system

Abstract

Provide a system. [Solution] A means for analyzing agricultural data in real time using AI technology; A means of obtaining the latest weather information; means for collecting soil conditions from a sensor; a means of monitoring the health of the crop; means for generating a farming strategy for maximizing crop growth based on the analysis results and the acquired meteorological information; a means for providing a farming strategy to a user; A system including:

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] Traditional agriculture faces the challenge of managing weather information, soil conditions, and crop growth conditions individually, making it difficult to formulate optimal farming strategies based on this information. In particular, there has been no comprehensive system for collecting and analyzing data in real time to achieve efficient and sustainable agriculture. As a result, agricultural work such as irrigation, fertilization, and pest management cannot be carried out efficiently, which can lead to reduced yields and crop quality. [Means for solving the problem]

[0005] This invention provides a means for analyzing agricultural data in real time using AI technology. This system includes a means for acquiring the latest weather information, a means for collecting soil conditions from sensors, and a means for monitoring the health of crops. It also includes a means for generating farming strategies to maximize crop growth based on this data and providing them to users. By suggesting specific strategies for irrigation, fertilization, and pest management to users, the system aims to achieve efficient and sustainable agriculture. Furthermore, when collecting soil conditions from sensors, using sensors that detect soil nutrient and moisture levels enables more accurate data collection. This provides a system that is expected to optimize farming and improve crop yields.

[0006] "AI technology" is a technology that uses artificial intelligence to analyze data and support human judgment and actions.

[0007] "Agricultural data" refers to information related to agriculture, such as meteorological information, soil conditions, and crop conditions.

[0008] "Real-time" means that data acquisition and processing occurs immediately, without delay.

[0009] "Weather information" refers to climate data such as temperature, precipitation, humidity, and wind speed in a specific area.

[0010] "Soil condition" refers to information that describes the physical and chemical properties of the soil, including specific nutrient and moisture levels.

[0011] A "sensor" is a device that detects physical or chemical changes and captures them as data.

[0012] "Crop health status" refers to data that indicates the health and growth stage of a crop, including its response to the effects of pests and diseases and stress in the cultivation environment.

[0013] An "agricultural strategy" is a specific method or procedure, such as irrigation, fertilization, or pest management, designed to optimize agricultural activities.

[0014] "Users" are those who use this system and receive proposals for agricultural work strategies, specifically agricultural managers and agricultural cooperatives. [Brief explanation of the drawings]

[0015] [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. 3 is a sequence diagram showing a processing flow of the data processing system according to the first embodiment. [Figure 12] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in Application Example 1. [Figure 13]FIG. 10 is a sequence diagram showing the flow of processing in the data processing system according to the second embodiment when an emotion engine is combined. [Figure 14] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in Application Example 2 when an emotion engine is combined. DETAILED DESCRIPTION OF THE INVENTION

[0016] 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.

[0017] First, the terms used in the following description will be explained.

[0018] 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, a 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), and an APU (Accelerated Processing Unit).

[0019] 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.

[0020] 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.

[0021] 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.

[0022] 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."

[0023] [First embodiment]

[0024] FIG. 1 shows an example of the configuration of a data processing system 10 according to the first embodiment.

[0025] 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.

[0026] 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).

[0027] 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.

[0028] 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.

[0029] 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.

[0030] 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.

[0031] FIG. 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0032] 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.

[0033] 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.

[0034] 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.

[0035] Next, a description will be given of the specific processing performed by the specific processing unit 290 of the data processing device 12. 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."

[0036] To implement this invention, it is first necessary to build an agricultural data analysis system using AI technology. This system consists of a server, terminals, and users, each of which plays a specific role.

[0037] Server-side processing

[0038] The server retrieves weather information, analyzes soil data and crop health, and generates optimal farming strategies.

[0039] Obtaining weather information

[0040] The server retrieves the latest weather information through an API. Using the API key and location, it collects data such as current temperature, precipitation, humidity, and wind speed from the weather API.

[0041] Soil data analysis

[0042] The server analyzes data collected from the soil sensors, including soil nutrient and moisture levels, and makes fertilization recommendations if nutrient levels are low and irrigation suggestions if moisture levels are low.

[0043] Crop health monitoring

[0044] The server monitors the health and growth stage of the crops based on data sent from the plant's sensors, and suggests appropriate measures if the plant's health index is low or if the plant is threatened by pests.

[0045] Generation of farming strategies

[0046] The server integrates weather, soil, and crop data and uses AI algorithms to generate optimal farming strategies, including specific instructions for irrigation, fertilization, and pest management.

[0047] Processing on the terminal side

[0048] The terminal is responsible for collecting data from soil and crop sensors and transmitting it to a server.

[0049] Acquiring Sensor Data

[0050] The device uses soil sensors to measure soil nutrient and moisture levels, and crop sensors to measure the growth stage and health index of the crop, and these data are periodically transmitted to the server.

[0051] User-side processing

[0052] The user receives the farming strategy provided by the server and carries out the farming work according to the instructions.

[0053] Viewing Optimization Strategies

[0054] Through a screen or application provided by the device, the user can view farming strategies sent from the server, including specific instructions for irrigation, fertilization, and pest management.

[0055] Specific examples

[0056] For example, if a farmer uses this system, he or she can implement an optimized farming strategy by following the steps below.

[0057] 1. Acquiring sensor data:

[0058] The device uses soil sensors to measure soil nutrient and moisture levels, and crop sensors to measure crop growth stages and health indices.

[0059] 2. Obtaining weather information:

[0060] The server retrieves the latest weather information through a weather API.

[0061] 3. Data Analysis:

[0062] The server analyzes the weather, soil, and crop data it acquires and uses AI algorithms to generate farming strategies.

[0063] 4. Display Strategy:

[0064] The user then uses the terminal to review specific instructions provided by the server regarding irrigation, fertilization, and pest management.

[0065] 5. Performing agricultural work:

[0066] The user performs farming operations based on the strategies provided, such as "increase irrigation," "apply balanced fertilizer," and "conduct pest inspections."

[0067] In this way, by using the AI ​​system of the present invention, farmers can implement optimal farming strategies in real time and achieve efficient and sustainable agriculture.

[0068] The processing flow will be explained below.

[0069] Step 1:

[0070] The device collects data from soil and crop sensors, specifically using the soil sensor to measure soil nutrient and moisture levels, and the crop sensor to measure the growth stage and health index of the crop.

[0071] Step 2:

[0072] The device transmits the collected sensor data to a server, including soil nutrient levels, moisture levels, crop growth stage, and health index.

[0073] Step 3:

[0074] The server retrieves weather information by sending a request to the weather API using the API key and location to retrieve weather data such as the current temperature, precipitation, humidity, and wind speed.

[0075] Step 4:

[0076] The server analyzes the soil data, specifically extracting nutrient and moisture levels from the transmitted soil data and evaluating each measurement.

[0077] Step 5:

[0078] The server monitors the health of the crops based on the crop data, checking the growth stage and health index of the crops and considering measures to improve their health if necessary.

[0079] Step 6:

[0080] The server combines the analyzed soil data, monitored crop data, and acquired weather information, and uses AI algorithms to generate optimal farming strategies, specifically creating instructions for irrigation, fertilization, and pest management.

[0081] Step 7:

[0082] The server provides the generated farming strategy to the user by sending it to the terminal and allowing the user to check it through the terminal.

[0083] Step 8:

[0084] The user views the farming strategies displayed on the device, including increased irrigation, fertilization suggestions, and pest management instructions.

[0085] Step 9:

[0086] The user performs farming operations based on the provided farming strategies, such as adjusting irrigation systems, applying fertilizers, and conducting pest checks.

[0087] Through the above steps, efficient and sustainable agriculture will be realized through collaboration between servers, terminals, and users.

[0088] Example 1

[0089] Next, a description will be given of 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."

[0090] Conventional agricultural processes lack sufficient data collection and analysis to optimize crop growth and health, resulting in unstable crop yields and quality. It is also difficult to provide real-time farming strategies based on meteorological and soil data, creating a need for methods to achieve efficient and sustainable agriculture.

[0091] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[0092] In this invention, the server includes means for analyzing agricultural data in real time using AI technology, means for acquiring the latest weather information, means for collecting soil conditions from sensors, means for monitoring the health of crops, means for generating a farming strategy for maximizing crop growth based on the analysis results and the acquired weather information, means for providing the farming strategy to a user, means for collecting data from soil sensors and crop sensors via a terminal and transmitting it to the server, and means for the user to carry out farming based on the farming strategy provided. This makes it possible to provide optimal farming strategies in real time and achieve efficient and sustainable agriculture.

[0093] "AI technology" refers to technology that uses artificial intelligence, and in particular, systems that perform data analysis, pattern recognition, and predictive analysis.

[0094] "Real-time" means that data is acquired, processed, and results are provided without delay.

[0095] "Weather information" refers to data on weather conditions such as temperature, precipitation, humidity, and wind speed.

[0096] "Soil condition" is data about soil properties, such as soil nutrient and moisture levels.

[0097] A "sensor" is a device that detects changes in the physical or chemical environment and acquires data.

[0098] "Crop health" refers to data about the overall health of the crop, such as the stage of growth, nutritional status, and the presence or absence of pests and diseases.

[0099] An "agricultural strategy" is a set of specific operations and activities, such as irrigation, fertilization, and pest management, that optimize crop growth.

[0100] "User" refers to a farm manager or farm worker who uses the system to carry out farm work.

[0101] A "terminal" is a communication device that collects data from sensors and transmits it to a server.

[0102] A "generative model" is an algorithm or AI model that predicts and generates farming strategies based on input data.

[0103] MODE FOR CARRYING OUT THE INVENTION

[0104] To implement this invention, it is first necessary to build an agricultural data analysis system using AI technology. This system consists of a server, terminals, and users, each of which plays a specific role.

[0105] Server-side processing

[0106] The server retrieves weather information, analyzes soil data and crop health, and generates optimal farming strategies.

[0107] Obtaining weather information

[0108] The server retrieves the latest weather information using weather APIs such as OpenWeatherMap, and periodically collects data such as temperature, precipitation, humidity, and wind speed using the API key and location information.

[0109] Soil data analysis

[0110] The server analyzes data collected from the soil sensors, including soil nutrient and moisture levels, and, for example, makes fertilization recommendations if nutrient levels are low and irrigation suggestions if moisture levels are declining.

[0111] Crop health monitoring

[0112] The server uses data from the plant sensors to monitor the health of the plants, and if the plant's health index declines or there are signs of pest infestation, it will suggest appropriate measures.

[0113] Generation of farming strategies

[0114] The server integrates weather, soil, and crop data and uses AI algorithms to generate optimal farming strategies, including specific instructions for irrigation, fertilization, and pest management.

[0115] Processing on the terminal side

[0116] The terminal is responsible for collecting data from soil and crop sensors and transmitting it to a server.

[0117] Acquiring Sensor Data

[0118] The device uses soil sensors to measure soil nutrient and moisture levels, and crop sensors to measure the growth stage and health index of the crop, and periodically transmits this data to the server.

[0119] User-side processing

[0120] The user receives the farming strategy provided by the server and carries out the farming work according to the instructions.

[0121] Viewing Optimization Strategies

[0122] Through a screen or application provided by the device, the user can view farming strategies sent from the server, including specific instructions for irrigation, fertilization, and pest management.

[0123] Specific examples

[0124] For example, if a farmer uses this system, he or she can implement an optimized farming strategy by following the steps below.

[0125] 1. Acquiring sensor data:

[0126] The device uses soil sensors to measure soil nutrient and moisture levels, and crop sensors to measure crop growth stages and health indices.

[0127] 2. Obtaining weather information:

[0128] The server retrieves the latest weather information through a weather API.

[0129] 3. Data Analysis:

[0130] The server analyzes the weather, soil, and crop data it acquires and uses AI algorithms to generate farming strategies.

[0131] 4. Display Strategy:

[0132] The user then uses the terminal to review specific instructions provided by the server regarding irrigation, fertilization, and pest management.

[0133] 5. Performing agricultural work:

[0134] The user performs farming operations based on the strategies provided, such as "increase irrigation," "apply balanced fertilizer," and "conduct pest inspections."

[0135] In this way, by using the agricultural data analysis system that employs the AI ​​technology of the present invention, farmers can implement optimal farming strategies in real time and achieve efficient and sustainable agriculture.

[0136] Example prompt sentences to use

[0137] Please explain in detail your approach to developing a system that analyzes agricultural data and generates optimal farming strategies. Please describe in detail the roles of the server, terminal, and user.

[0138] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0139] Step 1:

[0140] The server calls the weather API every hour and obtains the latest weather information using the API key and location information. The API key and location information are required as input data, and based on this, weather data such as temperature, precipitation, humidity, and wind speed is obtained and stored in a database. Specifically, the server first generates a URL, then sends a request to the API, analyzes the response, and extracts the weather data.

[0141] Step 2:

[0142] The server receives soil data sent from the device every five minutes and records it in a database. The input data required is the nutrient and moisture levels sent from the soil sensor, which is analyzed to determine whether fertilization or irrigation is necessary. Specifically, each time new data is received, it is added to the database and compared with past data.

[0143] Step 3:

[0144] The server receives data from the crop sensors in real time and analyzes the growth and health of the crops. The input data is the growth stage and health index sent from the crop sensors, and based on this, it evaluates the presence of pests and the growth status. Specifically, it analyzes the received data and issues an alert if an abnormal value is detected.

[0145] Step 4:

[0146] The server integrates the acquired weather, soil, and crop data and generates farming strategies using AI algorithms. The input data consists of weather, soil, and crop data, which are then fed into an AI model that outputs specific instructions for irrigation, fertilization, and pest management. Specifically, the server preprocesses the data, inputs it into the AI ​​model, and stores the output strategy in a database.

[0147] Step 5:

[0148] The terminal acquires data from the soil and crop sensors every five minutes and sends it to the server. The input data is real-time data acquired from the sensors, and is sent to the server via wireless communication or an internet connection. Specifically, the terminal aggregates the data from the sensors, converts it into packet format, and sends it.

[0149] Step 6:

[0150] The user checks the farming strategy provided by the server through the terminal. The input data is the optimized farming instructions from the server, which are displayed in the application. Specifically, the application sends a request to the server and visually displays the received strategy data.

[0151] Step 7:

[0152] The user performs actual farm work based on the provided farming strategy. The input data are specific instructions from the server, and based on these, the user performs irrigation, fertilization, and pest management. Specific actions include applying the instructed amount of water and fertilizer, and carrying out necessary pest control.

[0153] Through the above processing steps, the system of the present invention can provide a real-time farming strategy for achieving efficient and sustainable agriculture.

[0154] (Application example 1)

[0155] Next, a description will be given of Application 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."

[0156] Conventional agricultural systems have had difficulty effectively understanding weather information, soil conditions, and crop health to derive optimal farming strategies. Similarly, in factory production lines, it has been challenging to automatically generate optimal production strategies based on environmental data and raw material quality, and then instruct robots to execute them.

[0157] 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.

[0158] In this invention, the server includes means for analyzing agricultural data in real time using AI technology, means for acquiring the latest weather information, means for collecting soil condition information from sensors, means for monitoring the health of crops, means for generating a farming strategy to maximize crop growth based on the analysis results and the acquired weather information, means for analyzing environmental data and raw material quality data and generating an optimal production strategy, means for instructing a work robot to execute the production strategy, and means for providing the farming strategy to a user. This enables both agricultural efficiency improvement and production line optimization.

[0159] "AI technology" is a technology that uses artificial intelligence to analyze data and make decisions.

[0160] "Agricultural data" refers to various data related to agriculture, such as crop growth, soil condition, and weather information.

[0161] "Real-time" means processing and providing data immediately, without delay.

[0162] "Weather information" refers to data related to weather, such as temperature, precipitation, humidity, and wind speed.

[0163] A "sensor" is a device that detects physical properties of the environment or a substance.

[0164] "Soil condition" refers to the characteristics and state of the soil, such as its nutrient levels and moisture content.

[0165] "Health" refers to whether the crop is disease-free, growing well, etc.

[0166] "Agricultural strategy" refers to specific plans and methods for agricultural work, such as irrigation, fertilization, and pest management.

[0167] "Environmental data" refers to data related to the environment, such as temperature and humidity within the factory.

[0168] "Raw Material Quality Data" refers to data relating to the quality of raw materials used.

[0169] "Production strategy" refers to a plan to optimize material inputs and work processes in factory production.

[0170] A "working robot" is a mechanical device that can perform work automatically.

[0171] This invention provides a data analysis system using AI technology that can be applied to both agriculture and factory production. Each element, server, terminal, and user, plays a specific role to efficiently collect, analyze, and execute data. Specific embodiments are shown below.

[0172] 1. Server Processing

[0173] The server performs the following functions:

[0174] Data analysis using AI technology:

[0175] The server analyzes agricultural and factory data in real time, including using machine learning algorithms and data mining techniques, such as building AI models using Python's Tensorflow® library.

[0176] Get the latest weather information:

[0177] The server collects weather information through API. Specifically, it uses the Weather API to obtain the current temperature, precipitation, humidity, wind speed, etc. It obtains the data using the API key and location information.

[0178] Analysis of environmental and raw material quality data:

[0179] Environmental data (temperature, humidity, etc.) and raw material quality data collected from sensors in the factory are analyzed to generate production strategies, such as issuing instructions for heating or cooling if the temperature is not within a certain range.

[0180] 2. Terminal Processing

[0181] The terminal is responsible for collecting and transmitting the following data to the server:

[0182] Sensor data collection:

[0183] In agriculture, the device will use soil sensors to measure soil nutrient and moisture levels, crop sensors to measure crop growth stage and health index, and in factories, environmental sensors to measure temperature and humidity, and raw material quality sensors to measure material quality.

[0184] Sending data:

[0185] The collected data is periodically sent to a server, which then analyzes the data in real time.

[0186] 3. User Actions

[0187] A user uses the system as follows:

[0188] View optimization strategies:

[0189] Through a screen or application provided by the device, the user can view the optimization strategies sent from the server, which in the case of agriculture can include specific instructions for irrigation, fertilization, and pest management, and in the case of a factory, can include optimization strategies for material inputs and work processes.

[0190] Carrying out agricultural and production activities:

[0191] Users perform agricultural and production tasks based on the provided strategies, such as increasing irrigation and applying balanced fertilizers, and factory tasks such as increasing temperature and adjusting input amounts of materials.

[0192] Specific examples

[0193] For example, if a farmer uses this system, he or she can implement an optimized farming strategy by following the steps below.

[0194] 1. Acquiring sensor data:

[0195] The device uses soil sensors to measure soil nutrient and moisture levels, and crop sensors to measure crop growth stages and health indices.

[0196] 2. Obtaining weather information:

[0197] The server retrieves the latest weather information through a weather API.

[0198] 3. Data Analysis:

[0199] The server analyzes the weather, soil, and crop data it acquires and uses AI algorithms to generate farming strategies.

[0200] 4. Display Strategy:

[0201] The user then uses the terminal to review specific instructions provided by the server regarding irrigation, fertilization, and pest management.

[0202] 5. Performing agricultural work:

[0203] The user performs farming operations based on the strategies provided, such as "increase irrigation," "apply balanced fertilizer," and "conduct pest inspections."

[0204] The following are some specific examples in factories:

[0205] Based on data on the temperature, humidity, and material quality in the factory, the server generates an optimal production strategy and sends instructions to the work robots via terminals, thereby maximizing production efficiency.

[0206] Prompt Sentence Examples

[0207] "Generate optimal production strategies based on temperature, humidity, and material quality data in a factory in Tokyo."

[0208] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0209] Step 1:

[0210] The device collects data using soil and crop sensors. Specifically, it measures soil nutrient and moisture levels, as well as the growth stage and health index of the crop. This data is then sent to a server in a unified format, such as JSON. The input is the data obtained from the sensors, and the output is the data sent to the server for analysis.

[0211] Step 2:

[0212] The server obtains weather information using an API. Specifically, it uses the API key and location from the Weather API to collect data such as the current temperature, precipitation, humidity, and wind speed. The input is the API key and location information, and the output is the weather data. This allows you to obtain the latest weather information.

[0213] Step 3:

[0214] The server integrates and analyzes the soil and crop data sent from the devices and the acquired weather information. It uses AI algorithms (e.g., machine learning models using TensorFlow) to analyze this data and generate a farming strategy. The inputs are soil data, crop data, and weather information, and the output is an optimized farming strategy. This strategy includes specific instructions for irrigation, fertilization, and pest management.

[0215] Step 4:

[0216] The server analyzes environmental data and raw material quality data within the factory. It combines data from environmental sensors, such as temperature and humidity, and raw material quality sensors to generate an optimal production strategy using an AI model. The inputs are environmental data and raw material quality data, and the output is an optimized production strategy. This production strategy includes instructions for optimizing material inputs and work processes.

[0217] Step 5:

[0218] The user checks the farming strategy provided by the server through the terminal. The terminal displays the farming strategy and presents it in a format that is easy for the user to understand. The input is the strategy data sent from the server, and the output is visual instructions for the user. The user then carries out the farming work based on this.

[0219] Step 6:

[0220] The user checks the production strategy provided by the server through a terminal. The terminal displays the production strategy and presents it in a format that is easy for the user to understand. The input is the production strategy data sent from the server, and the output is visual instructions for the user. The user carries out factory work based on this.

[0221] Through these steps, agricultural and factory production can be optimized.

[0222] 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.

[0223] To implement this invention, it is necessary to combine an agricultural data analysis system using AI technology with an emotion engine that recognizes user emotions. This system is composed of a server, terminals, and users, each of which plays a specific role and cooperates effectively.

[0224] Server-side processing

[0225] The server acquires weather information, analyzes soil data and crop health, and generates optimal farming strategies. It also acquires user emotion data and adjusts farming strategies accordingly.

[0226] Obtaining weather information

[0227] The server retrieves the latest weather information through an API. Using the API key and location, it collects data such as current temperature, precipitation, humidity, and wind speed from the weather API.

[0228] Soil data analysis

[0229] The server analyzes data collected from the soil sensors, including soil nutrient and moisture levels, and makes assessments to suggest necessary fertilization and irrigation applications.

[0230] Crop health monitoring

[0231] The server monitors the health and growth stage of the crops based on data sent from the crop sensors, and suggests appropriate measures if the crop health index is low or if there are signs of pests.

[0232] Generation of farming strategies

[0233] The server integrates weather, soil, and crop data and uses AI algorithms to generate optimal farming strategies, including specific instructions for irrigation, fertilization, and pest management.

[0234] Obtaining sentiment data and adjusting strategies

[0235] The server acquires the user's emotional data using an emotion engine. The emotion engine recognizes emotions from the user's voice, facial expressions, and text input. Based on the recognized emotional data, the farming strategy is further adjusted to make it more user-acceptable.

[0236] Processing on the terminal side

[0237] The device collects data from soil and crop sensors and transmits it to the server, and also collects user emotion data through an emotion engine.

[0238] Acquiring Sensor Data

[0239] The device uses soil sensors to measure soil nutrient and moisture levels, and crop sensors to measure the growth stage and health index of the crop, and these data are periodically transmitted to the server.

[0240] Acquiring emotion data

[0241] The device recognizes the user's emotions through an emotion engine, which analyzes voice, facial expressions, and text input obtained from interactions and observations with the user to generate emotion data.

[0242] User-side processing

[0243] The user receives the farming strategy provided by the server, performs farming according to the instructions, and cooperates by providing emotion data.

[0244] Viewing Optimization Strategies

[0245] Through a screen or application provided by the device, the user can view farming strategies sent from the server, including specific instructions for irrigation, fertilization, and pest management.

[0246] Providing emotion data

[0247] Users can express their emotions through their devices and provide data to the emotion engine, which allows the system to provide farming strategies that take the user's emotions into account.

[0248] Specific examples

[0249] For example, if a farmer uses this system, he or she can implement an optimized farming strategy by following the steps below.

[0250] 1. Acquiring sensor data:

[0251] The device uses soil sensors to measure soil nutrient and moisture levels, and crop sensors to measure crop growth stages and health indices.

[0252] 2. Obtaining weather information:

[0253] The server retrieves the latest weather information through a weather API.

[0254] 3. Data Analysis:

[0255] The server analyzes the weather, soil, and crop data it acquires and uses AI algorithms to generate farming strategies.

[0256] 4. Acquiring and using emotion data:

[0257] The server obtains the user's emotions through an emotion engine and adjusts the farming strategy.

[0258] 5. Display Strategy:

[0259] The user then uses the terminal to review specific instructions provided by the server regarding irrigation, fertilization, and pest management.

[0260] 6. Performing agricultural work:

[0261] The user performs farming operations based on the strategies provided, such as "increase irrigation," "apply balanced fertilizer," and "conduct pest inspections."

[0262] In this way, by using the AI ​​system and emotion engine of the present invention, farmers can implement optimal farming strategies in real time and achieve efficient and sustainable agriculture. Furthermore, by taking the user's emotions into consideration, it is possible to provide a farming strategy that is more suited to the user and reduce the burden of implementation.

[0263] The processing flow will be explained below.

[0264] Step 1:

[0265] The device collects data from soil and crop sensors, specifically using the soil sensor to measure soil nutrient and moisture levels, and the crop sensor to measure the growth stage and health index of the crop.

[0266] Step 2:

[0267] The device transmits the collected sensor data to a server, including soil nutrient levels, moisture levels, crop growth stage, and health index.

[0268] Step 3:

[0269] The server retrieves weather information by sending a request to the weather API using the API key and location to retrieve weather data such as the current temperature, precipitation, humidity, and wind speed.

[0270] Step 4:

[0271] The server analyzes the soil data, specifically extracting nutrient and moisture levels from the transmitted soil data and evaluating each measurement.

[0272] Step 5:

[0273] The server monitors the health of the crops based on the crop data, checking the growth stage and health index of the crops and considering measures to improve their health if necessary.

[0274] Step 6:

[0275] The device collects user emotion data through an emotion engine, which analyzes the user's voice, facial expressions, and text input to generate emotion data.

[0276] Step 7:

[0277] The device transmits the collected emotion data to the server, specifically, data regarding the user's current emotional state.

[0278] Step 8:

[0279] The server combines the analyzed soil data, monitored crop data, and acquired weather information, and uses AI algorithms to generate optimal farming strategies, specifically creating instructions for irrigation, fertilization, and pest management.

[0280] Step 9:

[0281] The server adjusts the farming strategy based on the collected emotional data, specifically taking into account the user's stress level and satisfaction level.

[0282] Step 10:

[0283] The server provides the generated farming strategy to the user by sending it to the terminal and allowing the user to check it through the terminal.

[0284] Step 11:

[0285] The user views the farming strategies displayed on the device, including increased irrigation, fertilization suggestions, and pest management instructions.

[0286] Step 12:

[0287] The user performs farming operations based on the provided farming strategies, such as adjusting irrigation systems, applying fertilizers, and conducting pest checks.

[0288] Through these steps, efficient and sustainable agriculture can be achieved through collaboration between the server, terminals, and users. By taking into account the user's emotions, a more compatible farming strategy can be provided, reducing the burden of implementation.

[0289] Example 2

[0290] Next, a description will be given of 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."

[0291] Conventional agricultural data analysis systems only propose optimal farming strategies based on physical data such as weather information, soil data, and crop health, but do not consider the user's emotions or psychological state. This can lead to users feeling burdened when implementing the proposed strategy or being unable to accept the strategy. Therefore, there is a need to provide more personalized farming strategies that take user emotions into account.

[0292] 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.

[0293] In this invention, the server includes means for analyzing agricultural data in real time using AI technology, means for acquiring the latest weather information, means for collecting soil condition information from sensors, means for monitoring the health of crops, means for acquiring user emotion data, means for generating a farming strategy for maximizing crop growth based on the analysis results, the acquired weather information, and the emotion data, and means for providing the farming strategy to the user, thereby making it possible to provide an optimal farming strategy that takes user emotion into consideration.

[0294] "AI technology" refers to technology that uses artificial intelligence to automate data analysis and decision-making.

[0295] "Agricultural data" refers to data related to agricultural activities, including, for example, weather information, soil conditions, and crop health.

[0296] "Means of real-time analysis" refers to technology that instantly analyzes acquired data and provides the results in a usable form.

[0297] "Weather information" refers to data on weather conditions such as temperature, precipitation, humidity, and wind speed.

[0298] "Sensor" refers to a device for measuring a physical condition and collecting that data.

[0299] "Soil condition" refers to the physical and chemical characteristics of the soil that affect crop growth, such as soil nutrient and moisture levels.

[0300] A "soil sensor" refers to a device used to measure nutrient and moisture levels in the soil.

[0301] "Crop health" refers to data on the growth status of crops, such as the stage of growth and health index of the crop.

[0302] A "crop sensor" refers to a device used to monitor the health and growth stage of crops.

[0303] "Emotion data" refers to data related to emotions recognized from the user's voice, facial expressions, text input, etc.

[0304] "Emotion engine" refers to an analysis engine for recognizing the user's emotions.

[0305] An "agricultural strategy" refers to a specific implementation plan for irrigation, fertilization, pest management, etc. to maximize crop growth.

[0306] "User" refers to a person who uses the system to carry out agricultural activities.

[0307] To implement this invention, it is necessary to combine an emotion engine that recognizes user emotions based on an agricultural data analysis system using AI technology. This system is composed of a server, terminals, and users, each of which plays a specific role and cooperates effectively.

[0308] The server acquires weather information, analyzes soil data and crop health, and generates optimal farming strategies. It also acquires user emotion data and adjusts farming strategies accordingly.

[0309] Specifically, the server obtains the latest weather information through a weather API. A standard server computer is required as the hardware, and API services such as OpenWeatherMap and WeatherAPI are used as the software. Data collected from soil and crop sensors is analyzed using AI algorithms (e.g., TensorFlow and PyTorch) to evaluate soil nutrient and moisture levels and crop health indices. Furthermore, an emotion engine (e.g., Amazon Rekognition and Microsoft® Azure® Emotion API) is used to obtain user emotion data, which is then integrated with the analysis results to generate optimal farming strategies.

[0310] The device collects data from soil and plant sensors and transmits it to a server. Specifically, it uses sensor devices such as the Adafruit STEMMA Soil Sensor and MicaSense to measure soil nutrient and moisture levels, and plant sensors to measure the growth stage and health index of plants. It also collects user emotion data through an emotion engine.

[0311] The user receives farming strategies provided by the server and performs farming according to those instructions. The user also cooperates by providing emotional data. The user checks specific instructions on irrigation, fertilization, and pest management provided by the server through the device's display or application. Furthermore, the user expresses their own emotions through the device and provides data to the emotion engine.

[0312] Specific examples include the following steps:

[0313] 1. The device uses soil sensors to measure soil nutrient and moisture levels, and crop sensors to measure crop growth stages and health indices.

[0314] 2. The server retrieves the latest weather information through the weather API.

[0315] 3. The weather, soil, and crop data acquired by the server is analyzed using AI algorithms to generate farming strategies.

[0316] 4. The server obtains the user's emotions through the emotion engine and adjusts the farming strategy.

[0317] 5. The user reviews specific instructions for irrigation, fertilization, and pest management provided by the server through the terminal.

[0318] 6. The user performs farming operations based on the provided strategy.

[0319] An example prompt is:

[0320] "This system integrates sensor data and weather information and uses AI to generate optimal farming strategies. It also uses an emotion engine to adjust strategies that take the user's emotions into account. Specifically, sensors measure the moisture and nutrient levels in the soil and obtain the latest weather information, allowing the AI ​​to suggest the optimal growing environment for crops. It also has a function that collects user emotion data and optimizes the strategies provided to the user."

[0321] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0322] Step 1: Get weather information

[0323] The server obtains the latest weather information through a weather API. Specifically, it uses services such as OpenWeatherMap and WeatherAPI, and uses an API key and location information. Location information and an API key are required as input, and weather data such as temperature, precipitation, humidity, and wind speed are obtained based on this data. The obtained weather data is then output.

[0324] Step 2: Acquiring Sensor Data

[0325] The device collects data from soil and crop sensors. For example, it uses Adafruit soil sensors to measure soil nutrient and moisture levels, and MicaSense crop sensors to measure crop growth stages and health indices. It receives real-time data from the sensors as input and periodically transmits it to a server. The output is soil and crop data.

[0326] Step 3: Sending data from the sensor

[0327] The terminal transmits the data obtained from the sensors to the server. The soil nutrient and moisture levels, crop growth stage and health index measured by the sensors are provided as inputs and transmit this data to the server. As output, the server receives these data and stores them for analysis.

[0328] Step 4: Analyze the data

[0329] The server analyzes the acquired weather information, soil data, and crop data using AI algorithms. The AI ​​algorithms use TensorFlow and PyTorch to perform analysis based on the input data. For example, if the weather information indicates dryness, the soil data determines whether irrigation is necessary, and the crop data determines whether the crop is in a growth stage. The analysis results are obtained as output.

[0330] Step 5: Generate farming strategies

[0331] The server uses the analysis results to generate a farming strategy, which includes specific instructions for irrigation, fertilization, and pest management. The analysis results are used as input, and an AI algorithm derives the optimal farming strategy. The generated farming strategy is the output.

[0332] Step 6: Obtaining emotion data

[0333] The device uses an emotion engine to recognize the user's emotions. Specifically, it uses a camera and microphone to generate emotion data from the user's voice, facial expressions, and text input. The input is the user's emotional information (voice, facial expressions, text), which is analyzed by the emotion engine. The output is the recognized emotion data.

[0334] Step 7: Adjust your strategy based on emotions

[0335] The server adjusts the farming strategy based on the acquired emotional data. For example, if the user is feeling stressed, it will adjust the frequency of irrigation or fertilization. The inputs are the emotional data and the existing farming strategy, and the server regenerates the strategy based on these. The output is the adjusted farming strategy.

[0336] Step 8: View the optimization strategy

[0337] The user views the optimized farming strategy sent from the server through the device, including specific instructions for irrigation, fertilization, and pest management. The input is the adjusted farming strategy, which is displayed on the device's display or through an application. The output is the farming strategy presented to the user in an easy-to-understand format.

[0338] Step 9: Performing farm work

[0339] The user performs farming tasks based on the provided strategy. For example, the user acts according to specific instructions such as "increase irrigation," "apply balanced fertilizer," and "conduct pest inspection." The input is the displayed farming strategy, and the user performs actual farming tasks based on this. The output is the optimized farming results.

[0340] In this way, the system allows the server, terminal, and user to each play their respective roles and work together to provide and implement optimal farming strategies that take the user's emotions into consideration.

[0341] (Application example 2)

[0342] Next, a description will be given of Application 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."

[0343] In modern agriculture, improving productivity while reducing the burden on farmers is a key challenge. In particular, it is necessary to grasp the condition of soil and crops in real time and formulate optimal farming strategies. However, this requires the collection and analysis of a large amount of data, which places a heavy burden on farmers. Furthermore, strategies are not adjusted taking into account farmers' emotions and stress, which could increase the burden of actually implementing the farming strategies. Furthermore, there is a lack of efficient means to put the collected data and generated strategies into practice. To solve these challenges, it is necessary to integrate advanced data analysis, emotion recognition, and implementation methods.

[0344] The specific processing by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes means for analyzing agricultural data in real time using AI technology, means for acquiring the latest weather information, means for collecting soil conditions from sensors, means for monitoring the health of crops, means for generating a farming strategy for maximizing crop growth based on the analysis results and the acquired weather information, means for acquiring user emotion data and adjusting the farming strategy, means for instructing an autonomous vehicle to execute the farming strategy, and means for providing the farming strategy to the user. This makes it possible to formulate and execute an efficient and highly accurate farming strategy while reducing the burden on farmers.

[0345] "AI technology" is a technology that uses artificial intelligence to collect, analyze, predict, and optimize data.

[0346] "Real-time agricultural data analysis" is the process of instantly collecting and analyzing data such as soil and crop conditions and weather information.

[0347] "Latest weather information" refers to the latest data on current and future weather, obtained through APIs, etc.

[0348] "Soil condition" refers to the physical and chemical attributes of the soil, such as nutrient levels, moisture levels, and pH value.

[0349] A "sensor" is a device that detects physical or chemical changes and collects that information as data.

[0350] "Crop health monitoring" is the process of continuously monitoring the growth stage and health index of crops using sensors and cameras.

[0351] "Agricultural strategies" refer to specific instructions for irrigation, fertilization, pest management, etc. generated using AI technology to optimize agricultural operations.

[0352] "User emotion data" is data that indicates the user's emotional state, extracted from the user's voice, facial expression, text input, and the like.

[0353] An "autonomous vehicle" is a vehicle that operates autonomously and performs specific tasks without the need for human intervention.

[0354] "Instructions" are specific instructions or guidelines for performing a particular action or task.

[0355] A system for implementing this invention utilizes an autonomous vehicle (e.g., an agricultural tractor), a specific device, and a cloud server.

[0356] 1. Server-side processing

[0357] The server analyzes the agricultural data and generates an optimized farming strategy using the following means:

[0358] AI technology: The server uses a Python (registered trademark)-based AI framework (e.g., TensorFlow, Keras), which analyzes collected data in real time and performs advanced predictions and optimization.

[0359] Obtaining weather information: Use a weather API (e.g., OpenWeatherMap) to collect the latest weather information using your API key and location. Specifically, obtain data such as the current temperature, precipitation, humidity, and wind speed.

[0360] Soil and crop data collection and analysis: Analyzes soil nutrient and moisture levels collected from sensors to recommend fertilization and irrigation needs, and monitors crop health and growth stages in real time to recommend appropriate measures.

[0361] Emotion engine: Using voice recognition software (e.g., Google® Cloud Speech-to-Text) and facial recognition software (e.g., Amazon Rekognition), the engine obtains user emotion data, which allows it to detect farmers' stress and fatigue and adjust farming strategies accordingly.

[0362] 2. Terminal processing

[0363] The terminal (autonomous vehicle or other device) collects data and transmits it to the server using the following means:

[0364] Sensor data acquisition: Soil and crop sensors are used to measure soil nutrient and moisture levels, as well as crop health and growth stage. This data is sent to a server via Wi-Fi or Bluetooth.

[0365] Emotional data capture: Using a camera and microphone, the device collects user emotional data from their voice and facial expressions, using voice and facial recognition software.

[0366] 3. User-side processing

[0367] The user receives the farming strategy provided by the server and performs farming according to the instructions. The user also cooperates by providing emotion data.

[0368] View farming strategies: View specific instructions for irrigation, fertilization, pest management, etc. sent from the server through the terminal.

[0369] Providing emotional data: Expressing emotions through voice and facial expressions and providing data to the emotion engine.

[0370] Specific examples

[0371] For example, if the present invention is implemented in a farming field in Tokyo, soil and crop data will be collected through sensors mounted on tractors and drones and sent to a server. The server will then integrate the data with the latest weather information to generate an optimal farming strategy. If a farmer is feeling stressed, the emotion engine will detect this and adjust the strategy to reduce the farming burden. An example of a prompt sentence that can be generated is as follows:

[0372] "Please obtain weather data for Tokyo, analyze soil and crop data from sensors, and propose the optimal farming strategy based on the results. Also, obtain emotional data from farmers through their voices and facial expressions, and adjust the strategy to reduce the stress on farmers if they are under a high level of stress."

[0373] In this way, the system of the present invention provides optimal farming strategies in real time, reducing the burden on farmers and achieving efficient farming.

[0374] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0375] Step 1: Acquiring Sensor Data

[0376] The device uses soil and crop sensors to collect data such as soil nutrient levels, moisture levels, crop health index, and growth stage. It receives measurement data from the sensors as input and transmits the data to a server. The output is a dataset of the measurement results.

[0377] Step 2: Get weather information

[0378] The server uses a weather API to retrieve the latest weather information (current temperature, precipitation, humidity, wind speed, etc.). It uses the API key and location information as input to retrieve data from the weather API. The output is a dataset of the retrieved weather information.

[0379] Step 3: Analyze the data

[0380] The server integrates data from sensors and meteorological information and analyzes them using AI technology. It receives soil data, crop data, and meteorological data as input and combines each data into a data frame. Based on this data, the AI ​​model generates crop growth forecasts and optimal farming strategies. The output is a dataset of optimal farming strategies.

[0381] Step 4: Obtaining emotion data

[0382] The device uses a camera and microphone to collect the user's voice and facial expressions, which are then analyzed by an emotion engine. Using the collected voice and facial expression data as input, emotion analysis is performed using voice recognition software (e.g., Google Cloud Speech-to-Text) and facial recognition software (e.g., Amazon Rekognition). The output is the user's emotional data.

[0383] Step 5: Adjust your farming strategy

[0384] The server adjusts the generated farming strategy to match the user's emotional state based on the acquired emotional data. It receives the emotional data and the initial farming strategy dataset as input, and modifies the strategy to reduce the workload if the user is feeling stressed. The output is a dataset of the adjusted farming strategy.

[0385] Step 6: Prescribe and implement farming strategies

[0386] The terminal instructs the autonomous vehicle to execute the adjusted farming strategy sent from the server. It receives the adjusted farming strategy dataset as input, transfers its contents to the autonomous vehicle, and performs specific farming tasks. The output is log data of the farming tasks performed.

[0387] Step 7: Displaying farming strategies

[0388] The user checks the adjusted farming strategy sent from the server through a screen provided by the terminal. The adjusted farming strategy dataset is displayed as input, and specific instructions (e.g., irrigation, fertilization, pest management) are provided to the user. The output is the farming strategy information visually displayed to the user.

[0389] 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.

[0390] 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> ), Gemini (registered trademark) (Internet search <url: https: gemini.google.com ?hl="ja">) 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.

[0391] 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.

[0392] [Second embodiment]

[0393] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0394] 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.

[0395] 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).

[0396] 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.

[0397] 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.

[0398] 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).

[0399] 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.

[0400] 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.

[0401] 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.

[0402] 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.

[0403] 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.

[0404] Next, a description will be given of the identification process performed by the identification processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as the "server" and the smart glasses 214 will be referred to as the "terminal."

[0405] To implement this invention, it is first necessary to build an agricultural data analysis system using AI technology. This system consists of a server, terminals, and users, each of which plays a specific role.

[0406] Server-side processing

[0407] The server retrieves weather information, analyzes soil data and crop health, and generates optimal farming strategies.

[0408] Obtaining weather information

[0409] The server retrieves the latest weather information through an API. Using the API key and location, it collects data such as current temperature, precipitation, humidity, and wind speed from the weather API.

[0410] Soil data analysis

[0411] The server analyzes data collected from the soil sensors, including soil nutrient and moisture levels, and makes fertilization recommendations if nutrient levels are low and irrigation suggestions if moisture levels are low.

[0412] Crop health monitoring

[0413] The server monitors the health and growth stage of the crops based on data sent from the plant's sensors, and suggests appropriate measures if the plant's health index is low or if the plant is threatened by pests.

[0414] Generation of farming strategies

[0415] The server integrates weather, soil, and crop data and uses AI algorithms to generate optimal farming strategies, including specific instructions for irrigation, fertilization, and pest management.

[0416] Processing on the terminal side

[0417] The terminal is responsible for collecting data from soil and crop sensors and transmitting it to a server.

[0418] Acquiring Sensor Data

[0419] The device uses soil sensors to measure soil nutrient and moisture levels, and crop sensors to measure the growth stage and health index of the crop, and these data are periodically transmitted to the server.

[0420] User-side processing

[0421] The user receives the farming strategy provided by the server and carries out the farming work according to the instructions.

[0422] Viewing Optimization Strategies

[0423] Through a screen or application provided by the device, the user can view farming strategies sent from the server, including specific instructions for irrigation, fertilization, and pest management.

[0424] Specific examples

[0425] For example, if a farmer uses this system, he or she can implement an optimized farming strategy by following the steps below.

[0426] 1. Acquiring sensor data:

[0427] The device uses soil sensors to measure soil nutrient and moisture levels, and crop sensors to measure crop growth stages and health indices.

[0428] 2. Obtaining weather information:

[0429] The server retrieves the latest weather information through a weather API.

[0430] 3. Data Analysis:

[0431] The server analyzes the weather, soil, and crop data it acquires and uses AI algorithms to generate farming strategies.

[0432] 4. Display Strategy:

[0433] The user then uses the terminal to review specific instructions provided by the server regarding irrigation, fertilization, and pest management.

[0434] 5. Performing agricultural work:

[0435] The user performs farming operations based on the strategies provided, such as "increase irrigation," "apply balanced fertilizer," and "conduct pest inspections."

[0436] In this way, by using the AI ​​system of the present invention, farmers can implement optimal farming strategies in real time and achieve efficient and sustainable agriculture.

[0437] The processing flow will be explained below.

[0438] Step 1:

[0439] The device collects data from soil and crop sensors, specifically using the soil sensor to measure soil nutrient and moisture levels, and the crop sensor to measure the growth stage and health index of the crop.

[0440] Step 2:

[0441] The device transmits the collected sensor data to a server, including soil nutrient levels, moisture levels, crop growth stage, and health index.

[0442] Step 3:

[0443] The server retrieves weather information by sending a request to the weather API using the API key and location to retrieve weather data such as the current temperature, precipitation, humidity, and wind speed.

[0444] Step 4:

[0445] The server analyzes the soil data, specifically extracting nutrient and moisture levels from the transmitted soil data and evaluating each measurement.

[0446] Step 5:

[0447] The server monitors the health of the crops based on the crop data, checking the growth stage and health index of the crops and considering measures to improve their health if necessary.

[0448] Step 6:

[0449] The server combines the analyzed soil data, monitored crop data, and acquired weather information, and uses AI algorithms to generate optimal farming strategies, specifically creating instructions for irrigation, fertilization, and pest management.

[0450] Step 7:

[0451] The server provides the generated farming strategy to the user by sending it to the terminal and allowing the user to check it through the terminal.

[0452] Step 8:

[0453] The user views the farming strategies displayed on the device, including increased irrigation, fertilization suggestions, and pest management instructions.

[0454] Step 9:

[0455] The user performs farming operations based on the provided farming strategies, such as adjusting irrigation systems, applying fertilizers, and conducting pest checks.

[0456] Through the above steps, efficient and sustainable agriculture will be realized through collaboration between servers, terminals, and users.

[0457] Example 1

[0458] Next, a description will be given of 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."

[0459] Conventional agricultural processes lack sufficient data collection and analysis to optimize crop growth and health, resulting in unstable crop yields and quality. It is also difficult to provide real-time farming strategies based on meteorological and soil data, creating a need for methods to achieve efficient and sustainable agriculture.

[0460] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[0461] In this invention, the server includes means for analyzing agricultural data in real time using AI technology, means for acquiring the latest weather information, means for collecting soil conditions from sensors, means for monitoring the health of crops, means for generating a farming strategy for maximizing crop growth based on the analysis results and the acquired weather information, means for providing the farming strategy to a user, means for collecting data from soil sensors and crop sensors via a terminal and transmitting it to the server, and means for the user to carry out farming based on the farming strategy provided. This makes it possible to provide optimal farming strategies in real time and achieve efficient and sustainable agriculture.

[0462] "AI technology" refers to technology that uses artificial intelligence, and in particular, systems that perform data analysis, pattern recognition, and predictive analysis.

[0463] "Real-time" means that data is acquired, processed, and results are provided without delay.

[0464] "Weather information" refers to data on weather conditions such as temperature, precipitation, humidity, and wind speed.

[0465] "Soil condition" is data about soil properties, such as soil nutrient and moisture levels.

[0466] A "sensor" is a device that detects changes in the physical or chemical environment and acquires data.

[0467] "Crop health" refers to data about the overall health of the crop, such as the stage of growth, nutritional status, and the presence or absence of pests and diseases.

[0468] An "agricultural strategy" is a set of specific operations and activities, such as irrigation, fertilization, and pest management, that optimize crop growth.

[0469] "User" refers to a farm manager or farm worker who uses the system to carry out farm work.

[0470] A "terminal" is a communication device that collects data from sensors and transmits it to a server.

[0471] A "generative model" is an algorithm or AI model that predicts and generates farming strategies based on input data.

[0472] MODE FOR CARRYING OUT THE INVENTION

[0473] To implement this invention, it is first necessary to build an agricultural data analysis system using AI technology. This system consists of a server, terminals, and users, each of which plays a specific role.

[0474] Server-side processing

[0475] The server retrieves weather information, analyzes soil data and crop health, and generates optimal farming strategies.

[0476] Obtaining weather information

[0477] The server retrieves the latest weather information using weather APIs such as OpenWeatherMap, and periodically collects data such as temperature, precipitation, humidity, and wind speed using the API key and location information.

[0478] Soil data analysis

[0479] The server analyzes data collected from the soil sensors, including soil nutrient and moisture levels, and, for example, makes fertilization recommendations if nutrient levels are low and irrigation suggestions if moisture levels are declining.

[0480] Crop health monitoring

[0481] The server uses data from the plant sensors to monitor the health of the plants, and if the plant's health index declines or there are signs of pest infestation, it will suggest appropriate measures.

[0482] Generation of farming strategies

[0483] The server integrates weather, soil, and crop data and uses AI algorithms to generate optimal farming strategies, including specific instructions for irrigation, fertilization, and pest management.

[0484] Processing on the terminal side

[0485] The terminal is responsible for collecting data from soil and crop sensors and transmitting it to a server.

[0486] Acquiring Sensor Data

[0487] The device uses soil sensors to measure soil nutrient and moisture levels, and crop sensors to measure the growth stage and health index of the crop, and periodically transmits this data to the server.

[0488] User-side processing

[0489] The user receives the farming strategy provided by the server and carries out the farming work according to the instructions.

[0490] Viewing Optimization Strategies

[0491] Through a screen or application provided by the device, the user can view farming strategies sent from the server, including specific instructions for irrigation, fertilization, and pest management.

[0492] Specific examples

[0493] For example, if a farmer uses this system, he or she can implement an optimized farming strategy by following the steps below.

[0494] 1. Acquiring sensor data:

[0495] The device uses soil sensors to measure soil nutrient and moisture levels, and crop sensors to measure crop growth stages and health indices.

[0496] 2. Obtaining weather information:

[0497] The server retrieves the latest weather information through a weather API.

[0498] 3. Data Analysis:

[0499] The server analyzes the weather, soil, and crop data it acquires and uses AI algorithms to generate farming strategies.

[0500] 4. Display Strategy:

[0501] The user then uses the terminal to review specific instructions provided by the server regarding irrigation, fertilization, and pest management.

[0502] 5. Performing agricultural work:

[0503] The user performs farming operations based on the strategies provided, such as "increase irrigation," "apply balanced fertilizer," and "conduct pest inspections."

[0504] In this way, by using the agricultural data analysis system that employs the AI ​​technology of the present invention, farmers can implement optimal farming strategies in real time and achieve efficient and sustainable agriculture.

[0505] Example prompt sentences to use

[0506] Please explain in detail your approach to developing a system that analyzes agricultural data and generates optimal farming strategies. Please describe in detail the roles of the server, terminal, and user.

[0507] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0508] Step 1:

[0509] The server calls the weather API every hour and obtains the latest weather information using the API key and location information. The API key and location information are required as input data, and based on this, weather data such as temperature, precipitation, humidity, and wind speed is obtained and stored in a database. Specifically, the server first generates a URL, then sends a request to the API, analyzes the response, and extracts the weather data.

[0510] Step 2:

[0511] The server receives soil data sent from the device every five minutes and records it in a database. The input data required is the nutrient and moisture levels sent from the soil sensor, which is analyzed to determine whether fertilization or irrigation is necessary. Specifically, each time new data is received, it is added to the database and compared with past data.

[0512] Step 3:

[0513] The server receives data from the crop sensors in real time and analyzes the growth and health of the crops. The input data is the growth stage and health index sent from the crop sensors, and based on this, it evaluates the presence of pests and the growth status. Specifically, it analyzes the received data and issues an alert if an abnormal value is detected.

[0514] Step 4:

[0515] The server integrates the acquired weather, soil, and crop data and generates farming strategies using AI algorithms. The input data consists of weather, soil, and crop data, which are then fed into an AI model that outputs specific instructions for irrigation, fertilization, and pest management. Specifically, the server preprocesses the data, inputs it into the AI ​​model, and stores the output strategy in a database.

[0516] Step 5:

[0517] The terminal acquires data from the soil and crop sensors every five minutes and sends it to the server. The input data is real-time data acquired from the sensors, and is sent to the server via wireless communication or an internet connection. Specifically, the terminal aggregates the data from the sensors, converts it into packet format, and sends it.

[0518] Step 6:

[0519] The user checks the farming strategy provided by the server through the terminal. The input data is the optimized farming instructions from the server, which are displayed in the application. Specifically, the application sends a request to the server and visually displays the received strategy data.

[0520] Step 7:

[0521] The user performs actual farm work based on the provided farming strategy. The input data are specific instructions from the server, and based on these, the user performs irrigation, fertilization, and pest management. Specific actions include applying the instructed amount of water and fertilizer, and carrying out necessary pest control.

[0522] Through the above processing steps, the system of the present invention can provide a real-time farming strategy for achieving efficient and sustainable agriculture.

[0523] (Application example 1)

[0524] Next, a description will be given of Application 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."

[0525] Conventional agricultural systems have had difficulty effectively understanding weather information, soil conditions, and crop health to derive optimal farming strategies. Similarly, in factory production lines, it has been challenging to automatically generate optimal production strategies based on environmental data and raw material quality, and then instruct robots to execute them.

[0526] 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.

[0527] In this invention, the server includes means for analyzing agricultural data in real time using AI technology, means for acquiring the latest weather information, means for collecting soil condition information from sensors, means for monitoring the health of crops, means for generating a farming strategy to maximize crop growth based on the analysis results and the acquired weather information, means for analyzing environmental data and raw material quality data and generating an optimal production strategy, means for instructing a work robot to execute the production strategy, and means for providing the farming strategy to a user. This enables both agricultural efficiency improvement and production line optimization.

[0528] "AI technology" is a technology that uses artificial intelligence to analyze data and make decisions.

[0529] "Agricultural data" refers to various data related to agriculture, such as crop growth, soil condition, and weather information.

[0530] "Real-time" means processing and providing data immediately, without delay.

[0531] "Weather information" refers to data related to weather, such as temperature, precipitation, humidity, and wind speed.

[0532] A "sensor" is a device that detects physical properties of the environment or a substance.

[0533] "Soil condition" refers to the characteristics and state of the soil, such as its nutrient levels and moisture content.

[0534] "Health" refers to whether the crop is disease-free, growing well, etc.

[0535] "Agricultural strategy" refers to specific plans and methods for agricultural work, such as irrigation, fertilization, and pest management.

[0536] "Environmental data" refers to data related to the environment, such as temperature and humidity within the factory.

[0537] "Raw Material Quality Data" refers to data relating to the quality of raw materials used.

[0538] "Production strategy" refers to a plan to optimize material inputs and work processes in factory production.

[0539] A "working robot" is a mechanical device that can perform work automatically.

[0540] This invention provides a data analysis system using AI technology that can be applied to both agriculture and factory production. Each element, server, terminal, and user, plays a specific role to efficiently collect, analyze, and execute data. Specific embodiments are shown below.

[0541] 1. Server Processing

[0542] The server performs the following functions:

[0543] Data analysis using AI technology:

[0544] The server analyzes agricultural and factory data in real time, using machine learning algorithms and data mining techniques, such as building AI models using Python's TensorFlow library.

[0545] Get the latest weather information:

[0546] The server collects weather information through API. Specifically, it uses the Weather API to obtain the current temperature, precipitation, humidity, wind speed, etc. It obtains the data using the API key and location information.

[0547] Analysis of environmental and raw material quality data:

[0548] Environmental data (temperature, humidity, etc.) and raw material quality data collected from sensors in the factory are analyzed to generate production strategies, such as issuing instructions for heating or cooling if the temperature is not within a certain range.

[0549] 2. Terminal Processing

[0550] The terminal is responsible for collecting and transmitting the following data to the server:

[0551] Sensor data collection:

[0552] In agriculture, the device will use soil sensors to measure soil nutrient and moisture levels, crop sensors to measure crop growth stage and health index, and in factories, environmental sensors to measure temperature and humidity, and raw material quality sensors to measure material quality.

[0553] Sending data:

[0554] The collected data is periodically sent to a server, which then analyzes the data in real time.

[0555] 3. User Actions

[0556] A user uses the system as follows:

[0557] View optimization strategies:

[0558] Through a screen or application provided by the device, the user can view the optimization strategies sent from the server, which in the case of agriculture can include specific instructions for irrigation, fertilization, and pest management, and in the case of a factory, can include optimization strategies for material inputs and work processes.

[0559] Carrying out agricultural and production activities:

[0560] Users perform agricultural and production tasks based on the provided strategies, such as increasing irrigation and applying balanced fertilizers, and factory tasks such as increasing temperature and adjusting input amounts of materials.

[0561] Specific examples

[0562] For example, if a farmer uses this system, he or she can implement an optimized farming strategy by following the steps below.

[0563] 1. Acquiring sensor data:

[0564] The device uses soil sensors to measure soil nutrient and moisture levels, and crop sensors to measure crop growth stages and health indices.

[0565] 2. Obtaining weather information:

[0566] The server retrieves the latest weather information through a weather API.

[0567] 3. Data Analysis:

[0568] The server analyzes the weather, soil, and crop data it acquires and uses AI algorithms to generate farming strategies.

[0569] 4. Display Strategy:

[0570] The user then uses the terminal to review specific instructions provided by the server regarding irrigation, fertilization, and pest management.

[0571] 5. Performing agricultural work:

[0572] The user performs farming operations based on the strategies provided, such as "increase irrigation," "apply balanced fertilizer," and "conduct pest inspections."

[0573] The following are some specific examples in factories:

[0574] Based on data on the temperature, humidity, and material quality in the factory, the server generates an optimal production strategy and sends instructions to the work robots via terminals, thereby maximizing production efficiency.

[0575] Prompt Sentence Examples

[0576] "Generate optimal production strategies based on temperature, humidity, and material quality data in a factory in Tokyo."

[0577] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0578] Step 1:

[0579] The device collects data using soil and crop sensors. Specifically, it measures soil nutrient and moisture levels, as well as the growth stage and health index of the crop. This data is then sent to a server in a unified format, such as JSON. The input is the data obtained from the sensors, and the output is the data sent to the server for analysis.

[0580] Step 2:

[0581] The server obtains weather information using an API. Specifically, it uses the API key and location from the Weather API to collect data such as the current temperature, precipitation, humidity, and wind speed. The input is the API key and location information, and the output is the weather data. This allows you to obtain the latest weather information.

[0582] Step 3:

[0583] The server integrates and analyzes the soil and crop data sent from the devices and the acquired weather information. It uses AI algorithms (e.g., machine learning models using TensorFlow) to analyze this data and generate a farming strategy. The inputs are soil data, crop data, and weather information, and the output is an optimized farming strategy. This strategy includes specific instructions for irrigation, fertilization, and pest management.

[0584] Step 4:

[0585] The server analyzes environmental data and raw material quality data within the factory. It combines data from environmental sensors, such as temperature and humidity, and raw material quality sensors to generate an optimal production strategy using an AI model. The inputs are environmental data and raw material quality data, and the output is an optimized production strategy. This production strategy includes instructions for optimizing material inputs and work processes.

[0586] Step 5:

[0587] The user checks the farming strategy provided by the server through the terminal. The terminal displays the farming strategy and presents it in a format that is easy for the user to understand. The input is the strategy data sent from the server, and the output is visual instructions for the user. The user then carries out the farming work based on this.

[0588] Step 6:

[0589] The user checks the production strategy provided by the server through a terminal. The terminal displays the production strategy and presents it in a format that is easy for the user to understand. The input is the production strategy data sent from the server, and the output is visual instructions for the user. The user carries out factory work based on this.

[0590] Through these steps, agricultural and factory production can be optimized.

[0591] 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.

[0592] To implement this invention, it is necessary to combine an agricultural data analysis system using AI technology with an emotion engine that recognizes user emotions. This system is composed of a server, terminals, and users, each of which plays a specific role and cooperates effectively.

[0593] Server-side processing

[0594] The server acquires weather information, analyzes soil data and crop health, and generates optimal farming strategies. It also acquires user emotion data and adjusts farming strategies accordingly.

[0595] Obtaining weather information

[0596] The server retrieves the latest weather information through an API. Using the API key and location, it collects data such as current temperature, precipitation, humidity, and wind speed from the weather API.

[0597] Soil data analysis

[0598] The server analyzes data collected from the soil sensors, including soil nutrient and moisture levels, and makes assessments to suggest necessary fertilization and irrigation applications.

[0599] Crop health monitoring

[0600] The server monitors the health and growth stage of the crops based on data sent from the crop sensors, and suggests appropriate measures if the crop health index is low or if there are signs of pests.

[0601] Generation of farming strategies

[0602] The server integrates weather, soil, and crop data and uses AI algorithms to generate optimal farming strategies, including specific instructions for irrigation, fertilization, and pest management.

[0603] Obtaining sentiment data and adjusting strategies

[0604] The server acquires the user's emotional data using an emotion engine. The emotion engine recognizes emotions from the user's voice, facial expressions, and text input. Based on the recognized emotional data, the farming strategy is further adjusted to make it more user-acceptable.

[0605] Processing on the terminal side

[0606] The device collects data from soil and crop sensors and transmits it to the server, and also collects user emotion data through an emotion engine.

[0607] Acquiring Sensor Data

[0608] The device uses soil sensors to measure soil nutrient and moisture levels, and crop sensors to measure the growth stage and health index of the crop, and these data are periodically transmitted to the server.

[0609] Acquiring emotion data

[0610] The device recognizes the user's emotions through an emotion engine, which analyzes voice, facial expressions, and text input obtained from interactions and observations with the user to generate emotion data.

[0611] User-side processing

[0612] The user receives the farming strategy provided by the server, performs farming according to the instructions, and cooperates by providing emotion data.

[0613] Viewing Optimization Strategies

[0614] Through a screen or application provided by the device, the user can view farming strategies sent from the server, including specific instructions for irrigation, fertilization, and pest management.

[0615] Providing emotion data

[0616] Users can express their emotions through their devices and provide data to the emotion engine, which allows the system to provide farming strategies that take the user's emotions into account.

[0617] Specific examples

[0618] For example, if a farmer uses this system, he or she can implement an optimized farming strategy by following the steps below.

[0619] 1. Acquiring sensor data:

[0620] The device uses soil sensors to measure soil nutrient and moisture levels, and crop sensors to measure crop growth stages and health indices.

[0621] 2. Obtaining weather information:

[0622] The server retrieves the latest weather information through a weather API.

[0623] 3. Data Analysis:

[0624] The server analyzes the weather, soil, and crop data it acquires and uses AI algorithms to generate farming strategies.

[0625] 4. Acquiring and using emotion data:

[0626] The server obtains the user's emotions through an emotion engine and adjusts the farming strategy.

[0627] 5. Display Strategy:

[0628] The user then uses the terminal to review specific instructions provided by the server regarding irrigation, fertilization, and pest management.

[0629] 6. Performing agricultural work:

[0630] The user performs farming operations based on the strategies provided, such as "increase irrigation," "apply balanced fertilizer," and "conduct pest inspections."

[0631] In this way, by using the AI ​​system and emotion engine of the present invention, farmers can implement optimal farming strategies in real time and achieve efficient and sustainable agriculture. Furthermore, by taking the user's emotions into consideration, it is possible to provide a farming strategy that is more suited to the user and reduce the burden of implementation.

[0632] The processing flow will be explained below.

[0633] Step 1:

[0634] The device collects data from soil and crop sensors, specifically using the soil sensor to measure soil nutrient and moisture levels, and the crop sensor to measure the growth stage and health index of the crop.

[0635] Step 2:

[0636] The device transmits the collected sensor data to a server, including soil nutrient levels, moisture levels, crop growth stage, and health index.

[0637] Step 3:

[0638] The server retrieves weather information by sending a request to the weather API using the API key and location to retrieve weather data such as the current temperature, precipitation, humidity, and wind speed.

[0639] Step 4:

[0640] The server analyzes the soil data, specifically extracting nutrient and moisture levels from the transmitted soil data and evaluating each measurement.

[0641] Step 5:

[0642] The server monitors the health of the crops based on the crop data, checking the growth stage and health index of the crops and considering measures to improve their health if necessary.

[0643] Step 6:

[0644] The device collects user emotion data through an emotion engine, which analyzes the user's voice, facial expressions, and text input to generate emotion data.

[0645] Step 7:

[0646] The device transmits the collected emotion data to the server, specifically, data regarding the user's current emotional state.

[0647] Step 8:

[0648] The server combines the analyzed soil data, monitored crop data, and acquired weather information, and uses AI algorithms to generate optimal farming strategies, specifically creating instructions for irrigation, fertilization, and pest management.

[0649] Step 9:

[0650] The server adjusts the farming strategy based on the collected emotional data, specifically taking into account the user's stress level and satisfaction level.

[0651] Step 10:

[0652] The server provides the generated farming strategy to the user by sending it to the terminal and allowing the user to check it through the terminal.

[0653] Step 11:

[0654] The user views the farming strategies displayed on the device, including increased irrigation, fertilization suggestions, and pest management instructions.

[0655] Step 12:

[0656] The user performs farming operations based on the provided farming strategies, such as adjusting irrigation systems, applying fertilizers, and conducting pest checks.

[0657] Through these steps, efficient and sustainable agriculture can be achieved through collaboration between the server, terminals, and users. By taking into account the user's emotions, a more compatible farming strategy can be provided, reducing the burden of implementation.

[0658] Example 2

[0659] Next, a description will be given of 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."

[0660] Conventional agricultural data analysis systems only propose optimal farming strategies based on physical data such as weather information, soil data, and crop health, but do not consider the user's emotions or psychological state. This can lead to users feeling burdened when implementing the proposed strategy or being unable to accept the strategy. Therefore, there is a need to provide more personalized farming strategies that take user emotions into account.

[0661] 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.

[0662] In this invention, the server includes means for analyzing agricultural data in real time using AI technology, means for acquiring the latest weather information, means for collecting soil condition information from sensors, means for monitoring the health of crops, means for acquiring user emotion data, means for generating a farming strategy for maximizing crop growth based on the analysis results, the acquired weather information, and the emotion data, and means for providing the farming strategy to the user, thereby making it possible to provide an optimal farming strategy that takes user emotion into consideration.

[0663] "AI technology" refers to technology that uses artificial intelligence to automate data analysis and decision-making.

[0664] "Agricultural data" refers to data related to agricultural activities, including, for example, weather information, soil conditions, and crop health.

[0665] "Means of real-time analysis" refers to technology that instantly analyzes acquired data and provides the results in a usable form.

[0666] "Weather information" refers to data on weather conditions such as temperature, precipitation, humidity, and wind speed.

[0667] "Sensor" refers to a device for measuring a physical condition and collecting that data.

[0668] "Soil condition" refers to the physical and chemical characteristics of the soil that affect crop growth, such as soil nutrient and moisture levels.

[0669] A "soil sensor" refers to a device used to measure nutrient and moisture levels in the soil.

[0670] "Crop health" refers to data on the growth status of crops, such as the stage of growth and health index of the crop.

[0671] A "crop sensor" refers to a device used to monitor the health and growth stage of crops.

[0672] "Emotion data" refers to data related to emotions recognized from the user's voice, facial expressions, text input, etc.

[0673] "Emotion engine" refers to an analysis engine for recognizing the user's emotions.

[0674] An "agricultural strategy" refers to a specific implementation plan for irrigation, fertilization, pest management, etc. to maximize crop growth.

[0675] "User" refers to a person who uses the system to carry out agricultural activities.

[0676] To implement this invention, it is necessary to combine an emotion engine that recognizes user emotions based on an agricultural data analysis system using AI technology. This system is composed of a server, terminals, and users, each of which plays a specific role and cooperates effectively.

[0677] The server acquires weather information, analyzes soil data and crop health, and generates optimal farming strategies. It also acquires user emotion data and adjusts farming strategies accordingly.

[0678] Specifically, the server obtains the latest weather information through a weather API. A standard server computer is required as the hardware, and API services such as OpenWeatherMap and WeatherAPI are used as the software. Data collected from soil and crop sensors is analyzed using AI algorithms (e.g., TensorFlow and PyTorch) to evaluate soil nutrient and moisture levels and crop health indices. Furthermore, an emotion engine (e.g., Amazon Rekognition and Microsoft Azure Emotion API) is used to obtain user emotion data, which is then integrated with the analysis results to generate optimal farming strategies.

[0679] The device collects data from soil and plant sensors and transmits it to a server. Specifically, it uses sensor devices such as the Adafruit STEMMA Soil Sensor and MicaSense to measure soil nutrient and moisture levels, and plant sensors to measure the growth stage and health index of plants. It also collects user emotion data through an emotion engine.

[0680] The user receives farming strategies provided by the server and performs farming according to those instructions. The user also cooperates by providing emotional data. The user checks specific instructions on irrigation, fertilization, and pest management provided by the server through the device's display or application. Furthermore, the user expresses their own emotions through the device and provides data to the emotion engine.

[0681] Specific examples include the following steps:

[0682] 1. The device uses soil sensors to measure soil nutrient and moisture levels, and crop sensors to measure crop growth stages and health indices.

[0683] 2. The server retrieves the latest weather information through the weather API.

[0684] 3. The weather, soil, and crop data acquired by the server is analyzed using AI algorithms to generate farming strategies.

[0685] 4. The server obtains the user's emotions through the emotion engine and adjusts the farming strategy.

[0686] 5. The user reviews specific instructions for irrigation, fertilization, and pest management provided by the server through the terminal.

[0687] 6. The user performs farming operations based on the provided strategy.

[0688] An example prompt is:

[0689] "This system integrates sensor data and weather information and uses AI to generate optimal farming strategies. It also uses an emotion engine to adjust strategies that take the user's emotions into account. Specifically, sensors measure the moisture and nutrient levels in the soil and obtain the latest weather information, allowing the AI ​​to suggest the optimal growing environment for crops. It also has a function that collects user emotion data and optimizes the strategies provided to the user."

[0690] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0691] Step 1: Get weather information

[0692] The server obtains the latest weather information through a weather API. Specifically, it uses services such as OpenWeatherMap and WeatherAPI, and uses an API key and location information. Location information and an API key are required as input, and weather data such as temperature, precipitation, humidity, and wind speed are obtained based on this data. The obtained weather data is then output.

[0693] Step 2: Acquiring Sensor Data

[0694] The device collects data from soil and crop sensors. For example, it uses Adafruit soil sensors to measure soil nutrient and moisture levels, and MicaSense crop sensors to measure crop growth stages and health indices. It receives real-time data from the sensors as input and periodically transmits it to a server. The output is soil and crop data.

[0695] Step 3: Sending data from the sensor

[0696] The terminal transmits the data obtained from the sensors to the server. The soil nutrient and moisture levels, crop growth stage and health index measured by the sensors are provided as inputs and transmit this data to the server. As output, the server receives these data and stores them for analysis.

[0697] Step 4: Analyze the data

[0698] The server analyzes the acquired weather information, soil data, and crop data using AI algorithms. The AI ​​algorithms use TensorFlow and PyTorch to perform analysis based on the input data. For example, if the weather information indicates dryness, the soil data determines whether irrigation is necessary, and the crop data determines whether the crop is in a growth stage. The analysis results are obtained as output.

[0699] Step 5: Generate farming strategies

[0700] The server uses the analysis results to generate a farming strategy, which includes specific instructions for irrigation, fertilization, and pest management. The analysis results are used as input, and an AI algorithm derives the optimal farming strategy. The generated farming strategy is the output.

[0701] Step 6: Obtaining emotion data

[0702] The device uses an emotion engine to recognize the user's emotions. Specifically, it uses a camera and microphone to generate emotion data from the user's voice, facial expressions, and text input. The input is the user's emotional information (voice, facial expressions, text), which is analyzed by the emotion engine. The output is the recognized emotion data.

[0703] Step 7: Adjust your strategy based on emotions

[0704] The server adjusts the farming strategy based on the acquired emotional data. For example, if the user is feeling stressed, it will adjust the frequency of irrigation or fertilization. The inputs are the emotional data and the existing farming strategy, and the server regenerates the strategy based on these. The output is the adjusted farming strategy.

[0705] Step 8: View the optimization strategy

[0706] The user views the optimized farming strategy sent from the server through the device, including specific instructions for irrigation, fertilization, and pest management. The input is the adjusted farming strategy, which is displayed on the device's display or through an application. The output is the farming strategy presented to the user in an easy-to-understand format.

[0707] Step 9: Performing farm work

[0708] The user performs farming tasks based on the provided strategy. For example, the user acts according to specific instructions such as "increase irrigation," "apply balanced fertilizer," and "conduct pest inspection." The input is the displayed farming strategy, and the user performs actual farming tasks based on this. The output is the optimized farming results.

[0709] In this way, the system allows the server, terminal, and user to each play their respective roles and work together to provide and implement optimal farming strategies that take the user's emotions into consideration.

[0710] (Application example 2)

[0711] Next, a description will be given of Application 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."

[0712] In modern agriculture, improving productivity while reducing the burden on farmers is a key challenge. In particular, it is necessary to grasp the condition of soil and crops in real time and formulate optimal farming strategies. However, this requires the collection and analysis of a large amount of data, which places a heavy burden on farmers. Furthermore, strategies are not adjusted taking into account farmers' emotions and stress, which could increase the burden of actually implementing the farming strategies. Furthermore, there is a lack of efficient means to put the collected data and generated strategies into practice. To solve these challenges, it is necessary to integrate advanced data analysis, emotion recognition, and implementation methods.

[0713] The specific processing by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes means for analyzing agricultural data in real time using AI technology, means for acquiring the latest weather information, means for collecting soil conditions from sensors, means for monitoring the health of crops, means for generating a farming strategy for maximizing crop growth based on the analysis results and the acquired weather information, means for acquiring user emotion data and adjusting the farming strategy, means for instructing an autonomous vehicle to execute the farming strategy, and means for providing the farming strategy to the user. This makes it possible to formulate and execute an efficient and highly accurate farming strategy while reducing the burden on farmers.

[0714] "AI technology" is a technology that uses artificial intelligence to collect, analyze, predict, and optimize data.

[0715] "Real-time agricultural data analysis" is the process of instantly collecting and analyzing data such as soil and crop conditions and weather information.

[0716] "Latest weather information" refers to the latest data on current and future weather, obtained through APIs, etc.

[0717] "Soil condition" refers to the physical and chemical attributes of the soil, such as nutrient levels, moisture levels, and pH value.

[0718] A "sensor" is a device that detects physical or chemical changes and collects that information as data.

[0719] "Crop health monitoring" is the process of continuously monitoring the growth stage and health index of crops using sensors and cameras.

[0720] "Agricultural strategies" refer to specific instructions for irrigation, fertilization, pest management, etc. generated using AI technology to optimize agricultural operations.

[0721] "User emotion data" is data that indicates the user's emotional state, extracted from the user's voice, facial expression, text input, and the like.

[0722] An "autonomous vehicle" is a vehicle that operates autonomously and performs specific tasks without the need for human intervention.

[0723] "Instructions" are specific instructions or guidelines for performing a particular action or task.

[0724] A system for implementing this invention utilizes an autonomous vehicle (e.g., an agricultural tractor), a specific device, and a cloud server.

[0725] 1. Server-side processing

[0726] The server analyzes the agricultural data and generates an optimized farming strategy using the following means:

[0727] AI technology: The server uses Python-based AI frameworks (e.g., TensorFlow, Keras) to analyze collected data in real time and perform advanced predictions and optimization.

[0728] Obtaining weather information: Use a weather API (e.g., OpenWeatherMap) to collect the latest weather information using your API key and location. Specifically, obtain data such as the current temperature, precipitation, humidity, and wind speed.

[0729] Soil and crop data collection and analysis: Analyzes soil nutrient and moisture levels collected from sensors to recommend fertilization and irrigation needs, and monitors crop health and growth stages in real time to recommend appropriate measures.

[0730] Emotion engine: Using voice recognition software (e.g., Google Cloud Speech-to-Text) and facial recognition software (e.g., Amazon Rekognition), the engine obtains user emotion data, which can then detect farmers' stress and fatigue and adjust farming strategies.

[0731] 2. Terminal processing

[0732] The terminal (autonomous vehicle or other device) collects data and transmits it to the server using the following means:

[0733] Sensor data acquisition: Soil and crop sensors are used to measure soil nutrient and moisture levels, as well as crop health and growth stage. This data is sent to a server via Wi-Fi or Bluetooth.

[0734] Emotional data capture: Using a camera and microphone, the device collects user emotional data from their voice and facial expressions, using voice and facial recognition software.

[0735] 3. User-side processing

[0736] The user receives the farming strategy provided by the server and performs farming according to the instructions. The user also cooperates by providing emotion data.

[0737] View farming strategies: View specific instructions for irrigation, fertilization, pest management, etc. sent from the server through the terminal.

[0738] Providing emotional data: Expressing emotions through voice and facial expressions and providing data to the emotion engine.

[0739] Specific examples

[0740] For example, if the present invention is implemented in a farming field in Tokyo, soil and crop data will be collected through sensors mounted on tractors and drones and sent to a server. The server will then integrate the data with the latest weather information to generate an optimal farming strategy. If a farmer is feeling stressed, the emotion engine will detect this and adjust the strategy to reduce the farming burden. An example of a prompt sentence that can be generated is as follows:

[0741] "Please obtain weather data for Tokyo, analyze soil and crop data from sensors, and propose the optimal farming strategy based on the results. Also, obtain emotional data from farmers through their voices and facial expressions, and adjust the strategy to reduce the stress on farmers if they are under a high level of stress."

[0742] In this way, the system of the present invention provides optimal farming strategies in real time, reducing the burden on farmers and achieving efficient farming.

[0743] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0744] Step 1: Acquiring Sensor Data

[0745] The device uses soil and crop sensors to collect data such as soil nutrient levels, moisture levels, crop health index, and growth stage. It receives measurement data from the sensors as input and transmits the data to a server. The output is a dataset of the measurement results.

[0746] Step 2: Get weather information

[0747] The server uses a weather API to retrieve the latest weather information (current temperature, precipitation, humidity, wind speed, etc.). It uses the API key and location information as input to retrieve data from the weather API. The output is a dataset of the retrieved weather information.

[0748] Step 3: Analyze the data

[0749] The server integrates data from sensors and meteorological information and analyzes them using AI technology. It receives soil data, crop data, and meteorological data as input and combines each data into a data frame. Based on this data, the AI ​​model generates crop growth forecasts and optimal farming strategies. The output is a dataset of optimal farming strategies.

[0750] Step 4: Obtaining emotion data

[0751] The device uses a camera and microphone to collect the user's voice and facial expressions, which are then analyzed by an emotion engine. Using the collected voice and facial expression data as input, emotion analysis is performed using voice recognition software (e.g., Google Cloud Speech-to-Text) and facial recognition software (e.g., Amazon Rekognition). The output is the user's emotional data.

[0752] Step 5: Adjust your farming strategy

[0753] The server adjusts the generated farming strategy to match the user's emotional state based on the acquired emotional data. It receives the emotional data and the initial farming strategy dataset as input, and modifies the strategy to reduce the workload if the user is feeling stressed. The output is a dataset of the adjusted farming strategy.

[0754] Step 6: Prescribe and implement farming strategies

[0755] The terminal instructs the autonomous vehicle to execute the adjusted farming strategy sent from the server. It receives the adjusted farming strategy dataset as input, transfers its contents to the autonomous vehicle, and performs specific farming tasks. The output is log data of the farming tasks performed.

[0756] Step 7: Displaying farming strategies

[0757] The user checks the adjusted farming strategy sent from the server through a screen provided by the terminal. The adjusted farming strategy dataset is displayed as input, and specific instructions (e.g., irrigation, fertilization, pest management) are provided to the user. The output is the farming strategy information visually displayed to the user.

[0758] 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.

[0759] 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> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) 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.

[0760] 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.

[0761] [Third embodiment]

[0762] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0763] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0764] 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).

[0765] 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.

[0766] 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.

[0767] 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).

[0768] 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.

[0769] 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.

[0770] 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.

[0771] 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.

[0772] 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.

[0773] Next, a description will be given of the identification process performed by the identification processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as the "server" and the headset type terminal 314 will be referred to as the "terminal."

[0774] To implement this invention, it is first necessary to build an agricultural data analysis system using AI technology. This system consists of a server, terminals, and users, each of which plays a specific role.

[0775] Server-side processing

[0776] The server retrieves weather information, analyzes soil data and crop health, and generates optimal farming strategies.

[0777] Obtaining weather information

[0778] The server retrieves the latest weather information through an API. Using the API key and location, it collects data such as current temperature, precipitation, humidity, and wind speed from the weather API.

[0779] Soil data analysis

[0780] The server analyzes data collected from the soil sensors, including soil nutrient and moisture levels, and makes fertilization recommendations if nutrient levels are low and irrigation suggestions if moisture levels are low.

[0781] Crop health monitoring

[0782] The server monitors the health and growth stage of the crops based on data sent from the plant's sensors, and suggests appropriate measures if the plant's health index is low or if the plant is threatened by pests.

[0783] Generation of farming strategies

[0784] The server integrates weather, soil, and crop data and uses AI algorithms to generate optimal farming strategies, including specific instructions for irrigation, fertilization, and pest management.

[0785] Processing on the terminal side

[0786] The terminal is responsible for collecting data from soil and crop sensors and transmitting it to a server.

[0787] Acquiring Sensor Data

[0788] The device uses soil sensors to measure soil nutrient and moisture levels, and crop sensors to measure the growth stage and health index of the crop, and these data are periodically transmitted to the server.

[0789] User-side processing

[0790] The user receives the farming strategy provided by the server and carries out the farming work according to the instructions.

[0791] Viewing Optimization Strategies

[0792] Through a screen or application provided by the device, the user can view farming strategies sent from the server, including specific instructions for irrigation, fertilization, and pest management.

[0793] Specific examples

[0794] For example, if a farmer uses this system, he or she can implement an optimized farming strategy by following the steps below.

[0795] 1. Acquiring sensor data:

[0796] The device uses soil sensors to measure soil nutrient and moisture levels, and crop sensors to measure crop growth stages and health indices.

[0797] 2. Obtaining weather information:

[0798] The server retrieves the latest weather information through a weather API.

[0799] 3. Data Analysis:

[0800] The server analyzes the weather, soil, and crop data it acquires and uses AI algorithms to generate farming strategies.

[0801] 4. Display Strategy:

[0802] The user then uses the terminal to review specific instructions provided by the server regarding irrigation, fertilization, and pest management.

[0803] 5. Performing agricultural work:

[0804] The user performs farming operations based on the strategies provided, such as "increase irrigation," "apply balanced fertilizer," and "conduct pest inspections."

[0805] In this way, by using the AI ​​system of the present invention, farmers can implement optimal farming strategies in real time and achieve efficient and sustainable agriculture.

[0806] The processing flow will be explained below.

[0807] Step 1:

[0808] The device collects data from soil and crop sensors, specifically using the soil sensor to measure soil nutrient and moisture levels, and the crop sensor to measure the growth stage and health index of the crop.

[0809] Step 2:

[0810] The device transmits the collected sensor data to a server, including soil nutrient levels, moisture levels, crop growth stage, and health index.

[0811] Step 3:

[0812] The server retrieves weather information by sending a request to the weather API using the API key and location to retrieve weather data such as the current temperature, precipitation, humidity, and wind speed.

[0813] Step 4:

[0814] The server analyzes the soil data, specifically extracting nutrient and moisture levels from the transmitted soil data and evaluating each measurement.

[0815] Step 5:

[0816] The server monitors the health of the crops based on the crop data, checking the growth stage and health index of the crops and considering measures to improve their health if necessary.

[0817] Step 6:

[0818] The server combines the analyzed soil data, monitored crop data, and acquired weather information, and uses AI algorithms to generate optimal farming strategies, specifically creating instructions for irrigation, fertilization, and pest management.

[0819] Step 7:

[0820] The server provides the generated farming strategy to the user by sending it to the terminal and allowing the user to check it through the terminal.

[0821] Step 8:

[0822] The user views the farming strategies displayed on the device, including increased irrigation, fertilization suggestions, and pest management instructions.

[0823] Step 9:

[0824] The user performs farming operations based on the provided farming strategies, such as adjusting irrigation systems, applying fertilizers, and conducting pest checks.

[0825] Through the above steps, efficient and sustainable agriculture will be realized through collaboration between servers, terminals, and users.

[0826] Example 1

[0827] Next, a description will be given of 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."

[0828] Conventional agricultural processes lack sufficient data collection and analysis to optimize crop growth and health, resulting in unstable crop yields and quality. It is also difficult to provide real-time farming strategies based on meteorological and soil data, creating a need for methods to achieve efficient and sustainable agriculture.

[0829] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[0830] In this invention, the server includes means for analyzing agricultural data in real time using AI technology, means for acquiring the latest weather information, means for collecting soil conditions from sensors, means for monitoring the health of crops, means for generating a farming strategy for maximizing crop growth based on the analysis results and the acquired weather information, means for providing the farming strategy to a user, means for collecting data from soil sensors and crop sensors via a terminal and transmitting it to the server, and means for the user to carry out farming based on the farming strategy provided. This makes it possible to provide optimal farming strategies in real time and achieve efficient and sustainable agriculture.

[0831] "AI technology" refers to technology that uses artificial intelligence, and in particular, systems that perform data analysis, pattern recognition, and predictive analysis.

[0832] "Real-time" means that data is acquired, processed, and results are provided without delay.

[0833] "Weather information" refers to data on weather conditions such as temperature, precipitation, humidity, and wind speed.

[0834] "Soil condition" is data about soil properties, such as soil nutrient and moisture levels.

[0835] A "sensor" is a device that detects changes in the physical or chemical environment and acquires data.

[0836] "Crop health" refers to data about the overall health of the crop, such as the stage of growth, nutritional status, and the presence or absence of pests and diseases.

[0837] An "agricultural strategy" is a set of specific operations and activities, such as irrigation, fertilization, and pest management, that optimize crop growth.

[0838] "User" refers to a farm manager or farm worker who uses the system to carry out farm work.

[0839] A "terminal" is a communication device that collects data from sensors and transmits it to a server.

[0840] A "generative model" is an algorithm or AI model that predicts and generates farming strategies based on input data.

[0841] MODE FOR CARRYING OUT THE INVENTION

[0842] To implement this invention, it is first necessary to build an agricultural data analysis system using AI technology. This system consists of a server, terminals, and users, each of which plays a specific role.

[0843] Server-side processing

[0844] The server retrieves weather information, analyzes soil data and crop health, and generates optimal farming strategies.

[0845] Obtaining weather information

[0846] The server retrieves the latest weather information using weather APIs such as OpenWeatherMap, and periodically collects data such as temperature, precipitation, humidity, and wind speed using the API key and location information.

[0847] Soil data analysis

[0848] The server analyzes data collected from the soil sensors, including soil nutrient and moisture levels, and, for example, makes fertilization recommendations if nutrient levels are low and irrigation suggestions if moisture levels are declining.

[0849] Crop health monitoring

[0850] The server uses data from the plant sensors to monitor the health of the plants, and if the plant's health index declines or there are signs of pest infestation, it will suggest appropriate measures.

[0851] Generation of farming strategies

[0852] The server integrates weather, soil, and crop data and uses AI algorithms to generate optimal farming strategies, including specific instructions for irrigation, fertilization, and pest management.

[0853] Processing on the terminal side

[0854] The terminal is responsible for collecting data from soil and crop sensors and transmitting it to a server.

[0855] Acquiring Sensor Data

[0856] The device uses soil sensors to measure soil nutrient and moisture levels, and crop sensors to measure the growth stage and health index of the crop, and periodically transmits this data to the server.

[0857] User-side processing

[0858] The user receives the farming strategy provided by the server and carries out the farming work according to the instructions.

[0859] Viewing Optimization Strategies

[0860] Through a screen or application provided by the device, the user can view farming strategies sent from the server, including specific instructions for irrigation, fertilization, and pest management.

[0861] Specific examples

[0862] For example, if a farmer uses this system, he or she can implement an optimized farming strategy by following the steps below.

[0863] 1. Acquiring sensor data:

[0864] The device uses soil sensors to measure soil nutrient and moisture levels, and crop sensors to measure crop growth stages and health indices.

[0865] 2. Obtaining weather information:

[0866] The server retrieves the latest weather information through a weather API.

[0867] 3. Data Analysis:

[0868] The server analyzes the weather, soil, and crop data it acquires and uses AI algorithms to generate farming strategies.

[0869] 4. Display Strategy:

[0870] The user then uses the terminal to review specific instructions provided by the server regarding irrigation, fertilization, and pest management.

[0871] 5. Performing agricultural work:

[0872] The user performs farming operations based on the strategies provided, such as "increase irrigation," "apply balanced fertilizer," and "conduct pest inspections."

[0873] In this way, by using the agricultural data analysis system that employs the AI ​​technology of the present invention, farmers can implement optimal farming strategies in real time and achieve efficient and sustainable agriculture.

[0874] Example prompt sentences to use

[0875] Please explain in detail your approach to developing a system that analyzes agricultural data and generates optimal farming strategies. Please describe in detail the roles of the server, terminal, and user.

[0876] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0877] Step 1:

[0878] The server calls the weather API every hour and obtains the latest weather information using the API key and location information. The API key and location information are required as input data, and based on this, weather data such as temperature, precipitation, humidity, and wind speed is obtained and stored in a database. Specifically, the server first generates a URL, then sends a request to the API, analyzes the response, and extracts the weather data.

[0879] Step 2:

[0880] The server receives soil data sent from the device every five minutes and records it in a database. The input data required is the nutrient and moisture levels sent from the soil sensor, which is analyzed to determine whether fertilization or irrigation is necessary. Specifically, each time new data is received, it is added to the database and compared with past data.

[0881] Step 3:

[0882] The server receives data from the crop sensors in real time and analyzes the growth and health of the crops. The input data is the growth stage and health index sent from the crop sensors, and based on this, it evaluates the presence of pests and the growth status. Specifically, it analyzes the received data and issues an alert if an abnormal value is detected.

[0883] Step 4:

[0884] The server integrates the acquired weather, soil, and crop data and generates farming strategies using AI algorithms. The input data consists of weather, soil, and crop data, which are then fed into an AI model that outputs specific instructions for irrigation, fertilization, and pest management. Specifically, the server preprocesses the data, inputs it into the AI ​​model, and stores the output strategy in a database.

[0885] Step 5:

[0886] The terminal acquires data from the soil and crop sensors every five minutes and sends it to the server. The input data is real-time data acquired from the sensors, and is sent to the server via wireless communication or an internet connection. Specifically, the terminal aggregates the data from the sensors, converts it into packet format, and sends it.

[0887] Step 6:

[0888] The user checks the farming strategy provided by the server through the terminal. The input data is the optimized farming instructions from the server, which are displayed in the application. Specifically, the application sends a request to the server and visually displays the received strategy data.

[0889] Step 7:

[0890] The user performs actual farm work based on the provided farming strategy. The input data are specific instructions from the server, and based on these, the user performs irrigation, fertilization, and pest management. Specific actions include applying the instructed amount of water and fertilizer, and carrying out necessary pest control.

[0891] Through the above processing steps, the system of the present invention can provide a real-time farming strategy for achieving efficient and sustainable agriculture.

[0892] (Application example 1)

[0893] Next, a description will be given of Application 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."

[0894] Conventional agricultural systems have had difficulty effectively understanding weather information, soil conditions, and crop health to derive optimal farming strategies. Similarly, in factory production lines, it has been challenging to automatically generate optimal production strategies based on environmental data and raw material quality, and then instruct robots to execute them.

[0895] 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.

[0896] In this invention, the server includes means for analyzing agricultural data in real time using AI technology, means for acquiring the latest weather information, means for collecting soil condition information from sensors, means for monitoring the health of crops, means for generating a farming strategy to maximize crop growth based on the analysis results and the acquired weather information, means for analyzing environmental data and raw material quality data and generating an optimal production strategy, means for instructing a work robot to execute the production strategy, and means for providing the farming strategy to a user. This enables both agricultural efficiency improvement and production line optimization.

[0897] "AI technology" is a technology that uses artificial intelligence to analyze data and make decisions.

[0898] "Agricultural data" refers to various data related to agriculture, such as crop growth, soil condition, and weather information.

[0899] "Real-time" means processing and providing data immediately, without delay.

[0900] "Weather information" refers to data related to weather, such as temperature, precipitation, humidity, and wind speed.

[0901] A "sensor" is a device that detects physical properties of the environment or a substance.

[0902] "Soil condition" refers to the characteristics and state of the soil, such as its nutrient levels and moisture content.

[0903] "Health" refers to whether the crop is disease-free, growing well, etc.

[0904] "Agricultural strategy" refers to specific plans and methods for agricultural work, such as irrigation, fertilization, and pest management.

[0905] "Environmental data" refers to data related to the environment, such as temperature and humidity within the factory.

[0906] "Raw Material Quality Data" refers to data relating to the quality of raw materials used.

[0907] "Production strategy" refers to a plan to optimize material inputs and work processes in factory production.

[0908] A "working robot" is a mechanical device that can perform work automatically.

[0909] This invention provides a data analysis system using AI technology that can be applied to both agriculture and factory production. Each element, server, terminal, and user, plays a specific role to efficiently collect, analyze, and execute data. Specific embodiments are shown below.

[0910] 1. Server Processing

[0911] The server performs the following functions:

[0912] Data analysis using AI technology:

[0913] The server analyzes agricultural and factory data in real time, using machine learning algorithms and data mining techniques, such as building AI models using Python's TensorFlow library.

[0914] Get the latest weather information:

[0915] The server collects weather information through API. Specifically, it uses the Weather API to obtain the current temperature, precipitation, humidity, wind speed, etc. It obtains the data using the API key and location information.

[0916] Analysis of environmental and raw material quality data:

[0917] Environmental data (temperature, humidity, etc.) and raw material quality data collected from sensors in the factory are analyzed to generate production strategies, such as issuing instructions for heating or cooling if the temperature is not within a certain range.

[0918] 2. Terminal Processing

[0919] The terminal is responsible for collecting and transmitting the following data to the server:

[0920] Sensor data collection:

[0921] In agriculture, the device will use soil sensors to measure soil nutrient and moisture levels, crop sensors to measure crop growth stage and health index, and in factories, environmental sensors to measure temperature and humidity, and raw material quality sensors to measure material quality.

[0922] Sending data:

[0923] The collected data is periodically sent to a server, which then analyzes the data in real time.

[0924] 3. User Actions

[0925] A user uses the system as follows:

[0926] View optimization strategies:

[0927] Through a screen or application provided by the device, the user can view the optimization strategies sent from the server, which in the case of agriculture can include specific instructions for irrigation, fertilization, and pest management, and in the case of a factory, can include optimization strategies for material inputs and work processes.

[0928] Carrying out agricultural and production activities:

[0929] Users perform agricultural and production tasks based on the provided strategies, such as increasing irrigation and applying balanced fertilizers, and factory tasks such as increasing temperature and adjusting input amounts of materials.

[0930] Specific examples

[0931] For example, if a farmer uses this system, he or she can implement an optimized farming strategy by following the steps below.

[0932] 1. Acquiring sensor data:

[0933] The device uses soil sensors to measure soil nutrient and moisture levels, and crop sensors to measure crop growth stages and health indices.

[0934] 2. Obtaining weather information:

[0935] The server retrieves the latest weather information through a weather API.

[0936] 3. Data Analysis:

[0937] The server analyzes the weather, soil, and crop data it acquires and uses AI algorithms to generate farming strategies.

[0938] 4. Display Strategy:

[0939] The user then uses the terminal to review specific instructions provided by the server regarding irrigation, fertilization, and pest management.

[0940] 5. Performing agricultural work:

[0941] The user performs farming operations based on the strategies provided, such as "increase irrigation," "apply balanced fertilizer," and "conduct pest inspections."

[0942] The following are some specific examples in factories:

[0943] Based on data on the temperature, humidity, and material quality in the factory, the server generates an optimal production strategy and sends instructions to the work robots via terminals, thereby maximizing production efficiency.

[0944] Prompt Sentence Examples

[0945] "Generate optimal production strategies based on temperature, humidity, and material quality data in a factory in Tokyo."

[0946] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0947] Step 1:

[0948] The device collects data using soil and crop sensors. Specifically, it measures soil nutrient and moisture levels, as well as the growth stage and health index of the crop. This data is then sent to a server in a unified format, such as JSON. The input is the data obtained from the sensors, and the output is the data sent to the server for analysis.

[0949] Step 2:

[0950] The server obtains weather information using an API. Specifically, it uses the API key and location from the Weather API to collect data such as the current temperature, precipitation, humidity, and wind speed. The input is the API key and location information, and the output is the weather data. This allows you to obtain the latest weather information.

[0951] Step 3:

[0952] The server integrates and analyzes the soil and crop data sent from the devices and the acquired weather information. It uses AI algorithms (e.g., machine learning models using TensorFlow) to analyze this data and generate a farming strategy. The inputs are soil data, crop data, and weather information, and the output is an optimized farming strategy. This strategy includes specific instructions for irrigation, fertilization, and pest management.

[0953] Step 4:

[0954] The server analyzes environmental data and raw material quality data within the factory. It combines data from environmental sensors, such as temperature and humidity, and raw material quality sensors to generate an optimal production strategy using an AI model. The inputs are environmental data and raw material quality data, and the output is an optimized production strategy. This production strategy includes instructions for optimizing material inputs and work processes.

[0955] Step 5:

[0956] The user checks the farming strategy provided by the server through the terminal. The terminal displays the farming strategy and presents it in a format that is easy for the user to understand. The input is the strategy data sent from the server, and the output is visual instructions for the user. The user then carries out the farming work based on this.

[0957] Step 6:

[0958] The user checks the production strategy provided by the server through a terminal. The terminal displays the production strategy and presents it in a format that is easy for the user to understand. The input is the production strategy data sent from the server, and the output is visual instructions for the user. The user carries out factory work based on this.

[0959] Through these steps, agricultural and factory production can be optimized.

[0960] 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.

[0961] To implement this invention, it is necessary to combine an agricultural data analysis system using AI technology with an emotion engine that recognizes user emotions. This system is composed of a server, terminals, and users, each of which plays a specific role and cooperates effectively.

[0962] Server-side processing

[0963] The server acquires weather information, analyzes soil data and crop health, and generates optimal farming strategies. It also acquires user emotion data and adjusts farming strategies accordingly.

[0964] Obtaining weather information

[0965] The server retrieves the latest weather information through an API. Using the API key and location, it collects data such as current temperature, precipitation, humidity, and wind speed from the weather API.

[0966] Soil data analysis

[0967] The server analyzes data collected from the soil sensors, including soil nutrient and moisture levels, and makes assessments to suggest necessary fertilization and irrigation applications.

[0968] Crop health monitoring

[0969] The server monitors the health and growth stage of the crops based on data sent from the crop sensors, and suggests appropriate measures if the crop health index is low or if there are signs of pests.

[0970] Generation of farming strategies

[0971] The server integrates weather, soil, and crop data and uses AI algorithms to generate optimal farming strategies, including specific instructions for irrigation, fertilization, and pest management.

[0972] Obtaining sentiment data and adjusting strategies

[0973] The server acquires the user's emotional data using an emotion engine. The emotion engine recognizes emotions from the user's voice, facial expressions, and text input. Based on the recognized emotional data, the farming strategy is further adjusted to make it more user-acceptable.

[0974] Processing on the terminal side

[0975] The device collects data from soil and crop sensors and transmits it to the server, and also collects user emotion data through an emotion engine.

[0976] Acquiring Sensor Data

[0977] The device uses soil sensors to measure soil nutrient and moisture levels, and crop sensors to measure the growth stage and health index of the crop, and these data are periodically transmitted to the server.

[0978] Acquiring emotion data

[0979] The device recognizes the user's emotions through an emotion engine, which analyzes voice, facial expressions, and text input obtained from interactions and observations with the user to generate emotion data.

[0980] User-side processing

[0981] The user receives the farming strategy provided by the server, performs farming according to the instructions, and cooperates by providing emotion data.

[0982] Viewing Optimization Strategies

[0983] Through a screen or application provided by the device, the user can view farming strategies sent from the server, including specific instructions for irrigation, fertilization, and pest management.

[0984] Providing emotion data

[0985] Users can express their emotions through their devices and provide data to the emotion engine, which allows the system to provide farming strategies that take the user's emotions into account.

[0986] Specific examples

[0987] For example, if a farmer uses this system, he or she can implement an optimized farming strategy by following the steps below.

[0988] 1. Acquiring sensor data:

[0989] The device uses soil sensors to measure soil nutrient and moisture levels, and crop sensors to measure crop growth stages and health indices.

[0990] 2. Obtaining weather information:

[0991] The server retrieves the latest weather information through a weather API.

[0992] 3. Data Analysis:

[0993] The server analyzes the weather, soil, and crop data it acquires and uses AI algorithms to generate farming strategies.

[0994] 4. Acquiring and using emotion data:

[0995] The server obtains the user's emotions through an emotion engine and adjusts the farming strategy.

[0996] 5. Display Strategy:

[0997] The user then uses the terminal to review specific instructions provided by the server regarding irrigation, fertilization, and pest management.

[0998] 6. Performing agricultural work:

[0999] The user performs farming operations based on the strategies provided, such as "increase irrigation," "apply balanced fertilizer," and "conduct pest inspections."

[1000] In this way, by using the AI ​​system and emotion engine of the present invention, farmers can implement optimal farming strategies in real time and achieve efficient and sustainable agriculture. Furthermore, by taking the user's emotions into consideration, it is possible to provide a farming strategy that is more suited to the user and reduce the burden of implementation.

[1001] The processing flow will be explained below.

[1002] Step 1:

[1003] The device collects data from soil and crop sensors, specifically using the soil sensor to measure soil nutrient and moisture levels, and the crop sensor to measure the growth stage and health index of the crop.

[1004] Step 2:

[1005] The device transmits the collected sensor data to a server, including soil nutrient levels, moisture levels, crop growth stage, and health index.

[1006] Step 3:

[1007] The server retrieves weather information by sending a request to the weather API using the API key and location to retrieve weather data such as the current temperature, precipitation, humidity, and wind speed.

[1008] Step 4:

[1009] The server analyzes the soil data, specifically extracting nutrient and moisture levels from the transmitted soil data and evaluating each measurement.

[1010] Step 5:

[1011] The server monitors the health of the crops based on the crop data, checking the growth stage and health index of the crops and considering measures to improve their health if necessary.

[1012] Step 6:

[1013] The device collects user emotion data through an emotion engine, which analyzes the user's voice, facial expressions, and text input to generate emotion data.

[1014] Step 7:

[1015] The device transmits the collected emotion data to the server, specifically, data regarding the user's current emotional state.

[1016] Step 8:

[1017] The server combines the analyzed soil data, monitored crop data, and acquired weather information, and uses AI algorithms to generate optimal farming strategies, specifically creating instructions for irrigation, fertilization, and pest management.

[1018] Step 9:

[1019] The server adjusts the farming strategy based on the collected emotional data, specifically taking into account the user's stress level and satisfaction level.

[1020] Step 10:

[1021] The server provides the generated farming strategy to the user by sending it to the terminal and allowing the user to check it through the terminal.

[1022] Step 11:

[1023] The user views the farming strategies displayed on the device, including increased irrigation, fertilization suggestions, and pest management instructions.

[1024] Step 12:

[1025] The user performs farming operations based on the provided farming strategies, such as adjusting irrigation systems, applying fertilizers, and conducting pest checks.

[1026] Through these steps, efficient and sustainable agriculture can be achieved through collaboration between the server, terminals, and users. By taking into account the user's emotions, a more compatible farming strategy can be provided, reducing the burden of implementation.

[1027] Example 2

[1028] Next, a description will be given of 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."

[1029] Conventional agricultural data analysis systems only propose optimal farming strategies based on physical data such as weather information, soil data, and crop health, but do not consider the user's emotions or psychological state. This can lead to users feeling burdened when implementing the proposed strategy or being unable to accept the strategy. Therefore, there is a need to provide more personalized farming strategies that take user emotions into account.

[1030] 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.

[1031] In this invention, the server includes means for analyzing agricultural data in real time using AI technology, means for acquiring the latest weather information, means for collecting soil condition information from sensors, means for monitoring the health of crops, means for acquiring user emotion data, means for generating a farming strategy for maximizing crop growth based on the analysis results, the acquired weather information, and the emotion data, and means for providing the farming strategy to the user, thereby making it possible to provide an optimal farming strategy that takes user emotion into consideration.

[1032] "AI technology" refers to technology that uses artificial intelligence to automate data analysis and decision-making.

[1033] "Agricultural data" refers to data related to agricultural activities, including, for example, weather information, soil conditions, and crop health.

[1034] "Means of real-time analysis" refers to technology that instantly analyzes acquired data and provides the results in a usable form.

[1035] "Weather information" refers to data on weather conditions such as temperature, precipitation, humidity, and wind speed.

[1036] "Sensor" refers to a device for measuring a physical condition and collecting that data.

[1037] "Soil condition" refers to the physical and chemical characteristics of the soil that affect crop growth, such as soil nutrient and moisture levels.

[1038] A "soil sensor" refers to a device used to measure nutrient and moisture levels in the soil.

[1039] "Crop health" refers to data on the growth status of crops, such as the stage of growth and health index of the crop.

[1040] A "crop sensor" refers to a device used to monitor the health and growth stage of crops.

[1041] "Emotion data" refers to data related to emotions recognized from the user's voice, facial expressions, text input, etc.

[1042] "Emotion engine" refers to an analysis engine for recognizing the user's emotions.

[1043] An "agricultural strategy" refers to a specific implementation plan for irrigation, fertilization, pest management, etc. to maximize crop growth.

[1044] "User" refers to a person who uses the system to carry out agricultural activities.

[1045] To implement this invention, it is necessary to combine an emotion engine that recognizes user emotions based on an agricultural data analysis system using AI technology. This system is composed of a server, terminals, and users, each of which plays a specific role and cooperates effectively.

[1046] The server acquires weather information, analyzes soil data and crop health, and generates optimal farming strategies. It also acquires user emotion data and adjusts farming strategies accordingly.

[1047] Specifically, the server obtains the latest weather information through a weather API. A standard server computer is required as the hardware, and API services such as OpenWeatherMap and WeatherAPI are used as the software. Data collected from soil and crop sensors is analyzed using AI algorithms (e.g., TensorFlow and PyTorch) to evaluate soil nutrient and moisture levels and crop health indices. Furthermore, an emotion engine (e.g., Amazon Rekognition and Microsoft Azure Emotion API) is used to obtain user emotion data, which is then integrated with the analysis results to generate optimal farming strategies.

[1048] The device collects data from soil and plant sensors and transmits it to a server. Specifically, it uses sensor devices such as the Adafruit STEMMA Soil Sensor and MicaSense to measure soil nutrient and moisture levels, and plant sensors to measure the growth stage and health index of plants. It also collects user emotion data through an emotion engine.

[1049] The user receives farming strategies provided by the server and performs farming according to those instructions. The user also cooperates by providing emotional data. The user checks specific instructions on irrigation, fertilization, and pest management provided by the server through the device's display or application. Furthermore, the user expresses their own emotions through the device and provides data to the emotion engine.

[1050] Specific examples include the following steps:

[1051] 1. The device uses soil sensors to measure soil nutrient and moisture levels, and crop sensors to measure crop growth stages and health indices.

[1052] 2. The server retrieves the latest weather information through the weather API.

[1053] 3. The weather, soil, and crop data acquired by the server is analyzed using AI algorithms to generate farming strategies.

[1054] 4. The server obtains the user's emotions through the emotion engine and adjusts the farming strategy.

[1055] 5. The user reviews specific instructions for irrigation, fertilization, and pest management provided by the server through the terminal.

[1056] 6. The user performs farming operations based on the provided strategy.

[1057] An example prompt is:

[1058] "This system integrates sensor data and weather information and uses AI to generate optimal farming strategies. It also uses an emotion engine to adjust strategies that take the user's emotions into account. Specifically, sensors measure the moisture and nutrient levels in the soil and obtain the latest weather information, allowing the AI ​​to suggest the optimal growing environment for crops. It also has a function that collects user emotion data and optimizes the strategies provided to the user."

[1059] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1060] Step 1: Get weather information

[1061] The server obtains the latest weather information through a weather API. Specifically, it uses services such as OpenWeatherMap and WeatherAPI, and uses an API key and location information. Location information and an API key are required as input, and weather data such as temperature, precipitation, humidity, and wind speed are obtained based on this data. The obtained weather data is then output.

[1062] Step 2: Acquiring Sensor Data

[1063] The device collects data from soil and crop sensors. For example, it uses Adafruit soil sensors to measure soil nutrient and moisture levels, and MicaSense crop sensors to measure crop growth stages and health indices. It receives real-time data from the sensors as input and periodically transmits it to a server. The output is soil and crop data.

[1064] Step 3: Sending data from the sensor

[1065] The terminal transmits the data obtained from the sensors to the server. The soil nutrient and moisture levels, crop growth stage and health index measured by the sensors are provided as inputs and transmit this data to the server. As output, the server receives these data and stores them for analysis.

[1066] Step 4: Analyze the data

[1067] The server analyzes the acquired weather information, soil data, and crop data using AI algorithms. The AI ​​algorithms use TensorFlow and PyTorch to perform analysis based on the input data. For example, if the weather information indicates dryness, the soil data determines whether irrigation is necessary, and the crop data determines whether the crop is in a growth stage. The analysis results are obtained as output.

[1068] Step 5: Generate farming strategies

[1069] The server uses the analysis results to generate a farming strategy, which includes specific instructions for irrigation, fertilization, and pest management. The analysis results are used as input, and an AI algorithm derives the optimal farming strategy. The generated farming strategy is the output.

[1070] Step 6: Obtaining emotion data

[1071] The device uses an emotion engine to recognize the user's emotions. Specifically, it uses a camera and microphone to generate emotion data from the user's voice, facial expressions, and text input. The input is the user's emotional information (voice, facial expressions, text), which is analyzed by the emotion engine. The output is the recognized emotion data.

[1072] Step 7: Adjust your strategy based on emotions

[1073] The server adjusts the farming strategy based on the acquired emotional data. For example, if the user is feeling stressed, it will adjust the frequency of irrigation or fertilization. The inputs are the emotional data and the existing farming strategy, and the server regenerates the strategy based on these. The output is the adjusted farming strategy.

[1074] Step 8: View the optimization strategy

[1075] The user views the optimized farming strategy sent from the server through the device, including specific instructions for irrigation, fertilization, and pest management. The input is the adjusted farming strategy, which is displayed on the device's display or through an application. The output is the farming strategy presented to the user in an easy-to-understand format.

[1076] Step 9: Performing farm work

[1077] The user performs farming tasks based on the provided strategy. For example, the user acts according to specific instructions such as "increase irrigation," "apply balanced fertilizer," and "conduct pest inspection." The input is the displayed farming strategy, and the user performs actual farming tasks based on this. The output is the optimized farming results.

[1078] In this way, the system allows the server, terminal, and user to each play their respective roles and work together to provide and implement optimal farming strategies that take the user's emotions into consideration.

[1079] (Application example 2)

[1080] Next, a description will be given of Application 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."

[1081] In modern agriculture, improving productivity while reducing the burden on farmers is a key challenge. In particular, it is necessary to grasp the condition of soil and crops in real time and formulate optimal farming strategies. However, this requires the collection and analysis of a large amount of data, which places a heavy burden on farmers. Furthermore, strategies are not adjusted taking into account farmers' emotions and stress, which could increase the burden of actually implementing the farming strategies. Furthermore, there is a lack of efficient means to put the collected data and generated strategies into practice. To solve these challenges, it is necessary to integrate advanced data analysis, emotion recognition, and implementation methods.

[1082] The specific processing by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes means for analyzing agricultural data in real time using AI technology, means for acquiring the latest weather information, means for collecting soil conditions from sensors, means for monitoring the health of crops, means for generating a farming strategy for maximizing crop growth based on the analysis results and the acquired weather information, means for acquiring user emotion data and adjusting the farming strategy, means for instructing an autonomous vehicle to execute the farming strategy, and means for providing the farming strategy to the user. This makes it possible to formulate and execute an efficient and highly accurate farming strategy while reducing the burden on farmers.

[1083] "AI technology" is a technology that uses artificial intelligence to collect, analyze, predict, and optimize data.

[1084] "Real-time agricultural data analysis" is the process of instantly collecting and analyzing data such as soil and crop conditions and weather information.

[1085] "Latest weather information" refers to the latest data on current and future weather, obtained through APIs, etc.

[1086] "Soil condition" refers to the physical and chemical attributes of the soil, such as nutrient levels, moisture levels, and pH value.

[1087] A "sensor" is a device that detects physical or chemical changes and collects that information as data.

[1088] "Crop health monitoring" is the process of continuously monitoring the growth stage and health index of crops using sensors and cameras.

[1089] "Agricultural strategies" refer to specific instructions for irrigation, fertilization, pest management, etc. generated using AI technology to optimize agricultural operations.

[1090] "User emotion data" is data that indicates the user's emotional state, extracted from the user's voice, facial expression, text input, and the like.

[1091] An "autonomous vehicle" is a vehicle that operates autonomously and performs specific tasks without the need for human intervention.

[1092] "Instructions" are specific instructions or guidelines for performing a particular action or task.

[1093] A system for implementing this invention utilizes an autonomous vehicle (e.g., an agricultural tractor), a specific device, and a cloud server.

[1094] 1. Server-side processing

[1095] The server analyzes the agricultural data and generates an optimized farming strategy using the following means:

[1096] AI technology: The server uses Python-based AI frameworks (e.g., TensorFlow, Keras) to analyze collected data in real time and perform advanced predictions and optimization.

[1097] Obtaining weather information: Use a weather API (e.g., OpenWeatherMap) to collect the latest weather information using your API key and location. Specifically, obtain data such as the current temperature, precipitation, humidity, and wind speed.

[1098] Soil and crop data collection and analysis: Analyzes soil nutrient and moisture levels collected from sensors to recommend fertilization and irrigation needs, and monitors crop health and growth stages in real time to recommend appropriate measures.

[1099] Emotion engine: Using voice recognition software (e.g., Google Cloud Speech-to-Text) and facial recognition software (e.g., Amazon Rekognition), the engine obtains user emotion data, which can then detect farmers' stress and fatigue and adjust farming strategies.

[1100] 2. Terminal processing

[1101] The terminal (autonomous vehicle or other device) collects data and transmits it to the server using the following means:

[1102] Sensor data acquisition: Soil and crop sensors are used to measure soil nutrient and moisture levels, as well as crop health and growth stage. This data is sent to a server via Wi-Fi or Bluetooth.

[1103] Emotional data capture: Using a camera and microphone, the device collects user emotional data from their voice and facial expressions, using voice and facial recognition software.

[1104] 3. User-side processing

[1105] The user receives the farming strategy provided by the server and performs farming according to the instructions. The user also cooperates by providing emotion data.

[1106] View farming strategies: View specific instructions for irrigation, fertilization, pest management, etc. sent from the server through the terminal.

[1107] Providing emotional data: Expressing emotions through voice and facial expressions and providing data to the emotion engine.

[1108] Specific examples

[1109] For example, if the present invention is implemented in a farming field in Tokyo, soil and crop data will be collected through sensors mounted on tractors and drones and sent to a server. The server will then integrate the data with the latest weather information to generate an optimal farming strategy. If a farmer is feeling stressed, the emotion engine will detect this and adjust the strategy to reduce the farming burden. An example of a prompt sentence that can be generated is as follows:

[1110] "Please obtain weather data for Tokyo, analyze soil and crop data from sensors, and propose the optimal farming strategy based on the results. Also, obtain emotional data from farmers through their voices and facial expressions, and adjust the strategy to reduce the stress on farmers if they are under a high level of stress."

[1111] In this way, the system of the present invention provides optimal farming strategies in real time, reducing the burden on farmers and achieving efficient farming.

[1112] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1113] Step 1: Acquiring Sensor Data

[1114] The device uses soil and crop sensors to collect data such as soil nutrient levels, moisture levels, crop health index, and growth stage. It receives measurement data from the sensors as input and transmits the data to a server. The output is a dataset of the measurement results.

[1115] Step 2: Get weather information

[1116] The server uses a weather API to retrieve the latest weather information (current temperature, precipitation, humidity, wind speed, etc.). It uses the API key and location information as input to retrieve data from the weather API. The output is a dataset of the retrieved weather information.

[1117] Step 3: Analyze the data

[1118] The server integrates data from sensors and meteorological information and analyzes them using AI technology. It receives soil data, crop data, and meteorological data as input and combines each data into a data frame. Based on this data, the AI ​​model generates crop growth forecasts and optimal farming strategies. The output is a dataset of optimal farming strategies.

[1119] Step 4: Obtaining emotion data

[1120] The device uses a camera and microphone to collect the user's voice and facial expressions, which are then analyzed by an emotion engine. Using the collected voice and facial expression data as input, emotion analysis is performed using voice recognition software (e.g., Google Cloud Speech-to-Text) and facial recognition software (e.g., Amazon Rekognition). The output is the user's emotional data.

[1121] Step 5: Adjust your farming strategy

[1122] The server adjusts the generated farming strategy to match the user's emotional state based on the acquired emotional data. It receives the emotional data and the initial farming strategy dataset as input, and modifies the strategy to reduce the workload if the user is feeling stressed. The output is a dataset of the adjusted farming strategy.

[1123] Step 6: Prescribe and implement farming strategies

[1124] The terminal instructs the autonomous vehicle to execute the adjusted farming strategy sent from the server. It receives the adjusted farming strategy dataset as input, transfers its contents to the autonomous vehicle, and performs specific farming tasks. The output is log data of the farming tasks performed.

[1125] Step 7: Displaying farming strategies

[1126] The user checks the adjusted farming strategy sent from the server through a screen provided by the terminal. The adjusted farming strategy dataset is displayed as input, and specific instructions (e.g., irrigation, fertilization, pest management) are provided to the user. The output is the farming strategy information visually displayed to the user.

[1127] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 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.

[1128] 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> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) 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.

[1129] In the above embodiment, an example was given in which the specific processing is performed by the data processing device 12, but the technology of the present disclosure is not limited to this, and the specific processing may be performed by the headset type terminal 314.

[1130] [Fourth embodiment]

[1131] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[1132] 7, a data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[1133] 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).

[1134] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. 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 control target 443 are also connected to the bus 52.

[1135] 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.

[1136] 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).

[1137] 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.

[1138] The control object 443 includes a display device, LEDs in the eyes, and motors for driving the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[1139] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, 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.

[1140] 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.

[1141] 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.

[1142] In the robot 414, the processor 46 performs the reception output process. 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.

[1143] Next, a description will be given of the specific processing performed by the specific processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1144] To implement this invention, it is first necessary to build an agricultural data analysis system using AI technology. This system consists of a server, terminals, and users, each of which plays a specific role.

[1145] Server-side processing

[1146] The server retrieves weather information, analyzes soil data and crop health, and generates optimal farming strategies.

[1147] Obtaining weather information

[1148] The server retrieves the latest weather information through an API. Using the API key and location, it collects data such as current temperature, precipitation, humidity, and wind speed from the weather API.

[1149] Soil data analysis

[1150] The server analyzes data collected from the soil sensors, including soil nutrient and moisture levels, and makes fertilization recommendations if nutrient levels are low and irrigation suggestions if moisture levels are low.

[1151] Crop health monitoring

[1152] The server monitors the health and growth stage of the crops based on data sent from the plant's sensors, and suggests appropriate measures if the plant's health index is low or if the plant is threatened by pests.

[1153] Generation of farming strategies

[1154] The server integrates weather, soil, and crop data and uses AI algorithms to generate optimal farming strategies, including specific instructions for irrigation, fertilization, and pest management.

[1155] Processing on the terminal side

[1156] The terminal is responsible for collecting data from soil and crop sensors and transmitting it to a server.

[1157] Acquiring Sensor Data

[1158] The device uses soil sensors to measure soil nutrient and moisture levels, and crop sensors to measure the growth stage and health index of the crop, and these data are periodically transmitted to the server.

[1159] User-side processing

[1160] The user receives the farming strategy provided by the server and carries out the farming work according to the instructions.

[1161] Viewing Optimization Strategies

[1162] Through a screen or application provided by the device, the user can view farming strategies sent from the server, including specific instructions for irrigation, fertilization, and pest management.

[1163] Specific examples

[1164] For example, if a farmer uses this system, he or she can implement an optimized farming strategy by following the steps below.

[1165] 1. Acquiring sensor data:

[1166] The device uses soil sensors to measure soil nutrient and moisture levels, and crop sensors to measure crop growth stages and health indices.

[1167] 2. Obtaining weather information:

[1168] The server retrieves the latest weather information through a weather API.

[1169] 3. Data Analysis:

[1170] The server analyzes the weather, soil, and crop data it acquires and uses AI algorithms to generate farming strategies.

[1171] 4. Display Strategy:

[1172] The user then uses the terminal to review specific instructions provided by the server regarding irrigation, fertilization, and pest management.

[1173] 5. Performing agricultural work:

[1174] The user performs farming operations based on the strategies provided, such as "increase irrigation," "apply balanced fertilizer," and "conduct pest inspections."

[1175] In this way, by using the AI ​​system of the present invention, farmers can implement optimal farming strategies in real time and achieve efficient and sustainable agriculture.

[1176] The processing flow will be explained below.

[1177] Step 1:

[1178] The device collects data from soil and crop sensors, specifically using the soil sensor to measure soil nutrient and moisture levels, and the crop sensor to measure the growth stage and health index of the crop.

[1179] Step 2:

[1180] The device transmits the collected sensor data to a server, including soil nutrient levels, moisture levels, crop growth stage, and health index.

[1181] Step 3:

[1182] The server retrieves weather information by sending a request to the weather API using the API key and location to retrieve weather data such as the current temperature, precipitation, humidity, and wind speed.

[1183] Step 4:

[1184] The server analyzes the soil data, specifically extracting nutrient and moisture levels from the transmitted soil data and evaluating each measurement.

[1185] Step 5:

[1186] The server monitors the health of the crops based on the crop data, checking the growth stage and health index of the crops and considering measures to improve their health if necessary.

[1187] Step 6:

[1188] The server combines the analyzed soil data, monitored crop data, and acquired weather information, and uses AI algorithms to generate optimal farming strategies, specifically creating instructions for irrigation, fertilization, and pest management.

[1189] Step 7:

[1190] The server provides the generated farming strategy to the user by sending it to the terminal and allowing the user to check it through the terminal.

[1191] Step 8:

[1192] The user views the farming strategies displayed on the device, including increased irrigation, fertilization suggestions, and pest management instructions.

[1193] Step 9:

[1194] The user performs farming operations based on the provided farming strategies, such as adjusting irrigation systems, applying fertilizers, and conducting pest checks.

[1195] Through the above steps, efficient and sustainable agriculture will be realized through collaboration between servers, terminals, and users.

[1196] Example 1

[1197] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1198] Conventional agricultural processes lack sufficient data collection and analysis to optimize crop growth and health, resulting in unstable crop yields and quality. It is also difficult to provide real-time farming strategies based on meteorological and soil data, creating a need for methods to achieve efficient and sustainable agriculture.

[1199] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[1200] In this invention, the server includes means for analyzing agricultural data in real time using AI technology, means for acquiring the latest weather information, means for collecting soil conditions from sensors, means for monitoring the health of crops, means for generating a farming strategy for maximizing crop growth based on the analysis results and the acquired weather information, means for providing the farming strategy to a user, means for collecting data from soil sensors and crop sensors via a terminal and transmitting it to the server, and means for the user to carry out farming based on the farming strategy provided. This makes it possible to provide optimal farming strategies in real time and achieve efficient and sustainable agriculture.

[1201] "AI technology" refers to technology that uses artificial intelligence, and in particular, systems that perform data analysis, pattern recognition, and predictive analysis.

[1202] "Real-time" means that data is acquired, processed, and results are provided without delay.

[1203] "Weather information" refers to data on weather conditions such as temperature, precipitation, humidity, and wind speed.

[1204] "Soil condition" is data about soil properties, such as soil nutrient and moisture levels.

[1205] A "sensor" is a device that detects changes in the physical or chemical environment and acquires data.

[1206] "Crop health" refers to data about the overall health of the crop, such as the stage of growth, nutritional status, and the presence or absence of pests and diseases.

[1207] An "agricultural strategy" is a set of specific operations and activities, such as irrigation, fertilization, and pest management, that optimize crop growth.

[1208] "User" refers to a farm manager or farm worker who uses the system to carry out farm work.

[1209] A "terminal" is a communication device that collects data from sensors and transmits it to a server.

[1210] A "generative model" is an algorithm or AI model that predicts and generates farming strategies based on input data.

[1211] MODE FOR CARRYING OUT THE INVENTION

[1212] To implement this invention, it is first necessary to build an agricultural data analysis system using AI technology. This system consists of a server, terminals, and users, each of which plays a specific role.

[1213] Server-side processing

[1214] The server retrieves weather information, analyzes soil data and crop health, and generates optimal farming strategies.

[1215] Obtaining weather information

[1216] The server retrieves the latest weather information using weather APIs such as OpenWeatherMap, and periodically collects data such as temperature, precipitation, humidity, and wind speed using the API key and location information.

[1217] Soil data analysis

[1218] The server analyzes data collected from the soil sensors, including soil nutrient and moisture levels, and, for example, makes fertilization recommendations if nutrient levels are low and irrigation suggestions if moisture levels are declining.

[1219] Crop health monitoring

[1220] The server uses data from the plant sensors to monitor the health of the plants, and if the plant's health index declines or there are signs of pest infestation, it will suggest appropriate measures.

[1221] Generation of farming strategies

[1222] The server integrates weather, soil, and crop data and uses AI algorithms to generate optimal farming strategies, including specific instructions for irrigation, fertilization, and pest management.

[1223] Processing on the terminal side

[1224] The terminal is responsible for collecting data from soil and crop sensors and transmitting it to a server.

[1225] Acquiring Sensor Data

[1226] The device uses soil sensors to measure soil nutrient and moisture levels, and crop sensors to measure the growth stage and health index of the crop, and periodically transmits this data to the server.

[1227] User-side processing

[1228] The user receives the farming strategy provided by the server and carries out the farming work according to the instructions.

[1229] Viewing Optimization Strategies

[1230] Through a screen or application provided by the device, the user can view farming strategies sent from the server, including specific instructions for irrigation, fertilization, and pest management.

[1231] Specific examples

[1232] For example, if a farmer uses this system, he or she can implement an optimized farming strategy by following the steps below.

[1233] 1. Acquiring sensor data:

[1234] The device uses soil sensors to measure soil nutrient and moisture levels, and crop sensors to measure crop growth stages and health indices.

[1235] 2. Obtaining weather information:

[1236] The server retrieves the latest weather information through a weather API.

[1237] 3. Data Analysis:

[1238] The server analyzes the weather, soil, and crop data it acquires and uses AI algorithms to generate farming strategies.

[1239] 4. Display Strategy:

[1240] The user then uses the terminal to review specific instructions provided by the server regarding irrigation, fertilization, and pest management.

[1241] 5. Performing agricultural work:

[1242] The user performs farming operations based on the strategies provided, such as "increase irrigation," "apply balanced fertilizer," and "conduct pest inspections."

[1243] In this way, by using the agricultural data analysis system that employs the AI ​​technology of the present invention, farmers can implement optimal farming strategies in real time and achieve efficient and sustainable agriculture.

[1244] Example prompt sentences to use

[1245] Please explain in detail your approach to developing a system that analyzes agricultural data and generates optimal farming strategies. Please describe in detail the roles of the server, terminal, and user.

[1246] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1247] Step 1:

[1248] The server calls the weather API every hour and obtains the latest weather information using the API key and location information. The API key and location information are required as input data, and based on this, weather data such as temperature, precipitation, humidity, and wind speed is obtained and stored in a database. Specifically, the server first generates a URL, then sends a request to the API, analyzes the response, and extracts the weather data.

[1249] Step 2:

[1250] The server receives soil data sent from the device every five minutes and records it in a database. The input data required is the nutrient and moisture levels sent from the soil sensor, which is analyzed to determine whether fertilization or irrigation is necessary. Specifically, each time new data is received, it is added to the database and compared with past data.

[1251] Step 3:

[1252] The server receives data from the crop sensors in real time and analyzes the growth and health of the crops. The input data is the growth stage and health index sent from the crop sensors, and based on this, it evaluates the presence of pests and the growth status. Specifically, it analyzes the received data and issues an alert if an abnormal value is detected.

[1253] Step 4:

[1254] The server integrates the acquired weather, soil, and crop data and generates farming strategies using AI algorithms. The input data consists of weather, soil, and crop data, which are then fed into an AI model that outputs specific instructions for irrigation, fertilization, and pest management. Specifically, the server preprocesses the data, inputs it into the AI ​​model, and stores the output strategy in a database.

[1255] Step 5:

[1256] The terminal acquires data from the soil and crop sensors every five minutes and sends it to the server. The input data is real-time data acquired from the sensors, and is sent to the server via wireless communication or an internet connection. Specifically, the terminal aggregates the data from the sensors, converts it into packet format, and sends it.

[1257] Step 6:

[1258] The user checks the farming strategy provided by the server through the terminal. The input data is the optimized farming instructions from the server, which are displayed in the application. Specifically, the application sends a request to the server and visually displays the received strategy data.

[1259] Step 7:

[1260] The user performs actual farm work based on the provided farming strategy. The input data are specific instructions from the server, and based on these, the user performs irrigation, fertilization, and pest management. Specific actions include applying the instructed amount of water and fertilizer, and carrying out necessary pest control.

[1261] Through the above processing steps, the system of the present invention can provide a real-time farming strategy for achieving efficient and sustainable agriculture.

[1262] (Application example 1)

[1263] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1264] Conventional agricultural systems have had difficulty effectively understanding weather information, soil conditions, and crop health to derive optimal farming strategies. Similarly, in factory production lines, it has been challenging to automatically generate optimal production strategies based on environmental data and raw material quality, and then instruct robots to execute them.

[1265] 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.

[1266] In this invention, the server includes means for analyzing agricultural data in real time using AI technology, means for acquiring the latest weather information, means for collecting soil condition information from sensors, means for monitoring the health of crops, means for generating a farming strategy to maximize crop growth based on the analysis results and the acquired weather information, means for analyzing environmental data and raw material quality data and generating an optimal production strategy, means for instructing a work robot to execute the production strategy, and means for providing the farming strategy to a user. This enables both agricultural efficiency improvement and production line optimization.

[1267] "AI technology" is a technology that uses artificial intelligence to analyze data and make decisions.

[1268] "Agricultural data" refers to various data related to agriculture, such as crop growth, soil condition, and weather information.

[1269] "Real-time" means processing and providing data immediately, without delay.

[1270] "Weather information" refers to data related to weather, such as temperature, precipitation, humidity, and wind speed.

[1271] A "sensor" is a device that detects physical properties of the environment or a substance.

[1272] "Soil condition" refers to the characteristics and state of the soil, such as its nutrient levels and moisture content.

[1273] "Health" refers to whether the crop is disease-free, growing well, etc.

[1274] "Agricultural strategy" refers to specific plans and methods for agricultural work, such as irrigation, fertilization, and pest management.

[1275] "Environmental data" refers to data related to the environment, such as temperature and humidity within the factory.

[1276] "Raw Material Quality Data" refers to data relating to the quality of raw materials used.

[1277] "Production strategy" refers to a plan to optimize material inputs and work processes in factory production.

[1278] A "working robot" is a mechanical device that can perform work automatically.

[1279] This invention provides a data analysis system using AI technology that can be applied to both agriculture and factory production. Each element, server, terminal, and user, plays a specific role to efficiently collect, analyze, and execute data. Specific embodiments are shown below.

[1280] 1. Server Processing

[1281] The server performs the following functions:

[1282] Data analysis using AI technology:

[1283] The server analyzes agricultural and factory data in real time, using machine learning algorithms and data mining techniques, such as building AI models using Python's TensorFlow library.

[1284] Get the latest weather information:

[1285] The server collects weather information through API. Specifically, it uses the Weather API to obtain the current temperature, precipitation, humidity, wind speed, etc. It obtains the data using the API key and location information.

[1286] Analysis of environmental and raw material quality data:

[1287] Environmental data (temperature, humidity, etc.) and raw material quality data collected from sensors in the factory are analyzed to generate production strategies, such as issuing instructions for heating or cooling if the temperature is not within a certain range.

[1288] 2. Terminal Processing

[1289] The terminal is responsible for collecting and transmitting the following data to the server:

[1290] Sensor data collection:

[1291] In agriculture, the device will use soil sensors to measure soil nutrient and moisture levels, crop sensors to measure crop growth stage and health index, and in factories, environmental sensors to measure temperature and humidity, and raw material quality sensors to measure material quality.

[1292] Sending data:

[1293] The collected data is periodically sent to a server, which then analyzes the data in real time.

[1294] 3. User Actions

[1295] A user uses the system as follows:

[1296] View optimization strategies:

[1297] Through a screen or application provided by the device, the user can view the optimization strategies sent from the server, which in the case of agriculture can include specific instructions for irrigation, fertilization, and pest management, and in the case of a factory, can include optimization strategies for material inputs and work processes.

[1298] Carrying out agricultural and production activities:

[1299] Users perform agricultural and production tasks based on the provided strategies, such as increasing irrigation and applying balanced fertilizers, and factory tasks such as increasing temperature and adjusting input amounts of materials.

[1300] Specific examples

[1301] For example, if a farmer uses this system, he or she can implement an optimized farming strategy by following the steps below.

[1302] 1. Acquiring sensor data:

[1303] The device uses soil sensors to measure soil nutrient and moisture levels, and crop sensors to measure crop growth stages and health indices.

[1304] 2. Obtaining weather information:

[1305] The server retrieves the latest weather information through a weather API.

[1306] 3. Data Analysis:

[1307] The server analyzes the weather, soil, and crop data it acquires and uses AI algorithms to generate farming strategies.

[1308] 4. Display Strategy:

[1309] The user then uses the terminal to review specific instructions provided by the server regarding irrigation, fertilization, and pest management.

[1310] 5. Performing agricultural work:

[1311] The user performs farming operations based on the strategies provided, such as "increase irrigation," "apply balanced fertilizer," and "conduct pest inspections."

[1312] The following are some specific examples in factories:

[1313] Based on data on the temperature, humidity, and material quality in the factory, the server generates an optimal production strategy and sends instructions to the work robots via terminals, thereby maximizing production efficiency.

[1314] Prompt Sentence Examples

[1315] "Generate optimal production strategies based on temperature, humidity, and material quality data in a factory in Tokyo."

[1316] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1317] Step 1:

[1318] The device collects data using soil and crop sensors. Specifically, it measures soil nutrient and moisture levels, as well as the growth stage and health index of the crop. This data is then sent to a server in a unified format, such as JSON. The input is the data obtained from the sensors, and the output is the data sent to the server for analysis.

[1319] Step 2:

[1320] The server obtains weather information using an API. Specifically, it uses the API key and location from the Weather API to collect data such as the current temperature, precipitation, humidity, and wind speed. The input is the API key and location information, and the output is the weather data. This allows you to obtain the latest weather information.

[1321] Step 3:

[1322] The server integrates and analyzes the soil and crop data sent from the devices and the acquired weather information. It uses AI algorithms (e.g., machine learning models using TensorFlow) to analyze this data and generate a farming strategy. The inputs are soil data, crop data, and weather information, and the output is an optimized farming strategy. This strategy includes specific instructions for irrigation, fertilization, and pest management.

[1323] Step 4:

[1324] The server analyzes environmental data and raw material quality data within the factory. It combines data from environmental sensors, such as temperature and humidity, and raw material quality sensors to generate an optimal production strategy using an AI model. The inputs are environmental data and raw material quality data, and the output is an optimized production strategy. This production strategy includes instructions for optimizing material inputs and work processes.

[1325] Step 5:

[1326] The user checks the farming strategy provided by the server through the terminal. The terminal displays the farming strategy and presents it in a format that is easy for the user to understand. The input is the strategy data sent from the server, and the output is visual instructions for the user. The user then carries out the farming work based on this.

[1327] Step 6:

[1328] The user checks the production strategy provided by the server through a terminal. The terminal displays the production strategy and presents it in a format that is easy for the user to understand. The input is the production strategy data sent from the server, and the output is visual instructions for the user. The user carries out factory work based on this.

[1329] Through these steps, agricultural and factory production can be optimized.

[1330] 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.

[1331] To implement this invention, it is necessary to combine an agricultural data analysis system using AI technology with an emotion engine that recognizes user emotions. This system is composed of a server, terminals, and users, each of which plays a specific role and cooperates effectively.

[1332] Server-side processing

[1333] The server acquires weather information, analyzes soil data and crop health, and generates optimal farming strategies. It also acquires user emotion data and adjusts farming strategies accordingly.

[1334] Obtaining weather information

[1335] The server retrieves the latest weather information through an API. Using the API key and location, it collects data such as current temperature, precipitation, humidity, and wind speed from the weather API.

[1336] Soil data analysis

[1337] The server analyzes data collected from the soil sensors, including soil nutrient and moisture levels, and makes assessments to suggest necessary fertilization and irrigation applications.

[1338] Crop health monitoring

[1339] The server monitors the health and growth stage of the crops based on data sent from the crop sensors, and suggests appropriate measures if the crop health index is low or if there are signs of pests.

[1340] Generation of farming strategies

[1341] The server integrates weather, soil, and crop data and uses AI algorithms to generate optimal farming strategies, including specific instructions for irrigation, fertilization, and pest management.

[1342] Obtaining sentiment data and adjusting strategies

[1343] The server acquires the user's emotional data using an emotion engine. The emotion engine recognizes emotions from the user's voice, facial expressions, and text input. Based on the recognized emotional data, the farming strategy is further adjusted to make it more user-acceptable.

[1344] Processing on the terminal side

[1345] The device collects data from soil and crop sensors and transmits it to the server, and also collects user emotion data through an emotion engine.

[1346] Acquiring Sensor Data

[1347] The device uses soil sensors to measure soil nutrient and moisture levels, and crop sensors to measure the growth stage and health index of the crop, and these data are periodically transmitted to the server.

[1348] Acquiring emotion data

[1349] The device recognizes the user's emotions through an emotion engine, which analyzes voice, facial expressions, and text input obtained from interactions and observations with the user to generate emotion data.

[1350] User-side processing

[1351] The user receives the farming strategy provided by the server, performs farming according to the instructions, and cooperates by providing emotion data.

[1352] Viewing Optimization Strategies

[1353] Through a screen or application provided by the device, the user can view farming strategies sent from the server, including specific instructions for irrigation, fertilization, and pest management.

[1354] Providing emotion data

[1355] Users can express their emotions through their devices and provide data to the emotion engine, which allows the system to provide farming strategies that take the user's emotions into account.

[1356] Specific examples

[1357] For example, if a farmer uses this system, he or she can implement an optimized farming strategy by following the steps below.

[1358] 1. Acquiring sensor data:

[1359] The device uses soil sensors to measure soil nutrient and moisture levels, and crop sensors to measure crop growth stages and health indices.

[1360] 2. Obtaining weather information:

[1361] The server retrieves the latest weather information through a weather API.

[1362] 3. Data Analysis:

[1363] The server analyzes the weather, soil, and crop data it acquires and uses AI algorithms to generate farming strategies.

[1364] 4. Acquiring and using emotion data:

[1365] The server obtains the user's emotions through an emotion engine and adjusts the farming strategy.

[1366] 5. Display Strategy:

[1367] The user then uses the terminal to review specific instructions provided by the server regarding irrigation, fertilization, and pest management.

[1368] 6. Performing agricultural work:

[1369] The user performs farming operations based on the strategies provided, such as "increase irrigation," "apply balanced fertilizer," and "conduct pest inspections."

[1370] In this way, by using the AI ​​system and emotion engine of the present invention, farmers can implement optimal farming strategies in real time and achieve efficient and sustainable agriculture. Furthermore, by taking the user's emotions into consideration, it is possible to provide a farming strategy that is more suited to the user and reduce the burden of implementation.

[1371] The processing flow will be explained below.

[1372] Step 1:

[1373] The device collects data from soil and crop sensors, specifically using the soil sensor to measure soil nutrient and moisture levels, and the crop sensor to measure the growth stage and health index of the crop.

[1374] Step 2:

[1375] The device transmits the collected sensor data to a server, including soil nutrient levels, moisture levels, crop growth stage, and health index.

[1376] Step 3:

[1377] The server retrieves weather information by sending a request to the weather API using the API key and location to retrieve weather data such as the current temperature, precipitation, humidity, and wind speed.

[1378] Step 4:

[1379] The server analyzes the soil data, specifically extracting nutrient and moisture levels from the transmitted soil data and evaluating each measurement.

[1380] Step 5:

[1381] The server monitors the health of the crops based on the crop data, checking the growth stage and health index of the crops and considering measures to improve their health if necessary.

[1382] Step 6:

[1383] The device collects user emotion data through an emotion engine, which analyzes the user's voice, facial expressions, and text input to generate emotion data.

[1384] Step 7:

[1385] The device transmits the collected emotion data to the server, specifically, data regarding the user's current emotional state.

[1386] Step 8:

[1387] The server combines the analyzed soil data, monitored crop data, and acquired weather information, and uses AI algorithms to generate optimal farming strategies, specifically creating instructions for irrigation, fertilization, and pest management.

[1388] Step 9:

[1389] The server adjusts the farming strategy based on the collected emotional data, specifically taking into account the user's stress level and satisfaction level.

[1390] Step 10:

[1391] The server provides the generated farming strategy to the user by sending it to the terminal and allowing the user to check it through the terminal.

[1392] Step 11:

[1393] The user views the farming strategies displayed on the device, including increased irrigation, fertilization suggestions, and pest management instructions.

[1394] Step 12:

[1395] The user performs farming operations based on the provided farming strategies, such as adjusting irrigation systems, applying fertilizers, and conducting pest checks.

[1396] Through these steps, efficient and sustainable agriculture can be achieved through collaboration between the server, terminals, and users. By taking into account the user's emotions, a more compatible farming strategy can be provided, reducing the burden of implementation.

[1397] Example 2

[1398] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1399] Conventional agricultural data analysis systems only propose optimal farming strategies based on physical data such as weather information, soil data, and crop health, but do not consider the user's emotions or psychological state. This can lead to users feeling burdened when implementing the proposed strategy or being unable to accept the strategy. Therefore, there is a need to provide more personalized farming strategies that take user emotions into account.

[1400] 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.

[1401] In this invention, the server includes means for analyzing agricultural data in real time using AI technology, means for acquiring the latest weather information, means for collecting soil condition information from sensors, means for monitoring the health of crops, means for acquiring user emotion data, means for generating a farming strategy for maximizing crop growth based on the analysis results, the acquired weather information, and the emotion data, and means for providing the farming strategy to the user, thereby making it possible to provide an optimal farming strategy that takes user emotion into consideration.

[1402] "AI technology" refers to technology that uses artificial intelligence to automate data analysis and decision-making.

[1403] "Agricultural data" refers to data related to agricultural activities, including, for example, weather information, soil conditions, and crop health.

[1404] "Means of real-time analysis" refers to technology that instantly analyzes acquired data and provides the results in a usable form.

[1405] "Weather information" refers to data on weather conditions such as temperature, precipitation, humidity, and wind speed.

[1406] "Sensor" refers to a device for measuring a physical condition and collecting that data.

[1407] "Soil condition" refers to the physical and chemical characteristics of the soil that affect crop growth, such as soil nutrient and moisture levels.

[1408] A "soil sensor" refers to a device used to measure nutrient and moisture levels in the soil.

[1409] "Crop health" refers to data on the growth status of crops, such as the stage of growth and health index of the crop.

[1410] A "crop sensor" refers to a device used to monitor the health and growth stage of crops.

[1411] "Emotion data" refers to data related to emotions recognized from the user's voice, facial expressions, text input, etc.

[1412] "Emotion engine" refers to an analysis engine for recognizing the user's emotions.

[1413] An "agricultural strategy" refers to a specific implementation plan for irrigation, fertilization, pest management, etc. to maximize crop growth.

[1414] "User" refers to a person who uses the system to carry out agricultural activities.

[1415] To implement this invention, it is necessary to combine an emotion engine that recognizes user emotions based on an agricultural data analysis system using AI technology. This system is composed of a server, terminals, and users, each of which plays a specific role and cooperates effectively.

[1416] The server acquires weather information, analyzes soil data and crop health, and generates optimal farming strategies. It also acquires user emotion data and adjusts farming strategies accordingly.

[1417] Specifically, the server obtains the latest weather information through a weather API. A standard server computer is required as the hardware, and API services such as OpenWeatherMap and WeatherAPI are used as the software. Data collected from soil and crop sensors is analyzed using AI algorithms (e.g., TensorFlow and PyTorch) to evaluate soil nutrient and moisture levels and crop health indices. Furthermore, an emotion engine (e.g., Amazon Rekognition and Microsoft Azure Emotion API) is used to obtain user emotion data, which is then integrated with the analysis results to generate optimal farming strategies.

[1418] The device collects data from soil and plant sensors and transmits it to a server. Specifically, it uses sensor devices such as the Adafruit STEMMA Soil Sensor and MicaSense to measure soil nutrient and moisture levels, and plant sensors to measure the growth stage and health index of plants. It also collects user emotion data through an emotion engine.

[1419] The user receives farming strategies provided by the server and performs farming according to those instructions. The user also cooperates by providing emotional data. The user checks specific instructions on irrigation, fertilization, and pest management provided by the server through the device's display or application. Furthermore, the user expresses their own emotions through the device and provides data to the emotion engine.

[1420] Specific examples include the following steps:

[1421] 1. The device uses soil sensors to measure soil nutrient and moisture levels, and crop sensors to measure crop growth stages and health indices.

[1422] 2. The server retrieves the latest weather information through the weather API.

[1423] 3. The weather, soil, and crop data acquired by the server is analyzed using AI algorithms to generate farming strategies.

[1424] 4. The server obtains the user's emotions through the emotion engine and adjusts the farming strategy.

[1425] 5. The user reviews specific instructions for irrigation, fertilization, and pest management provided by the server through the terminal.

[1426] 6. The user performs farming operations based on the provided strategy.

[1427] An example prompt is:

[1428] "This system integrates sensor data and weather information and uses AI to generate optimal farming strategies. It also uses an emotion engine to adjust strategies that take the user's emotions into account. Specifically, sensors measure the moisture and nutrient levels in the soil and obtain the latest weather information, allowing the AI ​​to suggest the optimal growing environment for crops. It also has a function that collects user emotion data and optimizes the strategies provided to the user."

[1429] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1430] Step 1: Get weather information

[1431] The server obtains the latest weather information through a weather API. Specifically, it uses services such as OpenWeatherMap and WeatherAPI, and uses an API key and location information. Location information and an API key are required as input, and weather data such as temperature, precipitation, humidity, and wind speed are obtained based on this data. The obtained weather data is then output.

[1432] Step 2: Acquiring Sensor Data

[1433] The device collects data from soil and crop sensors. For example, it uses Adafruit soil sensors to measure soil nutrient and moisture levels, and MicaSense crop sensors to measure crop growth stages and health indices. It receives real-time data from the sensors as input and periodically transmits it to a server. The output is soil and crop data.

[1434] Step 3: Sending data from the sensor

[1435] The terminal transmits the data obtained from the sensors to the server. The soil nutrient and moisture levels, crop growth stage and health index measured by the sensors are provided as inputs and transmit this data to the server. As output, the server receives these data and stores them for analysis.

[1436] Step 4: Analyze the data

[1437] The server analyzes the acquired weather information, soil data, and crop data using AI algorithms. The AI ​​algorithms use TensorFlow and PyTorch to perform analysis based on the input data. For example, if the weather information indicates dryness, the soil data determines whether irrigation is necessary, and the crop data determines whether the crop is in a growth stage. The analysis results are obtained as output.

[1438] Step 5: Generate farming strategies

[1439] The server uses the analysis results to generate a farming strategy, which includes specific instructions for irrigation, fertilization, and pest management. The analysis results are used as input, and an AI algorithm derives the optimal farming strategy. The generated farming strategy is the output.

[1440] Step 6: Obtaining emotion data

[1441] The device uses an emotion engine to recognize the user's emotions. Specifically, it uses a camera and microphone to generate emotion data from the user's voice, facial expressions, and text input. The input is the user's emotional information (voice, facial expressions, text), which is analyzed by the emotion engine. The output is the recognized emotion data.

[1442] Step 7: Adjust your strategy based on emotions

[1443] The server adjusts the farming strategy based on the acquired emotional data. For example, if the user is feeling stressed, it will adjust the frequency of irrigation or fertilization. The inputs are the emotional data and the existing farming strategy, and the server regenerates the strategy based on these. The output is the adjusted farming strategy.

[1444] Step 8: View the optimization strategy

[1445] The user views the optimized farming strategy sent from the server through the device, including specific instructions for irrigation, fertilization, and pest management. The input is the adjusted farming strategy, which is displayed on the device's display or through an application. The output is the farming strategy presented to the user in an easy-to-understand format.

[1446] Step 9: Performing farm work

[1447] The user performs farming tasks based on the provided strategy. For example, the user acts according to specific instructions such as "increase irrigation," "apply balanced fertilizer," and "conduct pest inspection." The input is the displayed farming strategy, and the user performs actual farming tasks based on this. The output is the optimized farming results.

[1448] In this way, the system allows the server, terminal, and user to each play their respective roles and work together to provide and implement optimal farming strategies that take the user's emotions into consideration.

[1449] (Application example 2)

[1450] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1451] In modern agriculture, improving productivity while reducing the burden on farmers is a key challenge. In particular, it is necessary to grasp the condition of soil and crops in real time and formulate optimal farming strategies. However, this requires the collection and analysis of a large amount of data, which places a heavy burden on farmers. Furthermore, strategies are not adjusted taking into account farmers' emotions and stress, which could increase the burden of actually implementing the farming strategies. Furthermore, there is a lack of efficient means to put the collected data and generated strategies into practice. To solve these challenges, it is necessary to integrate advanced data analysis, emotion recognition, and implementation methods.

[1452] The specific processing by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes means for analyzing agricultural data in real time using AI technology, means for acquiring the latest weather information, means for collecting soil conditions from sensors, means for monitoring the health of crops, means for generating a farming strategy for maximizing crop growth based on the analysis results and the acquired weather information, means for acquiring user emotion data and adjusting the farming strategy, means for instructing an autonomous vehicle to execute the farming strategy, and means for providing the farming strategy to the user. This makes it possible to formulate and execute an efficient and highly accurate farming strategy while reducing the burden on farmers.

[1453] "AI technology" is a technology that uses artificial intelligence to collect, analyze, predict, and optimize data.

[1454] "Real-time agricultural data analysis" is the process of instantly collecting and analyzing data such as soil and crop conditions and weather information.

[1455] "Latest weather information" refers to the latest data on current and future weather, obtained through APIs, etc.

[1456] "Soil condition" refers to the physical and chemical attributes of the soil, such as nutrient levels, moisture levels, and pH value.

[1457] A "sensor" is a device that detects physical or chemical changes and collects that information as data.

[1458] "Crop health monitoring" is the process of continuously monitoring the growth stage and health index of crops using sensors and cameras.

[1459] "Agricultural strategies" refer to specific instructions for irrigation, fertilization, pest management, etc. generated using AI technology to optimize agricultural operations.

[1460] "User emotion data" is data that indicates the user's emotional state, extracted from the user's voice, facial expression, text input, and the like.

[1461] An "autonomous vehicle" is a vehicle that operates autonomously and performs specific tasks without the need for human intervention.

[1462] "Instructions" are specific instructions or guidelines for performing a particular action or task.

[1463] A system for implementing this invention utilizes an autonomous vehicle (e.g., an agricultural tractor), a specific device, and a cloud server.

[1464] 1. Server-side processing

[1465] The server analyzes the agricultural data and generates an optimized farming strategy using the following means:

[1466] AI technology: The server uses Python-based AI frameworks (e.g., TensorFlow, Keras) to analyze collected data in real time and perform advanced predictions and optimization.

[1467] Obtaining weather information: Use a weather API (e.g., OpenWeatherMap) to collect the latest weather information using your API key and location. Specifically, obtain data such as the current temperature, precipitation, humidity, and wind speed.

[1468] Soil and crop data collection and analysis: Analyzes soil nutrient and moisture levels collected from sensors to recommend fertilization and irrigation needs, and monitors crop health and growth stages in real time to recommend appropriate measures.

[1469] Emotion engine: Using voice recognition software (e.g., Google Cloud Speech-to-Text) and facial recognition software (e.g., Amazon Rekognition), the engine obtains user emotion data, which can then detect farmers' stress and fatigue and adjust farming strategies.

[1470] 2. Terminal processing

[1471] The terminal (autonomous vehicle or other device) collects data and transmits it to the server using the following means:

[1472] Sensor data acquisition: Soil and crop sensors are used to measure soil nutrient and moisture levels, as well as crop health and growth stage. This data is sent to a server via Wi-Fi or Bluetooth.

[1473] Emotional data capture: Using a camera and microphone, the device collects user emotional data from their voice and facial expressions, using voice and facial recognition software.

[1474] 3. User-side processing

[1475] The user receives the farming strategy provided by the server and performs farming according to the instructions. The user also cooperates by providing emotion data.

[1476] View farming strategies: View specific instructions for irrigation, fertilization, pest management, etc. sent from the server through the terminal.

[1477] Providing emotional data: Expressing emotions through voice and facial expressions and providing data to the emotion engine.

[1478] Specific examples

[1479] For example, if the present invention is implemented in a farming field in Tokyo, soil and crop data will be collected through sensors mounted on tractors and drones and sent to a server. The server will then integrate the data with the latest weather information to generate an optimal farming strategy. If a farmer is feeling stressed, the emotion engine will detect this and adjust the strategy to reduce the farming burden. An example of a prompt sentence that can be generated is as follows:

[1480] "Please obtain weather data for Tokyo, analyze soil and crop data from sensors, and propose the optimal farming strategy based on the results. Also, obtain emotional data from farmers through their voices and facial expressions, and adjust the strategy to reduce the stress on farmers if they are under a high level of stress."

[1481] In this way, the system of the present invention provides optimal farming strategies in real time, reducing the burden on farmers and achieving efficient farming.

[1482] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1483] Step 1: Acquiring Sensor Data

[1484] The device uses soil and crop sensors to collect data such as soil nutrient levels, moisture levels, crop health index, and growth stage. It receives measurement data from the sensors as input and transmits the data to a server. The output is a dataset of the measurement results.

[1485] Step 2: Get weather information

[1486] The server uses a weather API to retrieve the latest weather information (current temperature, precipitation, humidity, wind speed, etc.). It uses the API key and location information as input to retrieve data from the weather API. The output is a dataset of the retrieved weather information.

[1487] Step 3: Analyze the data

[1488] The server integrates data from sensors and meteorological information and analyzes them using AI technology. It receives soil data, crop data, and meteorological data as input and combines each data into a data frame. Based on this data, the AI ​​model generates crop growth forecasts and optimal farming strategies. The output is a dataset of optimal farming strategies.

[1489] Step 4: Obtaining emotion data

[1490] The device uses a camera and microphone to collect the user's voice and facial expressions, which are then analyzed by an emotion engine. Using the collected voice and facial expression data as input, emotion analysis is performed using voice recognition software (e.g., Google Cloud Speech-to-Text) and facial recognition software (e.g., Amazon Rekognition). The output is the user's emotional data.

[1491] Step 5: Adjust your farming strategy

[1492] The server adjusts the generated farming strategy to match the user's emotional state based on the acquired emotional data. It receives the emotional data and the initial farming strategy dataset as input, and modifies the strategy to reduce the workload if the user is feeling stressed. The output is a dataset of the adjusted farming strategy.

[1493] Step 6: Prescribe and implement farming strategies

[1494] The terminal instructs the autonomous vehicle to execute the adjusted farming strategy sent from the server. It receives the adjusted farming strategy dataset as input, transfers its contents to the autonomous vehicle, and performs specific farming tasks. The output is log data of the farming tasks performed.

[1495] Step 7: Displaying farming strategies

[1496] The user checks the adjusted farming strategy sent from the server through a screen provided by the terminal. The adjusted farming strategy dataset is displayed as input, and specific instructions (e.g., irrigation, fertilization, pest management) are provided to the user. The output is the farming strategy information visually displayed to the user.

[1497] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice 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 voice data.

[1498] 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> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) 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.

[1499] In the above embodiment, an example was given in which the specific processing is performed by the data processing device 12, but the technology of the present disclosure is not limited to this, and the specific processing may be performed by the robot 414.

[1500] The emotion identification model 59 as an emotion engine may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[1501] FIG. 9 is a diagram illustrating an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and actions arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion includes both affect and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[1502] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[1503] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[1504] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is indicated, and when they approach the ideal, a state of pleasure is indicated. Emotions can also be created for robots, automobiles, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is indicated, and when they approach the ideal, a state of pleasure is indicated. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on Voice Emotion Recognition and Emotional Brain Physiological Signal Analysis Systems, Tokushima University, Doctoral Dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the "reaction" domain, where sensation is dominant. The right half of the emotion map lists emotions belonging to the "situation" domain, where situational awareness is dominant.

[1505] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[1506] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[1507] The system according to the present disclosure has been described above mainly with respect to the functions of the data processing device 12, but the system according to the present disclosure is not necessarily implemented on a server. The system according to the present disclosure may be implemented as a general information processing system. The present disclosure may be implemented, for example, as a software program running on a personal computer or an application running on a smartphone, etc. The method according to the present disclosure may be provided to users in the form of SaaS (Software as a Service).

[1508] In the above embodiment, an example was given in which the specific processing is performed by one computer 22, but the technology of the present disclosure is not limited to this, and the specific processing may be distributed and performed by a plurality of computers including the computer 22. For example, the data generation model 58 may be provided in an external device of the data processing device 12, and data may be generated in the external device in accordance with input data.

[1509] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[1510] Alternatively, the specific processing program 56 may be stored in a storage device such as a server connected to the data processing device 12 via the network 54, and the specific processing program 56 may be downloaded and installed on the computer 22 in response to a request from the data processing device 12.

[1511] It is not necessary to store all of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[1512] The hardware resource for executing a specific process can be any of the following processors: An example of a processor is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. Another example of a processor is a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[1513] The hardware resource that executes the specific processing may be configured with one of these various processors, or may be configured with a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific processing may be a single processor.

[1514] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[1515] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[1516] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[1517] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference.

[1518] The following is further disclosed regarding the above embodiment.

[1519] (Claim 1)

[1520] A means of analyzing agricultural data in real time using AI technology,

[1521] A means of obtaining the latest weather information;

[1522] means for collecting soil conditions from a sensor;

[1523] a means of monitoring the health of the crop;

[1524] means for generating a farming strategy for maximizing crop growth based on the analysis results and the acquired meteorological information;

[1525] a means for providing a farming strategy to a user;

[1526] A system including:

[1527] (Claim 2)

[1528] 2. The system of claim 1, wherein the farming strategy generator suggests irrigation, fertilization, and pest management strategies to the user.

[1529] (Claim 3)

[1530] 10. The system of claim 1, wherein the means for collecting soil conditions from sensors includes sensors for detecting soil nutrient and moisture levels.

[1531] "Example 1"

[1532] (Claim 1)

[1533] A means of analyzing agricultural data in real time using AI technology,

[1534] A means of obtaining the latest weather information;

[1535] means for collecting soil conditions from a sensor;

[1536] a means of monitoring the health of the crop;

[1537] means for generating a farming strategy for maximizing crop growth based on the analysis results and the acquired meteorological information;

[1538] a means for providing a farming strategy to a user;

[1539] means for collecting data from the soil sensor and the crop sensor via the terminal and transmitting the data to a server;

[1540] A means for carrying out farming operations based on a farming operation strategy provided by a user;

[1541] A system including:

[1542] (Claim 2)

[1543] 2. The system of claim 1, wherein the farming strategy generator suggests irrigation, fertilization, and pest management strategies to the user.

[1544] (Claim 3)

[1545] 10. The system of claim 1, wherein the means for collecting soil conditions from sensors includes sensors for detecting soil nutrient and moisture levels.

[1546] "Application Example 1"

[1547] (Claim 1)

[1548] A means of analyzing agricultural data in real time using AI technology,

[1549] A means of obtaining the latest weather information;

[1550] means for collecting soil conditions from a sensor;

[1551] a means of monitoring the health of the crop;

[1552] means for generating a farming strategy for maximizing crop growth based on the analysis results and the acquired meteorological information;

[1553] A means of analyzing environmental data and raw material quality data to generate optimal production strategies;

[1554] a means for instructing a work robot to execute the production strategy;

[1555] a means for providing a farming strategy to a user;

[1556] A system including:

[1557] (Claim 2)

[1558] 2. The system of claim 1, wherein the farming strategy generator suggests irrigation, fertilization, and pest management strategies to the user.

[1559] (Claim 3)

[1560] 10. The system of claim 1, wherein the means for collecting soil conditions from sensors includes sensors for detecting soil nutrient and moisture levels.

[1561] "Example 2: Combining Emotion Engines"

[1562] (Claim 1)

[1563] A means of analyzing agricultural data in real time using AI technology,

[1564] A means of obtaining the latest weather information;

[1565] means for collecting soil conditions from a sensor;

[1566] a means of monitoring the health of the crop;

[1567] A means for acquiring user emotion data;

[1568] means for generating a farming strategy for maximizing crop growth based on the analysis results, the acquired meteorological information, and the emotion data;

[1569] a means for providing a farming strategy to a user;

[1570] A system including:

[1571] (Claim 2)

[1572] 2. The system of claim 1, wherein the farming strategy generation means proposes irrigation, fertilization, and pest management strategies to the user, and further adjusts the strategies based on the user's emotion data.

[1573] (Claim 3)

[1574] 10. The system of claim 1, wherein the means for collecting soil conditions from sensors includes sensors for detecting soil nutrient and moisture levels.

[1575] "Application example 2 when combining emotion engines"

[1576] (Claim 1)

[1577] A means of analyzing agricultural data in real time using AI technology,

[1578] A means of obtaining the latest weather information;

[1579] means for collecting soil conditions from a sensor;

[1580] a means of monitoring the health of the crop;

[1581] means for generating a farming strategy for maximizing crop growth based on the analysis results and the acquired meteorological information;

[1582] a means for acquiring user emotion data and adjusting the farming strategy;

[1583] means for instructing and executing the farming strategy on an autonomous vehicle;

[1584] a means for providing a farming strategy to a user;

[1585] A system including:

[1586] (Claim 2)

[1587] 2. The system of claim 1, wherein the farming strategy generator suggests irrigation, fertilization, and pest management strategies to the user.

[1588] (Claim 3)

[1589] 10. The system of claim 1, wherein the means for collecting soil conditions from sensors includes sensors for detecting soil nutrient and moisture levels. [Explanation of symbols]

[1590] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot< / url:> < / url:> < / url:> < / url:>

Claims

1. A means of analyzing agricultural data in real time using AI technology, A means of obtaining the latest weather information; means for collecting soil conditions from a sensor; a means of monitoring the health of the crop; means for generating a farming strategy for maximizing crop growth based on the analysis results and the acquired meteorological information; a means for providing a farming strategy to a user; A system including:

2. 2. The system of claim 1, wherein the farming strategy generator suggests irrigation, fertilization, and pest management strategies to the user.

3. 10. The system of claim 1, wherein the means for collecting soil conditions from sensors includes sensors for detecting soil nutrient and moisture levels.

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