[0208] software

[0209] Machine learning libraries (e.g. TensorFlow)

[0210] Control systems (e.g., computer control circuits)

[0211] Specific examples

[0212] For example, when growing tomatoes in a factory, the following prompt sentences can be input into the generative AI model:

[0213] Prompt Sentence Examples

[0214] "When soil moisture levels drop below 30%, use a generative AI model to predict the appropriate amount of watering and then use a pump to provide water."

[0215] In this way, the system can efficiently and automatically manage plant growth.

[0216] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0217] Step 1:

[0218] The server collects environmental data such as soil moisture, temperature, and amount of sunlight from various sensors.

[0219] Input: Data from soil moisture sensor, temperature sensor, and light sensor

[0220] Output: Acquired environmental data

[0221] Specific operation: The server periodically polls data from each sensor and collects environmental data in real time.

[0222] Step 2:

[0223] The server inputs the acquired environmental data into a generative AI model to predict the optimal timing and amount of watering.

[0224] Input: Obtained environmental data

[0225] Output: Predicted watering timing and amount

[0226] Specific operation: The server inputs environmental data into a generative AI model and calculates the optimal watering timing and amount based on past and current environmental data.

[0227] Step 3:

[0228] The server sends instructions to the plant-growing robot based on the timing and amount of watering predicted by the generative AI model.

[0229] Input: Predicted watering timing and amount

[0230] Output: Watering instructions

[0231] Specific operation: Based on the prediction results obtained from the generative AI model, the server sends specific watering instructions to the plant-growing robot.

[0232] Step 4:

[0233] The plant-growing robot follows instructions from the server to control the water pump and supply the appropriate amount of water.

[0234] Input: Watering instructions from the server

[0235] Output: Actual watering behavior

[0236] Specific operation: The plant growing robot starts the water pump and supplies the specified amount of water to the plant.

[0237] Step 5:

[0238] The server receives feedback from the plant-growing robot and stores it as data for the next watering.

[0239] Input: Feedback data from a plant-growing robot

[0240] Output: Updated environment data

[0241] Specific operation: The server receives feedback data from the plant-growing robot, stores it in a database, and uses it to predict the next watering.

[0242] Example 3

[0243] Next, a description will be given of a third embodiment of the third embodiment. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0244] In conventional agricultural systems, it is difficult to respond to market price fluctuations in crop cultivation, and the optimal harvest timing is often missed. In addition, adjusting environmental conditions manually makes efficient management difficult. This makes it difficult to maximize profits, and it takes time and effort.

[0245] The specific processing by the specific processing unit 290 of the data processing device 12 in the third embodiment is realized by the following means.

[0246] In this invention, the server includes means for proposing optimal crops and varieties to cultivate based on data acquired from various sensors, means for automating optimal watering for crops, means for proposing the type of fertilizer and the timing of application, means for automating adjustment of sunlight and temperature so that crops can be shipped at a time when market prices are high, means for analyzing market price data and crop maturity data using a generative AI model and calculating the optimal sunlight and temperature, means for adjusting environmental conditions in the greenhouse based on the calculation results, and means for monitoring and providing feedback on environmental conditions.This enables efficient environmental management that can respond to fluctuations in market prices and ensures that the optimal harvest timing is not missed.

[0247] "Various sensors" are devices used to measure environmental conditions and the growth status of crops, such as temperature sensors, moisture sensors, and light sensors.

[0248] "Data" refers to information that serves as the basis for analysis and judgment, such as information obtained from various sensors and market price information.

[0249] "Proposing the best crops and varieties to cultivate" means selecting the crops and varieties that are best suited to specific environmental conditions based on collected data and recommending them to the user.

[0250] "Automated watering" is a system that automatically supplies the water necessary for crop growth.

[0251] "Suggestion of fertilizer type and application timing" means recommending the type of fertilizer and the application timing that are optimal for crop growth based on data.

[0252] "High market price period" refers to a period when the market price of a crop is higher than usual.

[0253] "Automated sunlight and temperature adjustment" is a system that automatically adjusts the amount of sunlight and temperature to optimal levels for crop growth.

[0254] A "generative AI model" is a model that uses artificial intelligence technology to analyze data and make predictions about optimal environmental conditions and crop growth.

[0255] "Market price data" refers to price information for agricultural products in the market.

[0256] "Crop maturity data" is information relating to the growth status and maturity of crops.

[0257] "Environmental conditions inside the greenhouse" refer to factors that affect crop growth, such as the temperature, humidity, and amount of sunlight inside the greenhouse.

[0258] "Monitoring" refers to the continuous monitoring of environmental conditions and crop status using sensors and other devices.

[0259] "Feedback" refers to adjusting environmental conditions and providing information to users based on the monitoring results.

[0260] This invention is a system that uses data obtained from various sensors to suggest the best crops and varieties to cultivate, automates the optimal watering of crops, suggests the type of fertilizer and the timing of application, automates the adjustment of sunlight and temperature so that products can be shipped at a time when market prices are high, and provides sales support.

[0261] 1. Program Generation

[0262] The server uses a generative AI model to generate a program that takes market price data and crop maturity data as input, and includes algorithms for automatically adjusting the amount of sunlight and temperature.

[0263] 2. Program processing explanation

[0264] The server executes the generated program and performs the following processes.

[0265] Data collection

[0266] Market price data collection:

[0267] The server retrieves the latest price information from a market price database through an API. For example, it uses the API of an agricultural market to get the current market price of tomatoes. Specifically, the server sends a "GET" request to the API endpoint and receives price data in JSON format.

[0268] Collecting Crop Maturity Data:

[0269] The server collects crop maturity data from sensors installed in the greenhouse, such as temperature, humidity, and light sensors. For example, the server periodically polls the sensors to obtain the current temperature, humidity, and amount of sunlight.

[0270] Data analysis

[0271] Market price and maturity data analysis:

[0272] The server inputs the collected market price data and crop maturity data into the generative AI model. The generative AI model calculates the optimal amount of sunlight and temperature based on this data. Specifically, the server sends a prompt to the generative AI model, such as "Please tell me the optimal settings for adjusting sunlight and temperature so that tomatoes can be harvested in May 2023."

[0273] environmental adjustment

[0274] Adjusting sunlight and temperature:

[0275] The server adjusts the environmental conditions in the greenhouse based on the optimal settings obtained from the generative AI model. Specifically, it turns lights on and off and changes the settings of the temperature control device. For example, the server sends instructions to the smart greenhouse control system such as "turn on the lights for 8 hours and set the temperature to 25 degrees."

[0276] Monitoring and Feedback

[0277] Environmental condition monitoring:

[0278] The server monitors whether the set environmental conditions are being maintained properly, periodically checking data from sensors and making readjustments as necessary. Specifically, the server monitors data from sensors in real time and makes immediate adjustments if, for example, the temperature deviates from the set value.

[0279] Providing feedback:

[0280] The server provides feedback to the user on the state of environmental conditions and the growth status of the crops. The user can check this information through a dedicated device or application. For example, the server might send a notification to the user's device saying, "The current temperature is 25 degrees, and the amount of sunlight is set to 8 hours. The tomatoes are growing well."

[0281] Examples and prompts

[0282] As a concrete example, consider adjusting environmental conditions to coincide with periods when market prices for tomatoes are high.

[0283] Examples:

[0284] Market price data: The market price of tomatoes in May 2023 is high.

[0285] Crop maturity data: The current growth state of tomatoes is immature.

[0286] The server uses a generative AI model to adjust sunlight and temperature to ensure tomatoes ripen in May.

[0287] Example prompt sentence:

[0288] Using market price data and crop maturity data as inputs, generate a program that will adjust the amount of sunlight and temperature so that tomatoes can be harvested in May 2023.

[0289] In this way, the server realizes a system that automatically adjusts environmental conditions so that crops can be harvested at a timing when market prices are high. The flow of the identification process in the third embodiment will be described with reference to FIG.

[0290] Step 1: Data collection

[0291] Input: Market price data, crop maturity data

[0292] Specific behavior:

[0293] The server retrieves the latest price information from the market price database through the API by sending a "GET" request to the API endpoint and receiving price data in JSON format.

[0294] The server collects crop maturity data from temperature, humidity, and light sensors installed in the greenhouse. Specifically, the server periodically polls the data from the sensors to obtain the current temperature, humidity, and amount of sunlight.

[0295] Output: Collected market price data, crop maturity data

[0296] Step 2: Data analysis

[0297] Inputs: Collected market price data, crop maturity data

[0298] Specific behavior:

[0299] The server inputs the collected market price data and crop maturity data into the generative AI model. Specifically, the server sends a prompt to the generative AI model, such as, "Please tell me the optimal settings for adjusting the amount of sunlight and temperature so that tomatoes can be harvested in May 2023."

[0300] The generative AI model analyzes the input data and calculates the optimal amount of sunlight and temperature.

[0301] Output: Optimal light and temperature settings

[0302] Step 3: Adjusting the environment

[0303] Input: Optimal sunlight and temperature settings

[0304] Specific behavior:

[0305] The server then adjusts the environmental conditions in the greenhouse based on the optimal settings obtained from the generative AI model, sending instructions to the smart greenhouse's control system, such as "turn on the lights for 8 hours and set the temperature to 25 degrees."

[0306] The server turns lights on and off and changes the settings on thermostats.

[0307] Output: Controlled greenhouse environmental conditions

[0308] Step 4: Monitoring and feedback

[0309] Input: Controlled greenhouse environmental conditions

[0310] Specific behavior:

[0311] The server monitors whether the set environmental conditions are being maintained properly by periodically checking data from sensors and making readjustments if necessary.

[0312] The server provides feedback to the user about the state of environmental conditions and the growth status of the crops. Specifically, the server sends a notification to the user's device, such as, "The current temperature is 25 degrees, and the amount of sunlight is set to 8 hours. The tomatoes are growing well."

[0313] Output: Monitoring results, feedback to users

[0314] In this way, the server creates a system that automatically adjusts environmental conditions so that crops can be harvested at a time when market prices are high.

[0315] (Application example 3)

[0316] Next, a description will be given of Application Example 3 of Form Example 3. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0317] Conventional agricultural systems have difficulty optimizing crop harvest times in response to fluctuations in market prices. Furthermore, because adjustments to sunlight and temperature are made manually, efficient cultivation management is difficult, making it difficult to maximize profits. Furthermore, there was a need for a system that could monitor crop maturity in real time and determine the optimal harvest time based on market prices.

[0318] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 3 is realized by the following means.

[0319] In this invention, the server includes means for proposing the optimal crop and variety to cultivate based on data acquired from various sensors, means for automating the optimal watering for the crops, means for proposing the type of fertilizer and the timing of application, means for automating the adjustment of sunlight and temperature so that the crops can be shipped when the market price is high, means for adjusting sunlight and temperature based on market price data and the maturity of the crops using a generative AI model so that the crops can be harvested when the market price is high, and means for providing sales support.This enables the optimal harvest time to be determined in response to fluctuations in market price, enabling efficient cultivation management and maximization of profits.

[0320] "Various sensors" are devices for acquiring environmental data such as temperature, humidity, light intensity, and soil moisture content.

[0321] "Data" refers to a collection of information such as environmental information, market price information, and crop maturity information obtained from various sensors.

[0322] The "means for proposing the optimal crops and varieties to cultivate" is a function that analyzes the acquired data and selects and proposes the most suitable crops and varieties.

[0323] "Automated watering means" is a function for automatically supplying the water necessary for crop growth.

[0324] The "means for suggesting the type of fertilizer and the timing of application" is a function for suggesting the type of fertilizer that is optimal for crop growth and the timing of application.

[0325] "Automated means for adjusting sunlight and temperature" is a function for automatically adjusting the amount of sunlight and temperature required for crop growth.

[0326] A "generative AI model" is a model that uses artificial intelligence technology to analyze data and predict optimal cultivation conditions and harvest times.

[0327] "Market price data" refers to price information for crops in the market.

[0328] "Crop maturity" is an index that indicates the degree of growth of a crop before it is ready for harvest.

[0329] The "means for providing sales support" is a function for supporting the sale of harvested crops.

[0330] The system for implementing this invention includes various sensors, a generative AI model, data analysis software, and an automated control device. The specific configuration and operation of the system are described below.

[0331] System Configuration

[0332] 1. Various sensors: Install sensors to acquire environmental data such as temperature, humidity, light intensity, soil moisture, etc. These sensors collect environmental information necessary for crop growth in real time.

[0333] 2. Generative AI model: An artificial intelligence model is used to calculate the optimal amount of sunlight and temperature based on market price data and crop maturity. This model learns from past data and predicts future market prices.

[0334] 3. Data analysis software: Software is used to analyze the data acquired from various sensors and input it into the generative AI model. Specifically, programming languages ​​such as Python and R are used.

[0335] 4. Automation Control Devices: Install devices to automatically adjust the amount of sunlight and temperature, including LED lights, heaters, and cooling devices.

[0336] Operation explanation

[0337] 1. Data collection: Various sensors collect environmental data and send it to a server, thereby obtaining real-time environmental information.

[0338] 2. Data analysis: The server analyzes the collected data using data analysis software, and the analysis results are input into the generative AI model.

[0339] 3. Run the generative AI model: The generative AI model calculates the optimal amount of sunlight and temperature based on market price data and crop maturity, allowing crops to be harvested when market prices are high.

[0340] 4. Environmental adjustment: The automated control system adjusts the amount of sunlight and temperature based on the calculations of the generative AI model, thereby optimizing the growing environment for the crops.

[0341] Specific examples

[0342] For example, if you want to harvest tomatoes at a time when market prices are high, you can input the following prompt sentence into the generative AI model:

[0343] Example prompt sentence:

[0344] Create a generative AI model that adjusts sunlight and temperature to harvest tomatoes at the peak market price.

[0345] By inputting this prompt into a generative AI model, an AI model is generated that suggests optimal sun exposure and temperature adjustments, allowing tomatoes to be harvested at a time when market prices are high.

[0346] This system will enable the determination of the optimal harvest time in response to fluctuations in market prices, enabling efficient cultivation management and maximizing profits.

[0347] The flow of the specific processing in Application Example 3 will be described with reference to FIG.

[0348] Step 1:

[0349] Various sensors collect environmental data and send it to a server.

[0350] Input: Environmental data such as temperature, humidity, light, and soil moisture.

[0351] Output: The environment data sent to the server.

[0352] How it works: The temperature sensor measures the current temperature, the humidity sensor measures the humidity, the light sensor measures the amount of sunlight, and the soil moisture sensor measures the amount of moisture in the soil. These data are sent to the server in real time.

[0353] Step 2:

[0354] The server analyzes the collected environmental data using data analysis software.

[0355] Input: The environment data sent to the server.

[0356] Output: Parsed environmental data.

[0357] Specific operation: The server analyzes the collected environmental data using data analysis software such as Python or R. For example, it calculates the average value and fluctuation of temperature data and analyzes trends in humidity data.

[0358] Step 3:

[0359] The server inputs the analysis results into a generative AI model.

[0360] Input: Parsed environmental data.

[0361] Output: The data that is input into a generative AI model.

[0362] How it works: The server inputs the analysis results into the generative AI model. For example, it passes data such as average temperature values ​​and humidity trends to the AI ​​model.

[0363] Step 4:

[0364] A generative AI model calculates optimal sunlight and temperature based on market price data and crop maturity.

[0365] Inputs: Analyzed environmental data, market price data, crop maturity.

[0366] Output: Optimal light and temperature settings.

[0367] How it works: The generative AI model predicts future market prices based on past market price data and the current maturity of crops. Based on this prediction, it calculates the optimal amount of sunlight and temperature.

[0368] Step 5:

[0369] The server controls the automation control device based on the calculation results of the generated AI model.

[0370] Input: Optimal sunlight and temperature settings.

[0371] Output: Control signal to automation controller.

[0372] Specific operation: The server receives the calculation results of the generative AI model and controls the automation control devices accordingly, for example, adjusting the brightness of LED lights and controlling the operation of heaters and cooling devices.

[0373] Step 6:

[0374] Automated controls regulate the amount of sunlight and temperature.

[0375] Input: Control signal from the server.

[0376] Output: Adjusted solar radiation and temperature.

[0377] How it works: Based on control signals from the server, the automated control device adjusts the brightness of LED lights and activates heaters and cooling devices, optimizing the growing environment for crops.

[0378] Furthermore, an emotion engine that estimates the user's emotion may be combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0379] "Example 1"

[0380] One embodiment of the present invention is a system that combines an emotion engine that recognizes the user's emotions. This system recognizes the user's emotions and, based on those emotions, the generative AI suggests the optimal crops and varieties to cultivate. Specifically, if the user feels stressed, the emotion engine communicates this information to the generative AI, which then suggests growing a crop that may reduce stress, such as lavender.

[0381] "Example 2"

[0382] Another embodiment is a system in which an emotion engine recognizes a user's emotions, and based on those emotions, a generation AI automates the adjustment of sunlight and temperature so that products can be shipped at a time when market prices are high. Specifically, if a user feels joy, the emotion engine conveys that information to the generation AI, and the generation AI automates the adjustment of sunlight and temperature so that products can be shipped at a time when market prices are high. For example, if a user feels joy on a particular holiday, the generation AI adjusts sunlight and temperature so that crops can be harvested in time for that holiday.

[0383] "Example 3"

[0384] One embodiment of the present invention is a system that combines an emotion engine that recognizes the user's emotions. This system recognizes the user's emotions and, based on those emotions, the generative AI suggests the optimal crops and varieties to cultivate. Specifically, if the user feels stressed, the emotion engine communicates this information to the generative AI, which then suggests growing a crop that may reduce stress, such as lavender.

[0385] The processing flow of each embodiment will be described below.

[0386] "Example 1"

[0387] Step 1: The emotion engine recognizes the user's emotion.

[0388] Step 2: The emotion engine communicates the emotion information to the generative AI.

[0389] Step 3: The generative AI suggests the best crops and varieties based on the emotion. For example, if the user feels stressed, the generative AI suggests growing lavender, a crop that may reduce stress.

[0390] "Example 2"

[0391] Step 1: The emotion engine recognizes the user's emotion.

[0392] Step 2: The emotion engine communicates the emotion information to the generative AI.

[0393] Step 3: Based on that emotion, the AI ​​automates the adjustment of sunlight and temperature so that crops can be shipped at a time when market prices are high. For example, if a user feels joy on a particular holiday, the AI ​​adjusts sunlight and temperature so that crops can be harvested in time for that holiday.

[0394] Example 1

[0395] Next, a description will be given of Example 1 of Form Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0396] Conventional agricultural systems have difficulty selecting optimal crops and varieties based on environmental data, and are unable to make suggestions based on user emotions. This makes it difficult to achieve efficient cultivation and improve user satisfaction.

[0397] The identification process by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means. In this invention, the server includes a means for proposing optimal crops and varieties to cultivate based on data acquired from various sensors, a means for analyzing the acquired data and generating a prompt sentence to input to the generative AI model, a means for inputting the prompt sentence to the generative AI model and receiving a proposal for optimal crops and varieties, and a means for making a proposal based on the emotion using an emotion engine that recognizes the user's emotion. This makes it possible to propose optimal crops and varieties based on environmental data and user emotion data.

[0398] "Various sensors" are devices used to acquire environmental data such as soil humidity, temperature, and hours of sunlight.

[0399] A "generative AI model" is an artificial intelligence algorithm that suggests optimal crops and varieties based on input prompts.

[0400] A "prompt" is text containing a question or instruction to be input to a generative AI model.

[0401] An "emotion engine" is software or hardware that recognizes a user's emotions and analyzes that information.

[0402] A "user" is someone who uses this system to cultivate crops and select varieties.

[0403] The "server" is a central processing unit that analyzes data obtained from various sensors and inputs prompt sentences into the generative AI model.

[0404] A "terminal" is a device that acquires data from various sensors and transmits it to a server.

[0405] "Data analysis" is the process of generating prompt sentences to input into the generative AI model based on the acquired environmental data.

[0406] "Proposing optimal crops and varieties" means that the generative AI model selects crops and varieties suitable for cultivation based on environmental data and user emotional data.

[0407] This invention is a system that uses generative AI to suggest optimal crops and varieties for cultivation based on data acquired from various sensors. This system acquires data such as soil humidity, temperature, and sunlight hours, and the generative AI uses this data to suggest optimal crops and varieties. In addition, by combining it with an emotion engine that recognizes the user's emotions, it can also make suggestions based on the user's emotions.

[0408] Hardware and software used

[0409] 1. Hardware

[0410] Various sensors: Use a soil moisture sensor, temperature sensor, light sensor, etc. For example, use DHT11 as the soil moisture sensor.

[0411] Terminal: Uses a small computer such as a computer control circuit.

[0412] Server: Uses a central processing unit to analyze data and run generative AI models.

[0413] 2. Software

[0414] Generative AI models: Use generative AI algorithms such as OpenAI's GPT-4.

[0415] Emotion engine: Uses Microsoft(R) Azure(R) Emotion API, etc.

[0416] Specific operation of the system

[0417] 1. Data Acquisition

[0418] The device acquires environmental data from various sensors, specifically using soil humidity, temperature, and light sensors to collect data on soil humidity, temperature, and sunlight duration.

[0419] Specific operation: The soil humidity sensor (e.g., DHT11) connected to the device measures the soil humidity, the temperature sensor measures the ambient temperature, and the light sensor measures the hours of sunlight.

[0420] 2. Data Transmission

[0421] The device sends the acquired environmental data to a server using Wi-Fi or a wired connection.

[0422] How it works: The device (e.g., a computer control circuit) transmits data acquired from sensors to a server via Wi-Fi. The transmitted data includes soil humidity of 80%, temperature of 25°C, and sunlight duration of 10 hours.

[0423] 3. Data Analysis

[0424] The server analyzes the received environmental data and generates prompts to input into the generative AI model, using a programming language such as Python.

[0425] Specific operation: Based on the data received by the server, it generates a prompt such as, "What is the best crop if the soil moisture is 80%, the temperature is 25 degrees, and there are 10 hours of sunlight?"

[0426] 4. Generative AI suggestions

[0427] The server inputs a prompt into the generative AI model and receives recommendations for optimal crops and varieties, using OpenAI's GPT-4 and other models.

[0428] Specific operation: The server sends a prompt to the generative AI model, and the generative AI model replies, "I suggest growing rice."

[0429] 5. Use of Emotion Engine

[0430] To recognize the user's emotions, an emotion engine is used, such as Microsoft Azure's Emotion API.

[0431] Specific operation: When a user shows an expression to the device's camera, the emotion engine analyzes the expression and determines that the user is feeling stressed.

[0432] 6. Emotion-based recommendations

[0433] Based on the information received from the emotion engine, the server generates prompts for the generative AI model and suggests optimal crops and varieties based on emotions.

[0434] Specific behavior: The server generates a prompt sentence: "If the user is feeling stressed, what crops might reduce stress?", and the generative AI model responds: "I suggest growing lavender."

[0435] Examples and prompts

[0436] Example 1: Proposals based on environmental data

[0437] Prompt: "If the soil moisture is 80%, the temperature is 25 degrees, and there are 10 hours of sunshine, what is the best crop to grow?"

[0438] Generative AI's answer: "I suggest growing rice."

[0439] Example 2: Suggestions based on emotional data

[0440] Prompt: "If the user is feeling stressed, what crops might reduce stress?"

[0441] Generative AI response: "I suggest growing lavender."

[0442] In this way, the system proposes optimal crops and varieties based on environmental data and user emotional data.

[0443] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0444] Step 1: Data Acquisition

[0445] The device acquires environmental data from various sensors, specifically using soil humidity, temperature, and light sensors to collect data on soil humidity, temperature, and sunlight duration.

[0446] Input: Real-time data from soil moisture sensor, temperature sensor, and light sensor.

[0447] Output: Soil moisture, temperature, and sunshine duration datasets.

[0448] Specific operation: The soil humidity sensor (e.g., DHT11) connected to the device measures the soil humidity, the temperature sensor measures the ambient temperature, and the light sensor measures the hours of sunlight.

[0449] Step 2: Send data

[0450] The device sends the acquired environmental data to a server using Wi-Fi or a wired connection.

[0451] Input: Obtained environmental data (e.g. soil humidity 80%, temperature 25°C, sunshine duration 10 hours).

[0452] Output: The environment data sent to the server.

[0453] Specific operation: The device (e.g., a computer control circuit) transmits data obtained from the sensor to a server via Wi-Fi.

[0454] Step 3: Data analysis

[0455] The server analyzes the received environmental data and generates prompts to input into the generative AI model, using a programming language such as Python.

[0456] Input: The environment data sent to the server.

[0457] Output: A prompt to be fed into the generative AI model.

[0458] Specific operation: Based on the data received by the server, it generates a prompt such as, "What is the best crop if the soil moisture is 80%, the temperature is 25 degrees, and there are 10 hours of sunlight?"

[0459] Step 4: Generative AI suggestions

[0460] The server inputs a prompt into the generative AI model, which uses OpenAI's GPT-4 and other models, and receives recommendations for optimal crops and varieties.

[0461] Input: The generated prompt statement.

[0462] Output: Suggested results from the generative AI model.

[0463] Specific operation: The server sends a prompt to the generative AI model, and the generative AI model replies, "I suggest growing rice."

[0464] Step 5: Use the Emotion Engine

[0465] To recognize the user's emotions, an emotion engine is used, such as Microsoft Azure's Emotion API.

[0466] Input: User's facial expression data.

[0467] Output: User emotion data.

[0468] Specific operation: When a user shows an expression to the device's camera, the emotion engine analyzes the expression and determines that the user is feeling stressed.

[0469] Step 6: Emotion-based suggestions

[0470] Based on the information received from the emotion engine, the server generates prompts for the generative AI model and suggests optimal crops and varieties based on emotions.

[0471] Input: User emotion data from the emotion engine.

[0472] Output: Sentiment-based recommendation results from the generative AI model.

[0473] Specific behavior: The server generates a prompt sentence: "If the user is feeling stressed, what crops might reduce stress?", and the generative AI model responds: "I suggest growing lavender."

[0474] (Application example 1)

[0475] Next, a description will be given of Application Example 1 of Embodiment Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0476] Conventional agricultural systems can optimize crop cultivation based on data obtained from various sensors, but they are unable to make suggestions that take into account the emotions of workers, making it difficult to reduce worker stress. Furthermore, because crop suggestions are not based on emotions, there is a risk that this could have a negative impact on worker health and work efficiency.

[0477] The specific processing by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means. In this invention, the server includes a means for proposing the optimal crop and variety to cultivate based on data acquired from various sensors, a means for automating the optimal watering for the crops, a means for proposing the type of fertilizer and the timing of application, a means for automating the amount of sunlight and temperature to ship at a time when market prices are high, a sales support means, and a means for recognizing the emotions of the worker using an emotion recognition engine and having the generation AI propose the optimal crop and variety to cultivate based on that emotion. This makes it possible to propose crops that take the worker's emotions into consideration, thereby reducing worker stress and improving their health and work efficiency.

[0478] "Various sensors" are devices used to acquire environmental data such as soil humidity, temperature, and hours of sunlight.

[0479] The "means for suggesting optimal crops and varieties to cultivate" is a function that uses generative AI to suggest optimal crops and varieties based on acquired environmental data.

[0480] "Automated watering method that is optimal for crops" is a function that automatically supplies the amount of water required for crops based on data from sensors.

[0481] "Means for suggesting fertilizer type and spraying timing" is a function that analyzes environmental data and suggests the optimal type of fertilizer and the timing to spray it.

[0482] "Automated means of adjusting sunlight and temperature to enable shipments at times when market prices are high" is a function that automatically adjusts sunlight and temperature based on market price data to optimize the timing of crop shipments.

[0483] "Sales support means" is a function for supporting the sale of crops, such as predicting market prices and proposing sales strategies.

[0484] An "emotion recognition engine" is a technology for recognizing a worker's emotions, and uses cameras and sensors to analyze the worker's facial expressions and behavior.

[0485] "Generative AI" is an artificial intelligence technology that suggests optimal crops and varieties based on acquired data.

[0486] "Means for recognizing the emotions of workers and having the generation AI suggest the optimal crops and varieties to cultivate based on those emotions" is a function that uses an emotion recognition engine to analyze the emotions of workers, conveys the results to the generation AI, and suggests crops and varieties that are suitable for the emotions of the workers.

[0487] The following system configuration will be described as an embodiment of the present invention.

[0488] System Configuration

[0489] Hardware

[0490] Various sensors: Devices for acquiring environmental data such as soil humidity, temperature, and sunlight hours.

[0491] Emotion recognition camera: A camera for recognizing the emotions of workers.

[0492] Server: A computer system for analyzing data and running generative AI models.

[0493] software

[0494] Generative AI model: An artificial intelligence technology that suggests optimal crops and varieties based on acquired data (e.g., GPT-3 (registered trademark)).

[0495] Emotion recognition engine: Software for analyzing worker emotions (e.g., Affectiva).

[0496] Data analysis program: A program for analyzing data obtained from various sensors.

[0497] Processing flow

[0498] 1. Data acquisition: Collect data on soil humidity, temperature, and sunlight hours from various sensors. Collect worker emotion data using an emotion recognition camera.

[0499] 2. Data analysis: The acquired environmental data and emotion data are sent to the server and analyzed using a data analysis program.

[0500] 3. Recommendations by generative AI: Based on the analysis results, the generative AI model will suggest the optimal crops and varieties.

[0501] 4. Emotion-based suggestions: The worker's emotional data analyzed by the emotion recognition engine is input into a generative AI model to suggest crops and varieties that suit the worker's emotions.

[0502] Specific examples

[0503] For example, consider the case where the data obtained from the sensor is as follows:

[0504] Soil moisture is 60%

[0505] The temperature is 25 degrees

[0506] 8 hours of sunshine

[0507] Furthermore, if the emotion recognition camera recognizes the worker's emotion as "feeling stressed," the generative AI model will make the following suggestions:

[0508] Prompt Sentence Examples

[0509] "If the soil moisture is 60%, the temperature is 25°C, there are 8 hours of sunshine, and the farmer is feeling stressed, please suggest the best crop and variety."

[0510] By inputting this prompt into a generative AI model, it can suggest, for example, the cultivation of "lavender." In this way, it becomes possible to suggest crops that take into account the worker's emotions, reducing worker stress and improving their health and work efficiency.

[0511] This system makes it possible to comprehensively analyze environmental data and worker emotional data, and to suggest optimal crops and varieties.

[0512] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0513] Step 1:

[0514] Data on soil humidity, temperature, and sunlight hours are acquired from various sensors. The sensors measure this environmental data in real time and send it to the server. The input is the environmental data from the sensors, and the output is the environmental data sent to the server.

[0515] Step 2:

[0516] An emotion recognition camera is used to acquire the worker's emotional data. The camera analyzes the worker's facial expressions and behavior and sends the data to the emotion recognition engine. The input is the video data from the camera, and the output is the analyzed emotional data.

[0517] Step 3:

[0518] The server analyzes the acquired environmental data and emotional data using a data analysis program. The data analysis program integrates the environmental data and emotional data and generates a prompt sentence to input into the generative AI model. The input is the environmental data and emotional data, and the output is the generated prompt sentence.

[0519] Step 4:

[0520] A prompt is input into the generative AI model, which then suggests the optimal crops and varieties. The generative AI model analyzes the prompt and outputs the optimal crops and varieties. The input is the prompt, and the output is the suggested crops and varieties.

[0521] Step 5:

[0522] The server notifies the worker of the proposed results from the generative AI model. The notification is sent via a terminal or display. The input is the proposed results from the generative AI model, and the output is the notification sent to the worker.

[0523] Step 6:

[0524] The worker starts cultivating based on the suggested crops and varieties. The worker receives notifications from the server and works according to the suggestions. The input is the notification from the server, and the output is the worker's cultivation behavior.

[0525] In this way, it becomes possible to perform an integrated analysis of environmental data and worker emotional data to suggest optimal crops and varieties.

[0526] Example 2

[0527] Next, a description will be given of Example 2 of Form Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0528] In conventional agricultural management systems, watering crops and adjusting the environment are often done manually, making efficient management difficult. Furthermore, it is difficult to maximize profits because it is not possible to adjust shipping timing based on user sentiment or market prices. Furthermore, there is a lack of ways to effectively utilize data obtained from various sensors, making it difficult to propose optimal cultivation methods.

[0529] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[0530] In this invention, the server includes a means for proposing optimal crops and varieties to cultivate based on data acquired from various sensors, a means for acquiring soil humidity data and automatically adjusting the timing and amount of watering using a generative AI model, and a means for acquiring user emotion data and automating adjustment of sunlight and temperature using a generative AI model so that products can be shipped at a time when market prices are high. This enables efficient agricultural management and maximizes profits.

[0531] "Various sensors" are devices used to acquire environmental data such as soil humidity, temperature, and amount of sunlight.

[0532] A "generative AI model" is an artificial intelligence algorithm that analyzes acquired data and adjusts the optimal timing and amount of watering, as well as the amount of sunlight and temperature.

[0533] "Soil moisture data" is data that indicates the amount of moisture in the soil and is used to determine the timing and amount of watering for crops.

[0534] "Watering timing and amount" refers to instructions for providing crops with the right amount of water at the right time.

[0535] "User emotion data" is data that indicates the user's emotional state, and is acquired using emotion recognition technology.

[0536] "High market price timing" refers to the period when the market price of crops is highest, and the aim is to maximize profits by shipping at that time.

[0537] "Sunlight and temperature adjustment" refers to adjusting the environment to maintain the optimum amount of sunlight and temperature for crop growth.

[0538] "Automation" refers to the system operating automatically without the need for manual operation.

[0539] This invention is a system that automates crop cultivation management using a generative AI model based on data acquired from various sensors. Specifically, it includes functions to acquire soil moisture data and automatically adjust the timing and amount of watering, and to acquire user emotion data and automatically adjust the amount of sunlight and temperature so that crops can be shipped at a time when market prices are high.

[0540] The server collects data obtained from various sensors and stores it in a database. An example of the database used is MySQL (registered trademark). The server analyzes the data using a generative AI model and generates appropriate instructions. An example of the generative AI model used is OpenAI's GPT-4.

[0541] The terminals receive instructions from the server and actually water the plants and adjust the environment. Examples of terminals include computer control circuits and Arduino. These terminals work in conjunction with sensors and actuators (such as solenoid valves, LED lights, and heaters).

[0542] Users provide emotion data through an emotion engine, such as Affectiva's emotion recognition API, which is then sent to a server and analyzed by a generative AI model.

[0543] As a concrete example, consider the following scenario:

[0544] 1. Automated watering:

[0545] The sensor measures the soil moisture, and when the humidity drops below 30%, the device sends the data to the server.

[0546] The server stores the data in a database, and the generation AI analyzes the data to generate the instruction to "start watering."

[0547] The terminal receives the instructions and the watering system supplies water.

[0548] Example prompt sentence:

[0549] "When soil moisture drops below 30 percent, give instructions to start watering."

[0550] 2. Automated solar radiation and temperature control:

[0551] When a user feels joy for a particular holiday, the emotion engine recognizes the emotion and sends the data to the server.

[0552] The server provides the data to the generation AI, which then generates instructions to "adjust the amount of sunlight and temperature so that shipments can be made at a time when market prices are high."

[0553] The terminal receives the instructions, and the environmental control system adjusts the amount of sunlight and temperature.

[0554] Example prompt sentence:

[0555] "If users are happy, give instructions to adjust the amount of sunlight and temperature so that shipments can be made at a time when market prices are high."

[0556] This system enables efficient agricultural management and maximizes profits.

[0557] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0558] Step 1:

[0559] The sensor measures the moisture content of the soil.

[0560] Input: Soil moisture

[0561] Output: Humidity data

[0562] Specific operation: A humidity sensor installed in the soil measures the humidity of the soil in real time and transmits the data to the terminal.

[0563] Step 2:

[0564] The device receives humidity data from the sensor and sends it to the server.

[0565] Input: Humidity data

[0566] Output: Humidity data sent to the server

[0567] Specific operation: A terminal such as a computer control circuit transmits the humidity data received from the sensor to a server.

[0568] Step 3:

[0569] The server stores the humidity data in a database.

[0570] Input: Humidity data

[0571] Output: Humidity data stored in a database

[0572] Specific operation: The server stores the received humidity data in a database such as MySQL.

[0573] Step 4:

[0574] The server provides humidity data to the generative AI model for analysis.

[0575] Input: Humidity data stored in the database

[0576] Output: Watering timing and amount instructions

[0577] How it works: The server retrieves humidity data from the database and provides it to a generative AI model (e.g., OpenAI's GPT-4) for analysis. The generative AI model then determines when and how much water to use.

[0578] Step 5:

[0579] The server sends instructions from the generated AI model to the device.

[0580] Input: Watering timing and amount instructions

[0581] Output: Instructions sent to the terminal

[0582] Specific operation: The server receives instructions from the generative AI model and sends them to the device.

[0583] Step 6:

[0584] The terminal controls the watering system and performs watering.

[0585] Input: Watering timing and amount instructions

[0586] Output: Watering performed

[0587] Specific operation: Based on the instructions received by the terminal, it controls watering systems such as solenoid valves connected to a computer control circuit, and supplies water at the specified timing and amount.

[0588] Step 7:

[0589] The user provides emotion data through the emotion engine.

[0590] Input: User's emotions

[0591] Output: Emotion data

[0592] Specific behavior: When a user feels a certain emotion (e.g., joy), the emotion engine (e.g., Affectiva's emotion recognition API) recognizes the emotion and generates data.

[0593] Step 8:

[0594] The emotion engine sends the emotion data to the server.

[0595] Input: Emotion data

[0596] Output: Emotion data sent to the server

[0597] Specific operation: The emotion engine sends the recognized emotion data to the server.

[0598] Step 9:

[0599] The server provides the emotion data to the generative AI model for analysis.

[0600] Input: Emotion data

[0601] Output: Indication of sunlight and temperature adjustment

[0602] How it works: The server provides emotion data to the generative AI model, which then determines how to adjust sunlight and temperature to ensure shipments are made at a time when market prices are high.

[0603] Step 10:

[0604] The server sends instructions from the generated AI model to the device.

[0605] Input: sunlight and temperature control instructions

[0606] Output: Instructions sent to the terminal

[0607] Specific operation: The server receives instructions from the generative AI model and sends them to the device.

[0608] Step 11:

[0609] The device controls the environmental control system, adjusting the amount of sunlight and temperature.

[0610] Input: sunlight and temperature control instructions

[0611] Output: Adjusted sunlight and temperature

[0612] Specific operation: Based on the instructions received by the terminal, it controls environmental adjustment systems such as LED lights and heaters connected to Arduino, adjusting the amount of sunlight and temperature at the specified time and in the specified manner.

[0613] (Application example 2)

[0614] Next, a description will be given of Application Example 2 of Form Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0615] In conventional agricultural systems, watering crops and adjusting the environment are often done manually, making efficient cultivation difficult. Furthermore, because the environment is not adjusted taking into account the user's emotions, optimizing the harvest time is difficult. Furthermore, there is insufficient automation to ensure shipments at times when market prices are high.

[0616] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[0617] In this invention, the server includes a means for proposing the optimal crop and variety to cultivate based on data acquired from various sensors, a means for automating the optimal watering for the crops, a means for proposing the type of fertilizer and the timing of application, a means for automating the amount of sunlight and temperature adjustment so that the crops can be shipped at a time when the market price is high, and a means for recognizing the user's emotions and adjusting the environment based on those emotions, thereby enabling efficient crop cultivation and optimizing the harvest time.

[0618] "Various sensors" are devices used to acquire environmental data such as soil humidity, temperature, and amount of sunlight.

[0619] "Generative AI" is an artificial intelligence technology that analyzes acquired data and automatically determines the optimal crop cultivation method and environmental adjustments.

[0620] "Automated watering" is a system that automatically supplies water at the appropriate time and in the appropriate amount based on soil moisture data.

[0621] "Suggestion of fertilizer type and application timing" is a function that suggests the type of fertilizer that is best suited for crop growth and the timing of its application.

[0622] "Automated sunlight and temperature adjustment" is a system that automatically adjusts the amount of sunlight and temperature to suit crop growth.

[0623] "Recognizing user emotions" is a technology that uses an emotion engine to detect the user's emotional state.

[0624] The "means for adjusting the environment" is a function that makes adjustments to optimize the crop cultivation environment based on the user's feelings.

[0625] "Shipping when market prices are high" is a system that aims to harvest and ship crops when market prices are highest.

[0626] The system for implementing this invention includes various sensors, a generative AI, an emotion engine, and an agricultural robot. The specific configuration and operation of the system are described below.

[0627] System Configuration

[0628] 1. Various sensors: These are devices for acquiring environmental data such as soil humidity, temperature, and amount of sunlight. These sensors are attached to agricultural robots.

[0629] 2. Generative AI: This is an artificial intelligence technology that analyzes acquired data and automatically determines optimal crop cultivation methods and environmental adjustments. Generative AI runs on a server.

[0630] 3. Emotion Engine: This technology detects the user's emotional state. The emotion engine is installed on the user's smartphone or tablet.

[0631] 4. Agricultural robot: A robot that waters crops and adjusts the environment. The robot operates based on instructions from the server.

[0632] Operation explanation

[0633] 1. Data Acquisition

[0634] Various sensors collect environmental data such as soil humidity, temperature, and amount of sunlight, which is then sent to a server in real time.

[0635] 2. Data Analysis

[0636] The server-based AI analyzes the acquired environmental data and, based on the results of the analysis, determines the optimal crop cultivation method, watering timing, and type and application of fertilizer.

[0637] 3. Emotion recognition

[0638] An emotion engine installed on the user's smartphone or tablet detects the user's emotional state, and the emotion data is sent to a server.

[0639] 4. Environmental adjustment

[0640] The server-based AI adjusts the amount of sunlight and temperature based on the user's emotional data, allowing for shipments to be made at a time when market prices are high.

[0641] 5. Execution

[0642] Agricultural robots water crops and adjust the environment based on instructions from a server, enabling efficient crop cultivation and optimizing harvest times.

[0643] Specific examples

[0644] If the soil moisture is below 20%, the robot will automatically start watering.

[0645] If the user is recognized as "happy": The robot adjusts the environment to optimize harvesting.

[0646] Prompt Sentence Examples

[0647] If the soil moisture is below 20%, begin watering.

[0648] If the user is recognized as "happy," adjust the environment to be optimal for harvesting.

[0649] In this way, the system enables efficient crop cultivation and optimized harvest times.

[0650] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0651] Step 1:

[0652] Various sensors collect environmental data such as soil humidity, temperature, and sunlight. This data is sent to a server in real time. The input is the environmental data from the sensors, and the output is the environmental data sent to the server.

[0653] Step 2:

[0654] The generation AI on the server analyzes the acquired environmental data. Based on the analysis results, it determines the optimal crop cultivation method, watering timing, type of fertilizer and when to apply it. The input is environmental data, and the output is a proposal for the optimal cultivation method, watering timing, type of fertilizer and when to apply it.

[0655] Step 3:

[0656] An emotion engine installed on the user's smartphone or tablet detects the user's emotional state. The emotion data is sent to the server. The input is the user's emotional state, and the output is the emotion data sent to the server.

[0657] Step 4:

[0658] The AI ​​generator on the server adjusts the amount of sunlight and temperature based on the user's emotional data. This allows for shipment at a time when market prices are high. The input is emotional data, and the output is instructions to adjust the amount of sunlight and temperature.

[0659] Step 5:

[0660] Agricultural robots water crops and adjust the environment based on instructions from a server. This allows for efficient crop cultivation and optimal harvest times. The input is instructions from the server, and the output is the watering and environmental adjustments that have been performed.

[0661] Example 3

[0662] Next, a description will be given of a third embodiment of the third embodiment. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0663] With conventional agricultural systems, it was difficult to provide an optimal cultivation environment that took into account market price fluctuations and user sentiment when cultivating crops. It was also difficult to integrate and manage multiple factors, such as adjusting sunlight and temperature, automating watering, and proposing the timing of fertilizer application. This led to issues that prevented sufficient improvements in crop quality and profitability.

[0664] The identification process by the identification processing unit 290 of the data processing device 12 in Example 3 is realized by the following means. In this invention, the server includes a means for proposing the optimal crop and variety to cultivate based on data acquired from various sensors, a means for automating the optimal watering of crops, a means for proposing the type of fertilizer and the timing of application, a means for automating the amount of sunlight and temperature adjustment so that the product can be shipped at a time when the market price is high, and a means for recognizing the user's emotions and proposing the optimal crop and variety based on the emotions. This makes it possible to improve the quality and profitability of crops.

[0665] "Various sensors" are devices used to measure environmental data and crop conditions, such as temperature sensors, light sensors, and moisture sensors.

[0666] A "generative AI model" is an artificial intelligence algorithm that analyzes collected data and suggests optimal cultivation conditions and crops.

[0667] "Suggesting the best crops and varieties to cultivate" means recommending the most suitable crops and varieties to users based on collected data.

[0668] "Automated watering" is a system that automatically supplies the necessary water to crops based on data from sensors.

[0669] "Suggesting the type of fertilizer and the timing of application" means suggesting to the user the optimal type of fertilizer and the timing of application depending on the growth stage of the crop and environmental conditions.

[0670] "Automated sunlight and temperature adjustment" is a system that automatically adjusts the amount of sunlight and temperature to suit crops based on data from sensors.

[0671] "Recognizing the user's emotions" means reading the user's emotions from their facial expressions, voice, etc., and reflecting that information in the system.

[0672] "Suggesting optimal crops and varieties" means recommending the most suitable crops and varieties for a user based on the user's emotional data.

[0673] MODE FOR CARRYING OUT THE INVENTION

[0674] This invention is a system that uses data obtained from various sensors to suggest the best crops and varieties to cultivate, automates the optimal watering for crops, suggests the type of fertilizer and the timing of application, automates the adjustment of sunlight and temperature so that crops can be shipped at a time when market prices are high, and recognizes the user's emotions and suggests the best crops and varieties based on those emotions.

[0675] Hardware and software used

[0676] Hardware: temperature sensors, light sensors, moisture sensors, servers, terminals (devices operated by users)

[0677] Software: Generative AI models, emotion engine, databases (market price data, crop maturity data)

[0678] Specific operation of the system

[0679] 1. Data Collection

[0680] The server collects real-time sunlight and temperature data from the temperature sensor and light sensor. For example, the temperature sensor measures the current temperature as 25 degrees, and the light sensor measures the sunlight as 500 lux.

[0681] The server retrieves the latest market price data from the market price database. For example, it retrieves that the market price of tomatoes is 300 yen per kilogram.

[0682] The server collects crop maturity data, for example, determining that tomatoes are 80% ripe.

[0683] 2. Data analysis and adjustment

[0684] The server inputs the collected market price data and crop maturity data into the generative AI model. For example, it inputs data such as "the market price of tomatoes is 300 yen and the maturity level is 80%."

[0685] The generative AI model analyzes input data and calculates the optimal amount of sunlight and temperature, for example, "Adjust the amount of sunlight to 600 lux and the temperature to 28 degrees to coincide with the period when market prices for tomatoes are high."

[0686] Based on the results of the generative AI model, the server controls the temperature control device and lighting devices to adjust the amount of sunlight and temperature, for example, setting the temperature control device to 28 degrees and the lighting device to 600 lux.

[0687] 3. User Emotion Recognition

[0688] The device recognizes the user's emotions using an emotion engine. For example, the emotion engine determines that the user is feeling stressed.

[0689] The emotion engine sends the user's emotion data to the generative AI model. For example, it sends data that says "the user is feeling stressed."

[0690] 4. Recommendations for optimal crops

[0691] The generative AI model suggests optimal crops and varieties based on the user's emotional data, for example, "Since the user is feeling stressed, we suggest growing lavender, which has stress-reducing properties."

[0692] The server notifies the user of the generative AI model's suggestions, for example by displaying a message on the device saying, "We suggest growing lavender."

[0693] Examples of concrete examples and prompts

[0694] Examples:

[0695] If a user is growing tomatoes, the generative AI model will analyze market price data and adjust the amount of sunlight and temperature to coincide with periods when tomatoes are at their highest market price.

[0696] If the user is feeling stressed, the emotion engine relays that information to the generative AI model, which then suggests growing lavender.

[0697] Example prompt sentence:

[0698] "How can I optimize the timing of tomato harvest based on market price data and crop maturity data?"

[0699] "If a user is stressed, how can you suggest crops that will reduce stress?"

[0700] In this way, the system collects, analyzes, adjusts, and proposes specific data at each step to provide the user with the optimal cultivation environment and crops. The flow of the identification process in Example 3 will be described with reference to FIG.

[0701] Step 1: Data collection

[0702] The server collects real-time sunlight and temperature data from the temperature sensor and light sensor. For example, the temperature sensor measures the current temperature as 25 degrees, and the light sensor measures the sunlight as 500 lux.

[0703] Input: Data from temperature and light sensors

[0704] Output: Collected solar radiation and temperature data

[0705] Specific operation: The server periodically polls the data from the sensors and stores it in a database.

[0706] Step 2: Obtain market price data

[0707] The server retrieves the latest market price data from the market price database. For example, it retrieves that the market price of tomatoes is 300 yen per kilogram.

[0708] Input: Query to market price database

[0709] Output: Obtained market price data

[0710] Specific operation: The server periodically accesses the market price database, obtains the latest price information, and stores it in the database.

[0711] Step 3: Collect crop maturity data

[0712] The server collects crop maturity data, for example, determining that tomatoes are 80% ripe.

[0713] Input: Data from crop maturity sensors

[0714] Output: Collected crop maturity data

[0715] What it does: The server periodically polls data from the crop maturity sensors and stores it in a database.

[0716] Step 4: Data analysis and adjustment

[0717] The server inputs the collected market price data and crop maturity data into the generative AI model. For example, it inputs data such as "the market price of tomatoes is 300 yen and the maturity level is 80%."

[0718] Input: Market price data and crop maturity data

[0719] Output: Analysis results from the generative AI model (optimal amount of sunlight and temperature)

[0720] How it works: The generative AI model analyzes input data and calculates the optimal amount of sunlight and temperature. For example, it will output a result such as "Adjust the amount of sunlight to 600 lux and the temperature to 28 degrees to coincide with the period when market prices for tomatoes are high."

[0721] Step 5: Adjusting the environment

[0722] Based on the results of the generative AI model, the server controls the temperature control device and lighting devices to adjust the amount of sunlight and temperature, for example, setting the temperature control device to 28 degrees and the lighting device to 600 lux.

[0723] Input: Analysis results of the generative AI model

[0724] Output: Adjusted solar radiation and temperature

[0725] Specific operation: The server sends control signals to the temperature control device and lighting device to change their settings.

[0726] Step 6: Recognizing User Emotions

[0727] The device recognizes the user's emotions using an emotion engine. For example, the emotion engine determines that the user is feeling stressed.

[0728] Input: User facial and voice data

[0729] Output: Recognized user emotion data

[0730] Specific operation: The device inputs the user's facial expressions and voice into the emotion engine and analyzes emotions.

[0731] Step 7: Recommend the best crop

[0732] The generative AI model suggests optimal crops and varieties based on the user's emotional data, for example, "Since the user is feeling stressed, we suggest growing lavender, which has stress-reducing properties."

[0733] Input: User emotion data

[0734] Output: Suggested optimal crops and varieties

[0735] How it works: The generative AI model analyzes emotion data and calculates the best crops and varieties.

[0736] Step 8: Notify users

[0737] The server notifies the user of the generative AI model's suggestions, for example by displaying a message on the device saying, "We suggest growing lavender."

[0738] Input: Proposal results of generative AI model

[0739] Output: A message to inform the user

[0740] Specific operation: The server sends the proposal results to the terminal and notifies the user.

[0741] (Application example 3)

[0742] Next, a description will be given of Application Example 3 of Form Example 3. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0743] Conventional agricultural systems have the problem that it is difficult to optimize crop harvest timing in response to fluctuations in market prices, making it difficult to maximize profits. Furthermore, they are unable to suggest optimal crops and varieties based on user sentiment, which makes it difficult to increase user satisfaction.

[0744] The specific processing by the specific processing unit 290 of the data processing device 12 in Application Example 3 is realized by the following means. In this invention, the server includes: a means for proposing the optimal crop and variety to cultivate based on data acquired from various sensors; a means for automating optimal watering for crops; a means for proposing the type of fertilizer and the timing of application; a means for automating sunlight and temperature adjustment so that the crops can be shipped at a time when market prices are high; a means for proposing the optimal crop and variety based on the user's emotions using an emotion engine that recognizes the user's emotions; a means for acquiring market price data in real time and using a generation AI to calculate the optimal sunlight and temperature; and a means for a robot to operate temperature and sunlight adjustment devices to maintain an optimal environment. This makes it possible to maximize profits in response to fluctuations in market prices and to propose the optimal crop and variety based on the user's emotions.

[0745] The "various sensors" are devices used to acquire environmental data such as the temperature, humidity, amount of light, and soil moisture of the soil where crops are to be grown.

[0746] The "means for proposing optimal crops and varieties to cultivate" is a function that selects and proposes the most suitable crops and varieties based on the acquired environmental data.

[0747] "The best automated watering method for crops" is a system that automatically supplies the amount of water needed for crop growth.

[0748] "Means for suggesting the type of fertilizer and the timing of application" is a function that suggests the optimal type of fertilizer and the timing of application depending on the stage of crop growth and environmental conditions.

[0749] "Automated means of adjusting sunlight and temperature to allow shipment at a time when market prices are high" is a system that automatically adjusts sunlight and temperature based on market price data so that crops can be harvested at the optimal time.

[0750] The "emotion engine that recognizes user emotions" is software that analyzes and recognizes emotions from the user's facial expressions and voice.

[0751] "Generative AI" is a system that uses artificial intelligence technology to analyze data and make optimal decisions and suggestions.

[0752] "Means of obtaining market price data in real time" refers to the ability to continuously obtain the latest market price information via the Internet or a dedicated data feed.

[0753] "Means for the robot to operate temperature control devices and sunlight control devices" refers to a function for the robot to automatically control devices that adjust the temperature and amount of sunlight.

[0754] As an embodiment of the present invention, a smart agricultural robot system will be described as an example. This system is configured using hardware and software such as various sensors, generative AI, emotion engines, temperature control devices, sunshine control devices, and robots.

[0755] First, the server collects environmental data such as temperature, humidity, light intensity, and soil moisture from various sensors. This data is then analyzed by the generative AI, which then suggests the optimal crops and varieties to cultivate. Based on this data, the generative AI calculates the amount of water needed for crop growth and automates optimal watering for the crops.

[0756] The server then suggests the type of fertilizer and the timing of its application. This is also achieved by the AI ​​analyzing environmental data and the stage of crop growth. Furthermore, the AI ​​obtains market price data in real time and calculates the optimal amount of sunlight and temperature. This allows the robot to operate the temperature and sunlight control devices to maintain the optimal environment.

[0757] It also has an emotion engine that recognizes the user's emotions and analyzes their facial expressions and voice. For example, if the user is feeling stressed, the emotion engine conveys that information to the generation AI, which then suggests growing crops that may reduce stress, such as lavender.

[0758] For example, if the emotion engine detects that a user is feeling stressed, the generative AI will suggest growing lavender, optimizing temperature and sunlight to ensure the lavender grows in the best conditions. In this way, the system can respond to fluctuations in market prices to maximize profits and also recommend the best crops and varieties based on the user's emotions.

[0759] Examples of prompts include:

[0760] “If the user is stressed, what crops will the generative AI suggest?”

[0761] This system is expected to not only improve agricultural efficiency and maximize profits, but also increase user satisfaction.

[0762] The flow of the specific processing in Application Example 3 will be described with reference to FIG.

[0763] Step 1:

[0764] The server acquires environmental data such as temperature, humidity, light intensity, and soil moisture from various sensors. This data is sent from the sensors to the server, which then receives it. The input is the environmental data from the various sensors, and the output is the environmental data stored on the server.

[0765] Step 2:

[0766] The server sends the acquired environmental data to the generation AI, which analyzes it. The generation AI selects the optimal crops and their varieties based on the environmental data. The input is environmental data, and the output is a proposal for the optimal crop and variety.

[0767] Step 3:

[0768] The server automates optimal watering for crops based on suggestions from the generative AI. Specifically, the server controls the watering device and supplies the required amount of water. The input is suggestions from the generative AI and environmental data, and the output is the execution of automated watering.

[0769] Step 4:

[0770] The server uses a generation AI to suggest the type of fertilizer and the timing of its application. The generation AI analyzes environmental data and the growth stage of the crop, and calculates the optimal type of fertilizer and the timing of its application. The input is the environmental data and the growth stage of the crop, and the output is a proposal for the type of fertilizer and the timing of its application.

[0771] Step 5:

[0772] The server retrieves market price data in real time, and the generation AI calculates the optimal amount of sunlight and temperature. Market price data is obtained via the internet or a dedicated data feed. The input is market price data, and the output is the optimal set value for sunlight and temperature.

[0773] Step 6:

[0774] The server sends the optimal sunlight and temperature settings to the robot, which then operates the temperature and sunlight control devices, maintaining an optimal environment. The input is the optimal sunlight and temperature settings, and the output is the adjusted environmental conditions.

[0775] Step 7:

[0776] The emotion engine recognizes the user's emotions and analyzes their emotions from their facial expressions and voice. The emotion engine then sends the user's emotional data to the generation AI. The input is the user's facial expressions and voice data, and the output is the analyzed emotional data.

[0777] Step 8:

[0778] The generative AI uses emotional data from the emotion engine to suggest the best crops and varieties based on the user's emotions. For example, if the user is feeling stressed, the generative AI will suggest growing lavender. The input is emotional data, and the output is a recommendation of the best crops and varieties.

[0779] Step 9:

[0780] Based on suggestions from the AI ​​generator, the server optimizes temperature and sunlight to ensure the crops grow in the best conditions. This allows the optimal crops to be grown according to the user's emotions. The input is the suggestions from the AI ​​generator, and the output is the optimized environmental conditions.

[0781] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0782] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> ) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0783] Another example of generative AI is Gemini (registered trademark) (Internet search engine). <url: https: gemini.google.com ?hl="ja">) are mentioned.

[0784] In the above embodiment, an example in which the specific process is performed by the data processing device 12 has been given, but the technology of the present disclosure is not limited to this, and the specific process may be performed by the smart device 14.

[0785] [Second embodiment]

[0786] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0787] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0788] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0789] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0790] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio in accordance with instructions from the processor 46.

[0791] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the surroundings of user 20 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0792] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0793] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0794] The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0795] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0796] In the smart glasses 214, the reception output process is performed by the processor 46. A reception output program 60 is stored in the storage 50. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0797] Next, the specific processing by the specific processing unit 290 of the data processing device 12 will be described.

[0798] "Example 1"

[0799] This invention is a system that uses a generative AI to propose optimal crops and varieties for cultivation based on data acquired from various sensors. Specifically, data such as soil humidity, temperature, and sunshine hours are acquired, and the generative AI proposes optimal crops and varieties based on this data. For example, if the soil humidity is high and the sunshine hours are long, the generative AI will propose rice cultivation.

[0800] "Example 2"

[0801] Another aspect of the present invention is a system that automates optimal watering for crops. Specifically, the AI ​​automatically adjusts the timing and amount of watering based on soil humidity data acquired from sensors. For example, if the soil humidity drops, the AI ​​automatically waters the crops.

[0802] "Example 3"

[0803] Furthermore, one aspect of the present invention is a system that automates the adjustment of sunlight and temperature so that crops can be shipped at a time when market prices are high. Specifically, the generation AI automatically adjusts sunlight and temperature based on market price data and the maturity of the crops, allowing crops to be harvested at a time when market prices are high. For example, the generation AI adjusts sunlight and temperature to coincide with the period when the market price of tomatoes is high, allowing tomatoes to be harvested at that time.

[0804] The processing flow of each embodiment will be described below.

[0805] "Example 1"

[0806] Step 1: Obtain data such as soil moisture, temperature, and sunlight hours from various sensors.

[0807] Step 2: Input the acquired data into the generation AI.

[0808] Step 3: The AI ​​analyzes the data and suggests the best crop and variety. For example, if the soil moisture is high and the sunshine hours are long, the AI ​​will suggest growing rice.

[0809] "Example 2"

[0810] Step 1: Obtain soil moisture data from the sensor.

[0811] Step 2: Input the acquired humidity data into the generation AI.

[0812] Step 3: The AI ​​analyzes the humidity data and automatically adjusts the timing and amount of watering. For example, if the soil humidity drops, the AI ​​will automatically water the plants.

[0813] "Example 3"

[0814] Step 1: Generative AI automatically adjusts the amount of sunlight and temperature based on market price data and crop maturity.

[0815] Step 2: The AI ​​adjusts the amount of sunlight and temperature so that crops can be harvested at a time when market prices are high. For example, when the market price of tomatoes is high, the AI ​​adjusts the amount of sunlight and temperature so that tomatoes can be harvested at that time.

[0816] Example 1

[0817] Next, a description will be given of Example 1 of Form Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0818] With conventional agricultural systems, it was difficult to effectively utilize data acquired from various sensors to propose optimal crops and varieties and manage cultivation.It was also difficult to adjust shipping timing in response to fluctuations in market prices, which made it difficult to maximize profits.

[0819] The identification processing by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means. In this invention, the server includes a means for proposing optimal crops and varieties to cultivate based on data acquired from various sensors, a means for preprocessing the collected data, a means for analyzing the preprocessed data, a means for inputting a prompt statement to the generative AI model based on the analysis results, a means for acquiring a proposal from the generative AI model, and a means for displaying the acquired proposal to the user. This makes it possible to effectively utilize data acquired from various sensors to propose optimal crops and varieties and manage cultivation. In addition, it is possible to adjust shipping timing in response to fluctuations in market prices, thereby maximizing profits.

[0820] "Various sensors" are devices used to acquire environmental data on agricultural land, such as soil moisture sensors, temperature sensors, and sunlight sensors.

[0821] The "means for preprocessing data" is a function for performing processing to remove outliers from collected data and normalize the data.

[0822] "Means for analyzing data" refers to a function that performs statistical analysis and calculates average values ​​based on preprocessed data.

[0823] A "generative AI model" is an artificial intelligence model that generates optimal crop and variety recommendations based on input prompt statements.

[0824] A "prompt sentence" is an instruction sentence to be input into a generative AI model, and is a sentence that contains the analysis results.

[0825] The "means for obtaining proposals" is a function for receiving proposals generated from a generative AI model.

[0826] The "means for displaying proposals to the user" is a function for displaying the acquired proposals on the user's terminal.

[0827] "Automated sunlight and temperature adjustment" is a function that automatically adjusts sunlight and temperature based on data from environmental sensors.

[0828] This invention is a system that uses a generative AI model to propose optimal crops and varieties for cultivation based on data acquired from various sensors. Specifically, data such as soil humidity, temperature, and sunlight hours are acquired, and the generative AI model proposes optimal crops and varieties based on this data.

[0829] Hardware and software used

[0830] The server collects data from various sensors (e.g., soil moisture sensors, temperature sensors, and sunlight sensors). These sensors are installed in the farmland and provide data in real time. The server uses Python libraries such as Pandas and NumPy to preprocess and analyze the data. It also uses OpenAI's GPT-4 as a generative AI model.

[0831] The terminal is a device for displaying suggestions to the user, such as a smartphone or tablet.

[0832] Data collection and preprocessing

[0833] The server collects data from various sensors every day at 9:00 AM. For example, the soil humidity sensor gives data such as "Humidity: 70%," the temperature sensor gives data such as "Temperature: 25°C," and the sunshine sensor gives data such as "Sunshine hours: 8 hours." The collected data is then normalized and outliers are removed. For example, if an outlier such as "Humidity: 150%" is obtained from the soil humidity sensor, that data is removed. Temperature data is also normalized by converting it from Celsius to Fahrenheit.

[0834] Data analysis and the use of generative AI models

[0835] The server performs statistical analysis based on the preprocessed data. For example, if the soil humidity data for the past week was "65%, 70%, 75%, 70%, 68%, 72%, and 71%, the average value is calculated and set to "humidity: 70%." Based on the analysis results, a prompt is input to the generative AI model. The prompt includes the analysis results. For example, it could be "Please suggest the optimal crop and variety if the soil humidity is 70%, the temperature is 25 degrees, and there are 8 hours of sunshine."

[0836] Generate and view suggestions

[0837] The generative AI model generates a suggestion, "Rice cultivation is optimal," based on the prompt text. This suggestion is returned to the server. The server then sends the generated suggestion to the device. The device then displays "Rice cultivation is optimal" to the user. The user can view this suggestion on the screen of their smartphone or tablet.

[0838] Specific examples

[0839] Prompt Sentence Examples

[0840] "Please suggest the best crop and variety for a soil with 70% moisture, 25°C temperature, and 8 hours of sunshine."

[0841] Example proposal

[0842] "Rice cultivation is the best."

[0843] In this way, users can receive real-time recommendations on the best crops and varieties to suit the conditions of their farmland. They can also adjust shipping timing in response to fluctuations in market prices, maximizing profits.

[0844] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0845] Step 1: Data collection

[0846] The server collects data from various sensors. Specifically, soil humidity sensors, temperature sensors, sunlight sensors, etc. are installed on the farmland, and data is obtained from these sensors in real time. For example, the soil humidity sensor obtains data such as "humidity: 70%," the temperature sensor obtains data such as "temperature: 25 degrees," and the sunlight sensor obtains data such as "sunshine hours: 8 hours." The input is data from the various sensors, and the output is the collected raw data.

[0847] Step 2: Preprocessing the data

[0848] The server preprocesses the collected data. Specifically, it removes outliers and normalizes the data. For example, if an abnormal value such as "Humidity: 150%" is obtained from a soil humidity sensor, the data is removed. It also performs normalization such as converting temperature data from Celsius to Fahrenheit. The input is the collected raw data, and the output is the preprocessed data.

[0849] Step 3: Analyze the data

[0850] The server analyzes the preprocessed data. For the analysis, it uses Python libraries such as Pandas and NumPy. Specifically, it calculates the average and standard deviation of soil humidity, temperature, and sunshine hours. For example, it calculates the average soil humidity based on data from the past week. The input is the preprocessed data, and the output is the analysis results.

[0851] Step 4: Using the generative AI model

[0852] The server inputs a prompt statement to a generative AI model (for example, OpenAI's GPT-4) based on the analysis results. The prompt statement includes the analysis results. For example, it could say, "Please suggest the optimal crop and variety when the soil humidity is 70%, the temperature is 25 degrees, and the sunshine hours are 8 hours." The input is the analysis result and the prompt statement, and the output is the input to the generative AI model.

[0853] Step 5: Generate proposals

[0854] The generative AI model suggests the best crop and variety based on the input prompt. For example, it generates a suggestion such as "Rice cultivation is best." The generated suggestion is returned to the server. The input is the prompt, and the output is the generated suggestion.

[0855] Step 6: Viewing Proposals

[0856] The server sends the generated proposal to the terminal. The terminal displays the proposal to the user. For example, "Rice cultivation is optimal" may be displayed on the screen of a smartphone or tablet. The input is the generated proposal, and the output is the proposal displayed to the user.

[0857] (Application example 1)

[0858] Next, a description will be given of Application Example 1 of Form Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0859] Conventional plant cultivation systems can suggest optimal crops and varieties based on data obtained from various sensors, but in automated plant cultivation systems within factories, it has been difficult to integrate the suggestion of optimal crops and varieties with the automation of the cultivation process.In addition, it has been impossible to issue instructions to maintain optimal environmental conditions based on the suggested crops, making efficient cultivation difficult.

[0860] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[0861] In this invention, the server includes: means for proposing the optimal crop and variety to cultivate based on data acquired from various sensors; means for automating optimal watering for crops; means for proposing the type of fertilizer and the timing of application; means for automating sunlight and temperature adjustment so that products can be shipped at a time when market prices are high; means for providing sales support; means for collecting data from various sensors installed in the plant cultivation area within the factory and proposing the optimal crop and variety using a generative AI; and means for automating the cultivation process based on the proposed crop and issuing instructions for maintaining optimal environmental conditions. This enables the automated plant cultivation system within the factory to comprehensively propose the optimal crop and variety and automate the cultivation process.

[0862] "Various sensors" are devices for acquiring environmental data, such as humidity sensors, temperature sensors, and light sensors.

[0863] "Data" refers to information such as humidity, temperature, and hours of sunlight obtained from various sensors.

[0864] "Generative AI" is a system that uses artificial intelligence technology to suggest optimal crops and varieties based on input data.

[0865] "Crop and variety proposal" refers to the generative AI analyzing data obtained from various sensors and selecting the most suitable crop and its variety.

[0866] "Automated watering" is a system that automatically supplies the necessary water to crops.

[0867] "Fertilizer type" refers to the type of nutrients used to promote crop growth.

[0868] "Application timing" refers to the optimal time or period to apply fertilizer or water to crops.

[0869] "Automated sunlight and temperature adjustment" is a system that automatically adjusts the amount of sunlight and temperature to suit crop growth.

[0870] "Sales support" is a system that supports sales strategies based on market prices and demand for crops.

[0871] A "plant cultivation area" is a specific area within a factory set aside for growing plants.

[0872] "Cultivation process automation" refers to a system that automatically performs a series of tasks related to crop cultivation.

[0873] "Environmental conditions" are factors such as humidity, temperature, and hours of sunlight that affect crop growth.

[0874] "Instruct" refers to issuing instructions for the system to take any action or adjustment necessary to maintain optimal environmental conditions.

[0875] The system for implementing this invention collects data from various sensors installed in the plant cultivation area within the factory, uses generative AI to propose optimal crops and varieties, and automates the cultivation process. A specific embodiment of this system is shown below.

[0876] The server collects data from various sensors, including humidity sensors, temperature sensors, and light sensors, which are installed in the plant cultivation area and obtain environmental data such as soil humidity, temperature, and sunlight hours in real time.

[0877] The collected data is sent to a server and input into a generative AI model. The generative AI model analyzes this data and suggests the optimal crop and variety. For example, if the soil humidity is 70%, the temperature is 25 degrees, and the sunshine hours are 10 hours, the generative AI model will suggest "rice" as the optimal crop.

[0878] Based on the proposed crop, the server issues instructions to automate the cultivation process, such as determining the optimal timing and amount of watering, the type of fertilizer and when to apply it, and adjusting the amount of sunlight and temperature, thereby maintaining optimal environmental conditions and enabling efficient cultivation.

[0879] The server also collects market price data and suggests optimal shipping times, adjusting the amount of sunlight and temperature to control crop growth so that products can be shipped at a time when market prices are high.

[0880] For example, by inputting the following prompt into the generative AI model, you can receive suggestions for the optimal crop and variety:

[0881] "What is the best crop / variety to grow if the soil moisture is 70%, the temperature is 25°C, and there are 10 hours of sunshine?"

[0882] In this way, the server uses a generative AI model to suggest optimal crops and varieties based on data from various sensors, automating the cultivation process and achieving efficient plant cultivation.

[0883] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0884] Step 1:

[0885] The server collects data from various sensors, such as humidity sensors, temperature sensors, and light sensors. These sensors are installed in the plant cultivation area and obtain environmental data such as soil humidity, temperature, and sunlight hours in real time. The input is the environmental data from the various sensors, and the output is the collected data.

[0886] Step 2:

[0887] The server inputs the collected data into the generative AI model, which analyzes this data and proposes the optimal crop and variety. The input is the collected environmental data, and the output is the optimal crop and variety proposed by the generative AI model. Specifically, the server generates a prompt sentence and sends it to the generative AI model.

[0888] Step 3:

[0889] The server receives suggestions from the generative AI model and issues instructions to automate the cultivation process based on the optimal crop and variety. The input is the suggestions from the generative AI model, and the output is instructions for the automated cultivation process. Specifically, the server automatically sets the timing and amount of watering, the type of fertilizer and when to apply it, and adjusts the amount of sunlight and temperature.

[0890] Step 4:

[0891] The server collects market price data and proposes the optimal shipping timing. The input is market price data, and the output is a proposal for the optimal shipping timing. Specifically, the server accesses a market price database and obtains the current market price.

[0892] Step 5:

[0893] The server adjusts the amount of sunlight and temperature based on the optimal shipping timing to control crop growth. The input is the proposal for the optimal shipping timing, and the output is the adjusted amount of sunlight and temperature. Specifically, the server sends control signals to adjust the amount of sunlight and temperature.

[0894] Step 6:

[0895] The user checks the suggestions and instructions from the server and makes manual adjustments as necessary. The input is the suggestions and instructions from the server, and the output is the result of manual adjustments made by the user. Specifically, the user checks the server settings through the management screen and makes changes as necessary.

[0896] Example 2

[0897] Next, a description will be given of Example 2 of Form Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0898] Conventional agricultural systems require manual operations such as watering crops, spreading fertilizer, and adjusting sunlight and temperature, which is inefficient and often results in missing the optimal timing. It is also difficult to plan shipments at times when market prices are high. This leads to problems such as reduced crop quality and yields, and a decline in agricultural management efficiency.

[0899] The specific processing by the specific processing unit 290 of the data processing device 12 in Example 2 is realized by the following means. In this invention, the server includes a means for proposing the optimal crop and variety to cultivate based on data acquired from various sensors, a means for automatically adjusting the timing and amount of watering using a generative AI model based on humidity data acquired from a soil humidity sensor, a means for proposing the type of fertilizer and the timing of spraying, a means for automating the adjustment of sunlight and temperature so that products can be shipped at a time when market prices are high, and a means for providing sales support. This automates the entire process from crop cultivation to shipping, enabling efficient and optimal agricultural management.

[0900] "Various sensors" are devices used to acquire data on the crop cultivation environment, such as soil moisture sensors, temperature sensors, and sunlight sensors.

[0901] A "soil moisture sensor" is a device for measuring soil moisture and acquiring that data.

[0902] A "generative AI model" is an algorithm that uses artificial intelligence technology to analyze data and calculate the optimal timing and amount of watering.

[0903] The "means for automatically adjusting the timing and amount of watering" is a system that uses a generative AI model to determine the start time of watering and the amount of water to supply based on data obtained from a soil moisture sensor, and then automatically executes the process.

[0904] The "means for proposing the type of fertilizer and the timing of application" is a system that suggests the optimal type of fertilizer and the timing of its application based on the growth stage of the crop and environmental data.

[0905] The "means for automating sunlight and temperature adjustment" is a system that uses a generative AI model to optimally adjust sunlight and temperature based on data obtained from sunlight and temperature sensors.

[0906] "Sales support means" refers to a system that proposes optimal shipping times and sales strategies based on market prices and demand forecast data.

[0907] This invention is a system that automates optimal watering for crops. Specifically, a generative AI model automatically adjusts the timing and amount of watering based on soil moisture data obtained from sensors.

[0908] Hardware and software used

[0909] The server uses the following hardware and software to collect and analyze data:

[0910] Soil moisture sensor (e.g. DHT11, DHT22)

[0911] Temperature sensor, sunlight sensor

[0912] Generative AI models (e.g., OpenAI's GPT-4)

[0913] Communication modules (e.g. Wi-Fi, LoRa)

[0914] Watering systems (e.g. solenoid valves connected to computer control circuits)

[0915] The terminal uses the following hardware and software to collect data from the sensors and send it to the server:

[0916] soil moisture sensor

[0917] Communication Module

[0918] Computer Control Circuit

[0919] Data processing and calculation

[0920] The device collects data from the soil moisture sensor in real time and sends it to the server using wireless communication technologies such as Wi-Fi or LoRa. Specifically, the device converts the humidity data into JSON format and sends it to the server using an HTTP POST request.

[0921] The server inputs the received humidity data into the generative AI model. The generative AI model analyzes the humidity data and calculates the optimal timing and amount of watering. Specifically, the server converts the humidity data into a prompt sentence and sends it to the generative AI model. For example, it generates a prompt sentence such as, "The current soil humidity is 30%. Please tell me the optimal timing and amount of watering."

[0922] The server generates watering instructions based on the output from the generative AI model. The instructions include when to start watering and how much water to supply. Specifically, it analyzes the output from the generative AI model and generates the instruction "Start watering and supply 500ml of water."

[0923] The server sends the generated watering instructions to the device using wireless communication technologies such as Wi-Fi or LoRa. Specifically, the server converts the watering instructions into JSON format and sends them to the device using an HTTP POST request.

[0924] The terminal carries out watering based on the received watering instructions. Specifically, it controls a solenoid valve connected to a computer control circuit to supply the required amount of water to the crops. Specifically, the terminal opens the solenoid valve and supplies 500 ml of water. When watering is complete, it closes the solenoid valve.

[0925] Specific examples

[0926] For example, if the soil moisture drops below 30%, the generative AI model generates the instruction to "start watering and supply 500ml of water." The server receives this instruction and sends it to the terminal, which then opens the solenoid valve and supplies 500ml of water to the crops.

[0927] Example prompts to input to the generative AI model

[0928] "The current soil moisture level is 30%. What is the best time and amount to water?"

[0929] The system allows users to automate the watering of their crops and maintain an optimal environment.

[0930] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0931] Step 1:

[0932] The terminal collects soil humidity data using a soil humidity sensor. The sensor is inserted into the soil and measures the humidity at regular intervals (for example, every hour). The input is the soil humidity data, and the output is the collected humidity data. Specifically, the sensor acquires the humidity data as an analog signal, which the terminal converts into digital data.

[0933] Step 2:

[0934] The terminal sends the collected humidity data to a server. Communication is performed using wireless communication technologies such as Wi-Fi or LoRa. The input is the collected humidity data, and the output is the humidity data sent to the server. Specifically, the terminal converts the humidity data into JSON format and sends it to the server using an HTTP POST request.

[0935] Step 3:

[0936] The server inputs the received humidity data into the generative AI model. The generative AI model analyzes the humidity data and calculates the optimal timing and amount of watering. The input is the humidity data sent to the server, and the output is the analysis result from the generative AI model. Specifically, the server converts the humidity data into a prompt text and sends it to the generative AI model. For example, it generates a prompt text such as, "The current soil humidity is 30%. Please tell me the optimal timing and amount of watering."

[0937] Step 4:

[0938] The server generates watering instructions based on the output from the generative AI model. The instructions include when to start watering and the amount of water to supply. The input is the analysis result from the generative AI model, and the output is the generated watering instructions. Specifically, the server analyzes the output from the generative AI model and generates the instruction to "start watering and supply 500ml of water."

[0939] Step 5:

[0940] The server sends the generated watering instructions to the device. Communication is performed using wireless communication technologies such as Wi-Fi or LoRa. The input is the generated watering instructions, and the output is the watering instructions sent to the device. Specifically, the server converts the watering instructions into JSON format and sends them to the device using an HTTP POST request.

[0941] Step 6:

[0942] The terminal carries out watering based on the received watering instructions. Specifically, it controls a solenoid valve connected to a computer control circuit to supply the required amount of water to the crops. The input is the watering instruction sent to the terminal, and the output is the executed watering. Specifically, the terminal opens the solenoid valve and supplies 500 ml of water. When watering is complete, it closes the solenoid valve.

[0943] (Application example 2)

[0944] Next, a description will be given of Application Example 2 of Form Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0945] Conventional plant cultivation systems require manual control of soil humidity and watering timing, making efficient management difficult. Furthermore, because environmental conditions vary when cultivating plants within factories, there is a need for a system that can automatically adjust the appropriate watering timing and amount.

[0946] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[0947] In this invention, the server includes a means for proposing the optimal crop and variety to cultivate based on data obtained from various sensors, a means for automating the optimal watering for the crops, a means for proposing the type of fertilizer and the timing of application, a means for automating the amount of sunlight and temperature adjustment so that the crops can be shipped at a time when the market price is high, a means for providing sales support, and a means installed on a robot that grows plants in a factory, where the generating AI automatically adjusts the timing and amount of watering based on soil humidity data obtained from the sensors.This enables efficient and automatic plant growth management.

[0948] "Various sensors" are devices used to acquire environmental data such as soil humidity, temperature, and amount of sunlight.

[0949] The "means for proposing the optimal crops and varieties to cultivate" is a function that analyzes the acquired data and selects and proposes the optimal crops and varieties.

[0950] "Automated watering method that is optimal for crops" is a function that automatically waters crops at the appropriate time and in the appropriate amount based on soil humidity data.

[0951] The "means for suggesting the type of fertilizer and the timing of application" is a function for suggesting the type of fertilizer required for crop growth and the timing of application.

[0952] "Automated means for adjusting sunlight and temperature" is a function for automatically adjusting the amount of sunlight and temperature to suit crop growth.

[0953] "Sales support means" is a function for supporting sales strategies based on the market price and demand of crops.

[0954] A "factory plant growing robot" is an automated device designed to grow plants in a factory.

[0955] "Generative AI" is an artificial intelligence technology that analyzes acquired data and predicts and executes optimal actions.

[0956] "Soil moisture data" is data that indicates the amount of moisture in the soil.

[0957] "Means for automatically adjusting the timing and amount of watering" is a function that waters plants at the appropriate timing and amount based on soil humidity data.

[0958] The system for implementing this invention includes various sensors, a generative AI model, and a plant growing robot. The specific configuration and operation of the system are described below.

[0959] System configuration

[0960] 1. Various sensors

[0961] The various sensors are devices for acquiring environmental data such as soil humidity, temperature, and amount of sunlight. Specifically, they include soil humidity sensors, temperature sensors, and light sensors.

[0962] 2. Generative AI Models

[0963] A generative AI model is an artificial intelligence technology that analyzes acquired data and predicts and executes optimal actions. For example, a model trained using a machine learning library such as TensorFlow is used.

[0964] 3. Plant-growing robot

[0965] A plant-growing robot is an automated device designed to grow plants in a factory. The robot receives data from sensors and follows instructions from a generative AI model to water and fertilize the plants.

[0966] System Operation

[0967] 1. Data Acquisition

[0968] Various sensors collect real-time environmental data, such as soil humidity, temperature, and sunlight, which is then sent to the plant-growing robot's control system.

[0969] 2. Data Analysis

[0970] The plant-growing robot's control system inputs the acquired data into a generative AI model to predict the optimal timing and amount of watering, which then determines the optimal behavior based on past data and current environmental data.

[0971] 3. Watering

[0972] The plant-growing robot automatically waters plants based on the timing and amount of watering predicted by the generative AI model. Specifically, the robot's onboard water pump delivers the appropriate amount of water.

[0973] Hardware and software used

[0974] Hardware

[0975] Soil moisture sensor (e.g., Arduino Soil Moisture Sensor)

[0976] Temperature sensor (e.g. DHT22)

[0977] Optical sensor (e.g. BH1750)

[0978] Water pump (e.g. Peristaltic Liquid Pump)

[0979] software

[0980] Machine learning libraries (e.g. TensorFlow)

[0981] Control systems (e.g., computer control circuits)

[0982] Specific examples

[0983] For example, when growing tomatoes in a factory, the following prompt sentences can be input into the generative AI model:

[0984] Prompt Sentence Examples

[0985] "When soil moisture falls below 30%, use a generative AI model to predict the appropriate amount of watering and use a pump to provide water."

[0986] In this way, the system can efficiently and automatically manage plant growth.

[0987] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0988] Step 1:

[0989] The server collects environmental data such as soil humidity, temperature, and amount of sunlight from various sensors.

[0990] Input: Data from soil moisture sensor, temperature sensor, and light sensor

[0991] Output: Acquired environmental data

[0992] Specific operation: The server periodically polls data from each sensor and collects environmental data in real time.

[0993] Step 2:

[0994] The server inputs the acquired environmental data into a generative AI model to predict the optimal timing and amount of watering.

[0995] Input: Obtained environmental data

[0996] Output: Predicted watering timing and amount

[0997] Specific operation: The server inputs environmental data into a generative AI model and calculates the optimal watering timing and amount based on past and current environmental data.

[0998] Step 3:

[0999] The server sends instructions to the plant-growing robot based on the timing and amount of watering predicted by the generative AI model.

[1000] Input: Predicted watering timing and amount

[1001] Output: Watering instructions

[1002] Specific operation: Based on the prediction results obtained from the generative AI model, the server sends specific watering instructions to the plant-growing robot.

[1003] Step 4:

[1004] The plant-growing robot follows instructions from the server to control the water pump and supply the appropriate amount of water.

[1005] Input: Watering instructions from the server

[1006] Output: Actual watering behavior

[1007] Specific operation: The plant growing robot starts the water pump and supplies the specified amount of water to the plant.

[1008] Step 5:

[1009] The server receives feedback from the plant-growing robot and stores it as data for the next watering.

[1010] Input: Feedback data from a plant-growing robot

[1011] Output: Updated environment data

[1012] Specific operation: The server receives feedback data from the plant-growing robot, stores it in a database, and uses it to predict the next watering.

[1013] Example 3

[1014] Next, a description will be given of Example 3 of Form Example 3. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[1015] In conventional agricultural systems, it is difficult to respond to fluctuations in market prices when cultivating crops, and the optimal harvest timing is often missed. In addition, adjusting environmental conditions manually makes efficient management difficult. This makes it difficult to maximize profits, and it requires a lot of effort and time.

[1016] The specific processing by the specific processing unit 290 of the data processing device 12 in the third embodiment is realized by the following means.

[1017] In this invention, the server includes means for proposing optimal crops and varieties to cultivate based on data acquired from various sensors, means for automating optimal watering for crops, means for proposing the type of fertilizer and the timing of application, means for automating adjustment of sunlight and temperature so that crops can be shipped at a time when market prices are high, means for analyzing market price data and crop maturity data using a generative AI model and calculating the optimal sunlight and temperature, means for adjusting environmental conditions in the greenhouse based on the calculation results, and means for monitoring and providing feedback on environmental conditions.This enables efficient environmental management that can respond to fluctuations in market prices and ensures that the optimal harvest timing is not missed.

[1018] "Various sensors" are devices used to measure environmental conditions and the growth status of crops, such as temperature sensors, humidity sensors, and light sensors.

[1019] "Data" refers to information that serves as the basis for analysis and judgment, such as information obtained from various sensors and market price information.

[1020] "Proposing the optimal crops and varieties to cultivate" means selecting the crops and varieties that are best suited to specific environmental conditions based on collected data and recommending them to the user.

[1021] "Automated watering" is a system that automatically supplies the water necessary for crop growth.

[1022] "Suggestion of fertilizer type and application timing" means recommending the type of fertilizer and the application timing that are optimal for crop growth based on data.

[1023] "High market price period" refers to a period when the market price of a crop is higher than usual.

[1024] "Automated sunlight and temperature adjustment" is a system that automatically adjusts the amount of sunlight and temperature to optimal levels for crop growth.

[1025] A "generative AI model" is a model that uses artificial intelligence technology to analyze data and make predictions about optimal environmental conditions and crop growth.

[1026] "Market price data" refers to price information for agricultural products in the market.

[1027] "Crop maturity data" is information relating to the growth status and maturity of crops.

[1028] "Environmental conditions inside the greenhouse" refer to factors that affect crop growth, such as the temperature, humidity, and amount of sunlight inside the greenhouse.

[1029] "Monitoring" refers to the continuous monitoring of environmental conditions and crop status using sensors and other devices.

[1030] "Feedback" refers to adjusting environmental conditions and providing information to users based on the monitoring results.

[1031] This invention is a system that uses data obtained from various sensors to suggest the best crops and varieties to cultivate, automates the optimal watering of crops, suggests the type of fertilizer and the timing of application, automates the adjustment of sunlight and temperature so that products can be shipped at a time when market prices are high, and provides sales support.

[1032] 1. Program Generation

[1033] The server uses a generative AI model to generate a program that takes market price data and crop maturity data as input, and includes algorithms for automatically adjusting the amount of sunlight and temperature.

[1034] 2. Program processing explanation

[1035] The server executes the generated program and performs the following processes.

[1036] Data collection

[1037] Market price data collection:

[1038] The server retrieves the latest price information from a market price database through an API. For example, it uses the API of an agricultural market to get the current market price of tomatoes. Specifically, the server sends a "GET" request to the API endpoint and receives price data in JSON format.

[1039] Collecting Crop Maturity Data:

[1040] The server collects crop maturity data from sensors installed in the greenhouse, such as temperature, humidity, and light sensors. For example, the server periodically polls the sensors to obtain the current temperature, humidity, and amount of sunlight.

[1041] Data analysis

[1042] Market price and maturity data analysis:

[1043] The server inputs the collected market price data and crop maturity data into the generative AI model. The generative AI model calculates the optimal amount of sunlight and temperature based on this data. Specifically, the server sends a prompt to the generative AI model, such as "Please tell me the optimal settings for adjusting sunlight and temperature so that tomatoes can be harvested in May 2023."

[1044] environmental adjustment

[1045] Adjusting sunlight and temperature:

[1046] The server adjusts the environmental conditions in the greenhouse based on the optimal settings obtained from the generative AI model. Specifically, it turns lights on and off and changes the settings of the temperature control device. For example, the server sends instructions to the smart greenhouse control system such as "turn on the lights for 8 hours and set the temperature to 25 degrees."

[1047] Monitoring and Feedback

[1048] Environmental condition monitoring:

[1049] The server monitors whether the set environmental conditions are being maintained properly, periodically checking data from sensors and making readjustments as necessary. Specifically, the server monitors data from sensors in real time and makes immediate adjustments if, for example, the temperature deviates from the set value.

[1050] Providing feedback:

[1051] The server provides feedback to the user on the state of environmental conditions and the growth status of the crops. The user can check this information through a dedicated device or application. For example, the server might send a notification to the user's device saying, "The current temperature is 25 degrees, and the amount of sunlight is set to 8 hours. The tomatoes are growing well."

[1052] Examples and prompts

[1053] As a concrete example, consider adjusting environmental conditions to coincide with periods when market prices for tomatoes are high.

[1054] Examples:

[1055] Market price data: The market price of tomatoes in May 2023 is high.

[1056] Crop maturity data: The current growth state of tomatoes is immature.

[1057] The server uses a generative AI model to adjust sunlight and temperature to ensure tomatoes ripen in May.

[1058] Example prompt sentence:

[1059] Using market price data and crop maturity data as inputs, generate a program that will adjust the amount of sunlight and temperature so that tomatoes can be harvested in May 2023.

[1060] In this way, the server realizes a system that automatically adjusts environmental conditions so that crops can be harvested at a timing when market prices are high. The flow of the identification process in the third embodiment will be described with reference to FIG.

[1061] Step 1: Data collection

[1062] Input: Market price data, crop maturity data

[1063] Specific behavior:

[1064] The server retrieves the latest price information from the market price database through the API by sending a "GET" request to the API endpoint and receiving price data in JSON format.

[1065] The server collects crop maturity data from temperature, humidity, and light sensors installed in the greenhouse. Specifically, the server periodically polls the data from the sensors to obtain the current temperature, humidity, and amount of sunlight.

[1066] Output: Collected market price data, crop maturity data

[1067] Step 2: Data analysis

[1068] Inputs: Collected market price data, crop maturity data

[1069] Specific behavior:

[1070] The server inputs the collected market price data and crop maturity data into the generative AI model. Specifically, the server sends a prompt to the generative AI model, such as, "Please tell me the optimal settings for adjusting the amount of sunlight and temperature so that tomatoes can be harvested in May 2023."

[1071] The generative AI model analyzes the input data and calculates the optimal amount of sunlight and temperature.

[1072] Output: Optimal light and temperature settings

[1073] Step 3: Adjusting the environment

[1074] Input: Optimal sunlight and temperature settings

[1075] Specific behavior:

[1076] The server then adjusts the environmental conditions in the greenhouse based on the optimal settings obtained from the generative AI model, sending instructions to the smart greenhouse's control system, such as "turn on the lights for 8 hours and set the temperature to 25 degrees."

[1077] The server turns lights on and off and changes the settings on thermostats.

[1078] Output: Controlled greenhouse environmental conditions

[1079] Step 4: Monitoring and feedback

[1080] Input: Controlled greenhouse environmental conditions

[1081] Specific behavior:

[1082] The server monitors whether the set environmental conditions are being maintained properly by periodically checking data from sensors and making readjustments if necessary.

[1083] The server provides feedback to the user about the state of environmental conditions and the growth status of the crops. Specifically, the server sends a notification to the user's device, such as, "The current temperature is 25 degrees, and the amount of sunlight is set to 8 hours. The tomatoes are growing well."

[1084] Output: Monitoring results, feedback to users

[1085] In this way, the server creates a system that automatically adjusts environmental conditions so that crops can be harvested at a time when market prices are high.

[1086] (Application example 3)

[1087] Next, a description will be given of Application Example 3 of Form Example 3. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[1088] Conventional agricultural systems have difficulty optimizing crop harvest times in response to fluctuations in market prices. Furthermore, because adjustments to sunlight and temperature are made manually, efficient cultivation management is difficult, making it difficult to maximize profits. Furthermore, there was a need for a system that could monitor crop maturity in real time and determine the optimal harvest time based on market prices.

[1089] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 3 is realized by the following means.

[1090] In this invention, the server includes means for proposing the optimal crop and variety to cultivate based on data acquired from various sensors, means for automating the optimal watering for the crops, means for proposing the type of fertilizer and the timing of application, means for automating the adjustment of sunlight and temperature so that the crops can be shipped when the market price is high, means for adjusting sunlight and temperature based on market price data and the maturity of the crops using a generative AI model so that the crops can be harvested when the market price is high, and means for providing sales support.This enables the optimal harvest time to be determined in response to fluctuations in market price, enabling efficient cultivation management and maximization of profits.

[1091] "Various sensors" are devices for acquiring environmental data such as temperature, humidity, light intensity, and soil moisture.

[1092] "Data" refers to a collection of information such as environmental information, market price information, and crop maturity information obtained from various sensors.

[1093] The "means for proposing the optimal crops and varieties to cultivate" is a function that analyzes the acquired data and selects and proposes the most suitable crops and varieties.

[1094] "Automated watering means" is a function for automatically supplying the water necessary for crop growth.

[1095] The "means for suggesting the type of fertilizer and the timing of application" is a function for suggesting the type of fertilizer that is optimal for crop growth and the timing of application.

[1096] "Automated means for adjusting sunlight and temperature" is a function for automatically adjusting the amount of sunlight and temperature required for crop growth.

[1097] A "generative AI model" is a model that uses artificial intelligence technology to analyze data and predict optimal cultivation conditions and harvest times.

[1098] "Market price data" refers to price information for crops in the market.

[1099] "Crop maturity" is an index that indicates the degree of growth of a crop before it is ready for harvest.

[1100] The "means for providing sales support" is a function for supporting the sale of harvested crops.

[1101] The system for implementing this invention includes various sensors, a generative AI model, data analysis software, and an automated control device. The specific configuration and operation of the system are described below.

[1102] System Configuration

[1103] 1. Various sensors: Install sensors to acquire environmental data such as temperature, humidity, light intensity, soil moisture, etc. These sensors collect environmental information necessary for crop growth in real time.

[1104] 2. Generative AI model: An artificial intelligence model is used to calculate the optimal amount of sunlight and temperature based on market price data and crop maturity. This model learns from past data and predicts future market prices.

[1105] 3. Data analysis software: Software is used to analyze the data acquired from various sensors and input it into the generative AI model. Specifically, programming languages ​​such as Python and R are used.

[1106] 4. Automation Control Devices: Install devices to automatically adjust the amount of sunlight and temperature, including LED lights, heaters, and cooling devices.

[1107] Operation explanation

[1108] 1. Data collection: Various sensors collect environmental data and send it to a server, thereby obtaining real-time environmental information.

[1109] 2. Data analysis: The server analyzes the collected data using data analysis software, and the analysis results are input into the generative AI model.

[1110] 3. Run the generative AI model: The generative AI model calculates the optimal amount of sunlight and temperature based on market price data and crop maturity, allowing crops to be harvested when market prices are high.

[1111] 4. Environmental adjustment: The automated control system adjusts the amount of sunlight and temperature based on the calculations of the generative AI model, thereby optimizing the growing environment for the crops.

[1112] Specific examples

[1113] For example, if you want to harvest tomatoes at a time when market prices are high, you can input the following prompt sentence into the generative AI model:

[1114] Example prompt sentence:

[1115] Create a generative AI model that adjusts sunlight and temperature to harvest tomatoes at the peak market price.

[1116] By inputting this prompt into a generative AI model, an AI model is generated that suggests optimal sun exposure and temperature adjustments, allowing tomatoes to be harvested at a time when market prices are high.

[1117] This system will enable the determination of the optimal harvest time in response to fluctuations in market prices, enabling efficient cultivation management and maximizing profits.

[1118] The flow of the specific processing in Application Example 3 will be described with reference to FIG.

[1119] Step 1:

[1120] Various sensors collect environmental data and send it to a server.

[1121] Input: Environmental data such as temperature, humidity, light, and soil moisture.

[1122] Output: The environment data sent to the server.

[1123] How it works: The temperature sensor measures the current temperature, the humidity sensor measures the humidity, the light sensor measures the amount of sunlight, and the soil moisture sensor measures the amount of moisture in the soil. These data are sent to the server in real time.

[1124] Step 2:

[1125] The server analyzes the collected environmental data using data analysis software.

[1126] Input: The environment data sent to the server.

[1127] Output: Parsed environmental data.

[1128] Specific operation: The server analyzes the collected environmental data using data analysis software such as Python or R. For example, it calculates the average value and fluctuation of temperature data and analyzes trends in humidity data.

[1129] Step 3:

[1130] The server inputs the analysis results into a generative AI model.

[1131] Input: Parsed environmental data.

[1132] Output: The data that is input into a generative AI model.

[1133] How it works: The server inputs the analysis results into the generative AI model. For example, it passes data such as average temperature values ​​and humidity trends to the AI ​​model.

[1134] Step 4:

[1135] A generative AI model calculates optimal sunlight and temperature based on market price data and crop maturity.

[1136] Inputs: Analyzed environmental data, market price data, crop maturity.

[1137] Output: Optimal light and temperature settings.

[1138] How it works: The generative AI model predicts future market prices based on past market price data and the current maturity of crops. Based on this prediction, it calculates the optimal amount of sunlight and temperature.

[1139] Step 5:

[1140] The server controls the automation control device based on the calculation results of the generated AI model.

[1141] Input: Optimal sunlight and temperature settings.

[1142] Output: Control signal to automation controller.

[1143] Specific operation: The server receives the calculation results of the generative AI model and controls the automation control devices accordingly, for example, adjusting the brightness of LED lights and controlling the operation of heaters and cooling devices.

[1144] Step 6:

[1145] Automated controls regulate the amount of sunlight and temperature.

[1146] Input: Control signal from the server.

[1147] Output: Adjusted solar radiation and temperature.

[1148] How it works: Based on control signals from the server, the automated control device adjusts the brightness of LED lights and activates heaters and cooling devices, optimizing the growing environment for crops.

[1149] Furthermore, an emotion engine that estimates the user's emotion may be further combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59, and perform identification processing using the user's emotion.

[1150] "Example 1"

[1151] One embodiment of the present invention is a system that combines an emotion engine that recognizes the user's emotions. This system recognizes the user's emotions and, based on those emotions, the generative AI suggests the optimal crops and varieties to cultivate. Specifically, if the user feels stressed, the emotion engine communicates this information to the generative AI, which then suggests growing a crop that may reduce stress, such as lavender.

[1152] "Example 2"

[1153] Another embodiment is a system in which an emotion engine recognizes a user's emotions, and based on those emotions, a generation AI automates the adjustment of sunlight and temperature so that products can be shipped at a time when market prices are high. Specifically, if a user feels joy, the emotion engine conveys that information to the generation AI, and the generation AI automates the adjustment of sunlight and temperature so that products can be shipped at a time when market prices are high. For example, if a user feels joy on a particular holiday, the generation AI adjusts sunlight and temperature so that crops can be harvested in time for that holiday.

[1154] "Example 3"

[1155] One embodiment of the present invention is a system that combines an emotion engine that recognizes the user's emotions. This system recognizes the user's emotions and, based on those emotions, the generative AI suggests the optimal crops and varieties to cultivate. Specifically, if the user feels stressed, the emotion engine communicates this information to the generative AI, which then suggests growing a crop that may reduce stress, such as lavender.

[1156] The processing flow of each embodiment will be described below.

[1157] "Example 1"

[1158] Step 1: The emotion engine recognizes the user's emotion.

[1159] Step 2: The emotion engine communicates the emotion information to the generative AI.

[1160] Step 3: The generative AI suggests the best crops and varieties based on the emotion. For example, if the user feels stressed, the generative AI suggests growing lavender, a crop that may reduce stress.

[1161] "Example 2"

[1162] Step 1: The emotion engine recognizes the user's emotion.

[1163] Step 2: The emotion engine communicates the emotion information to the generative AI.

[1164] Step 3: Based on that emotion, the AI ​​automates the adjustment of sunlight and temperature so that crops can be shipped at a time when market prices are high. For example, if a user feels joy on a particular holiday, the AI ​​adjusts sunlight and temperature so that crops can be harvested in time for that holiday.

[1165] Example 1

[1166] Next, a description will be given of Example 1 of Form Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[1167] Conventional agricultural systems have difficulty selecting optimal crops and varieties based on environmental data, and are unable to make suggestions based on user emotions. This makes it difficult to achieve efficient cultivation and improve user satisfaction.

[1168] The identification process by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means. In this invention, the server includes a means for proposing optimal crops and varieties to cultivate based on data acquired from various sensors, a means for analyzing the acquired data and generating a prompt sentence to input to the generative AI model, a means for inputting the prompt sentence to the generative AI model and receiving a proposal for optimal crops and varieties, and a means for making a proposal based on the emotion using an emotion engine that recognizes the user's emotion. This makes it possible to propose optimal crops and varieties based on environmental data and user emotion data.

[1169] "Various sensors" are devices used to acquire environmental data such as soil humidity, temperature, and hours of sunlight.

[1170] A "generative AI model" is an artificial intelligence algorithm that suggests optimal crops and varieties based on input prompts.

[1171] A "prompt" is text containing a question or instruction to be input to a generative AI model.

[1172] An "emotion engine" is software or hardware that recognizes a user's emotions and analyzes that information.

[1173] A "user" is someone who uses this system to cultivate crops and select varieties.

[1174] The "server" is a central processing unit that analyzes data obtained from various sensors and inputs prompt sentences into the generative AI model.

[1175] A "terminal" is a device that acquires data from various sensors and transmits it to a server.

[1176] "Data analysis" is the process of generating prompt sentences to input into the generative AI model based on the acquired environmental data.

[1177] "Proposing optimal crops and varieties" means that the generative AI model selects crops and varieties suitable for cultivation based on environmental data and user emotional data.

[1178] This invention is a system that uses generative AI to suggest optimal crops and varieties for cultivation based on data acquired from various sensors. This system acquires data such as soil humidity, temperature, and sunlight hours, and the generative AI uses this data to suggest optimal crops and varieties. In addition, by combining it with an emotion engine that recognizes the user's emotions, it can also make suggestions based on the user's emotions.

[1179] Hardware and software used

[1180] 1. Hardware

[1181] Various sensors: Use a soil moisture sensor, temperature sensor, light sensor, etc. For example, use DHT11 as the soil moisture sensor.

[1182] Terminal: Uses a small computer such as a computer control circuit.

[1183] Server: Uses a central processing unit to analyze data and run generative AI models.

[1184] 2. Software

[1185] Generative AI models: Use generative AI algorithms such as OpenAI's GPT-4.

[1186] Emotion engine: Uses Microsoft Azure's Emotion API, etc.

[1187] Specific operation of the system

[1188] 1. Data Acquisition

[1189] The device acquires environmental data from various sensors, specifically using soil humidity, temperature, and light sensors to collect data on soil humidity, temperature, and sunlight duration.

[1190] Specific operation: The soil humidity sensor (e.g., DHT11) connected to the device measures the soil humidity, the temperature sensor measures the ambient temperature, and the light sensor measures the hours of sunlight.

[1191] 2. Data Transmission

[1192] The device sends the acquired environmental data to a server using Wi-Fi or a wired connection.

[1193] How it works: The device (e.g., a computer control circuit) transmits data acquired from sensors to a server via Wi-Fi. The transmitted data includes soil humidity of 80%, temperature of 25°C, and sunlight duration of 10 hours.

[1194] 3. Data Analysis

[1195] The server analyzes the received environmental data and generates prompts to input into the generative AI model, using a programming language such as Python.

[1196] Specific operation: Based on the data received by the server, it generates a prompt such as, "What is the best crop if the soil moisture is 80%, the temperature is 25 degrees, and there are 10 hours of sunlight?"

[1197] 4. Generative AI suggestions

[1198] The server inputs a prompt into the generative AI model, which uses OpenAI's GPT-4 and other models, and receives recommendations for optimal crops and varieties.

[1199] Specific operation: The server sends a prompt to the generative AI model, and the generative AI model replies, "I suggest growing rice."

[1200] 5. Use of Emotion Engine

[1201] To recognize the user's emotions, an emotion engine is used, such as Microsoft Azure's Emotion API.

[1202] Specific operation: When a user shows an expression to the device's camera, the emotion engine analyzes the expression and determines that the user is feeling stressed.

[1203] 6. Emotion-based recommendations

[1204] Based on the information received from the emotion engine, the server generates prompts for the generative AI model and suggests optimal crops and varieties based on emotions.

[1205] Specific behavior: The server generates a prompt sentence: "If the user is feeling stressed, what crops might reduce stress?", and the generative AI model responds: "I suggest growing lavender."

[1206] Examples and prompts

[1207] Example 1: Proposals based on environmental data

[1208] Prompt: "If the soil moisture is 80%, the temperature is 25 degrees, and there are 10 hours of sunshine, what is the best crop to grow?"

[1209] Generative AI's answer: "I suggest growing rice."

[1210] Example 2: Suggestions based on emotional data

[1211] Prompt: "If the user is feeling stressed, what crops might reduce stress?"

[1212] Generative AI response: "I suggest growing lavender."

[1213] In this way, the system proposes optimal crops and varieties based on environmental data and user emotional data.

[1214] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1215] Step 1: Data Acquisition

[1216] The device acquires environmental data from various sensors, specifically using soil humidity, temperature, and light sensors to collect data on soil humidity, temperature, and sunlight duration.

[1217] Input: Real-time data from soil moisture sensor, temperature sensor, and light sensor.

[1218] Output: Soil moisture, temperature, and sunshine duration datasets.

[1219] Specific operation: The soil humidity sensor (e.g., DHT11) connected to the device measures the soil humidity, the temperature sensor measures the ambient temperature, and the light sensor measures the hours of sunlight.

[1220] Step 2: Send data

[1221] The device sends the acquired environmental data to a server using Wi-Fi or a wired connection.

[1222] Input: Obtained environmental data (e.g. soil humidity 80%, temperature 25°C, sunshine duration 10 hours).

[1223] Output: The environment data sent to the server.

[1224] Specific operation: The device (e.g., a computer control circuit) transmits data obtained from the sensor to a server via Wi-Fi.

[1225] Step 3: Data analysis

[1226] The server analyzes the received environmental data and generates prompts to input into the generative AI model, using a programming language such as Python.

[1227] Input: The environment data sent to the server.

[1228] Output: A prompt to be fed into the generative AI model.

[1229] Specific operation: Based on the data received by the server, it generates a prompt such as, "What is the best crop if the soil moisture is 80%, the temperature is 25 degrees, and there are 10 hours of sunlight?"

[1230] Step 4: Generative AI suggestions

[1231] The server inputs a prompt into the generative AI model and receives recommendations for optimal crops and varieties, using OpenAI's GPT-4 and other models.

[1232] Input: The generated prompt statement.

[1233] Output: Suggested results from the generative AI model.

[1234] Specific operation: The server sends a prompt to the generative AI model, and the generative AI model replies, "I suggest growing rice."

[1235] Step 5: Use the Emotion Engine

[1236] To recognize the user's emotions, an emotion engine is used, such as Microsoft Azure's Emotion API.

[1237] Input: User's facial expression data.

[1238] Output: User emotion data.

[1239] Specific operation: When a user shows an expression to the device's camera, the emotion engine analyzes the expression and determines that the user is feeling stressed.

[1240] Step 6: Emotion-based suggestions

[1241] Based on the information received from the emotion engine, the server generates prompts for the generative AI model and suggests optimal crops and varieties based on emotions.

[1242] Input: User emotion data from the emotion engine.

[1243] Output: Sentiment-based recommendation results from the generative AI model.

[1244] Specific behavior: The server generates a prompt sentence: "If the user is feeling stressed, what crops might reduce stress?", and the generative AI model responds: "I suggest growing lavender."

[1245] (Application example 1)

[1246] Next, a description will be given of Application Example 1 of Form Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[1247] Conventional agricultural systems can optimize crop cultivation based on data obtained from various sensors, but they are unable to make suggestions that take into account the emotions of workers, making it difficult to reduce worker stress. Furthermore, because crop suggestions are not based on emotions, there is a risk that this could have a negative impact on worker health and work efficiency.

[1248] The specific processing by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means. In this invention, the server includes a means for proposing the optimal crop and variety to cultivate based on data acquired from various sensors, a means for automating the optimal watering for the crops, a means for proposing the type of fertilizer and the timing of application, a means for automating the amount of sunlight and temperature to ship at a time when market prices are high, a sales support means, and a means for recognizing the emotions of the worker using an emotion recognition engine and having the generation AI propose the optimal crop and variety to cultivate based on that emotion. This makes it possible to propose crops that take the worker's emotions into consideration, thereby reducing worker stress and improving their health and work efficiency.

[1249] "Various sensors" are devices used to acquire environmental data such as soil humidity, temperature, and hours of sunlight.

[1250] The "means for suggesting optimal crops and varieties to cultivate" is a function that uses generative AI to suggest optimal crops and varieties based on acquired environmental data.

[1251] "Automated watering method that is optimal for crops" is a function that automatically supplies the amount of water required for crops based on data from sensors.

[1252] "Means for suggesting fertilizer type and spraying timing" is a function that analyzes environmental data and suggests the optimal type of fertilizer and the timing to spray it.

[1253] "Automated means of adjusting sunlight and temperature to enable shipments at times when market prices are high" is a function that automatically adjusts sunlight and temperature based on market price data to optimize the timing of crop shipments.

[1254] "Sales support means" is a function for supporting the sale of crops, such as predicting market prices and proposing sales strategies.

[1255] An "emotion recognition engine" is a technology for recognizing a worker's emotions, and uses cameras and sensors to analyze the worker's facial expressions and behavior.

[1256] "Generative AI" is an artificial intelligence technology that suggests optimal crops and varieties based on acquired data.

[1257] "Means for recognizing the emotions of workers and having the generation AI suggest the optimal crops and varieties to cultivate based on those emotions" is a function that uses an emotion recognition engine to analyze the emotions of workers, conveys the results to the generation AI, and suggests crops and varieties that are suitable for the emotions of the workers.

[1258] The following system configuration will be described as an embodiment of the present invention.

[1259] System Configuration

[1260] Hardware

[1261] Various sensors: Devices for acquiring environmental data such as soil humidity, temperature, and sunlight hours.

[1262] Emotion recognition camera: A camera for recognizing the emotions of workers.

[1263] Server: A computer system for analyzing data and running generative AI models.

[1264] software

[1265] Generative AI model: An artificial intelligence technology that suggests optimal crops and varieties based on acquired data (e.g., GPT-3).

[1266] Emotion recognition engine: Software for analyzing worker emotions (e.g., Affectiva).

[1267] Data analysis program: A program for analyzing data obtained from various sensors.

[1268] Processing flow

[1269] 1. Data acquisition: Collect data on soil humidity, temperature, and sunlight hours from various sensors. Collect worker emotion data using an emotion recognition camera.

[1270] 2. Data analysis: The acquired environmental data and emotion data are sent to the server and analyzed using a data analysis program.

[1271] 3. Recommendations by generative AI: Based on the analysis results, the generative AI model will suggest the optimal crops and varieties.

[1272] 4. Emotion-based suggestions: The worker's emotional data analyzed by the emotion recognition engine is input into a generative AI model to suggest crops and varieties that suit the worker's emotions.

[1273] Specific examples

[1274] For example, consider the case where the data obtained from the sensor is as follows:

[1275] Soil moisture is 60%

[1276] The temperature is 25 degrees

[1277] 8 hours of sunshine

[1278] Furthermore, if the emotion recognition camera recognizes the worker's emotion as "feeling stressed," the generative AI model will make the following suggestions:

[1279] Prompt Sentence Examples

[1280] "If the soil moisture is 60%, the temperature is 25°C, there are 8 hours of sunshine, and the farmer is feeling stressed, please suggest the best crop and variety."

[1281] By inputting this prompt into a generative AI model, it can suggest, for example, the cultivation of "lavender." In this way, it becomes possible to suggest crops that take into account the worker's emotions, reducing worker stress and improving their health and work efficiency.

[1282] This system makes it possible to comprehensively analyze environmental data and worker emotional data, and to suggest optimal crops and varieties.

[1283] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1284] Step 1:

[1285] Data on soil humidity, temperature, and sunlight hours are acquired from various sensors. The sensors measure this environmental data in real time and send it to the server. The input is the environmental data from the sensors, and the output is the environmental data sent to the server.

[1286] Step 2:

[1287] An emotion recognition camera is used to acquire the worker's emotional data. The camera analyzes the worker's facial expressions and behavior and sends the data to the emotion recognition engine. The input is the video data from the camera, and the output is the analyzed emotional data.

[1288] Step 3:

[1289] The server analyzes the acquired environmental data and emotional data using a data analysis program. The data analysis program integrates the environmental data and emotional data and generates a prompt sentence to input into the generative AI model. The input is the environmental data and emotional data, and the output is the generated prompt sentence.

[1290] Step 4:

[1291] A prompt is input into the generative AI model, which then suggests the optimal crops and varieties. The generative AI model analyzes the prompt and outputs the optimal crops and varieties. The input is the prompt, and the output is the suggested crops and varieties.

[1292] Step 5:

[1293] The server notifies the worker of the proposed results from the generative AI model. The notification is sent via a terminal or display. The input is the proposed results from the generative AI model, and the output is the notification sent to the worker.

[1294] Step 6:

[1295] The worker starts cultivating based on the suggested crops and varieties. The worker receives notifications from the server and works according to the suggestions. The input is the notification from the server, and the output is the worker's cultivation behavior.

[1296] In this way, it becomes possible to perform an integrated analysis of environmental data and worker emotional data to suggest optimal crops and varieties.

[1297] Example 2

[1298] Next, a description will be given of Example 2 of Form Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[1299] In conventional agricultural management systems, watering crops and adjusting the environment are often done manually, making efficient management difficult. Furthermore, it is difficult to maximize profits because it is not possible to adjust shipping timing based on user sentiment or market prices. Furthermore, there is a lack of ways to effectively utilize data obtained from various sensors, making it difficult to propose optimal cultivation methods.

[1300] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[1301] In this invention, the server includes a means for proposing optimal crops and varieties to cultivate based on data acquired from various sensors, a means for acquiring soil humidity data and automatically adjusting the timing and amount of watering using a generative AI model, and a means for acquiring user emotion data and automating adjustment of sunlight and temperature using a generative AI model so that products can be shipped at a time when market prices are high. This enables efficient agricultural management and maximizes profits.

[1302] "Various sensors" are devices used to acquire environmental data such as soil humidity, temperature, and amount of sunlight.

[1303] A "generative AI model" is an artificial intelligence algorithm that analyzes acquired data and adjusts the optimal timing and amount of watering, as well as the amount of sunlight and temperature.

[1304] "Soil moisture data" is data that indicates the amount of moisture in the soil and is used to determine the timing and amount of watering for crops.

[1305] "Watering timing and amount" refers to instructions for providing crops with the right amount of water at the right time.

[1306] "User emotion data" is data that indicates the user's emotional state, and is acquired using emotion recognition technology.

[1307] "High market price timing" refers to the period when the market price of crops is highest, and the aim is to maximize profits by shipping at that time.

[1308] "Sunlight and temperature adjustment" refers to adjusting the environment to maintain the optimum amount of sunlight and temperature for crop growth.

[1309] "Automation" refers to the system operating automatically without the need for manual operation.

[1310] This invention is a system that automates crop cultivation management using a generative AI model based on data acquired from various sensors. Specifically, it includes functions to acquire soil moisture data and automatically adjust the timing and amount of watering, and to acquire user emotion data and automatically adjust the amount of sunlight and temperature so that crops can be shipped at a time when market prices are high.

[1311] The server collects data obtained from various sensors and stores it in a database. An example of the database used is MySQL. The server analyzes the data using a generative AI model and generates appropriate instructions. An example of the generative AI model used is OpenAI's GPT-4.

[1312] The terminals receive instructions from the server and actually water the plants and adjust the environment. Examples of terminals include computer control circuits and Arduino. These terminals work in conjunction with sensors and actuators (such as solenoid valves, LED lights, and heaters).

[1313] Users provide emotion data through an emotion engine, such as Affectiva's emotion recognition API, which is then sent to a server and analyzed by a generative AI model.

[1314] As a concrete example, consider the following scenario:

[1315] 1. Automated watering:

[1316] The sensor measures the soil moisture, and when the humidity drops below 30%, the device sends the data to the server.

[1317] The server stores the data in a database, and the generation AI analyzes the data to generate the instruction to "start watering."

[1318] The terminal receives the instructions and the watering system supplies water.

[1319] Example prompt sentence:

[1320] "When soil moisture drops below 30 percent, give instructions to start watering."

[1321] 2. Automated solar radiation and temperature control:

[1322] When a user feels joy for a particular holiday, the emotion engine recognizes the emotion and sends the data to the server.

[1323] The server provides the data to the generation AI, which then generates instructions to "adjust the amount of sunlight and temperature so that shipments can be made at a time when market prices are high."

[1324] The terminal receives the instructions, and the environmental control system adjusts the amount of sunlight and temperature.

[1325] Example prompt sentence:

[1326] "If users are happy, give instructions to adjust the amount of sunlight and temperature so that the product can be shipped at a time when market prices are high."

[1327] This system enables efficient agricultural management and maximizes profits.

[1328] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1329] Step 1:

[1330] The sensor measures the moisture content of the soil.

[1331] Input: Soil moisture

[1332] Output: Humidity data

[1333] Specific operation: A humidity sensor installed in the soil measures the humidity of the soil in real time and transmits the data to the terminal.

[1334] Step 2:

[1335] The device receives humidity data from the sensor and sends it to the server.

[1336] Input: Humidity data

[1337] Output: Humidity data sent to the server

[1338] Specific operation: A terminal such as a computer control circuit transmits the humidity data received from the sensor to a server.

[1339] Step 3:

[1340] The server stores the humidity data in a database.

[1341] Input: Humidity data

[1342] Output: Humidity data stored in a database

[1343] Specific operation: The server stores the received humidity data in a database such as MySQL.

[1344] Step 4:

[1345] The server provides humidity data to the generative AI model for analysis.

[1346] Input: Humidity data stored in the database

[1347] Output: Watering timing and amount instructions

[1348] How it works: The server retrieves humidity data from the database and provides it to a generative AI model (e.g., OpenAI's GPT-4) for analysis. The generative AI model then determines when and how much water to use.

[1349] Step 5:

[1350] The server sends instructions from the generated AI model to the device.

[1351] Input: Watering timing and amount instructions

[1352] Output: Instructions sent to the terminal

[1353] Specific operation: The server receives instructions from the generative AI model and sends them to the device.

[1354] Step 6:

[1355] The terminal controls the watering system and performs watering.

[1356] Input: Watering timing and amount instructions

[1357] Output: Watering performed

[1358] Specific operation: Based on the instructions received by the terminal, it controls watering systems such as solenoid valves connected to a computer control circuit, and supplies water at the specified timing and amount.

[1359] Step 7:

[1360] The user provides emotion data through the emotion engine.

[1361] Input: User's emotions

[1362] Output: Emotion data

[1363] Specific behavior: When a user feels a certain emotion (e.g., joy), the emotion engine (e.g., Affectiva's emotion recognition API) recognizes the emotion and generates data.

[1364] Step 8:

[1365] The emotion engine sends the emotion data to the server.

[1366] Input: Emotion data

[1367] Output: Emotion data sent to the server

[1368] Specific operation: The emotion engine sends the recognized emotion data to the server.

[1369] Step 9:

[1370] The server provides the emotion data to the generative AI model for analysis.

[1371] Input: Emotion data

[1372] Output: Indication of sunlight and temperature adjustment

[1373] How it works: The server provides emotion data to the generative AI model, which then determines how to adjust sunlight and temperature to ensure shipments are made at a time when market prices are high.

[1374] Step 10:

[1375] The server sends instructions from the generated AI model to the device.

[1376] Input: sunlight and temperature control instructions

[1377] Output: Instructions sent to the terminal

[1378] Specific operation: The server receives instructions from the generative AI model and sends them to the device.

[1379] Step 11:

[1380] The device controls the environmental control system, adjusting the amount of sunlight and temperature.

[1381] Input: sunlight and temperature control instructions

[1382] Output: Adjusted sunlight and temperature

[1383] Specific operation: Based on the instructions received by the terminal, it controls environmental adjustment systems such as LED lights and heaters connected to Arduino, adjusting the amount of sunlight and temperature at the specified time and in the specified manner.

[1384] (Application example 2)

[1385] Next, a description will be given of Application Example 2 of Form Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[1386] In conventional agricultural systems, watering crops and adjusting the environment are often done manually, making efficient cultivation difficult. Furthermore, because the environment is not adjusted taking into account the user's emotions, optimizing the harvest time is difficult. Furthermore, there is insufficient automation to ensure shipments at times when market prices are high.

[1387] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[1388] In this invention, the server includes a means for proposing the optimal crop and variety to cultivate based on data acquired from various sensors, a means for automating the optimal watering for the crops, a means for proposing the type of fertilizer and the timing of application, a means for automating the amount of sunlight and temperature adjustment so that the crops can be shipped at a time when the market price is high, and a means for recognizing the user's emotions and adjusting the environment based on those emotions, thereby enabling efficient crop cultivation and optimizing the harvest time.

[1389] "Various sensors" are devices used to acquire environmental data such as soil humidity, temperature, and amount of sunlight.

[1390] "Generative AI" is an artificial intelligence technology that analyzes acquired data and automatically determines the optimal crop cultivation method and environmental adjustments.

[1391] "Automated watering" is a system that automatically supplies water at the appropriate time and in the appropriate amount based on soil moisture data.

[1392] "Suggestion of fertilizer type and application timing" is a function that suggests the type of fertilizer that is best suited for crop growth and the timing of its application.

[1393] "Automated sunlight and temperature adjustment" is a system that automatically adjusts the amount of sunlight and temperature to suit crop growth.

[1394] "Recognizing user emotions" is a technology that uses an emotion engine to detect the user's emotional state.

[1395] The "means for adjusting the environment" is a function that makes adjustments to optimize the crop cultivation environment based on the user's feelings.

[1396] "Shipping when market prices are high" is a system that aims to harvest and ship crops when market prices are highest.

[1397] The system for implementing this invention includes various sensors, a generative AI, an emotion engine, and an agricultural robot. The specific configuration and operation of the system are described below.

[1398] System Configuration

[1399] 1. Various sensors: These are devices for acquiring environmental data such as soil humidity, temperature, and amount of sunlight. These sensors are attached to agricultural robots.

[1400] 2. Generative AI: This is an artificial intelligence technology that analyzes acquired data and automatically determines optimal crop cultivation methods and environmental adjustments. Generative AI runs on a server.

[1401] 3. Emotion Engine: This technology detects the user's emotional state. The emotion engine is installed on the user's smartphone or tablet.

[1402] 4. Agricultural robot: A robot that waters crops and adjusts the environment. The robot operates based on instructions from the server.

[1403] Operation explanation

[1404] 1. Data Acquisition

[1405] Various sensors collect environmental data such as soil humidity, temperature, and amount of sunlight, which is then sent to a server in real time.

[1406] 2. Data Analysis

[1407] The server-based AI analyzes the acquired environmental data and, based on the results of the analysis, determines the optimal crop cultivation method, watering timing, and type and application of fertilizer.

[1408] 3. Emotion recognition

[1409] An emotion engine installed on the user's smartphone or tablet detects the user's emotional state, and the emotion data is sent to a server.

[1410] 4. Environmental adjustment

[1411] The server-based AI adjusts the amount of sunlight and temperature based on the user's emotional data, allowing for shipments to be made at a time when market prices are high.

[1412] 5. Execution

[1413] Agricultural robots water crops and adjust the environment based on instructions from a server, enabling efficient crop cultivation and optimizing harvest times.

[1414] Specific examples

[1415] If the soil moisture is below 20%, the robot will automatically start watering.

[1416] If the user is recognized as "happy": The robot adjusts the environment to optimize harvesting.

[1417] Prompt Sentence Examples

[1418] "If the soil moisture is below 20 percent, begin watering."

[1419] If the user is recognized as "happy," adjust the environment to be optimal for harvesting.

[1420] In this way, the system enables efficient crop cultivation and optimized harvest times.

[1421] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1422] Step 1:

[1423] Various sensors collect environmental data such as soil humidity, temperature, and sunlight. This data is sent to a server in real time. The input is the environmental data from the sensors, and the output is the environmental data sent to the server.

[1424] Step 2:

[1425] The generation AI on the server analyzes the acquired environmental data. Based on the analysis results, it determines the optimal crop cultivation method, watering timing, type of fertilizer and when to apply it. The input is environmental data, and the output is a proposal for the optimal cultivation method, watering timing, type of fertilizer and when to apply it.

[1426] Step 3:

[1427] An emotion engine installed on the user's smartphone or tablet detects the user's emotional state. The emotion data is sent to the server. The input is the user's emotional state, and the output is the emotion data sent to the server.

[1428] Step 4:

[1429] The AI ​​generator on the server adjusts the amount of sunlight and temperature based on the user's emotional data. This allows for shipment at a time when market prices are high. The input is emotional data, and the output is instructions to adjust the amount of sunlight and temperature.

[1430] Step 5:

[1431] Agricultural robots water crops and adjust the environment based on instructions from a server. This allows for efficient crop cultivation and optimal harvest times. The input is instructions from the server, and the output is the watering and environmental adjustments that have been performed.

[1432] Example 3

[1433] Next, a description will be given of Example 3 of Form Example 3. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[1434] With conventional agricultural systems, it was difficult to provide an optimal cultivation environment that took into account market price fluctuations and user sentiment when cultivating crops. It was also difficult to integrate and manage multiple factors, such as adjusting sunlight and temperature, automating watering, and proposing the timing of fertilizer application. This led to issues that prevented sufficient improvements in crop quality and profitability.

[1435] The identification process by the identification processing unit 290 of the data processing device 12 in Example 3 is realized by the following means. In this invention, the server includes a means for proposing the optimal crop and variety to cultivate based on data acquired from various sensors, a means for automating the optimal watering of crops, a means for proposing the type of fertilizer and the timing of application, a means for automating the amount of sunlight and temperature adjustment so that the product can be shipped at a time when the market price is high, and a means for recognizing the user's emotions and proposing the optimal crop and variety based on the emotions. This makes it possible to improve the quality and profitability of crops.

[1436] "Various sensors" are devices used to measure environmental data and crop conditions, such as temperature sensors, light sensors, and moisture sensors.

[1437] A "generative AI model" is an artificial intelligence algorithm that analyzes collected data and suggests optimal cultivation conditions and crops.

[1438] "Suggesting the best crops and varieties to cultivate" means recommending the most suitable crops and varieties to users based on collected data.

[1439] "Automated watering" is a system that automatically supplies the necessary water to crops based on data from sensors.

[1440] "Suggesting the type of fertilizer and the timing of application" means suggesting to the user the optimal type of fertilizer and the timing of application depending on the growth stage of the crop and environmental conditions.

[1441] "Automated sunlight and temperature adjustment" is a system that automatically adjusts the amount of sunlight and temperature to suit crops based on data from sensors.

[1442] "Recognizing the user's emotions" means reading the user's emotions from their facial expressions, voice, etc., and reflecting that information in the system.

[1443] "Suggesting optimal crops and varieties" means recommending the most suitable crops and varieties for a user based on the user's emotional data.

[1444] MODE FOR CARRYING OUT THE INVENTION

[1445] This invention is a system that uses data obtained from various sensors to suggest the best crops and varieties to cultivate, automates the optimal watering for crops, suggests the type of fertilizer and the timing of application, automates the adjustment of sunlight and temperature so that crops can be shipped at a time when market prices are high, and recognizes the user's emotions and suggests the best crops and varieties based on those emotions.

[1446] Hardware and software used

[1447] Hardware: temperature sensors, light sensors, moisture sensors, servers, terminals (devices operated by users)

[1448] Software: Generative AI models, emotion engine, databases (market price data, crop maturity data)

[1449] Specific operation of the system

[1450] 1. Data Collection

[1451] The server collects real-time sunlight and temperature data from the temperature sensor and light sensor. For example, the temperature sensor measures the current temperature as 25 degrees, and the light sensor measures the sunlight as 500 lux.

[1452] The server retrieves the latest market price data from the market price database. For example, it retrieves that the market price of tomatoes is 300 yen per kilogram.

[1453] The server collects crop maturity data, for example, determining that tomatoes are 80% ripe.

[1454] 2. Data analysis and adjustment

[1455] The server inputs the collected market price data and crop maturity data into the generative AI model. For example, it inputs data such as "the market price of tomatoes is 300 yen and the maturity level is 80%."

[1456] The generative AI model analyzes input data and calculates the optimal amount of sunlight and temperature, for example, "Adjust the amount of sunlight to 600 lux and the temperature to 28 degrees to coincide with the period when market prices for tomatoes are high."

[1457] Based on the results of the generative AI model, the server controls the temperature control device and lighting devices to adjust the amount of sunlight and temperature, for example, setting the temperature control device to 28 degrees and the lighting device to 600 lux.

[1458] 3. User Emotion Recognition

[1459] The device recognizes the user's emotions using an emotion engine. For example, the emotion engine determines that the user is feeling stressed.

[1460] The emotion engine sends the user's emotion data to the generative AI model. For example, it sends data that says "the user is feeling stressed."

[1461] 4. Recommendations for optimal crops

[1462] The generative AI model suggests optimal crops and varieties based on the user's emotional data, for example, "Since the user is feeling stressed, we suggest growing lavender, which has stress-reducing properties."

[1463] The server notifies the user of the generative AI model's suggestions, for example by displaying a message on the device saying, "We suggest growing lavender."

[1464] Examples of concrete examples and prompts

[1465] Examples:

[1466] If a user is growing tomatoes, the generative AI model will analyze market price data and adjust the amount of sunlight and temperature to coincide with periods when tomatoes are at their highest market price.

[1467] If the user is feeling stressed, the emotion engine relays that information to the generative AI model, which then suggests growing lavender.

[1468] Example prompt sentence:

[1469] "How can I optimize the timing of tomato harvest based on market price data and crop maturity data?"

[1470] "If a user is stressed, how can you suggest crops that will reduce stress?"

[1471] In this way, the system collects, analyzes, adjusts, and proposes specific data at each step to provide the user with the optimal cultivation environment and crops. The flow of the identification process in Example 3 will be described with reference to FIG.

[1472] Step 1: Data collection

[1473] The server collects real-time sunlight and temperature data from the temperature sensor and light sensor. For example, the temperature sensor measures the current temperature as 25 degrees, and the light sensor measures the sunlight as 500 lux.

[1474] Input: Data from temperature and light sensors

[1475] Output: Collected solar radiation and temperature data

[1476] Specific operation: The server periodically polls the data from the sensors and stores it in a database.

[1477] Step 2: Obtain market price data

[1478] The server retrieves the latest market price data from the market price database. For example, it retrieves that the market price of tomatoes is 300 yen per kilogram.

[1479] Input: Query to market price database

[1480] Output: Obtained market price data

[1481] Specific operation: The server periodically accesses the market price database, obtains the latest price information, and stores it in the database.

[1482] Step 3: Collect crop maturity data

[1483] The server collects crop maturity data, for example, determining that tomatoes are 80% ripe.

[1484] Input: Data from crop maturity sensors

[1485] Output: Collected crop maturity data

[1486] What it does: The server periodically polls data from the crop maturity sensors and stores it in a database.

[1487] Step 4: Data analysis and adjustment

[1488] The server inputs the collected market price data and crop maturity data into the generative AI model. For example, it inputs data such as "the market price of tomatoes is 300 yen and the maturity level is 80%."

[1489] Input: Market price data and crop maturity data

[1490] Output: Analysis results from the generative AI model (optimal amount of sunlight and temperature)

[1491] How it works: The generative AI model analyzes input data and calculates the optimal amount of sunlight and temperature. For example, it will output a result such as "Adjust the amount of sunlight to 600 lux and the temperature to 28 degrees to coincide with the period when market prices for tomatoes are high."

[1492] Step 5: Adjusting the environment

[1493] Based on the results of the generative AI model, the server controls the temperature control device and lighting devices to adjust the amount of sunlight and temperature, for example, setting the temperature control device to 28 degrees and the lighting device to 600 lux.

[1494] Input: Analysis results of the generative AI model

[1495] Output: Adjusted solar radiation and temperature

[1496] Specific operation: The server sends control signals to the temperature control device and lighting device to change their settings.

[1497] Step 6: Recognizing User Emotions

[1498] The device recognizes the user's emotions using an emotion engine. For example, the emotion engine determines that the user is feeling stressed.

[1499] Input: User facial and voice data

[1500] Output: Recognized user emotion data

[1501] Specific operation: The device inputs the user's facial expressions and voice into the emotion engine and analyzes emotions.

[1502] Step 7: Recommend the best crop

[1503] The generative AI model suggests optimal crops and varieties based on the user's emotional data, for example, "Since the user is feeling stressed, we suggest growing lavender, which has stress-reducing properties."

[1504] Input: User emotion data

[1505] Output: Suggested optimal crops and varieties

[1506] How it works: The generative AI model analyzes emotion data and calculates the best crops and varieties.

[1507] Step 8: Notify users

[1508] The server notifies the user of the generative AI model's suggestions, for example by displaying a message on the device saying, "We suggest growing lavender."

[1509] Input: Proposal results of generative AI model

[1510] Output: A message to inform the user

[1511] Specific operation: The server sends the proposal results to the terminal and notifies the user.

[1512] (Application example 3)

[1513] Next, a description will be given of Application Example 3 of Form Example 3. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[1514] Conventional agricultural systems have the problem that it is difficult to optimize the timing of crop harvests in response to fluctuations in market prices, making it difficult to maximize profits. Furthermore, they are unable to suggest optimal crops and varieties based on user sentiment, which makes it difficult to increase user satisfaction.

[1515] The specific processing by the specific processing unit 290 of the data processing device 12 in Application Example 3 is realized by the following means. In this invention, the server includes: a means for proposing the optimal crop and variety to cultivate based on data acquired from various sensors; a means for automating optimal watering for crops; a means for proposing the type of fertilizer and the timing of application; a means for automating sunlight and temperature adjustment so that the crops can be shipped at a time when market prices are high; a means for proposing the optimal crop and variety based on the user's emotions using an emotion engine that recognizes the user's emotions; a means for acquiring market price data in real time and using a generation AI to calculate the optimal sunlight and temperature; and a means for a robot to operate temperature and sunlight adjustment devices to maintain an optimal environment. This makes it possible to maximize profits in response to fluctuations in market prices and to propose the optimal crop and variety based on the user's emotions.

[1516] "Various sensors" are devices for acquiring environmental data such as temperature, humidity, light intensity, and soil moisture.

[1517] The "means for proposing optimal crops and varieties to cultivate" is a function that selects and proposes the most suitable crops and varieties based on the acquired environmental data.

[1518] "The best automated watering method for crops" is a system that automatically supplies the amount of water needed for crop growth.

[1519] "Means for suggesting the type of fertilizer and the timing of application" is a function that suggests the optimal type of fertilizer and the timing of application depending on the stage of crop growth and environmental conditions.

[1520] "Automated means of adjusting sunlight and temperature to allow shipment at a time when market prices are high" is a system that automatically adjusts sunlight and temperature based on market price data so that crops can be harvested at the optimal time.

[1521] The "emotion engine that recognizes user emotions" is software that analyzes and recognizes emotions from the user's facial expressions and voice.

[1522] "Generative AI" is a system that uses artificial intelligence technology to analyze data and make optimal decisions and suggestions.

[1523] "Means of obtaining market price data in real time" refers to the ability to continuously obtain the latest market price information via the Internet or a dedicated data feed.

[1524] "Means for the robot to operate temperature control devices and sunlight control devices" refers to a function for the robot to automatically control devices that adjust the temperature and amount of sunlight.

[1525] As an embodiment of the present invention, a smart agricultural robot system will be described as an example. This system is configured using hardware and software such as various sensors, generative AI, emotion engines, temperature control devices, sunshine control devices, and robots.

[1526] First, the server collects environmental data such as temperature, humidity, light intensity, and soil moisture from various sensors. This data is then analyzed by the generative AI, which then suggests the optimal crops and varieties to cultivate. Based on this data, the generative AI calculates the amount of water needed for crop growth and automates optimal watering for the crops.

[1527] The server then suggests the type of fertilizer and the timing of its application. This is also achieved by the AI ​​analyzing environmental data and the stage of crop growth. Furthermore, the AI ​​obtains market price data in real time and calculates the optimal amount of sunlight and temperature. This allows the robot to operate the temperature and sunlight control devices to maintain the optimal environment.

[1528] It also has an emotion engine that recognizes the user's emotions and analyzes their facial expressions and voice. For example, if the user is feeling stressed, the emotion engine conveys that information to the generation AI, which then suggests growing crops that may reduce stress, such as lavender.

[1529] For example, if the emotion engine detects that a user is feeling stressed, the generative AI will suggest growing lavender, optimizing temperature and sunlight to ensure the lavender grows in the best conditions. In this way, the system can respond to fluctuations in market prices to maximize profits and also recommend the best crops and varieties based on the user's emotions.

[1530] Examples of prompts include:

[1531] “If the user is stressed, what crops will the generative AI suggest?”

[1532] This system is expected to not only improve agricultural efficiency and maximize profits, but also increase user satisfaction.

[1533] The flow of the specific processing in Application Example 3 will be described with reference to FIG.

[1534] Step 1:

[1535] The server acquires environmental data such as temperature, humidity, light intensity, and soil moisture from various sensors. This data is sent from the sensors to the server, which then receives it. The input is the environmental data from the various sensors, and the output is the environmental data stored on the server.

[1536] Step 2:

[1537] The server sends the acquired environmental data to the generation AI, which analyzes it. The generation AI selects the optimal crops and their varieties based on the environmental data. The input is environmental data, and the output is a proposal for the optimal crop and variety.

[1538] Step 3:

[1539] The server automates optimal watering for crops based on suggestions from the generative AI. Specifically, the server controls the watering device and supplies the required amount of water. The input is suggestions from the generative AI and environmental data, and the output is the execution of automated watering.

[1540] Step 4:

[1541] The server uses a generation AI to suggest the type of fertilizer and the timing of its application. The generation AI analyzes environmental data and the growth stage of the crop, and calculates the optimal type of fertilizer and the timing of its application. The input is the environmental data and the growth stage of the crop, and the output is a proposal for the type of fertilizer and the timing of its application.

[1542] Step 5:

[1543] The server retrieves market price data in real time, and the generation AI calculates the optimal amount of sunlight and temperature. Market price data is obtained via the internet or a dedicated data feed. The input is market price data, and the output is the optimal set value for sunlight and temperature.

[1544] Step 6:

[1545] The server sends the optimal sunlight and temperature settings to the robot, which then operates the temperature and sunlight control devices, maintaining an optimal environment. The input is the optimal sunlight and temperature settings, and the output is the adjusted environmental conditions.

[1546] Step 7:

[1547] The emotion engine recognizes the user's emotions and analyzes their emotions from their facial expressions and voice. The emotion engine then sends the user's emotional data to the generation AI. The input is the user's facial expressions and voice data, and the output is the analyzed emotional data.

[1548] Step 8:

[1549] The generative AI uses emotional data from the emotion engine to suggest the best crops and varieties based on the user's emotions. For example, if the user is feeling stressed, the generative AI will suggest growing lavender. The input is emotional data, and the output is a recommendation of the best crops and varieties.

[1550] Step 9:

[1551] Based on suggestions from the AI ​​generator, the server optimizes temperature and sunlight to ensure the crops grow in the best conditions. This allows the optimal crops to be grown according to the user's emotions. The input is the suggestions from the AI ​​generator, and the output is the optimized environmental conditions.

[1552] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[1553] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[1554] Another example of generative AI is Gemini (internet search engine). <url: https: gemini.google.com ?hl="ja">) are mentioned.

[1555] In the above embodiment, an example in which the specific processing is performed by the data processing device 12 has been given, but the technology of the present disclosure is not limited to this, and the specific processing may be performed by the smart glasses 214.

[1556] [Third embodiment]

[1557] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[1558] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[1559] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure.

[1560] The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[1561] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[1562] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio in accordance with instructions from the processor 46.

[1563] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the surroundings of user 20 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[1564] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[1565] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[1566] The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[1567] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[1568] In the headset type terminal 314, a reception output process is performed by the processor 46. A reception output program 60 is stored in the storage 50. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[1569] Next, the specific processing by the specific processing unit 290 of the data processing device 12 will be described.

[1570] "Example 1"

[1571] This invention is a system that uses a generative AI to propose optimal crops and varieties for cultivation based on data acquired from various sensors. Specifically, data such as soil humidity, temperature, and sunshine hours are acquired, and the generative AI proposes optimal crops and varieties based on this data. For example, if the soil humidity is high and the sunshine hours are long, the generative AI will propose rice cultivation.

[1572] "Example 2"

[1573] Another aspect of the present invention is a system that automates optimal watering for crops. Specifically, the AI ​​automatically adjusts the timing and amount of watering based on soil humidity data acquired from sensors. For example, if the soil humidity drops, the AI ​​automatically waters the crops.

[1574] "Example 3"

[1575] Furthermore, one aspect of the present invention is a system that automates the adjustment of sunlight and temperature so that crops can be shipped at a time when market prices are high. Specifically, the generation AI automatically adjusts sunlight and temperature based on market price data and the maturity of the crops, allowing crops to be harvested at a time when market prices are high. For example, the generation AI adjusts sunlight and temperature to coincide with the period when the market price of tomatoes is high, allowing tomatoes to be harvested at that time.

[1576] The processing flow of each embodiment will be described below.

[1577] "Example 1"

[1578] Step 1: Obtain data such as soil moisture, temperature, and sunlight hours from various sensors.

[1579] Step 2: Input the acquired data into the generation AI.

[1580] Step 3: The AI ​​analyzes the data and suggests the best crop and variety. For example, if the soil moisture is high and the sunshine hours are long, the AI ​​will suggest growing rice.

[1581] "Example 2"

[1582] Step 1: Obtain soil moisture data from the sensor.

[1583] Step 2: Input the acquired humidity data into the generation AI.

[1584] Step 3: The AI ​​analyzes the humidity data and automatically adjusts the timing and amount of watering. For example, if the soil humidity drops, the AI ​​will automatically water the plants.

[1585] "Example 3"

[1586] Step 1: Generative AI automatically adjusts the amount of sunlight and temperature based on market price data and crop maturity.

[1587] Step 2: The AI ​​adjusts the amount of sunlight and temperature so that crops can be harvested at a time when market prices are high. For example, when the market price of tomatoes is high, the AI ​​adjusts the amount of sunlight and temperature so that tomatoes can be harvested at that time.

[1588] Example 1

[1589] Next, a description will be given of Example 1 of Form Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[1590] With conventional agricultural systems, it was difficult to effectively utilize data acquired from various sensors to propose optimal crops and varieties and manage cultivation.It was also difficult to adjust shipping timing in response to fluctuations in market prices, which made it difficult to maximize profits.

[1591] The identification processing by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means. In this invention, the server includes a means for proposing optimal crops and varieties to cultivate based on data acquired from various sensors, a means for preprocessing the collected data, a means for analyzing the preprocessed data, a means for inputting a prompt statement to the generative AI model based on the analysis results, a means for acquiring a proposal from the generative AI model, and a means for displaying the acquired proposal to the user. This makes it possible to effectively utilize data acquired from various sensors to propose optimal crops and varieties and manage cultivation. In addition, it is possible to adjust shipping timing in response to fluctuations in market prices, thereby maximizing profits.

[1592] "Various sensors" are devices used to acquire environmental data on agricultural land, such as soil moisture sensors, temperature sensors, and sunlight sensors.

[1593] The "means for preprocessing data" is a function for performing processing to remove outliers from collected data and normalize the data.

[1594] "Means for analyzing data" refers to a function that performs statistical analysis and calculates average values ​​based on preprocessed data.

[1595] A "generative AI model" is an artificial intelligence model that generates optimal crop and variety recommendations based on input prompt statements.

[1596] A "prompt sentence" is an instruction sentence to be input into a generative AI model, and is a sentence that contains the analysis results.

[1597] The "means for obtaining proposals" is a function for receiving proposals generated from a generative AI model.

[1598] The "means for displaying proposals to the user" is a function for displaying the acquired proposals on the user's terminal.

[1599] "Automated sunlight and temperature adjustment" is a function that automatically adjusts sunlight and temperature based on data from environmental sensors.

[1600] This invention is a system that uses a generative AI model to propose optimal crops and varieties for cultivation based on data acquired from various sensors. Specifically, data such as soil humidity, temperature, and sunlight hours are acquired, and the generative AI model proposes optimal crops and varieties based on this data.

[1601] Hardware and software used

[1602] The server collects data from various sensors (e.g., soil moisture sensors, temperature sensors, and sunlight sensors). These sensors are installed in the farmland and provide data in real time. The server uses Python libraries such as Pandas and NumPy to preprocess and analyze the data. It also uses OpenAI's GPT-4 as a generative AI model.

[1603] The terminal is a device for displaying suggestions to the user, such as a smartphone or tablet.

[1604] Data collection and preprocessing

[1605] The server collects data from various sensors every day at 9:00 AM. For example, the soil humidity sensor gives data such as "Humidity: 70%," the temperature sensor gives data such as "Temperature: 25°C," and the sunshine sensor gives data such as "Sunshine hours: 8 hours." The collected data is then normalized and outliers are removed. For example, if an outlier such as "Humidity: 150%" is obtained from the soil humidity sensor, that data is removed. Temperature data is also normalized by converting it from Celsius to Fahrenheit.

[1606] Data analysis and the use of generative AI models

[1607] The server performs statistical analysis based on the preprocessed data. For example, if the soil humidity data for the past week was "65%, 70%, 75%, 70%, 68%, 72%, and 71%, the average value is calculated and set to "humidity: 70%." Based on the analysis results, a prompt is input to the generative AI model. The prompt includes the analysis results. For example, it could be "Please suggest the optimal crop and variety if the soil humidity is 70%, the temperature is 25 degrees, and there are 8 hours of sunshine."

[1608] Generate and view suggestions

[1609] The generative AI model generates a suggestion, "Rice cultivation is optimal," based on the prompt text. This suggestion is returned to the server. The server then sends the generated suggestion to the device. The device then displays "Rice cultivation is optimal" to the user. The user can view this suggestion on the screen of their smartphone or tablet.

[1610] Specific examples

[1611] Prompt Sentence Examples

[1612] "Please suggest the best crop and variety for a soil with 70% moisture, 25°C temperature, and 8 hours of sunshine."

[1613] Example proposal

[1614] "Rice cultivation is the best."

[1615] In this way, users can receive real-time recommendations on the best crops and varieties to suit the conditions of their farmland. They can also adjust shipping timing in response to fluctuations in market prices, maximizing profits.

[1616] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1617] Step 1: Data collection

[1618] The server collects data from various sensors. Specifically, soil humidity sensors, temperature sensors, sunlight sensors, etc. are installed on the farmland, and data is obtained from these sensors in real time. For example, the soil humidity sensor obtains data such as "humidity: 70%," the temperature sensor obtains data such as "temperature: 25 degrees," and the sunlight sensor obtains data such as "sunshine hours: 8 hours." The input is data from the various sensors, and the output is the collected raw data.

[1619] Step 2: Preprocessing the data

[1620] The server preprocesses the collected data. Specifically, it removes outliers and normalizes the data. For example, if an abnormal value such as "Humidity: 150%" is obtained from a soil humidity sensor, the data is removed. It also performs normalization such as converting temperature data from Celsius to Fahrenheit. The input is the collected raw data, and the output is the preprocessed data.

[1621] Step 3: Analyze the data

[1622] The server analyzes the preprocessed data. For the analysis, it uses Python libraries such as Pandas and NumPy. Specifically, it calculates the average and standard deviation of soil humidity, temperature, and sunshine hours. For example, it calculates the average soil humidity based on data from the past week. The input is the preprocessed data, and the output is the analysis results.

[1623] Step 4: Using the generative AI model

[1624] The server inputs a prompt statement to a generative AI model (for example, OpenAI's GPT-4) based on the analysis results. The prompt statement includes the analysis results. For example, it could say, "Please suggest the optimal crop and variety when the soil humidity is 70%, the temperature is 25 degrees, and the sunshine hours are 8 hours." The input is the analysis result and the prompt statement, and the output is the input to the generative AI model.

[1625] Step 5: Generate proposals

[1626] The generative AI model suggests the best crop and variety based on the input prompt. For example, it generates a suggestion such as "Rice cultivation is best." The generated suggestion is returned to the server. The input is the prompt, and the output is the generated suggestion.

[1627] Step 6: Viewing Proposals

[1628] The server sends the generated proposal to the terminal. The terminal displays the proposal to the user. For example, "Rice cultivation is optimal" may be displayed on the screen of a smartphone or tablet. The input is the generated proposal, and the output is the proposal displayed to the user.

[1629] (Application example 1)

[1630] Next, a description will be given of Application Example 1 of Form Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[1631] Conventional plant cultivation systems can suggest optimal crops and varieties based on data obtained from various sensors, but in automated plant cultivation systems within factories, it has been difficult to integrate the suggestion of optimal crops and varieties with the automation of the cultivation process.In addition, it has been impossible to issue instructions to maintain optimal environmental conditions based on the suggested crops, making efficient cultivation difficult.

[1632] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[1633] In this invention, the server includes: means for proposing the optimal crop and variety to cultivate based on data acquired from various sensors; means for automating optimal watering for crops; means for proposing the type of fertilizer and the timing of application; means for automating sunlight and temperature adjustment so that products can be shipped at a time when market prices are high; means for providing sales support; means for collecting data from various sensors installed in the plant cultivation area within the factory and proposing the optimal crop and variety using a generative AI; and means for automating the cultivation process based on the proposed crop and issuing instructions for maintaining optimal environmental conditions. This enables the automated plant cultivation system within the factory to comprehensively propose the optimal crop and variety and automate the cultivation process.

[1634] "Various sensors" are devices for acquiring environmental data, such as humidity sensors, temperature sensors, and light sensors.

[1635] "Data" refers to information such as humidity, temperature, and hours of sunlight obtained from various sensors.

[1636] "Generative AI" is a system that uses artificial intelligence technology to suggest optimal crops and varieties based on input data.

[1637] "Crop and variety proposal" refers to the generative AI analyzing data obtained from various sensors and selecting the most suitable crop and its variety.

[1638] "Automated watering" is a system that automatically supplies the necessary water to crops.

[1639] "Fertilizer type" refers to the type of nutrients used to promote crop growth.

[1640] "Application timing" refers to the optimal time or period to apply fertilizer or water to crops.

[1641] "Automated sunlight and temperature adjustment" is a system that automatically adjusts the amount of sunlight and temperature to suit crop growth.

[1642] "Sales support" is a system that supports sales strategies based on market prices and demand for crops.

[1643] A "plant cultivation area" is a specific area within a factory set aside for growing plants.

[1644] "Cultivation process automation" refers to a system that automatically performs a series of tasks related to crop cultivation.

[1645] "Environmental conditions" are factors such as humidity, temperature, and hours of sunlight that affect crop growth.

[1646] "Instruct" refers to issuing instructions for the system to take any action or adjustment necessary to maintain optimal environmental conditions.

[1647] The system for implementing this invention collects data from various sensors installed in the plant cultivation area within the factory, uses generative AI to propose optimal crops and varieties, and automates the cultivation process. A specific embodiment of this system is shown below.

[1648] The server collects data from various sensors, including humidity sensors, temperature sensors, and light sensors, which are installed in the plant cultivation area and obtain environmental data such as soil humidity, temperature, and sunlight hours in real time.

[1649] The collected data is sent to a server and input into a generative AI model. The generative AI model analyzes this data and suggests the optimal crop and variety. For example, if the soil humidity is 70%, the temperature is 25 degrees, and the sunshine hours are 10 hours, the generative AI model will suggest "rice" as the optimal crop.

[1650] Based on the proposed crop, the server issues instructions to automate the cultivation process, such as determining the optimal timing and amount of watering, the type of fertilizer and when to apply it, and adjusting the amount of sunlight and temperature, thereby maintaining optimal environmental conditions and enabling efficient cultivation.

[1651] The server also collects market price data and suggests optimal shipping times, adjusting the amount of sunlight and temperature to control crop growth so that products can be shipped at a time when market prices are high.

[1652] For example, by inputting the following prompt into the generative AI model, you can receive suggestions for the optimal crop and variety:

[1653] "What is the best crop / variety to grow if the soil moisture is 70%, the temperature is 25°C, and there are 10 hours of sunshine?"

[1654] In this way, the server uses a generative AI model to suggest optimal crops and varieties based on data from various sensors, automating the cultivation process and achieving efficient plant cultivation.

[1655] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1656] Step 1:

[1657] The server collects data from various sensors, such as humidity sensors, temperature sensors, and light sensors. These sensors are installed in the plant cultivation area and obtain environmental data such as soil humidity, temperature, and sunlight hours in real time. The input is the environmental data from the various sensors, and the output is the collected data.

[1658] Step 2:

[1659] The server inputs the collected data into the generative AI model, which analyzes this data and proposes the optimal crop and variety. The input is the collected environmental data, and the output is the optimal crop and variety proposed by the generative AI model. Specifically, the server generates a prompt sentence and sends it to the generative AI model.

[1660] Step 3:

[1661] The server receives suggestions from the generative AI model and issues instructions to automate the cultivation process based on the optimal crop and variety. The input is the suggestions from the generative AI model, and the output is instructions for the automated cultivation process. Specifically, the server automatically sets the timing and amount of watering, the type of fertilizer and when to apply it, and adjusts the amount of sunlight and temperature.

[1662] Step 4:

[1663] The server collects market price data and proposes the optimal shipping timing. The input is market price data, and the output is a proposal for the optimal shipping timing. Specifically, the server accesses a market price database and obtains the current market price.

[1664] Step 5:

[1665] The server adjusts the amount of sunlight and temperature based on the optimal shipping timing to control crop growth. The input is the proposal for the optimal shipping timing, and the output is the adjusted amount of sunlight and temperature. Specifically, the server sends control signals to adjust the amount of sunlight and temperature.

[1666] Step 6:

[1667] The user checks the suggestions and instructions from the server and makes manual adjustments as necessary. The input is the suggestions and instructions from the server, and the output is the result of manual adjustments made by the user. Specifically, the user checks the server settings through the management screen and makes changes as necessary.

[1668] Example 2

[1669] Next, a description will be given of Example 2 of Form Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[1670] Conventional agricultural systems require manual operations such as watering crops, spreading fertilizer, and adjusting sunlight and temperature, which is inefficient and often results in missing the optimal timing. It is also difficult to plan shipments at times when market prices are high. This leads to problems such as reduced crop quality and yields, and a decline in agricultural management efficiency.

[1671] The specific processing by the specific processing unit 290 of the data processing device 12 in Example 2 is realized by the following means. In this invention, the server includes a means for proposing the optimal crop and variety to cultivate based on data acquired from various sensors, a means for automatically adjusting the timing and amount of watering using a generative AI model based on humidity data acquired from a soil humidity sensor, a means for proposing the type of fertilizer and the timing of spraying, a means for automating the adjustment of sunlight and temperature so that products can be shipped at a time when market prices are high, and a means for providing sales support. This automates the entire process from crop cultivation to shipping, enabling efficient and optimal agricultural management.

[1672] "Various sensors" are devices used to acquire data on the crop cultivation environment, such as soil moisture sensors, temperature sensors, and sunlight sensors.

[1673] A "soil moisture sensor" is a device for measuring soil moisture and acquiring that data.

[1674] A "generative AI model" is an algorithm that uses artificial intelligence technology to analyze data and calculate the optimal timing and amount of watering.

[1675] The "means for automatically adjusting the timing and amount of watering" is a system that uses a generative AI model to determine the start time of watering and the amount of water to supply based on data obtained from a soil moisture sensor, and then automatically executes the process.

[1676] The "means for proposing the type of fertilizer and the timing of application" is a system that suggests the optimal type of fertilizer and the timing of its application based on the growth stage of the crop and environmental data.

[1677] The "means for automating sunlight and temperature adjustment" is a system that uses a generative AI model to optimally adjust sunlight and temperature based on data obtained from sunlight and temperature sensors.

[1678] "Sales support means" refers to a system that proposes optimal shipping times and sales strategies based on market prices and demand forecast data.

[1679] This invention is a system that automates optimal watering for crops. Specifically, a generative AI model automatically adjusts the timing and amount of watering based on soil moisture data obtained from sensors.

[1680] Hardware and software used

[1681] The server uses the following hardware and software to collect and analyze data:

[1682] Soil moisture sensor (e.g. DHT11, DHT22)

[1683] Temperature sensor, sunlight sensor

[1684] Generative AI models (e.g., OpenAI's GPT-4)

[1685] Communication modules (e.g. Wi-Fi, LoRa)

[1686] Watering systems (e.g. solenoid valves connected to computer control circuits)

[1687] The terminal uses the following hardware and software to collect data from the sensors and send it to the server:

[1688] soil moisture sensor

[1689] Communication Module

[1690] Computer Control Circuit

[1691] Data processing and calculation

[1692] The device collects data from the soil moisture sensor in real time and sends it to the server using wireless communication technologies such as Wi-Fi or LoRa. Specifically, the device converts the humidity data into JSON format and sends it to the server using an HTTP POST request.

[1693] The server inputs the received humidity data into the generative AI model. The generative AI model analyzes the humidity data and calculates the optimal timing and amount of watering. Specifically, the server converts the humidity data into a prompt sentence and sends it to the generative AI model. For example, it generates a prompt sentence such as, "The current soil humidity is 30%. Please tell me the optimal timing and amount of watering."

[1694] The server generates watering instructions based on the output from the generative AI model. The instructions include when to start watering and how much water to supply. Specifically, it analyzes the output from the generative AI model and generates the instruction "Start watering and supply 500ml of water."

[1695] The server sends the generated watering instructions to the device using wireless communication technologies such as Wi-Fi or LoRa. Specifically, the server converts the watering instructions into JSON format and sends them to the device using an HTTP POST request.

[1696] The terminal carries out watering based on the received watering instructions. Specifically, it controls a solenoid valve connected to a computer control circuit to supply the required amount of water to the crops. Specifically, the terminal opens the solenoid valve and supplies 500 ml of water. When watering is complete, it closes the solenoid valve.

[1697] Specific examples

[1698] For example, if the soil moisture drops below 30%, the generative AI model generates the instruction to "start watering and supply 500ml of water." The server receives this instruction and sends it to the terminal, which then opens the solenoid valve and supplies 500ml of water to the crops.

[1699] Example prompts to input to the generative AI model

[1700] "The current soil moisture level is 30%. What is the best time and amount to water?"

[1701] The system allows users to automate the watering of their crops and maintain an optimal environment.

[1702] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1703] Step 1:

[1704] The terminal collects soil humidity data using a soil humidity sensor. The sensor is inserted into the soil and measures the humidity at regular intervals (for example, every hour). The input is the soil humidity data, and the output is the collected humidity data. Specifically, the sensor acquires the humidity data as an analog signal, which the terminal converts into digital data.

[1705] Step 2:

[1706] The terminal sends the collected humidity data to a server. Communication is performed using wireless communication technologies such as Wi-Fi or LoRa. The input is the collected humidity data, and the output is the humidity data sent to the server. Specifically, the terminal converts the humidity data into JSON format and sends it to the server using an HTTP POST request.

[1707] Step 3:

[1708] The server inputs the received humidity data into the generative AI model. The generative AI model analyzes the humidity data and calculates the optimal timing and amount of watering. The input is the humidity data sent to the server, and the output is the analysis result from the generative AI model. Specifically, the server converts the humidity data into a prompt text and sends it to the generative AI model. For example, it generates a prompt text such as, "The current soil humidity is 30%. Please tell me the optimal timing and amount of watering."

[1709] Step 4:

[1710] The server generates watering instructions based on the output from the generative AI model. The instructions include when to start watering and the amount of water to supply. The input is the analysis result from the generative AI model, and the output is the generated watering instructions. Specifically, the server analyzes the output from the generative AI model and generates the instruction to "start watering and supply 500ml of water."

[1711] Step 5:

[1712] The server sends the generated watering instructions to the device. Communication is performed using wireless communication technologies such as Wi-Fi or LoRa. The input is the generated watering instructions, and the output is the watering instructions sent to the device. Specifically, the server converts the watering instructions into JSON format and sends them to the device using an HTTP POST request.

[1713] Step 6:

[1714] The terminal carries out watering based on the received watering instructions. Specifically, it controls a solenoid valve connected to a computer control circuit to supply the required amount of water to the crops. The input is the watering instruction sent to the terminal, and the output is the executed watering. Specifically, the terminal opens the solenoid valve and supplies 500 ml of water. When watering is complete, it closes the solenoid valve.

[1715] (Application example 2)

[1716] Next, a description will be given of Application Example 2 of Form Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[1717] Conventional plant cultivation systems require manual control of soil humidity and watering timing, making efficient management difficult. Furthermore, because environmental conditions vary when cultivating plants within factories, there is a need for a system that can automatically adjust the appropriate watering timing and amount.

[1718] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[1719] In this invention, the server includes a means for proposing the optimal crop and variety to cultivate based on data obtained from various sensors, a means for automating the optimal watering for the crops, a means for proposing the type of fertilizer and the timing of application, a means for automating the amount of sunlight and temperature adjustment so that the crops can be shipped at a time when the market price is high, a means for providing sales support, and a means installed on a robot that grows plants in a factory, where the generating AI automatically adjusts the timing and amount of watering based on soil humidity data obtained from the sensors.This enables efficient and automatic plant growth management.

[1720] "Various sensors" are devices used to acquire environmental data such as soil humidity, temperature, and amount of sunlight.

[1721] The "means for proposing the optimal crops and varieties to cultivate" is a function that analyzes the acquired data and selects and proposes the optimal crops and varieties.

[1722] "Automated watering method that is optimal for crops" is a function that automatically waters crops at the appropriate time and in the appropriate amount based on soil humidity data.

[1723] The "means for suggesting the type of fertilizer and the timing of application" is a function for suggesting the type of fertilizer required for crop growth and the timing of application.

[1724] "Automated means for adjusting sunlight and temperature" is a function for automatically adjusting the amount of sunlight and temperature to suit crop growth.

[1725] "Sales support means" is a function for supporting sales strategies based on the market price and demand of crops.

[1726] A "factory plant growing robot" is an automated device designed to grow plants in a factory.

[1727] "Generative AI" is an artificial intelligence technology that analyzes acquired data and predicts and executes optimal actions.

[1728] "Soil moisture data" is data that indicates the amount of moisture in the soil.

[1729] "Means for automatically adjusting the timing and amount of watering" is a function that waters plants at the appropriate timing and amount based on soil humidity data.

[1730] The system for implementing this invention includes various sensors, a generative AI model, and a plant growing robot. The specific configuration and operation of the system are described below.

[1731] System configuration

[1732] 1. Various sensors

[1733] The various sensors are devices for acquiring environmental data such as soil humidity, temperature, and amount of sunlight. Specifically, they include soil humidity sensors, temperature sensors, and light sensors.

[1734] 2. Generative AI Models

[1735] A generative AI model is an artificial intelligence technology that analyzes acquired data and predicts and executes optimal actions. For example, a model trained using a machine learning library such as TensorFlow is used.

[1736] 3. Plant-growing robot

[1737] A plant-growing robot is an automated device designed to grow plants in a factory. The robot receives data from sensors and follows instructions from a generative AI model to water and fertilize the plants.

[1738] System Operation

[1739] 1. Data Acquisition

[1740] Various sensors collect real-time environmental data, such as soil humidity, temperature, and sunlight, which is then sent to the plant-growing robot's control system.

[1741] 2. Data Analysis

[1742] The plant-growing robot's control system inputs the acquired data into a generative AI model to predict the optimal timing and amount of watering, which then determines the optimal behavior based on past data and current environmental data.

[1743] 3. Watering

[1744] The plant-growing robot automatically waters plants based on the timing and amount of watering predicted by the generative AI model. Specifically, the robot's onboard water pump delivers the appropriate amount of water.

[1745] Hardware and software used

[1746] Hardware

[1747] Soil moisture sensor (e.g., Arduino Soil Moisture Sensor)

[1748] Temperature sensor (e.g. DHT22)

[1749] Optical sensor (e.g. BH1750)

[1750] Water pump (e.g. Peristaltic Liquid Pump)

[1751] software

[1752] Machine learning libraries (e.g. TensorFlow)

[1753] Control systems (e.g., computer control circuits)

[1754] Specific examples

[1755] For example, when growing tomatoes in a factory, the following prompt sentences can be input into the generative AI model:

[1756] Prompt Sentence Examples

[1757] "When soil moisture falls below 30%, use a generative AI model to predict the appropriate amount of watering and use a pump to provide water."

[1758] In this way, the system can efficiently and automatically manage plant growth.

[1759] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1760] Step 1:

[1761] The server collects environmental data such as soil humidity, temperature, and amount of sunlight from various sensors.

[1762] Input: Data from soil moisture sensor, temperature sensor, and light sensor

[1763] Output: Acquired environmental data

[1764] Specific operation: The server periodically polls data from each sensor and collects environmental data in real time.

[1765] Step 2:

[1766] The server inputs the acquired environmental data into a generative AI model to predict the optimal timing and amount of watering.

[1767] Input: Obtained environmental data

[1768] Output: Predicted watering timing and amount

[1769] Specific operation: The server inputs environmental data into a generative AI model and calculates the optimal watering timing and amount based on past and current environmental data.

[1770] Step 3:

[1771] The server sends instructions to the plant-growing robot based on the timing and amount of watering predicted by the generative AI model.

[1772] Input: Predicted watering timing and amount

[1773] Output: Watering instructions

[1774] Specific operation: Based on the prediction results obtained from the generative AI model, the server sends specific watering instructions to the plant-growing robot.

[1775] Step 4:

[1776] The plant-growing robot follows instructions from the server to control the water pump and supply the appropriate amount of water.

[1777] Input: Watering instructions from the server

[1778] Output: Actual watering action

[1779] Specific operation: The plant growing robot starts the water pump and supplies the specified amount of water to the plant.

[1780] Step 5:

[1781] The server receives feedback from the plant-growing robot and stores it as data for the next watering.

[1782] Input: Feedback data from a plant-growing robot

[1783] Output: Updated environment data

[1784] Specific operation: The server receives feedback data from the plant-growing robot, stores it in a database, and uses it to predict the next watering.

[1785] Example 3

[1786] Next, a third embodiment of the third embodiment will be described. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[1787] In conventional agricultural systems, it is difficult to respond to fluctuations in market prices when cultivating crops, and the optimal harvest timing is often missed. In addition, adjusting environmental conditions manually makes efficient management difficult. This makes it difficult to maximize profits, and it requires a lot of effort and time.

[1788] The specific processing by the specific processing unit 290 of the data processing device 12 in the third embodiment is realized by the following means.

[1789] In this invention, the server includes means for proposing optimal crops and varieties to cultivate based on data acquired from various sensors, means for automating optimal watering for crops, means for proposing the type of fertilizer and the timing of application, means for automating adjustment of sunlight and temperature so that crops can be shipped at a time when market prices are high, means for analyzing market price data and crop maturity data using a generative AI model and calculating the optimal sunlight and temperature, means for adjusting environmental conditions in the greenhouse based on the calculation results, and means for monitoring and providing feedback on environmental conditions.This enables efficient environmental management that can respond to fluctuations in market prices and ensures that the optimal harvest timing is not missed.

[1790] "Various sensors" are devices used to measure environmental conditions and the growth status of crops, such as temperature sensors, humidity sensors, and light sensors.

[1791] "Data" refers to information that serves as the basis for analysis and judgment, such as information obtained from various sensors and market price information.

[1792] "Proposing the optimal crops and varieties to cultivate" means selecting the crops and varieties that are best suited to specific environmental conditions based on collected data and recommending them to the user.

[1793] "Automated watering" is a system that automatically supplies the water necessary for crop growth.

[1794] "Suggestion of fertilizer type and application timing" means recommending the type of fertilizer and the application timing that are optimal for crop growth based on data.

[1795] "High market price period" refers to a period when the market price of a crop is higher than usual.

[1796] "Automated sunlight and temperature adjustment" is a system that automatically adjusts the amount of sunlight and temperature to optimal levels for crop growth.

[1797] A "generative AI model" is a model that uses artificial intelligence technology to analyze data and make predictions about optimal environmental conditions and crop growth.

[1798] "Market price data" refers to price information for agricultural products in the market.

[1799] "Crop maturity data" is information relating to the growth status and maturity of crops.

[1800] "Environmental conditions inside the greenhouse" refer to factors that affect crop growth, such as the temperature, humidity, and amount of sunlight inside the greenhouse.

[1801] "Monitoring" refers to the continuous monitoring of environmental conditions and crop status using sensors and other devices.

[1802] "Feedback" refers to adjusting environmental conditions and providing information to users based on the monitoring results.

[1803] This invention is a system that uses data obtained from various sensors to suggest the best crops and varieties to cultivate, automates the optimal watering of crops, suggests the type of fertilizer and the timing of application, automates the adjustment of sunlight and temperature so that products can be shipped at a time when market prices are high, and provides sales support.

[1804] 1. Program Generation

[1805] The server uses a generative AI model to generate a program that takes market price data and crop maturity data as input, and includes algorithms for automatically adjusting the amount of sunlight and temperature.

[1806] 2. Program processing explanation

[1807] The server executes the generated program and performs the following processes.

[1808] Data collection

[1809] Market price data collection:

[1810] The server retrieves the latest price information from a market price database through an API. For example, it uses the API of an agricultural market to get the current market price of tomatoes. Specifically, the server sends a "GET" request to the API endpoint and receives price data in JSON format.

[1811] Collecting Crop Maturity Data:

[1812] The server collects crop maturity data from sensors installed in the greenhouse, such as temperature, humidity, and light sensors. For example, the server periodically polls the sensors to obtain the current temperature, humidity, and amount of sunlight.

[1813] Data analysis

[1814] Market price and maturity data analysis:

[1815] The server inputs the collected market price data and crop maturity data into the generative AI model. The generative AI model calculates the optimal amount of sunlight and temperature based on this data. Specifically, the server sends a prompt to the generative AI model, such as "Please tell me the optimal settings for adjusting sunlight and temperature so that tomatoes can be harvested in May 2023."

[1816] environmental adjustment

[1817] Adjusting sunlight and temperature:

[1818] The server adjusts the environmental conditions in the greenhouse based on the optimal settings obtained from the generative AI model. Specifically, it turns lights on and off and changes the settings of the temperature control device. For example, the server sends instructions to the smart greenhouse control system such as "turn on the lights for 8 hours and set the temperature to 25 degrees."

[1819] Monitoring and Feedback

[1820] Environmental condition monitoring:

[1821] The server monitors whether the set environmental conditions are being maintained properly, periodically checking data from sensors and making readjustments as necessary. Specifically, the server monitors data from sensors in real time and makes immediate adjustments if, for example, the temperature deviates from the set value.

[1822] Providing feedback:

[1823] The server provides feedback to the user on the state of environmental conditions and the growth status of the crops. The user can check this information through a dedicated device or application. For example, the server might send a notification to the user's device saying, "The current temperature is 25 degrees, and the amount of sunlight is set to 8 hours. The tomatoes are growing well."

[1824] Examples and prompts

[1825] As a concrete example, consider adjusting environmental conditions to coincide with periods when market prices for tomatoes are high.

[1826] Examples:

[1827] Market price data: The market price of tomatoes in May 2023 is high.

[1828] Crop maturity data: The current growth state of tomatoes is immature.

[1829] The server uses a generative AI model to adjust sunlight and temperature to ensure tomatoes ripen in May.

[1830] Example prompt sentence:

[1831] Using market price data and crop maturity data as inputs, generate a program that will adjust the amount of sunlight and temperature so that tomatoes can be harvested in May 2023.

[1832] In this way, the server realizes a system that automatically adjusts environmental conditions so that crops can be harvested at a timing when market prices are high. The flow of the identification process in the third embodiment will be described with reference to FIG.

[1833] Step 1: Data collection

[1834] Input: Market price data, crop maturity data

[1835] Specific behavior:

[1836] The server retrieves the latest price information from the market price database through the API by sending a "GET" request to the API endpoint and receiving price data in JSON format.

[1837] The server collects crop maturity data from temperature, humidity, and light sensors installed in the greenhouse. Specifically, the server periodically polls the data from the sensors to obtain the current temperature, humidity, and amount of sunlight.

[1838] Output: Collected market price data, crop maturity data

[1839] Step 2: Data analysis

[1840] Inputs: Collected market price data, crop maturity data

[1841] Specific behavior:

[1842] The server inputs the collected market price data and crop maturity data into the generative AI model. Specifically, the server sends a prompt to the generative AI model, such as, "What are the optimal settings for adjusting sunlight and temperature so that tomatoes can be harvested in M...

Claims

1. A sensor for acquiring environmental data including humidity, temperature, amount of sunlight, and soil moisture of the soil in which crops are to be cultivated; A means for recognizing an emotional state of a worker by analyzing the facial expressions and actions of the worker using an emotion engine; a means for generating a prompt sentence instructing the generation of a proposal for a crop or variety suitable for the soil condition and the worker's emotional state based on the environmental data acquired by the sensor and the recognized worker's emotional state; A means for generating crop and variety recommendations suited to the soil condition and the worker's emotional state using the generated prompt sentences and the generative AI model; A means for notifying a worker of a crop or variety suitable for the soil condition and the worker's emotional state output from the generating AI model; A system equipped with.

2. a means for automatically adjusting the timing and amount of watering of the soil based on the soil moisture data acquired by the sensor; A means for proposing a type of fertilizer to be applied to the soil and a timing for spreading the fertilizer based on the environmental condition of the soil acquired by the sensor; The system of claim 1 further comprising:

3. Based on market price data and the maturity of the crop, a method is developed to automatically adjust the amount of sunlight and temperature so that the crop can be harvested at a time when the market price is high. The system of claim 1 or 2 further comprising:

Images