system

Abstract

We provide the system. [Solution] A measuring device for acquiring environmental information and an information processing device that communicates with it, A data processing device equipped with an artificial intelligence module for analyzing environmental information received from the aforementioned information processing device and generating a plant cultivation plan, A means for notifying the user of agricultural activity proposals based on the aforementioned cultivation plan, The above proposal provides means for optimizing cultivation activities in limited spaces within urban environments, A system that includes this.

Description

Technical Field

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

Background Art

[0002] Patent Document 1 discloses a method for controlling a persona chatbot, which is performed by at least one processor and includes steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of a character of the chatbot, 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

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the agricultural field, manually performing appropriate cultivation management according to environmental conditions requires experience and knowledge and is a heavy burden. Therefore, in order for small and medium-sized agricultural operators to increase high yields efficiently and sustainably, a mechanism for supporting precise cultivation management adapted to the environment is necessary.

Means for Solving the Problems

[0005] The present invention uses a server equipped with a sensor for acquiring environmental data and an artificial intelligence module for analyzing the data to generate a cultivation plan for agricultural crops. By using this system to notify the user of specific agricultural work proposals based on this plan, an optimized cultivation process is realized, and the efficiency and profitability of agriculture are improved.

[0006] "Environmental data" refers to information related to crop cultivation, such as temperature, humidity, and soil conditions.

[0007] A "sensor" refers to an electronic device used to acquire environmental data in real time.

[0008] A "terminal" refers to a device equipped with communication capabilities for transmitting data acquired from sensors to a server.

[0009] An "artificial intelligence module" refers to a program that analyzes input data and makes predictions and decisions based on specific objectives.

[0010] A "server" refers to a computer system that generates cultivation plans based on the results analyzed by an artificial intelligence module and provides information to users.

[0011] A "cultivation plan" refers to a plan created to optimize crop growth based on environmental data.

[0012] "Agricultural work suggestions" refer to instructions that specify the agricultural tasks to be carried out according to the cultivation plan and the timing of those tasks.

[0013] "User" refers to agricultural workers who use this system to cultivate crops. [Brief explanation of the drawing]

[0014] [Figure 1] This is a conceptual diagram showing an example of the configuration of a data processing system according to the first embodiment. [Figure 2] This is a conceptual diagram showing an example of the essential functions of a data processing device and a smart device according to the first embodiment. [Figure 3] This is a conceptual diagram showing an example of the configuration of a data processing system according to the second embodiment. [Figure 4] This is a conceptual diagram showing an example of the main functions of a data processing device and smart glasses according to the second embodiment. [Figure 5] It is a conceptual diagram showing an example of the configuration of a data processing system according to the third embodiment. [Figure 6] It is a conceptual diagram showing an example of the main functions of a data processing device and a headset-type terminal according to the third embodiment. [Figure 7] It is a conceptual diagram showing an example of the configuration of a data processing system according to the fourth embodiment. [Figure 8] It is a conceptual diagram showing an example of the main functions of a data processing device and a robot according to the fourth embodiment. [Figure 9] It shows an emotion map to which a plurality of emotions are mapped. [Figure 10] It shows an emotion map to which a plurality of emotions are mapped. [Figure 11] It is a sequence diagram showing the processing flow of the data processing system in Example 1. [Figure 12] It is a sequence diagram showing the processing flow of the data processing system in Application Example 1. [Figure 13] It is a sequence diagram showing the processing flow of the data processing system in Example 2 when an emotion engine is combined. [Figure 14] It is a sequence diagram showing the processing flow of the data processing system in Application Example 2 when an emotion engine is combined.

Mode for Carrying Out the Invention

[0015] Hereinafter, an example of an embodiment of a system according to the technology of the present disclosure will be described according to the accompanying drawings.

[0016] First, the language used in the following description will be explained.

[0017] In the following embodiments, the signed processor (hereinafter simply referred to as "processor") may be a single arithmetic unit or a combination of multiple arithmetic units. Furthermore, the processor may be a single type of arithmetic unit or a combination of multiple types of arithmetic units. Examples of arithmetic units include CPU (Central Processing Unit), GPU (Graphics Processing Unit), GPGPU (General-Purpose computing on Graphics Processing Units), and APU (Accelerated Processing Unit).

[0018] In the following embodiments, signed RAM (Random Access Memory) is a memory that temporarily stores information and is used as work memory by the processor.

[0019] In the following embodiments, the signed storage is one or more non-volatile storage devices that store various programs and various parameters. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), or magnetic tapes.

[0020] In the following embodiments, the signed communication interface (I / F) is an interface that includes a communication processor and an antenna, etc. The communication interface manages communication between multiple computers. Examples of communication standards applicable to the communication interface include wireless communication standards such as 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark).

[0021] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." That is, "A and / or B" means that it may be A alone, or B alone, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" applies when expressing three or more things linked by "and / or."

[0022] [First Embodiment]

[0023] Figure 1 shows an example of the configuration of the data processing system 10 according to the first embodiment.

[0024] As shown in Figure 1, the 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.

[0025] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0026] The smart device 14 comprises a computer 36, a reception device 38, an output device 40, a camera 42, and a communication interface 44. The computer 36 comprises a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The reception device 38, output device 40, and camera 42 are also connected to the bus 52.

[0027] The reception device 38 is equipped with a touch panel 38A and a microphone 38B, etc., and receives user input. The touch panel 38A receives user input by detecting contact with an object (e.g., a pen or finger). The microphone 38B receives user input by detecting the user's voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and 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.

[0028] 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 perceptible to the user 20 (e.g., audio and / or text). The display 40A displays visible information such as text and images according to instructions from the processor 46. The speaker 40B outputs audio according to instructions from the processor 46. The camera 42 is a small digital camera equipped with an optical system such as a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0029] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various types of information between processor 46 and processor 28 via network 54.

[0030] Figure 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0031] As shown in Figure 2, in the data processing device 12, a specific processing 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" related to the technology of this 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 according to the specific processing program 56 executed on the RAM 30.

[0032] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0033] In the smart device 14, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The reception output program 60 is used in conjunction with a 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 processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0034] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the smart device 14 as the "terminal".

[0035] This invention is a system for efficiently managing crop cultivation and consists of the following elements. First, a terminal collects environmental data from sensors placed in the farmland. This includes temperature, humidity, soil moisture content, etc. The terminal transmits this data in real time to a server in the cloud.

[0036] The server uses an artificial intelligence module to analyze the received environmental data. This AI module, based on machine learning algorithms, compares historical data with real-time data to generate an optimal cultivation plan. This plan includes the timing and amount of watering and fertilization, as well as the timing of other farming tasks.

[0037] The generated cultivation plan is sent from the server to the terminal. Based on this plan, the user plans specific farming tasks. For example, they can check the amount of water and fertilizer to be given on a particular day, or measures to take when the temperature changes suddenly, and then reflect these in their actual work.

[0038] As a concrete example, consider a case where a tomato farmer uses this system. The terminal detects low soil moisture through sensors. When the server receives this data, the artificial intelligence module compares it to past climate patterns and predicts the likelihood of continued drought. Therefore, the server sends a notification to the user suggesting more frequent watering. Based on this notification, the user waters at the appropriate time, promoting healthy tomato growth.

[0039] Thus, the present invention is a system that supports the crop cultivation process through accurate analysis based on environmental data and the proposal of cultivation plans based on that analysis, thereby improving the efficiency and profitability of agriculture.

[0040] The following describes the processing flow.

[0041] Step 1:

[0042] The device collects environmental data such as temperature, humidity, and soil moisture from sensors placed in the farmland. This data is updated in real time.

[0043] Step 2:

[0044] The device sends the collected environmental data to a server in the cloud. A secure communication protocol is used for transmission.

[0045] Step 3:

[0046] The server stores the received environmental data in a database and prepares it for processing for analysis.

[0047] Step 4:

[0048] The server uses an artificial intelligence module to analyze the received data. This includes comparing it with past weather data and analyzing trends using machine learning algorithms.

[0049] Step 5:

[0050] The server generates a cultivation plan suitable for the crops based on the results of data analysis. This includes timing for watering and fertilizing, as well as preventative measures against pests and diseases.

[0051] Step 6:

[0052] The server generates a cultivation plan and notifies the user's terminal. The notification is sent in real time, making it immediately available for the user to check.

[0053] Step 7:

[0054] The system reviews the suggestions received from the user's device and plans farm work according to the cultivation plan. If necessary, it creates a work schedule.

[0055] Step 8:

[0056] The system executes farm work exactly as planned by the user. This enables efficient cultivation that is suited to the environment.

[0057] (Example 1)

[0058] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server," and the smart device 14 will be referred to as the "terminal."

[0059] Cultivating crops requires proper management of weather conditions and soil conditions, but conventional methods make it difficult to grasp real-time environmental information and efficiently formulate growth plans. Furthermore, if users are not provided with timely data transmission and accurate farming instructions based on analysis results, proper cultivation management may not be carried out, affecting crop yield and quality. A system is needed to solve these problems and improve the efficiency and feasibility of agriculture.

[0060] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0061] In this invention, the server includes means for an intelligent module for analyzing environmental information and generating a growth plan, means for referring to and comparing past weather information based on environmental information received from an information device, and means for transmitting data using wireless communication technology. This makes it possible to efficiently collect environmental information in real time and quickly provide users with optimal farming instructions based on the analysis results.

[0062] "Environmental information" refers to data that includes temperature, humidity, soil conditions, and other factors that affect crop cultivation.

[0063] A "measuring device" is a device installed in agricultural land that is equipped with sensors to acquire environmental information.

[0064] An "information device" is a terminal equipped with communication capabilities for processing environmental information received from a measuring device and transmitting it to a server.

[0065] An "intelligent module" is a program that runs on a computing device, analyzes environmental information using machine learning algorithms, and generates a growth plan.

[0066] A "computational device" is a computer system equipped with an intelligent module that formulates growth plans based on data received from an information device.

[0067] "Wireless communication technology" refers to technologies used to transmit data from information devices to computing devices without using cables, and includes Wi-Fi and LoRa.

[0068] A "growing plan" is a set of schedules and instructions that provide optimal guidelines for crop cultivation, generated by analyzing environmental information.

[0069] A "user" is a person who receives a growth plan sent from a computing device and then carries out the actual farming work.

[0070] This invention is a system for efficiently managing crop cultivation. This system primarily consists of the interaction between terminals, a server, and users.

[0071] The terminal collects environmental information in real time from measuring devices placed in the farmland. These measuring devices include sensors for measuring temperature and humidity, as well as sensors for measuring soil moisture. This information is transmitted to a server in the cloud using wireless communication technologies such as Wi-Fi and LoRa.

[0072] The server uses an intelligent module to analyze the received environmental information. This intelligent module uses machine learning algorithms (including, for example, those using TENSORFLOW®) to compare historical weather information with real-time environmental information and generate an optimal growth plan. The growth plan includes timing for watering and fertilizing, as well as instructions for farm work.

[0073] Users plan specific farming tasks based on growth plans notified by the server. They then carry out appropriate tasks based on these plans to promote healthy crop growth. For example, a tomato producer can improve tomato yield and quality by providing the correct amount of water and fertilizer at the right time, based on notifications from the system.

[0074] As a concrete example, here is an example of a prompt message:

[0075] "Based on historical and current environmental data, please generate the optimal watering schedule for the tomato farm."

[0076] This system aims to improve agricultural efficiency and profitability through real-time data collection and analysis, thereby contributing to the realization of sustainable agriculture.

[0077] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0078] Step 1:

[0079] The terminal collects environmental information from measuring devices installed in the farmland. This includes temperature, humidity, and soil moisture, and involves a process of converting analog data from the measuring devices into digital data. The input is raw data from each sensor, and the output is structured digital environmental information. This allows for an understanding of the current conditions of the farmland. Specifically, the sensor measures soil moisture every 10 minutes, and the terminal converts this into a digital signal.

[0080] Step 2:

[0081] The terminal transmits the collected environmental information to the server. The input is the digital environmental information acquired in step 1, and the output is a transmission completion signal to the server. Wireless communication technology (e.g., Wi-Fi) is used to transmit accurate information quickly in real time. Specifically, the terminal converts the collected environmental information into packet format and transmits it sequentially to the server.

[0082] Step 3:

[0083] The server analyzes the received environmental information. First, it compares the environmental information received as input with past weather information to verify data consistency. Next, it performs data analysis using a generative AI model and generates an optimal growth plan as output. A SQL database is used to store the data, and machine learning algorithms are applied to the analysis. Specifically, TensorFlow is used to learn the correlation between environmental conditions and growth patterns, and the optimal timing for watering and fertilizing is suggested.

[0084] Step 4:

[0085] The server notifies the terminal of the generated growth plan. The input is the growth plan created in step 3, and the output is a push notification to the terminal. The server generates notifications sequentially and sends the data to the terminal quickly. Specifically, the server converts the plan into text and sends the notification via the protocol.

[0086] Step 5:

[0087] The user receives notifications from their device and plans specific farming tasks. The input is the growth plan notified to the device, and the output is the details of the planned farming tasks. Based on the notifications, the user can also perform watering and fertilizing at the appropriate times and input this into a task management app. As a specific action, the user waters the tomato rows according to the schedule provided.

[0088] Through these steps, the system provides an appropriate growth plan based on real-time environmental information to help crops grow healthily.

[0089] (Application Example 1)

[0090] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart device 14 will be referred to as the "terminal."

[0091] In modern urban environments, cultivating plants in limited spaces presents numerous challenges. In particular, optimizing cultivation plans based on appropriate environmental information and efficiently utilizing resources in limited spaces are significant issues. Furthermore, responding quickly to climate change and urban-specific weather conditions is difficult.

[0092] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0093] In this invention, the server includes an information processing device that communicates with a measuring device for acquiring environmental information, a data processing device equipped with an artificial intelligence module for analyzing the environmental information received from the information processing device and generating a plant cultivation plan, means for notifying the user of suggestions for agricultural activities based on the cultivation plan, and means for optimizing the suggestions for cultivation activities in a limited space in an urban environment. This makes efficient and effective plant cultivation possible even in urban environments.

[0094] "Environmental information" refers to data on external conditions that affect plant cultivation, and specifically includes weather data, humidity, and soil conditions.

[0095] A "measuring device" is a device used to acquire environmental information, and specifically includes a sensor that measures temperature, humidity, soil moisture content, and other similar parameters.

[0096] An "information processing device" is a terminal that receives data from a measuring device and sends it to a server for analysis.

[0097] The "artificial intelligence module" is a program equipped with data analysis algorithms that uses received environmental information to predict plant growth and generate an optimal cultivation plan.

[0098] A "data processing device" is a server device equipped with an artificial intelligence module that analyzes and processes data related to plant cultivation.

[0099] A "suggestion" is specific instructions for agricultural activities that are sent to the user based on the generated cultivation plan.

[0100] "Limited space in urban environments" refers to limited spaces that can be used as agricultural land, such as rooftops and balconies of buildings located in urban areas.

[0101] "Means for optimization" refers to technologies that adjust proposals based on environmental information and effectively utilize resources in order to efficiently carry out cultivation activities in urban environments.

[0102] This invention is a system for efficiently and optimally cultivating plants in urban environments. This system is implemented by combining multiple hardware and software components.

[0103] Sensors and other measuring devices acquire environmental information such as temperature, humidity, and soil moisture. This information is transmitted to an information processing device. The information processing device, specifically a smartphone or tablet, then transmits this environmental information in real time to a data processing device in the cloud, such as a server on AWS® or Google® Cloud.

[0104] The server analyzes this data using an artificial intelligence module. The AI ​​module used is equipped with data analysis algorithms such as TensorFlow, and compares historical weather data with current environmental information to generate an optimal cultivation plan. This plan includes the timing and amount of watering and fertilization, as well as the timing of agricultural activities, taking into account urban-specific constraints.

[0105] Users receive the generated suggestions through a smartphone application. The app notifies users of specific cultivation suggestions tailored to their urban environment, and users then carry out appropriate agricultural activities based on these suggestions.

[0106] For example, if a resident of an apartment building is growing basil on their balcony, the app will detect changes in humidity and temperature and suggest adjusting the watering frequency as needed. This promotes healthy growth even in limited spaces.

[0107] As an example of a prompt, a user can input a message like, "Please tell me the optimal watering schedule considering this week's weather data," into the interface to receive specific suggestions.

[0108] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0109] Step 1:

[0110] The sensor acquires environmental information.

[0111] As input, data such as temperature, humidity, and soil moisture content are measured by sensors. The sensors transmit this data to an information processing device. The output is a data packet of environmental information obtained in real time.

[0112] Step 2:

[0113] The device sends environmental information to a server in the cloud.

[0114] The terminal takes environmental information received from sensors as input and transmits the data to a cloud server via an internet connection. The output is a record of the environmental information stored in a database on the cloud.

[0115] Step 3:

[0116] The server analyzes environmental information and generates the optimal cultivation plan.

[0117] The server takes environmental information stored in the cloud as input and performs data analysis by comparing it with historical data using generative AI models such as TensorFlow. As part of the data processing, it analyzes trends in temperature changes and humidity, and generates a cultivation plan as output, which includes optimal watering and fertilization schedules.

[0118] Step 4:

[0119] The server generates a cultivation plan and sends it to the user's terminal.

[0120] The server uses the generated cultivation plan as input and notifies the user's terminal. The output is specific agricultural activity suggestions displayed to the user through the application.

[0121] Step 5:

[0122] Users carry out agricultural activities based on their suggestions.

[0123] The user uses suggestions received through the application as input to plan actual farming activities. For example, they might water more than usual on one day and fertilize the next. The output is the agricultural activities performed and the resulting state of plant growth.

[0124] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0125] This invention provides a system that takes user emotions into account to support crop cultivation management, and by incorporating emotion recognition capabilities, it offers more personalized farm work suggestions. The system consists of sensors that collect environmental data, a terminal that communicates with sensors, and a server equipped with an emotion engine that recognizes user emotions.

[0126] First, the device collects environmental data such as temperature, humidity, and soil condition from sensors and sends it to the server. The server analyzes the received data using an artificial intelligence module and generates an optimal cultivation plan. The emotion engine is particularly important here. The emotion engine analyzes the user's voice tone and facial expression data to recognize their current emotional state. This emotional data is used to determine stress levels and emotional stability.

[0127] Unlike suggestions based solely on normal environmental data, the generated cultivation plan is adjusted according to the user's emotional state. For example, if the server determines that the user is stressed, it can suggest ways to reduce the workload. Specifically, it might suggest breaking down farm work into smaller tasks or prioritizing tasks that cause less psychological stress.

[0128] Users receive notifications from the server via their devices and perform farming tasks based on the suggestions. For example, if the server detects that the user is stressed and lacking mental and physical energy, it automatically reminds them when watering is needed and makes new suggestions when they feel more emotionally at ease.

[0129] Through these emotion-based suggestion features, this system not only optimizes the growing environment but also supports the efficiency of farm work while taking into account the user's own emotional state. This can contribute to the realization of sustainable agricultural management.

[0130] The following describes the processing flow.

[0131] Step 1:

[0132] The terminal collects environmental data such as temperature, humidity, and soil condition from sensors placed in the farmland. The collected data is transmitted to the server in real time.

[0133] Step 2:

[0134] The server stores the received environmental data in a database. Next, an artificial intelligence module is used to analyze this data and generate the cultivation plan best suited to the current situation.

[0135] Step 3:

[0136] The device collects voice tone and facial expressions from the user to obtain emotional data. This emotional data is used to assess the user's stress level and emotional stability.

[0137] Step 4:

[0138] The emotion engine built into the server analyzes the emotion data received from the terminal. Based on the analysis results, it determines the user's emotional state.

[0139] Step 5:

[0140] The server adjusts the cultivation plan it generates based on the user's emotional state. For example, if the server determines that the user is stressed, it modifies the cultivation plan to reduce the workload.

[0141] Step 6:

[0142] The server notifies the user's terminal of the adjusted cultivation plan and suggestions. This allows the user to obtain the most appropriate course of action at that time.

[0143] Step 7:

[0144] Users receive suggestions from the server and plan and carry out actual farm work. Users follow emotionally sensitive suggestions and work at a manageable pace.

[0145] Step 8:

[0146] The user feeds back the results of their farming activities and new environmental data from their terminal to the server. The server uses this information to create the next cultivation plan and to evaluate user sentiment.

[0147] (Example 2)

[0148] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server," and the smart device 14 will be referred to as the "terminal."

[0149] Conventional plant cultivation systems only provide general work recommendations based on environmental information, making it difficult to provide individualized support that takes into account the influence of the user's emotional state. Therefore, there is a need to combine both environmental and emotional information to provide more effective cultivation plans and work recommendations that are tailored to the user.

[0150] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0151] In this invention, the server includes means for analyzing environmental information to generate a cultivation plan, means for collecting emotions and adjusting the cultivation plan based on the user's emotional state, and means for notifying the user of work recommendations. This enables personalized responses that take into account the user's emotional state.

[0152] "Environmental information" refers to information that includes data such as temperature, humidity, and soil conditions that affect plant growth.

[0153] A "measuring device" refers to a device used to acquire environmental information, and includes electronic devices such as sensors.

[0154] A "mobile terminal" is a portable computing device that communicates with measuring devices to send and receive information.

[0155] An "automated learning model" is an artificial intelligence technology that performs analysis and predictions based on data to generate an optimal cultivation plan.

[0156] A "computer" refers to a computer system used for information processing and analysis.

[0157] "Recommendations for work" are instructions or advice to suggest the best course of action for the user regarding plant cultivation.

[0158] "Emotional information" refers to information that includes data about the user's psychological state obtained from their voice and facial expressions.

[0159] "Emotional state" refers to the user's psychological and emotional state, including stress levels and emotional stability.

[0160] To implement this invention, the following equipment and software are required.

[0161] Hardware and software

[0162] The terminal uses various measuring devices (e.g., temperature and humidity sensors and soil moisture sensors) to collect environmental information. The terminal may be configured on a computing platform such as Arduino or Raspberry Pi. This allows data to be acquired in real time and transmitted to a server via communication means.

[0163] The server uses general-purpose computers or cloud servers, which are typically used in data centers, as computing resources. Automated learning models such as TensorFlow and PyTorch are installed on the server, which analyze environmental information and generate optimal plant cultivation plans. Furthermore, emotion analysis software such as Amazon Rekognition and OpenFace is used to process emotional information. This allows the recommended cultivation plan to be adjusted based on the user's emotional state.

[0164] Specific example

[0165] As a concrete example, suppose a user is cultivating plants on a farm. The terminal collects environmental information such as temperature (25°C), humidity (60%), and soil moisture (40%) from a measuring device and sends it to the server. The server analyzes this information and provides a detailed watering plan that incorporates next week's weather forecast. Furthermore, if the user's voice tone indicates fatigue, additional suggestions to reduce the workload, such as encouraging rest on the weekend, will be added.

[0166] Examples of prompts for generative AI models

[0167] "Based on user emotional states and environmental data, customize farm work plans and generate suggestions."

[0168] By using this embodiment of the invention, effective plant cultivation becomes possible, taking into account environmental information and the user's emotional state, thereby improving work efficiency and reducing the psychological burden on the user.

[0169] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0170] Step 1:

[0171] The terminal collects environmental information from measuring devices. Specifically, the terminal acquires data from temperature and humidity sensors and soil moisture sensors. The input is an analog signal obtained from the sensors, which is converted into digital data within the terminal. This data is output in the form of temperature, humidity, soil moisture content, etc., and is ready to be sent to the server.

[0172] Step 2:

[0173] The server analyzes environmental information received from the terminal. Specifically, the server inputs this data into an AI model and processes it to predict weather patterns and evaluate the need for watering. As a result of the analysis, output data is obtained that represents an optimal cultivation plan. This output indicates the appropriate cultivation conditions for each type of plant.

[0174] Step 3:

[0175] The server collects and analyzes the user's emotional information. Input is obtained through voice messages and facial expression data provided by the user via a smartphone app. The server processes this emotional information using emotion analysis software to evaluate stress levels and emotional balance. The analysis results are output as a quantified emotional state, which is then used in the next step.

[0176] Step 4:

[0177] The server integrates environmental and emotional information to generate a customized cultivation plan. This is an optimization process using a generative AI model, which takes the previously obtained environmental analysis results and emotional state as input. As a result of processing this integrated information, specific work recommendations for the user are output.

[0178] Step 5:

[0179] The device notifies the user of recommended tasks received from the server. Specifically, this information is presented via push notifications through a smartphone app. The notification content includes specific action plans such as "Start watering at 2 PM" or "Add fertilizer to the soil." By following these instructions, the user can achieve optimal plant cultivation.

[0180] (Application Example 2)

[0181] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as a "server" and the smart device 14 as a "terminal".

[0182] In recent years, the agricultural sector has seen a growing demand for increased productivity while simultaneously reducing the psychological and physical burden on workers. However, conventional cultivation management systems have been limited to suggestions based on crop growth environments and have not taken into account the emotional state of users. As a result, farmers experience stress, leading to decreased productivity and work efficiency.

[0183] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0184] In this invention, the server includes a detection device for acquiring environmental information, a device for communicating with the detection device, an intelligent module for analyzing the environmental information received from the device and generating a cultivation plan, an emotion recognition means for recognizing the user's emotional state, and a means for notifying the user of optimized farm work suggestions according to the user's emotional state. This enables a flexible farm work schedule tailored to the individual emotional state of the user, thereby reducing psychological burden and improving work efficiency.

[0185] "Environmental information" refers to data related to the cultivation environment, such as temperature, humidity, and soil characteristics.

[0186] A "detection device" refers to a device such as a sensor used to acquire environmental information from the cultivation environment of agricultural products.

[0187] A "communicating device" refers to a terminal that receives environmental information obtained from a detection device and transfers it to the appropriate computing device.

[0188] An "intelligent module" refers to a system element that includes algorithms and computer programs for generating cultivation plans based on received environmental information.

[0189] "Emotion identification means" refers to technologies and programs that analyze a user's voice and facial expressions to identify their current emotional state.

[0190] "Notification means" refers to electronic devices or interfaces used to present users with optimized farming suggestions.

[0191] A system for carrying out the present invention first requires a detection device equipped with sensors for acquiring environmental information. This device detects environmental information such as temperature, humidity, and soil characteristics in real time and transmits the data to a communication device. The communication device is responsible for transferring the environmental information received from the detection device to an intelligent module on a server.

[0192] Upon receiving this data, the server uses a learning algorithm within its intelligent module to generate an optimal cultivation plan. In this process, the server utilizes emotion recognition technology to recognize the user's emotional state. This technology leverages the camera and microphone of a smartphone or tablet device to analyze the user's voice and facial expressions, and the resulting data is transmitted to the server.

[0193] Based on the user's emotional state, the server optimizes farm work suggestions and delivers them to the user through notification channels. These notifications are sent via a smartphone application, allowing the user to view the suggested work plan in real time. For example, if a user is inspecting their field with their smartphone in the morning, the app might send a specific suggestion such as, "Your stress level is high today. Take it easy this afternoon and prioritize lighter tasks."

[0194] This system manages data exchange using data platforms such as Firebase and leverages cloud-based artificial intelligence services such as Google Cloud AI to analyze emotional states and generate cultivation plans.

[0195] A concrete example of a prompt message would be an instruction such as, "Assess the user's emotional state today and propose a farming plan tailored to their stress level." In this way, agricultural support that flexibly responds to the user's emotions is realized.

[0196] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0197] Step 1:

[0198] The terminal collects environmental information such as temperature, humidity, and soil characteristics from the detection device. The input environmental information data is formatted and then transferred to the server.

[0199] Step 2:

[0200] The server passes the environmental information received from the terminal to the intelligent module, which then begins analyzing it as input data. The intelligent module's learning algorithm analyzes this data and generates an optimal cultivation plan for the crops. This plan is then output.

[0201] Step 3:

[0202] The device captures the user's voice and facial expressions using its camera and microphone and transmits them to an emotion recognition system. The input user emotion data is transferred to a server. The server analyzes this data using the emotion recognition system to identify the user's current emotional state.

[0203] Step 4:

[0204] The server integrates and optimizes the cultivation plan based on the user's emotional state and environmental information. If the emotional state is high due to stress, instructions will be issued to reduce the work schedule.

[0205] Step 5:

[0206] The server sends optimized farm work suggestions to the terminal via a notification system. The terminal displays these suggestions on the user's smartphone app, notifying the user visually and audibly. The user can then review these notifications and perform tasks according to the optimized plan in real time.

[0207] 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 user input for the result of the specific processing. The control unit 46A transmits the audio data indicating 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.

[0208] Data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> ), Gemini (registered trademark) (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0209] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the smart device 14.

[0210] [Second Embodiment]

[0211] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0212] As shown in Figure 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0213] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0214] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

[0215] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0216] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0217] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0218] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0219] The specific processing program 56 is an example of a "program" relating to the technology of this 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.

[0220] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0221] In the smart glasses 214, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. 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 processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0222] Next, the identification processing performed by the identification processing unit 290 of the data processing device 12 will be described. 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".

[0223] This invention is a system for efficiently managing crop cultivation and consists of the following elements. First, a terminal collects environmental data from sensors placed in the farmland. This includes temperature, humidity, soil moisture content, etc. The terminal transmits this data in real time to a server in the cloud.

[0224] The server uses an artificial intelligence module to analyze the received environmental data. This AI module, based on machine learning algorithms, compares historical data with real-time data to generate an optimal cultivation plan. This plan includes the timing and amount of watering and fertilization, as well as the timing of other farming tasks.

[0225] The generated cultivation plan is sent from the server to the terminal. Based on this plan, the user plans specific farming tasks. For example, they can check the amount of water and fertilizer to be given on a particular day, or measures to take when the temperature changes suddenly, and then reflect these in their actual work.

[0226] As a concrete example, consider a case where a tomato farmer uses this system. The terminal detects low soil moisture through sensors. When the server receives this data, the artificial intelligence module compares it to past climate patterns and predicts the likelihood of continued drought. Therefore, the server sends a notification to the user suggesting more frequent watering. Based on this notification, the user waters at the appropriate time, promoting healthy tomato growth.

[0227] Thus, the present invention is a system that supports the crop cultivation process through accurate analysis based on environmental data and the proposal of cultivation plans based on that analysis, thereby improving the efficiency and profitability of agriculture.

[0228] The following describes the processing flow.

[0229] Step 1:

[0230] The device collects environmental data such as temperature, humidity, and soil moisture from sensors placed in the farmland. This data is updated in real time.

[0231] Step 2:

[0232] The device sends the collected environmental data to a server in the cloud. A secure communication protocol is used for transmission.

[0233] Step 3:

[0234] The server stores the received environmental data in a database and prepares it for processing for analysis.

[0235] Step 4:

[0236] The server uses an artificial intelligence module to analyze the received data. This includes comparing it with past weather data and analyzing trends using machine learning algorithms.

[0237] Step 5:

[0238] The server generates a cultivation plan suitable for the crops based on the results of data analysis. This includes timing for watering and fertilizing, as well as preventative measures against pests and diseases.

[0239] Step 6:

[0240] The server generates a cultivation plan and notifies the user's terminal. The notification is sent in real time, making it immediately available for the user to check.

[0241] Step 7:

[0242] The system reviews the suggestions received from the user's device and plans farm work according to the cultivation plan. If necessary, it creates a work schedule.

[0243] Step 8:

[0244] The system executes farm work exactly as planned by the user. This enables efficient cultivation that is suited to the environment.

[0245] (Example 1)

[0246] Next, we will describe Example 1. 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."

[0247] Cultivating crops requires proper management of weather conditions and soil conditions, but conventional methods make it difficult to grasp real-time environmental information and efficiently formulate growth plans. Furthermore, if users are not provided with timely data transmission and accurate farming instructions based on analysis results, proper cultivation management may not be carried out, affecting crop yield and quality. A system is needed to solve these problems and improve the efficiency and feasibility of agriculture.

[0248] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0249] In this invention, the server includes means for an intelligent module for analyzing environmental information and generating a growth plan, means for referring to and comparing past weather information based on environmental information received from an information device, and means for transmitting data using wireless communication technology. This makes it possible to efficiently collect environmental information in real time and quickly provide users with optimal farming instructions based on the analysis results.

[0250] "Environmental information" refers to data that includes temperature, humidity, soil conditions, and other factors that affect crop cultivation.

[0251] A "measuring device" is a device installed in agricultural land that is equipped with sensors to acquire environmental information.

[0252] An "information device" is a terminal equipped with communication capabilities for processing environmental information received from a measuring device and transmitting it to a server.

[0253] An "intelligent module" is a program that runs on a computing device, analyzes environmental information using machine learning algorithms, and generates a growth plan.

[0254] A "computational device" is a computer system equipped with an intelligent module that formulates growth plans based on data received from an information device.

[0255] "Wireless communication technology" refers to technologies used to transmit data from information devices to computing devices without using cables, and includes Wi-Fi and LoRa.

[0256] A "growing plan" is a set of schedules and instructions that provide optimal guidelines for crop cultivation, generated by analyzing environmental information.

[0257] A "user" is a person who receives a growth plan sent from a computing device and then carries out the actual farming work.

[0258] This invention is a system for efficiently managing crop cultivation. This system primarily consists of the interaction between terminals, a server, and users.

[0259] The terminal collects environmental information in real time from measuring devices placed in the farmland. These measuring devices include sensors for measuring temperature and humidity, as well as sensors for measuring soil moisture. This information is transmitted to a server in the cloud using wireless communication technologies such as Wi-Fi and LoRa.

[0260] The server uses an intelligent module to analyze the received environmental information. This intelligent module uses machine learning algorithms (including, for example, those using TensorFlow) to compare historical weather information with real-time environmental information and generate an optimal growth plan. The growth plan includes timing for watering and fertilizing, as well as instructions for farm work.

[0261] Users plan specific farming tasks based on growth plans notified by the server. They then carry out appropriate tasks based on these plans to promote healthy crop growth. For example, a tomato producer can improve tomato yield and quality by providing the correct amount of water and fertilizer at the right time, based on notifications from the system.

[0262] As a concrete example, here is an example of a prompt message:

[0263] "Based on historical and current environmental data, please generate the optimal watering schedule for the tomato farm."

[0264] This system aims to improve agricultural efficiency and profitability through real-time data collection and analysis, thereby contributing to the realization of sustainable agriculture.

[0265] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0266] Step 1:

[0267] The terminal collects environmental information from measuring devices installed in the farmland. This includes temperature, humidity, and soil moisture, and involves a process of converting analog data from the measuring devices into digital data. The input is raw data from each sensor, and the output is structured digital environmental information. This allows for an understanding of the current conditions of the farmland. Specifically, the sensor measures soil moisture every 10 minutes, and the terminal converts this into a digital signal.

[0268] Step 2:

[0269] The terminal transmits the collected environmental information to the server. The input is the digital environmental information acquired in step 1, and the output is a transmission completion signal to the server. Wireless communication technology (e.g., Wi-Fi) is used to transmit accurate information quickly in real time. Specifically, the terminal converts the collected environmental information into packet format and transmits it sequentially to the server.

[0270] Step 3:

[0271] The server analyzes the received environmental information. First, it compares the environmental information received as input with past weather information to verify data consistency. Next, it performs data analysis using a generative AI model and generates an optimal growth plan as output. A SQL database is used to store the data, and machine learning algorithms are applied to the analysis. Specifically, TensorFlow is used to learn the correlation between environmental conditions and growth patterns, and the optimal timing for watering and fertilizing is suggested.

[0272] Step 4:

[0273] The server notifies the terminal of the generated growth plan. The input is the growth plan created in step 3, and the output is a push notification to the terminal. The server generates notifications sequentially and sends the data to the terminal quickly. Specifically, the server converts the plan into text and sends the notification via the protocol.

[0274] Step 5:

[0275] The user receives notifications from their device and plans specific farming tasks. The input is the growth plan notified to the device, and the output is the details of the planned farming tasks. Based on the notifications, the user can also perform watering and fertilizing at the appropriate times and input this into a task management app. As a specific action, the user waters the tomato rows according to the schedule provided.

[0276] Through these steps, the system provides an appropriate growth plan based on real-time environmental information to help crops grow healthily.

[0277] (Application Example 1)

[0278] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal."

[0279] In a modern urban environment, there are various challenges when cultivating plants using limited space. In particular, optimizing cultivation plans based on appropriate environmental information and efficient resource utilization in limited space are issues. Additionally, it is difficult to quickly respond to climate change and urban-specific weather conditions.

[0280] The specific processing by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0281] In this invention, the server includes an information processing device that communicates with a measuring device for acquiring environmental information, a data processing device equipped with an artificial intelligence module for analyzing the environmental information received from the information processing device to generate a plant cultivation plan, means for notifying a user of a proposal for agricultural activities based on the cultivation plan, and means for optimizing the proposal for cultivation activities in limited space in an urban environment. Thereby, efficient and effective plant cultivation is possible even in an urban environment.

[0282] "Environmental information" is data on external conditions that affect plant cultivation, specifically including meteorological data, humidity, and soil conditions.

[0283] "Measuring device" is a device for acquiring environmental information, specifically a device including sensors for measuring temperature, humidity, soil moisture content, etc.

[0284] "Information processing device" is a terminal that receives data from a measuring device and transmits it to a server for analysis.

[0285] "Artificial intelligence module" is a program equipped with a data analysis algorithm that uses the received environmental information to predict plant growth and generate an optimal cultivation plan.

[0286] The "data processing device" is a server device equipped with an artificial intelligence module and analyzes and processes data related to plant cultivation.

[0287] The "proposal" is specific agricultural activity instruction information notified to the user based on the generated cultivation plan.

[0288] The "limited space in the urban environment" refers to limited spaces such as rooftops and balconies of buildings located in urban areas that can be used as agricultural land.

[0289] The "means for optimization" is a technology that adjusts the proposed content based on environmental information and effectively utilizes resources in order to efficiently carry out cultivation activities in the urban environment.

[0290] This invention is a system for efficiently and optimizing plant cultivation in the urban environment. This system is realized by combining a plurality of hardware and software components.

[0291] Measuring devices such as sensors acquire environmental information such as temperature, humidity, and soil moisture. This information is transmitted to the information processing device. The information processing device, specifically a smartphone or tablet, etc., transmits this environmental information in real time to a data processing device on the cloud, such as a server of AWS or Google Cloud.

[0292] The server analyzes this data using an artificial intelligence module. The artificial intelligence module used has a data analysis algorithm such as TensorFlow, compares past weather data with current environmental information, and generates an optimal cultivation plan. This plan includes the timing and amount of watering and fertilization, and the timing of agricultural activities considering urban-specific constraints.

[0293] Users receive the generated suggestions through a smartphone application. The app notifies users of specific cultivation suggestions tailored to their urban environment, and users then carry out appropriate agricultural activities based on these suggestions.

[0294] For example, if a resident of an apartment building is growing basil on their balcony, the app will detect changes in humidity and temperature and suggest adjusting the watering frequency as needed. This promotes healthy growth even in limited spaces.

[0295] As an example of a prompt, a user can input a message like, "Please tell me the optimal watering schedule considering this week's weather data," into the interface to receive specific suggestions.

[0296] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0297] Step 1:

[0298] The sensor acquires environmental information.

[0299] As input, data such as temperature, humidity, and soil moisture content are measured by sensors. The sensors transmit this data to an information processing device. The output is a data packet of environmental information obtained in real time.

[0300] Step 2:

[0301] The device sends environmental information to a server in the cloud.

[0302] The terminal takes environmental information received from sensors as input and transmits the data to a cloud server via an internet connection. The output is a record of the environmental information stored in a database on the cloud.

[0303] Step 3:

[0304] The server analyzes environmental information and generates an optimal cultivation plan.

[0305] The server takes in the environmental information stored in the cloud as input, performs data analysis by comparing it with past data using a generative AI model such as TensorFlow. As data processing, it analyzes trends in temperature changes and humidity, and generates a cultivation plan including an optimal watering and fertilization schedule as output.

[0306] Step 4:

[0307] The server sends the cultivation plan generated by the server to the user's terminal.

[0308] Using the generated cultivation plan as input, the server notifies the user's terminal. The output is specific proposal information for agricultural activities that is displayed to the user through the application.

[0309] Step 5:

[0310] The user conducts agricultural activities based on the proposal.

[0311] The user uses the proposal received in the application as input and plans the actual farming work. For example, watering more than usual on one day and fertilizing on the next day, etc. The output is the implemented agricultural activities and the growth state of the plants as a result.

[0312] Furthermore, an emotion engine for estimating the user's emotion may be combined. That is, the specific processing unit 290 may estimate the user's emotion using the emotion recognition model 59 and perform specific processing using the user's emotion.

[0313] The present invention is a system that takes into account the user's emotion in order to support the cultivation management of agricultural crops, and by having an emotion recognition function, makes more individualized proposals for farming work. This system consists of a terminal that communicates with sensors for collecting environmental data, and a server equipped with an emotion engine for recognizing the user's emotion.

[0314] First, the device collects environmental data such as temperature, humidity, and soil condition from sensors and sends it to the server. The server analyzes the received data using an artificial intelligence module and generates an optimal cultivation plan. The emotion engine is particularly important here. The emotion engine analyzes the user's voice tone and facial expression data to recognize their current emotional state. This emotional data is used to determine stress levels and emotional stability.

[0315] Unlike suggestions based solely on normal environmental data, the generated cultivation plan is adjusted according to the user's emotional state. For example, if the server determines that the user is stressed, it can suggest ways to reduce the workload. Specifically, it might suggest breaking down farm work into smaller tasks or prioritizing tasks that cause less psychological stress.

[0316] Users receive notifications from the server via their devices and perform farming tasks based on the suggestions. For example, if the server detects that the user is stressed and lacking mental and physical energy, it automatically reminds them when watering is needed and makes new suggestions when they feel more emotionally at ease.

[0317] Through these emotion-based suggestion features, this system not only optimizes the growing environment but also supports the efficiency of farm work while taking into account the user's own emotional state. This can contribute to the realization of sustainable agricultural management.

[0318] The following describes the processing flow.

[0319] Step 1:

[0320] The terminal collects environmental data such as temperature, humidity, and soil condition from sensors placed in the farmland. The collected data is transmitted to the server in real time.

[0321] Step 2:

[0322] The server stores the received environmental data in a database. Next, an artificial intelligence module is used to analyze this data and generate the cultivation plan best suited to the current situation.

[0323] Step 3:

[0324] The device collects voice tone and facial expressions from the user to obtain emotional data. This emotional data is used to assess the user's stress level and emotional stability.

[0325] Step 4:

[0326] The emotion engine built into the server analyzes the emotion data received from the terminal. Based on the analysis results, it determines the user's emotional state.

[0327] Step 5:

[0328] The server adjusts the cultivation plan it generates based on the user's emotional state. For example, if the server determines that the user is stressed, it modifies the cultivation plan to reduce the workload.

[0329] Step 6:

[0330] The server notifies the user's terminal of the adjusted cultivation plan and suggestions. This allows the user to obtain the most appropriate course of action at that time.

[0331] Step 7:

[0332] Users receive suggestions from the server and plan and carry out actual farm work. Users follow emotionally sensitive suggestions and work at a manageable pace.

[0333] Step 8:

[0334] The user feeds back the results of their farming activities and new environmental data from their terminal to the server. The server uses this information to create the next cultivation plan and to evaluate user sentiment.

[0335] (Example 2)

[0336] Next, we will describe Example 2. 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".

[0337] Conventional plant cultivation systems only provide general work recommendations based on environmental information, making it difficult to provide individualized support that takes into account the influence of the user's emotional state. Therefore, there is a need to combine both environmental and emotional information to provide more effective cultivation plans and work recommendations that are tailored to the user.

[0338] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0339] In this invention, the server includes means for analyzing environmental information to generate a cultivation plan, means for collecting emotions and adjusting the cultivation plan based on the user's emotional state, and means for notifying the user of work recommendations. This enables personalized responses that take into account the user's emotional state.

[0340] "Environmental information" refers to information that includes data such as temperature, humidity, and soil conditions that affect plant growth.

[0341] A "measuring device" refers to a device used to acquire environmental information, and includes electronic devices such as sensors.

[0342] A "mobile terminal" is a portable computing device that communicates with measuring devices to send and receive information.

[0343] An "automated learning model" is an artificial intelligence technology that performs analysis and predictions based on data to generate an optimal cultivation plan.

[0344] A "computer" refers to a computer system used for information processing and analysis.

[0345] "Recommendations for work" are instructions or advice to suggest the best course of action for the user regarding plant cultivation.

[0346] "Emotional information" refers to information that includes data about the user's psychological state obtained from their voice and facial expressions.

[0347] "Emotional state" refers to the user's psychological and emotional state, including stress levels and emotional stability.

[0348] To implement this invention, the following equipment and software are required.

[0349] Hardware and software

[0350] The terminal uses various measuring devices (e.g., temperature and humidity sensors and soil moisture sensors) to collect environmental information. The terminal may be configured on a computing platform such as Arduino or Raspberry Pi. This allows data to be acquired in real time and transmitted to a server via communication means.

[0351] The server uses general-purpose computers or cloud servers, which are typically used in data centers, as computing resources. Automated learning models such as TensorFlow and PyTorch are installed on the server, which analyze environmental information and generate optimal plant cultivation plans. Furthermore, emotion analysis software such as Amazon Rekognition and OpenFace is used to process emotional information. This allows the recommended cultivation plan to be adjusted based on the user's emotional state.

[0352] Specific example

[0353] As a concrete example, suppose a user is cultivating plants on a farm. The terminal collects environmental information such as temperature (25°C), humidity (60%), and soil moisture (40%) from a measuring device and sends it to the server. The server analyzes this information and provides a detailed watering plan that incorporates next week's weather forecast. Furthermore, if the user's voice tone indicates fatigue, additional suggestions to reduce the workload, such as encouraging rest on the weekend, will be added.

[0354] Examples of prompts for generative AI models

[0355] "Based on user emotional states and environmental data, customize farm work plans and generate suggestions."

[0356] By using this embodiment of the invention, effective plant cultivation becomes possible, taking into account environmental information and the user's emotional state, thereby improving work efficiency and reducing the psychological burden on the user.

[0357] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0358] Step 1:

[0359] The terminal collects environmental information from measuring devices. Specifically, the terminal acquires data from temperature and humidity sensors and soil moisture sensors. The input is an analog signal obtained from the sensors, which is converted into digital data within the terminal. This data is output in the form of temperature, humidity, soil moisture content, etc., and is ready to be sent to the server.

[0360] Step 2:

[0361] The server analyzes environmental information received from the terminal. Specifically, the server inputs this data into an AI model and processes it to predict weather patterns and evaluate the need for watering. As a result of the analysis, output data is obtained that represents an optimal cultivation plan. This output indicates the appropriate cultivation conditions for each type of plant.

[0362] Step 3:

[0363] The server collects and analyzes the user's emotional information. Input is obtained through voice messages and facial expression data provided by the user via a smartphone app. The server processes this emotional information using emotion analysis software to evaluate stress levels and emotional balance. The analysis results are output as a quantified emotional state, which is then used in the next step.

[0364] Step 4:

[0365] The server integrates environmental and emotional information to generate a customized cultivation plan. This is an optimization process using a generative AI model, which takes the previously obtained environmental analysis results and emotional state as input. As a result of processing this integrated information, specific work recommendations for the user are output.

[0366] Step 5:

[0367] The device notifies the user of recommended tasks received from the server. Specifically, this information is presented via push notifications through a smartphone app. The notification content includes specific action plans such as "Start watering at 2 PM" or "Add fertilizer to the soil." By following these instructions, the user can achieve optimal plant cultivation.

[0368] (Application Example 2)

[0369] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal."

[0370] In recent years, the agricultural sector has seen a growing demand for increased productivity while simultaneously reducing the psychological and physical burden on workers. However, conventional cultivation management systems have been limited to suggestions based on crop growth environments and have not taken into account the emotional state of users. As a result, farmers experience stress, leading to decreased productivity and work efficiency.

[0371] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0372] In this invention, the server includes a detection device for acquiring environmental information, a device for communicating with the detection device, an intelligent module for analyzing the environmental information received from the device and generating a cultivation plan, an emotion recognition means for recognizing the user's emotional state, and a means for notifying the user of optimized farm work suggestions according to the user's emotional state. This enables a flexible farm work schedule tailored to the individual emotional state of the user, thereby reducing psychological burden and improving work efficiency.

[0373] "Environmental information" refers to data related to the cultivation environment, such as temperature, humidity, and soil characteristics.

[0374] A "detection device" refers to a device such as a sensor used to acquire environmental information from the cultivation environment of agricultural products.

[0375] A "communicating device" refers to a terminal that receives environmental information obtained from a detection device and transfers it to the appropriate computing device.

[0376] An "intelligent module" refers to a system element that includes algorithms and computer programs for generating cultivation plans based on received environmental information.

[0377] "Emotion identification means" refers to technologies and programs that analyze a user's voice and facial expressions to identify their current emotional state.

[0378] "Notification means" refers to electronic devices or interfaces used to present users with optimized farming suggestions.

[0379] A system for carrying out the present invention first requires a detection device equipped with sensors for acquiring environmental information. This device detects environmental information such as temperature, humidity, and soil characteristics in real time and transmits the data to a communication device. The communication device is responsible for transferring the environmental information received from the detection device to an intelligent module on a server.

[0380] Upon receiving this data, the server uses a learning algorithm within its intelligent module to generate an optimal cultivation plan. In this process, the server utilizes emotion recognition technology to recognize the user's emotional state. This technology leverages the camera and microphone of a smartphone or tablet device to analyze the user's voice and facial expressions, and the resulting data is transmitted to the server.

[0381] Based on the user's emotional state, the server optimizes farm work suggestions and delivers them to the user through notification channels. These notifications are sent via a smartphone application, allowing the user to view the suggested work plan in real time. For example, if a user is inspecting their field with their smartphone in the morning, the app might send a specific suggestion such as, "Your stress level is high today. Take it easy this afternoon and prioritize lighter tasks."

[0382] This system manages data exchange using data platforms such as Firebase and leverages cloud-based artificial intelligence services such as Google Cloud AI to analyze emotional states and generate cultivation plans.

[0383] A concrete example of a prompt message would be an instruction such as, "Assess the user's emotional state today and propose a farming plan tailored to their stress level." In this way, agricultural support that flexibly responds to the user's emotions is realized.

[0384] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0385] Step 1:

[0386] The terminal collects environmental information such as temperature, humidity, and soil characteristics from the detection device. The input environmental information data is formatted and then transferred to the server.

[0387] Step 2:

[0388] The server passes the environmental information received from the terminal to the intelligent module, which then begins analyzing it as input data. The intelligent module's learning algorithm analyzes this data and generates an optimal cultivation plan for the crops. This plan is then output.

[0389] Step 3:

[0390] The device captures the user's voice and facial expressions using its camera and microphone and transmits them to an emotion recognition system. The input user emotion data is transferred to a server. The server analyzes this data using the emotion recognition system to identify the user's current emotional state.

[0391] Step 4:

[0392] The server integrates and optimizes the cultivation plan based on the user's emotional state and environmental information. If the emotional state is high due to stress, instructions will be issued to reduce the work schedule.

[0393] Step 5:

[0394] The server sends optimized farm work suggestions to the terminal via a notification system. The terminal displays these suggestions on the user's smartphone app, notifying the user visually and audibly. The user can then review these notifications and perform tasks according to the optimized plan in real time.

[0395] 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 user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0396] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0397] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the smart glasses 214.

[0398] [Third Embodiment]

[0399] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0400] As shown in Figure 5, the data processing system 310 includes a data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0401] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0402] The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52.

[0403] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0404] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0405] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0406] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0407] The specific processing program 56 is an example of a "program" relating to the technology of this 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.

[0408] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0409] In the headset terminal 314, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. 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 processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0410] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the headset terminal 314 will be referred to as the "terminal".

[0411] This invention is a system for efficiently managing crop cultivation and consists of the following elements. First, a terminal collects environmental data from sensors placed in the farmland. This includes temperature, humidity, soil moisture content, etc. The terminal transmits this data in real time to a server in the cloud.

[0412] The server uses an artificial intelligence module to analyze the received environmental data. This AI module, based on machine learning algorithms, compares historical data with real-time data to generate an optimal cultivation plan. This plan includes the timing and amount of watering and fertilization, as well as the timing of other farming tasks.

[0413] The generated cultivation plan is sent from the server to the terminal. Based on this plan, the user plans specific farming tasks. For example, they can check the amount of water and fertilizer to be given on a particular day, or measures to take when the temperature changes suddenly, and then reflect these in their actual work.

[0414] As a concrete example, consider a case where a tomato farmer uses this system. The terminal detects low soil moisture through sensors. When the server receives this data, the artificial intelligence module compares it to past climate patterns and predicts the likelihood of continued drought. Therefore, the server sends a notification to the user suggesting more frequent watering. Based on this notification, the user waters at the appropriate time, promoting healthy tomato growth.

[0415] Thus, the present invention is a system that supports the crop cultivation process through accurate analysis based on environmental data and the proposal of cultivation plans based on that analysis, thereby improving the efficiency and profitability of agriculture.

[0416] The following describes the processing flow.

[0417] Step 1:

[0418] The device collects environmental data such as temperature, humidity, and soil moisture from sensors placed in the farmland. This data is updated in real time.

[0419] Step 2:

[0420] The device sends the collected environmental data to a server in the cloud. A secure communication protocol is used for transmission.

[0421] Step 3:

[0422] The server stores the received environmental data in a database and prepares it for processing for analysis.

[0423] Step 4:

[0424] The server uses an artificial intelligence module to analyze the received data. This includes comparing it with past weather data and analyzing trends using machine learning algorithms.

[0425] Step 5:

[0426] The server generates a cultivation plan suitable for the crops based on the results of data analysis. This includes timing for watering and fertilizing, as well as preventative measures against pests and diseases.

[0427] Step 6:

[0428] The server generates a cultivation plan and notifies the user's terminal. The notification is sent in real time, making it immediately available for the user to check.

[0429] Step 7:

[0430] The system reviews the suggestions received from the user's device and plans farm work according to the cultivation plan. If necessary, it creates a work schedule.

[0431] Step 8:

[0432] The system executes farm work exactly as planned by the user. This enables efficient cultivation that is suited to the environment.

[0433] (Example 1)

[0434] Next, we will describe Example 1. 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."

[0435] Cultivating crops requires proper management of weather conditions and soil conditions, but conventional methods make it difficult to grasp real-time environmental information and efficiently formulate growth plans. Furthermore, if users are not provided with timely data transmission and accurate farming instructions based on analysis results, proper cultivation management may not be carried out, affecting crop yield and quality. A system is needed to solve these problems and improve the efficiency and feasibility of agriculture.

[0436] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0437] In this invention, the server includes means for an intelligent module for analyzing environmental information and generating a growth plan, means for referring to and comparing past weather information based on environmental information received from an information device, and means for transmitting data using wireless communication technology. This makes it possible to efficiently collect environmental information in real time and quickly provide users with optimal farming instructions based on the analysis results.

[0438] "Environmental information" refers to data that includes temperature, humidity, soil conditions, and other factors that affect crop cultivation.

[0439] A "measuring device" is a device installed in agricultural land that is equipped with sensors to acquire environmental information.

[0440] An "information device" is a terminal equipped with communication capabilities for processing environmental information received from a measuring device and transmitting it to a server.

[0441] An "intelligent module" is a program that runs on a computing device, analyzes environmental information using machine learning algorithms, and generates a growth plan.

[0442] A "computational device" is a computer system equipped with an intelligent module that formulates growth plans based on data received from an information device.

[0443] "Wireless communication technology" refers to technologies used to transmit data from information devices to computing devices without using cables, and includes Wi-Fi and LoRa.

[0444] A "growing plan" is a set of schedules and instructions that provide optimal guidelines for crop cultivation, generated by analyzing environmental information.

[0445] A "user" is a person who receives a growth plan sent from a computing device and then carries out the actual farming work.

[0446] This invention is a system for efficiently managing crop cultivation. This system primarily consists of the interaction between terminals, a server, and users.

[0447] The terminal collects environmental information in real time from measuring devices placed in the farmland. These measuring devices include sensors for measuring temperature and humidity, as well as sensors for measuring soil moisture. This information is transmitted to a server in the cloud using wireless communication technologies such as Wi-Fi and LoRa.

[0448] The server uses an intelligent module to analyze the received environmental information. This intelligent module uses machine learning algorithms (including, for example, those using TensorFlow) to compare historical weather information with real-time environmental information and generate an optimal growth plan. The growth plan includes timing for watering and fertilizing, as well as instructions for farm work.

[0449] Users plan specific farming tasks based on growth plans notified by the server. They then carry out appropriate tasks based on these plans to promote healthy crop growth. For example, a tomato producer can improve tomato yield and quality by providing the correct amount of water and fertilizer at the right time, based on notifications from the system.

[0450] As a concrete example, here is an example of a prompt message:

[0451] "Based on historical and current environmental data, please generate the optimal watering schedule for the tomato farm."

[0452] This system aims to improve agricultural efficiency and profitability through real-time data collection and analysis, thereby contributing to the realization of sustainable agriculture.

[0453] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0454] Step 1:

[0455] The terminal collects environmental information from measuring devices installed in the farmland. This includes temperature, humidity, and soil moisture, and involves a process of converting analog data from the measuring devices into digital data. The input is raw data from each sensor, and the output is structured digital environmental information. This allows for an understanding of the current conditions of the farmland. Specifically, the sensor measures soil moisture every 10 minutes, and the terminal converts this into a digital signal.

[0456] Step 2:

[0457] The terminal transmits the collected environmental information to the server. The input is the digital environmental information acquired in step 1, and the output is a transmission completion signal to the server. Wireless communication technology (e.g., Wi-Fi) is used to transmit accurate information quickly in real time. Specifically, the terminal converts the collected environmental information into packet format and transmits it sequentially to the server.

[0458] Step 3:

[0459] The server analyzes the received environmental information. First, it compares the environmental information received as input with past weather information to verify data consistency. Next, it performs data analysis using a generative AI model and generates an optimal growth plan as output. A SQL database is used to store the data, and machine learning algorithms are applied to the analysis. Specifically, TensorFlow is used to learn the correlation between environmental conditions and growth patterns, and the optimal timing for watering and fertilizing is suggested.

[0460] Step 4:

[0461] The server notifies the terminal of the generated growth plan. The input is the growth plan created in step 3, and the output is a push notification to the terminal. The server generates notifications sequentially and sends the data to the terminal quickly. Specifically, the server converts the plan into text and sends the notification via the protocol.

[0462] Step 5:

[0463] The user receives notifications from their device and plans specific farming tasks. The input is the growth plan notified to the device, and the output is the details of the planned farming tasks. Based on the notifications, the user can also perform watering and fertilizing at the appropriate times and input this into a task management app. As a specific action, the user waters the tomato rows according to the schedule provided.

[0464] Through these steps, the system provides an appropriate growth plan based on real-time environmental information to help crops grow healthily.

[0465] (Application Example 1)

[0466] Next, we will explain Application Example 1. In the following explanation, 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."

[0467] In modern urban environments, cultivating plants in limited spaces presents numerous challenges. In particular, optimizing cultivation plans based on appropriate environmental information and efficiently utilizing resources in limited spaces are significant issues. Furthermore, responding quickly to climate change and urban-specific weather conditions is difficult.

[0468] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0469] In this invention, the server includes an information processing device that communicates with a measuring device for acquiring environmental information, a data processing device equipped with an artificial intelligence module for analyzing the environmental information received from the information processing device and generating a plant cultivation plan, means for notifying the user of suggestions for agricultural activities based on the cultivation plan, and means for optimizing the suggestions for cultivation activities in a limited space in an urban environment. This makes efficient and effective plant cultivation possible even in urban environments.

[0470] "Environmental information" refers to data on external conditions that affect plant cultivation, and specifically includes weather data, humidity, and soil conditions.

[0471] A "measuring device" is a device used to acquire environmental information, and specifically includes a sensor that measures temperature, humidity, soil moisture content, and other similar parameters.

[0472] An "information processing device" is a terminal that receives data from a measuring device and sends it to a server for analysis.

[0473] The "artificial intelligence module" is a program equipped with data analysis algorithms that uses received environmental information to predict plant growth and generate an optimal cultivation plan.

[0474] A "data processing device" is a server device equipped with an artificial intelligence module that analyzes and processes data related to plant cultivation.

[0475] A "suggestion" is specific instructions for agricultural activities that are sent to the user based on the generated cultivation plan.

[0476] "Limited space in urban environments" refers to limited spaces that can be used as agricultural land, such as rooftops and balconies of buildings located in urban areas.

[0477] "Means for optimization" refers to technologies that adjust proposals based on environmental information and effectively utilize resources in order to efficiently carry out cultivation activities in urban environments.

[0478] This invention is a system for efficiently and optimally cultivating plants in urban environments. This system is implemented by combining multiple hardware and software components.

[0479] Sensors and other measuring devices acquire environmental information such as temperature, humidity, and soil moisture. This information is transmitted to an information processing device. The information processing device, specifically a smartphone or tablet, then transmits this environmental information in real time to a data processing device in the cloud, such as a server on AWS or Google Cloud.

[0480] The server analyzes this data using an artificial intelligence module. The AI ​​module used is equipped with data analysis algorithms such as TensorFlow, and compares historical weather data with current environmental information to generate an optimal cultivation plan. This plan includes the timing and amount of watering and fertilization, as well as the timing of agricultural activities, taking into account urban-specific constraints.

[0481] Users receive the generated suggestions through a smartphone application. The app notifies users of specific cultivation suggestions tailored to their urban environment, and users then carry out appropriate agricultural activities based on these suggestions.

[0482] For example, if a resident of an apartment building is growing basil on their balcony, the app will detect changes in humidity and temperature and suggest adjusting the watering frequency as needed. This promotes healthy growth even in limited spaces.

[0483] As an example of a prompt, a user can input a message like, "Please tell me the optimal watering schedule considering this week's weather data," into the interface to receive specific suggestions.

[0484] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0485] Step 1:

[0486] The sensor acquires environmental information.

[0487] As input, data such as temperature, humidity, and soil moisture content are measured by sensors. The sensors transmit this data to an information processing device. The output is a data packet of environmental information obtained in real time.

[0488] Step 2:

[0489] The device sends environmental information to a server in the cloud.

[0490] The terminal takes environmental information received from sensors as input and transmits the data to a cloud server via an internet connection. The output is a record of the environmental information stored in a database on the cloud.

[0491] Step 3:

[0492] The server analyzes environmental information and generates the optimal cultivation plan.

[0493] The server takes environmental information stored in the cloud as input and performs data analysis by comparing it with historical data using generative AI models such as TensorFlow. As part of the data processing, it analyzes trends in temperature changes and humidity, and generates a cultivation plan as output, which includes optimal watering and fertilization schedules.

[0494] Step 4:

[0495] The server generates a cultivation plan and sends it to the user's terminal.

[0496] The server uses the generated cultivation plan as input and notifies the user's terminal. The output is specific agricultural activity suggestions displayed to the user through the application.

[0497] Step 5:

[0498] Users carry out agricultural activities based on their suggestions.

[0499] The user uses suggestions received through the application as input to plan actual farming activities. For example, they might water more than usual on one day and fertilize the next. The output is the agricultural activities performed and the resulting state of plant growth.

[0500] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0501] This invention provides a system that takes user emotions into account to support crop cultivation management, and by incorporating emotion recognition capabilities, it offers more personalized farm work suggestions. The system consists of sensors that collect environmental data, a terminal that communicates with sensors, and a server equipped with an emotion engine that recognizes user emotions.

[0502] First, the device collects environmental data such as temperature, humidity, and soil condition from sensors and sends it to the server. The server analyzes the received data using an artificial intelligence module and generates an optimal cultivation plan. The emotion engine is particularly important here. The emotion engine analyzes the user's voice tone and facial expression data to recognize their current emotional state. This emotional data is used to determine stress levels and emotional stability.

[0503] Unlike suggestions based solely on normal environmental data, the generated cultivation plan is adjusted according to the user's emotional state. For example, if the server determines that the user is stressed, it can suggest ways to reduce the workload. Specifically, it might suggest breaking down farm work into smaller tasks or prioritizing tasks that cause less psychological stress.

[0504] Users receive notifications from the server via their devices and perform farming tasks based on the suggestions. For example, if the server detects that the user is stressed and lacking mental and physical energy, it automatically reminds them when watering is needed and makes new suggestions when they feel more emotionally at ease.

[0505] Through these emotion-based suggestion features, this system not only optimizes the growing environment but also supports the efficiency of farm work while taking into account the user's own emotional state. This can contribute to the realization of sustainable agricultural management.

[0506] The following describes the processing flow.

[0507] Step 1:

[0508] The terminal collects environmental data such as temperature, humidity, and soil condition from sensors placed in the farmland. The collected data is transmitted to the server in real time.

[0509] Step 2:

[0510] The server stores the received environmental data in a database. Next, an artificial intelligence module is used to analyze this data and generate the cultivation plan best suited to the current situation.

[0511] Step 3:

[0512] The device collects voice tone and facial expressions from the user to obtain emotional data. This emotional data is used to assess the user's stress level and emotional stability.

[0513] Step 4:

[0514] The emotion engine built into the server analyzes the emotion data received from the terminal. Based on the analysis results, it determines the user's emotional state.

[0515] Step 5:

[0516] The server adjusts the cultivation plan it generates based on the user's emotional state. For example, if the server determines that the user is stressed, it modifies the cultivation plan to reduce the workload.

[0517] Step 6:

[0518] The server notifies the user's terminal of the adjusted cultivation plan and suggestions. This allows the user to obtain the most appropriate course of action at that time.

[0519] Step 7:

[0520] Users receive suggestions from the server and plan and carry out actual farm work. Users follow emotionally sensitive suggestions and work at a manageable pace.

[0521] Step 8:

[0522] The user feeds back the results of their farming activities and new environmental data from their terminal to the server. The server uses this information to create the next cultivation plan and to evaluate user sentiment.

[0523] (Example 2)

[0524] Next, we will describe Example 2. 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."

[0525] Conventional plant cultivation systems only provide general work recommendations based on environmental information, making it difficult to provide individualized support that takes into account the influence of the user's emotional state. Therefore, there is a need to combine both environmental and emotional information to provide more effective cultivation plans and work recommendations that are tailored to the user.

[0526] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0527] In this invention, the server includes means for analyzing environmental information to generate a cultivation plan, means for collecting emotions and adjusting the cultivation plan based on the user's emotional state, and means for notifying the user of work recommendations. This enables personalized responses that take into account the user's emotional state.

[0528] "Environmental information" refers to information that includes data such as temperature, humidity, and soil conditions that affect plant growth.

[0529] A "measuring device" refers to a device used to acquire environmental information, and includes electronic devices such as sensors.

[0530] A "mobile terminal" is a portable computing device that communicates with measuring devices to send and receive information.

[0531] An "automated learning model" is an artificial intelligence technology that performs analysis and predictions based on data to generate an optimal cultivation plan.

[0532] A "computer" refers to a computer system used for information processing and analysis.

[0533] "Recommendations for work" are instructions or advice to suggest the best course of action for the user regarding plant cultivation.

[0534] "Emotional information" refers to information that includes data about the user's psychological state obtained from their voice and facial expressions.

[0535] "Emotional state" refers to the user's psychological and emotional state, including stress levels and emotional stability.

[0536] To implement this invention, the following equipment and software are required.

[0537] Hardware and software

[0538] The terminal uses various measuring devices (e.g., temperature and humidity sensors and soil moisture sensors) to collect environmental information. The terminal may be configured on a computing platform such as Arduino or Raspberry Pi. This allows data to be acquired in real time and transmitted to a server via communication means.

[0539] The server uses general-purpose computers or cloud servers, which are typically used in data centers, as computing resources. Automated learning models such as TensorFlow and PyTorch are installed on the server, which analyze environmental information and generate optimal plant cultivation plans. Furthermore, emotion analysis software such as Amazon Rekognition and OpenFace is used to process emotional information. This allows the recommended cultivation plan to be adjusted based on the user's emotional state.

[0540] Specific example

[0541] As a concrete example, suppose a user is cultivating plants on a farm. The terminal collects environmental information such as temperature (25°C), humidity (60%), and soil moisture (40%) from a measuring device and sends it to the server. The server analyzes this information and provides a detailed watering plan that incorporates next week's weather forecast. Furthermore, if the user's voice tone indicates fatigue, additional suggestions to reduce the workload, such as encouraging rest on the weekend, will be added.

[0542] Examples of prompts for generative AI models

[0543] "Based on user emotional states and environmental data, customize farm work plans and generate suggestions."

[0544] By using this embodiment of the invention, effective plant cultivation becomes possible, taking into account environmental information and the user's emotional state, thereby improving work efficiency and reducing the psychological burden on the user.

[0545] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0546] Step 1:

[0547] The terminal collects environmental information from measuring devices. Specifically, the terminal acquires data from temperature and humidity sensors and soil moisture sensors. The input is an analog signal obtained from the sensors, which is converted into digital data within the terminal. This data is output in the form of temperature, humidity, soil moisture content, etc., and is ready to be sent to the server.

[0548] Step 2:

[0549] The server analyzes environmental information received from the terminal. Specifically, the server inputs this data into an AI model and processes it to predict weather patterns and evaluate the need for watering. As a result of the analysis, output data is obtained that represents an optimal cultivation plan. This output indicates the appropriate cultivation conditions for each type of plant.

[0550] Step 3:

[0551] The server collects and analyzes the user's emotional information. Input is obtained through voice messages and facial expression data provided by the user via a smartphone app. The server processes this emotional information using emotion analysis software to evaluate stress levels and emotional balance. The analysis results are output as a quantified emotional state, which is then used in the next step.

[0552] Step 4:

[0553] The server integrates environmental and emotional information to generate a customized cultivation plan. This is an optimization process using a generative AI model, which takes the previously obtained environmental analysis results and emotional state as input. As a result of processing this integrated information, specific work recommendations for the user are output.

[0554] Step 5:

[0555] The device notifies the user of recommended tasks received from the server. Specifically, this information is presented via push notifications through a smartphone app. The notification content includes specific action plans such as "Start watering at 2 PM" or "Add fertilizer to the soil." By following these instructions, the user can achieve optimal plant cultivation.

[0556] (Application Example 2)

[0557] Next, we will explain application example 2. In the following explanation, 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."

[0558] In recent years, the agricultural sector has seen a growing demand for increased productivity while simultaneously reducing the psychological and physical burden on workers. However, conventional cultivation management systems have been limited to suggestions based on crop growth environments and have not taken into account the emotional state of users. As a result, farmers experience stress, leading to decreased productivity and work efficiency.

[0559] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0560] In this invention, the server includes a detection device for acquiring environmental information, a device for communicating with the detection device, an intelligent module for analyzing the environmental information received from the device and generating a cultivation plan, an emotion recognition means for recognizing the user's emotional state, and a means for notifying the user of optimized farm work suggestions according to the user's emotional state. This enables a flexible farm work schedule tailored to the individual emotional state of the user, thereby reducing psychological burden and improving work efficiency.

[0561] "Environmental information" refers to data related to the cultivation environment, such as temperature, humidity, and soil characteristics.

[0562] A "detection device" refers to a device such as a sensor used to acquire environmental information from the cultivation environment of agricultural products.

[0563] A "communicating device" refers to a terminal that receives environmental information obtained from a detection device and transfers it to the appropriate computing device.

[0564] An "intelligent module" refers to a system element that includes algorithms and computer programs for generating cultivation plans based on received environmental information.

[0565] "Emotion identification means" refers to technologies and programs that analyze a user's voice and facial expressions to identify their current emotional state.

[0566] "Notification means" refers to electronic devices or interfaces used to present users with optimized farming suggestions.

[0567] A system for carrying out the present invention first requires a detection device equipped with sensors for acquiring environmental information. This device detects environmental information such as temperature, humidity, and soil characteristics in real time and transmits the data to a communication device. The communication device is responsible for transferring the environmental information received from the detection device to an intelligent module on a server.

[0568] Upon receiving this data, the server uses a learning algorithm within its intelligent module to generate an optimal cultivation plan. In this process, the server utilizes emotion recognition technology to recognize the user's emotional state. This technology leverages the camera and microphone of a smartphone or tablet device to analyze the user's voice and facial expressions, and the resulting data is transmitted to the server.

[0569] Based on the user's emotional state, the server optimizes farm work suggestions and delivers them to the user through notification channels. These notifications are sent via a smartphone application, allowing the user to view the suggested work plan in real time. For example, if a user is inspecting their field with their smartphone in the morning, the app might send a specific suggestion such as, "Your stress level is high today. Take it easy this afternoon and prioritize lighter tasks."

[0570] This system manages data exchange using data platforms such as Firebase and leverages cloud-based artificial intelligence services such as Google Cloud AI to analyze emotional states and generate cultivation plans.

[0571] A concrete example of a prompt message would be an instruction such as, "Assess the user's emotional state today and propose a farming plan tailored to their stress level." In this way, agricultural support that flexibly responds to the user's emotions is realized.

[0572] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0573] Step 1:

[0574] The terminal collects environmental information such as temperature, humidity, and soil characteristics from the detection device. The input environmental information data is formatted and then transferred to the server.

[0575] Step 2:

[0576] The server passes the environmental information received from the terminal to the intelligent module, which then begins analyzing it as input data. The intelligent module's learning algorithm analyzes this data and generates an optimal cultivation plan for the crops. This plan is then output.

[0577] Step 3:

[0578] The device captures the user's voice and facial expressions using its camera and microphone and transmits them to an emotion recognition system. The input user emotion data is transferred to a server. The server analyzes this data using the emotion recognition system to identify the user's current emotional state.

[0579] Step 4:

[0580] The server integrates and optimizes the cultivation plan based on the user's emotional state and environmental information. If the emotional state is high due to stress, instructions will be issued to reduce the work schedule.

[0581] Step 5:

[0582] The server sends optimized farm work suggestions to the terminal via a notification system. The terminal displays these suggestions on the user's smartphone app, notifying the user visually and audibly. The user can then review these notifications and perform tasks according to the optimized plan in real time.

[0583] The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0584] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0585] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and specific processing may also be performed by the headset terminal 314.

[0586] [Fourth Embodiment]

[0587] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0588] As shown in Figure 7, the 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.

[0589] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0590] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52.

[0591] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0592] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0593] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0594] The controlled object 443 includes a display device, LEDs in the eyes, and motors that drive 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 robot 414's emotions can be expressed by controlling these motors. Furthermore, the robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0595] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0596] The specific processing program 56 is an example of a "program" relating to the technology of this 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.

[0597] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0598] In robot 414, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. 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 processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0599] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0600] This invention is a system for efficiently managing crop cultivation and consists of the following elements. First, a terminal collects environmental data from sensors placed in the farmland. This includes temperature, humidity, soil moisture content, etc. The terminal transmits this data in real time to a server in the cloud.

[0601] The server uses an artificial intelligence module to analyze the received environmental data. This AI module, based on machine learning algorithms, compares historical data with real-time data to generate an optimal cultivation plan. This plan includes the timing and amount of watering and fertilization, as well as the timing of other farming tasks.

[0602] The generated cultivation plan is sent from the server to the terminal. Based on this plan, the user plans specific farming tasks. For example, they can check the amount of water and fertilizer to be given on a particular day, or measures to take when the temperature changes suddenly, and then reflect these in their actual work.

[0603] As a concrete example, consider a case where a tomato farmer uses this system. The terminal detects low soil moisture through sensors. When the server receives this data, the artificial intelligence module compares it to past climate patterns and predicts the likelihood of continued drought. Therefore, the server sends a notification to the user suggesting more frequent watering. Based on this notification, the user waters at the appropriate time, promoting healthy tomato growth.

[0604] Thus, the present invention is a system that supports the crop cultivation process through accurate analysis based on environmental data and the proposal of cultivation plans based on that analysis, thereby improving the efficiency and profitability of agriculture.

[0605] The following describes the processing flow.

[0606] Step 1:

[0607] The device collects environmental data such as temperature, humidity, and soil moisture from sensors placed in the farmland. This data is updated in real time.

[0608] Step 2:

[0609] The device sends the collected environmental data to a server in the cloud. A secure communication protocol is used for transmission.

[0610] Step 3:

[0611] The server stores the received environmental data in a database and prepares it for processing for analysis.

[0612] Step 4:

[0613] The server uses an artificial intelligence module to analyze the received data. This includes comparing it with past weather data and analyzing trends using machine learning algorithms.

[0614] Step 5:

[0615] The server generates a cultivation plan suitable for the crops based on the results of data analysis. This includes timing for watering and fertilizing, as well as preventative measures against pests and diseases.

[0616] Step 6:

[0617] The server generates a cultivation plan and notifies the user's terminal. The notification is sent in real time, making it immediately available for the user to check.

[0618] Step 7:

[0619] The system reviews the suggestions received from the user's device and plans farm work according to the cultivation plan. If necessary, it creates a work schedule.

[0620] Step 8:

[0621] The system executes farm work exactly as planned by the user. This enables efficient cultivation that is suited to the environment.

[0622] (Example 1)

[0623] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0624] Cultivating crops requires proper management of weather conditions and soil conditions, but conventional methods make it difficult to grasp real-time environmental information and efficiently formulate growth plans. Furthermore, if users are not provided with timely data transmission and accurate farming instructions based on analysis results, proper cultivation management may not be carried out, affecting crop yield and quality. A system is needed to solve these problems and improve the efficiency and feasibility of agriculture.

[0625] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0626] In this invention, the server includes means for an intelligent module for analyzing environmental information and generating a growth plan, means for referring to and comparing past weather information based on environmental information received from an information device, and means for transmitting data using wireless communication technology. This makes it possible to efficiently collect environmental information in real time and quickly provide users with optimal farming instructions based on the analysis results.

[0627] "Environmental information" refers to data that includes temperature, humidity, soil conditions, and other factors that affect crop cultivation.

[0628] A "measuring device" is a device installed in agricultural land that is equipped with sensors to acquire environmental information.

[0629] An "information device" is a terminal equipped with communication capabilities for processing environmental information received from a measuring device and transmitting it to a server.

[0630] An "intelligent module" is a program that runs on a computing device, analyzes environmental information using machine learning algorithms, and generates a growth plan.

[0631] A "computational device" is a computer system equipped with an intelligent module that formulates growth plans based on data received from an information device.

[0632] "Wireless communication technology" refers to technologies used to transmit data from information devices to computing devices without using cables, and includes Wi-Fi and LoRa.

[0633] A "growing plan" is a set of schedules and instructions that provide optimal guidelines for crop cultivation, generated by analyzing environmental information.

[0634] A "user" is a person who receives a growth plan sent from a computing device and then carries out the actual farming work.

[0635] This invention is a system for efficiently managing crop cultivation. This system primarily consists of the interaction between terminals, a server, and users.

[0636] The terminal collects environmental information in real time from measuring devices placed in the farmland. These measuring devices include sensors for measuring temperature and humidity, as well as sensors for measuring soil moisture. This information is transmitted to a server in the cloud using wireless communication technologies such as Wi-Fi and LoRa.

[0637] The server uses an intelligent module to analyze the received environmental information. This intelligent module uses machine learning algorithms (including, for example, those using TensorFlow) to compare historical weather information with real-time environmental information and generate an optimal growth plan. The growth plan includes timing for watering and fertilizing, as well as instructions for farm work.

[0638] Users plan specific farming tasks based on growth plans notified by the server. They then carry out appropriate tasks based on these plans to promote healthy crop growth. For example, a tomato producer can improve tomato yield and quality by providing the correct amount of water and fertilizer at the right time, based on notifications from the system.

[0639] As a concrete example, here is an example of a prompt message:

[0640] "Based on historical and current environmental data, please generate the optimal watering schedule for the tomato farm."

[0641] This system aims to improve agricultural efficiency and profitability through real-time data collection and analysis, thereby contributing to the realization of sustainable agriculture.

[0642] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0643] Step 1:

[0644] The terminal collects environmental information from measuring devices installed in the farmland. This includes temperature, humidity, and soil moisture, and involves a process of converting analog data from the measuring devices into digital data. The input is raw data from each sensor, and the output is structured digital environmental information. This allows for an understanding of the current conditions of the farmland. Specifically, the sensor measures soil moisture every 10 minutes, and the terminal converts this into a digital signal.

[0645] Step 2:

[0646] The terminal transmits the collected environmental information to the server. The input is the digital environmental information acquired in step 1, and the output is a transmission completion signal to the server. Wireless communication technology (e.g., Wi-Fi) is used to transmit accurate information quickly in real time. Specifically, the terminal converts the collected environmental information into packet format and transmits it sequentially to the server.

[0647] Step 3:

[0648] The server analyzes the received environmental information. First, it compares the environmental information received as input with past weather information to verify data consistency. Next, it performs data analysis using a generative AI model and generates an optimal growth plan as output. A SQL database is used to store the data, and machine learning algorithms are applied to the analysis. Specifically, TensorFlow is used to learn the correlation between environmental conditions and growth patterns, and the optimal timing for watering and fertilizing is suggested.

[0649] Step 4:

[0650] The server notifies the terminal of the generated growth plan. The input is the growth plan created in step 3, and the output is a push notification to the terminal. The server generates notifications sequentially and sends the data to the terminal quickly. Specifically, the server converts the plan into text and sends the notification via the protocol.

[0651] Step 5:

[0652] The user receives notifications from their device and plans specific farming tasks. The input is the growth plan notified to the device, and the output is the details of the planned farming tasks. Based on the notifications, the user can also perform watering and fertilizing at the appropriate times and input this into a task management app. As a specific action, the user waters the tomato rows according to the schedule provided.

[0653] Through these steps, the system provides an appropriate growth plan based on real-time environmental information to help crops grow healthily.

[0654] (Application Example 1)

[0655] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0656] In modern urban environments, cultivating plants in limited spaces presents numerous challenges. In particular, optimizing cultivation plans based on appropriate environmental information and efficiently utilizing resources in limited spaces are significant issues. Furthermore, responding quickly to climate change and urban-specific weather conditions is difficult.

[0657] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0658] In this invention, the server includes an information processing device that communicates with a measuring device for acquiring environmental information, a data processing device equipped with an artificial intelligence module for analyzing the environmental information received from the information processing device and generating a plant cultivation plan, means for notifying the user of suggestions for agricultural activities based on the cultivation plan, and means for optimizing the suggestions for cultivation activities in a limited space in an urban environment. This makes efficient and effective plant cultivation possible even in urban environments.

[0659] "Environmental information" refers to data on external conditions that affect plant cultivation, and specifically includes weather data, humidity, and soil conditions.

[0660] A "measuring device" is a device used to acquire environmental information, and specifically includes a sensor that measures temperature, humidity, soil moisture content, and other similar parameters.

[0661] An "information processing device" is a terminal that receives data from a measuring device and sends it to a server for analysis.

[0662] The "artificial intelligence module" is a program equipped with data analysis algorithms that uses received environmental information to predict plant growth and generate an optimal cultivation plan.

[0663] A "data processing device" is a server device equipped with an artificial intelligence module that analyzes and processes data related to plant cultivation.

[0664] A "suggestion" is specific instructions for agricultural activities that are sent to the user based on the generated cultivation plan.

[0665] "Limited space in urban environments" refers to limited spaces that can be used as agricultural land, such as rooftops and balconies of buildings located in urban areas.

[0666] "Means for optimization" refers to technologies that adjust proposals based on environmental information and effectively utilize resources in order to efficiently carry out cultivation activities in urban environments.

[0667] This invention is a system for efficiently and optimally cultivating plants in urban environments. This system is implemented by combining multiple hardware and software components.

[0668] Sensors and other measuring devices acquire environmental information such as temperature, humidity, and soil moisture. This information is transmitted to an information processing device. The information processing device, specifically a smartphone or tablet, then transmits this environmental information in real time to a data processing device in the cloud, such as a server on AWS or Google Cloud.

[0669] The server analyzes this data using an artificial intelligence module. The AI ​​module used is equipped with data analysis algorithms such as TensorFlow, and compares historical weather data with current environmental information to generate an optimal cultivation plan. This plan includes the timing and amount of watering and fertilization, as well as the timing of agricultural activities, taking into account urban-specific constraints.

[0670] Users receive the generated suggestions through a smartphone application. The app notifies users of specific cultivation suggestions tailored to their urban environment, and users then carry out appropriate agricultural activities based on these suggestions.

[0671] For example, if a resident of an apartment building is growing basil on their balcony, the app will detect changes in humidity and temperature and suggest adjusting the watering frequency as needed. This promotes healthy growth even in limited spaces.

[0672] As an example of a prompt, a user can input a message like, "Please tell me the optimal watering schedule considering this week's weather data," into the interface to receive specific suggestions.

[0673] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0674] Step 1:

[0675] The sensor acquires environmental information.

[0676] As input, data such as temperature, humidity, and soil moisture content are measured by sensors. The sensors transmit this data to an information processing device. The output is a data packet of environmental information obtained in real time.

[0677] Step 2:

[0678] The device sends environmental information to a server in the cloud.

[0679] The terminal takes environmental information received from sensors as input and transmits the data to a cloud server via an internet connection. The output is a record of the environmental information stored in a database on the cloud.

[0680] Step 3:

[0681] The server analyzes environmental information and generates the optimal cultivation plan.

[0682] The server takes environmental information stored in the cloud as input and performs data analysis by comparing it with historical data using generative AI models such as TensorFlow. As part of the data processing, it analyzes trends in temperature changes and humidity, and generates a cultivation plan as output, which includes optimal watering and fertilization schedules.

[0683] Step 4:

[0684] The server generates a cultivation plan and sends it to the user's terminal.

[0685] The server uses the generated cultivation plan as input and notifies the user's terminal. The output is specific agricultural activity suggestions displayed to the user through the application.

[0686] Step 5:

[0687] Users carry out agricultural activities based on their suggestions.

[0688] The user uses suggestions received through the application as input to plan actual farming activities. For example, they might water more than usual on one day and fertilize the next. The output is the agricultural activities performed and the resulting state of plant growth.

[0689] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0690] This invention provides a system that takes user emotions into account to support crop cultivation management, and by incorporating emotion recognition capabilities, it offers more personalized farm work suggestions. The system consists of sensors that collect environmental data, a terminal that communicates with sensors, and a server equipped with an emotion engine that recognizes user emotions.

[0691] First, the device collects environmental data such as temperature, humidity, and soil condition from sensors and sends it to the server. The server analyzes the received data using an artificial intelligence module and generates an optimal cultivation plan. The emotion engine is particularly important here. The emotion engine analyzes the user's voice tone and facial expression data to recognize their current emotional state. This emotional data is used to determine stress levels and emotional stability.

[0692] Unlike suggestions based solely on normal environmental data, the generated cultivation plan is adjusted according to the user's emotional state. For example, if the server determines that the user is stressed, it can suggest ways to reduce the workload. Specifically, it might suggest breaking down farm work into smaller tasks or prioritizing tasks that cause less psychological stress.

[0693] Users receive notifications from the server via their devices and perform farming tasks based on the suggestions. For example, if the server detects that the user is stressed and lacking mental and physical energy, it automatically reminds them when watering is needed and makes new suggestions when they feel more emotionally at ease.

[0694] Through these emotion-based suggestion features, this system not only optimizes the growing environment but also supports the efficiency of farm work while taking into account the user's own emotional state. This can contribute to the realization of sustainable agricultural management.

[0695] The following describes the processing flow.

[0696] Step 1:

[0697] The terminal collects environmental data such as temperature, humidity, and soil condition from sensors placed in the farmland. The collected data is transmitted to the server in real time.

[0698] Step 2:

[0699] The server stores the received environmental data in a database. Next, an artificial intelligence module is used to analyze this data and generate the cultivation plan best suited to the current situation.

[0700] Step 3:

[0701] The device collects voice tone and facial expressions from the user to obtain emotional data. This emotional data is used to assess the user's stress level and emotional stability.

[0702] Step 4:

[0703] The emotion engine built into the server analyzes the emotion data received from the terminal. Based on the analysis results, it determines the user's emotional state.

[0704] Step 5:

[0705] The server adjusts the cultivation plan it generates based on the user's emotional state. For example, if the server determines that the user is stressed, it modifies the cultivation plan to reduce the workload.

[0706] Step 6:

[0707] The server notifies the user's terminal of the adjusted cultivation plan and suggestions. This allows the user to obtain the most appropriate course of action at that time.

[0708] Step 7:

[0709] Users receive suggestions from the server and plan and carry out actual farm work. Users follow emotionally sensitive suggestions and work at a manageable pace.

[0710] Step 8:

[0711] The user feeds back the results of their farming activities and new environmental data from their terminal to the server. The server uses this information to create the next cultivation plan and to evaluate user sentiment.

[0712] (Example 2)

[0713] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0714] Conventional plant cultivation systems only provide general work recommendations based on environmental information, making it difficult to provide individualized support that takes into account the influence of the user's emotional state. Therefore, there is a need to combine both environmental and emotional information to provide more effective cultivation plans and work recommendations that are tailored to the user.

[0715] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0716] In this invention, the server includes means for analyzing environmental information to generate a cultivation plan, means for collecting emotions and adjusting the cultivation plan based on the user's emotional state, and means for notifying the user of work recommendations. This enables personalized responses that take into account the user's emotional state.

[0717] "Environmental information" refers to information that includes data such as temperature, humidity, and soil conditions that affect plant growth.

[0718] A "measuring device" refers to a device used to acquire environmental information, and includes electronic devices such as sensors.

[0719] A "mobile terminal" is a portable computing device that communicates with measuring devices to send and receive information.

[0720] An "automated learning model" is an artificial intelligence technology that performs analysis and predictions based on data to generate an optimal cultivation plan.

[0721] A "computer" refers to a computer system used for information processing and analysis.

[0722] "Recommendations for work" are instructions or advice to suggest the best course of action for the user regarding plant cultivation.

[0723] "Emotional information" refers to information that includes data about the user's psychological state obtained from their voice and facial expressions.

[0724] "Emotional state" refers to the user's psychological and emotional state, including stress levels and emotional stability.

[0725] To implement this invention, the following equipment and software are required.

[0726] Hardware and software

[0727] The terminal uses various measuring devices (e.g., temperature and humidity sensors and soil moisture sensors) to collect environmental information. The terminal may be configured on a computing platform such as Arduino or Raspberry Pi. This allows data to be acquired in real time and transmitted to a server via communication means.

[0728] The server uses general-purpose computers or cloud servers, which are typically used in data centers, as computing resources. Automated learning models such as TensorFlow and PyTorch are installed on the server, which analyze environmental information and generate optimal plant cultivation plans. Furthermore, emotion analysis software such as Amazon Rekognition and OpenFace is used to process emotional information. This allows the recommended cultivation plan to be adjusted based on the user's emotional state.

[0729] Specific example

[0730] As a concrete example, suppose a user is cultivating plants on a farm. The terminal collects environmental information such as temperature (25°C), humidity (60%), and soil moisture (40%) from a measuring device and sends it to the server. The server analyzes this information and provides a detailed watering plan that incorporates next week's weather forecast. Furthermore, if the user's voice tone indicates fatigue, additional suggestions to reduce the workload, such as encouraging rest on the weekend, will be added.

[0731] Examples of prompts for generative AI models

[0732] "Based on user emotional states and environmental data, customize farm work plans and generate suggestions."

[0733] By using this embodiment of the invention, effective plant cultivation becomes possible, taking into account environmental information and the user's emotional state, thereby improving work efficiency and reducing the psychological burden on the user.

[0734] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0735] Step 1:

[0736] The terminal collects environmental information from measuring devices. Specifically, the terminal acquires data from temperature and humidity sensors and soil moisture sensors. The input is an analog signal obtained from the sensors, which is converted into digital data within the terminal. This data is output in the form of temperature, humidity, soil moisture content, etc., and is ready to be sent to the server.

[0737] Step 2:

[0738] The server analyzes environmental information received from the terminal. Specifically, the server inputs this data into an AI model and processes it to predict weather patterns and evaluate the need for watering. As a result of the analysis, output data is obtained that represents an optimal cultivation plan. This output indicates the appropriate cultivation conditions for each type of plant.

[0739] Step 3:

[0740] The server collects and analyzes the user's emotional information. Input is obtained through voice messages and facial expression data provided by the user via a smartphone app. The server processes this emotional information using emotion analysis software to evaluate stress levels and emotional balance. The analysis results are output as a quantified emotional state, which is then used in the next step.

[0741] Step 4:

[0742] The server integrates environmental and emotional information to generate a customized cultivation plan. This is an optimization process using a generative AI model, which takes the previously obtained environmental analysis results and emotional state as input. As a result of processing this integrated information, specific work recommendations for the user are output.

[0743] Step 5:

[0744] The device notifies the user of recommended tasks received from the server. Specifically, this information is presented via push notifications through a smartphone app. The notification content includes specific action plans such as "Start watering at 2 PM" or "Add fertilizer to the soil." By following these instructions, the user can achieve optimal plant cultivation.

[0745] (Application Example 2)

[0746] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0747] In recent years, the agricultural sector has seen a growing demand for increased productivity while simultaneously reducing the psychological and physical burden on workers. However, conventional cultivation management systems have been limited to suggestions based on crop growth environments and have not taken into account the emotional state of users. As a result, farmers experience stress, leading to decreased productivity and work efficiency.

[0748] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0749] In this invention, the server includes a detection device for acquiring environmental information, a device for communicating with the detection device, an intelligent module for analyzing the environmental information received from the device and generating a cultivation plan, an emotion recognition means for recognizing the user's emotional state, and a means for notifying the user of optimized farm work suggestions according to the user's emotional state. This enables a flexible farm work schedule tailored to the individual emotional state of the user, thereby reducing psychological burden and improving work efficiency.

[0750] "Environmental information" refers to data related to the cultivation environment, such as temperature, humidity, and soil characteristics.

[0751] A "detection device" refers to a device such as a sensor used to acquire environmental information from the cultivation environment of agricultural products.

[0752] A "communicating device" refers to a terminal that receives environmental information obtained from a detection device and transfers it to the appropriate computing device.

[0753] An "intelligent module" refers to a system element that includes algorithms and computer programs for generating cultivation plans based on received environmental information.

[0754] "Emotion identification means" refers to technologies and programs that analyze a user's voice and facial expressions to identify their current emotional state.

[0755] "Notification means" refers to electronic devices or interfaces used to present users with optimized farming suggestions.

[0756] A system for carrying out the present invention first requires a detection device equipped with sensors for acquiring environmental information. This device detects environmental information such as temperature, humidity, and soil characteristics in real time and transmits the data to a communication device. The communication device is responsible for transferring the environmental information received from the detection device to an intelligent module on a server.

[0757] Upon receiving this data, the server uses a learning algorithm within its intelligent module to generate an optimal cultivation plan. In this process, the server utilizes emotion recognition technology to recognize the user's emotional state. This technology leverages the camera and microphone of a smartphone or tablet device to analyze the user's voice and facial expressions, and the resulting data is transmitted to the server.

[0758] Based on the user's emotional state, the server optimizes farm work suggestions and delivers them to the user through notification channels. These notifications are sent via a smartphone application, allowing the user to view the suggested work plan in real time. For example, if a user is inspecting their field with their smartphone in the morning, the app might send a specific suggestion such as, "Your stress level is high today. Take it easy this afternoon and prioritize lighter tasks."

[0759] This system manages data exchange using data platforms such as Firebase and leverages cloud-based artificial intelligence services such as Google Cloud AI to analyze emotional states and generate cultivation plans.

[0760] A concrete example of a prompt message would be an instruction such as, "Assess the user's emotional state today and propose a farming plan tailored to their stress level." In this way, agricultural support that flexibly responds to the user's emotions is realized.

[0761] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0762] Step 1:

[0763] The terminal collects environmental information such as temperature, humidity, and soil characteristics from the detection device. The input environmental information data is formatted and then transferred to the server.

[0764] Step 2:

[0765] The server passes the environmental information received from the terminal to the intelligent module, which then begins analyzing it as input data. The intelligent module's learning algorithm analyzes this data and generates an optimal cultivation plan for the crops. This plan is then output.

[0766] Step 3:

[0767] The device captures the user's voice and facial expressions using its camera and microphone and transmits them to an emotion recognition system. The input user emotion data is transferred to a server. The server analyzes this data using the emotion recognition system to identify the user's current emotional state.

[0768] Step 4:

[0769] The server integrates and optimizes the cultivation plan based on the user's emotional state and environmental information. If the emotional state is high due to stress, instructions will be issued to reduce the work schedule.

[0770] Step 5:

[0771] The server sends optimized farm work suggestions to the terminal via a notification system. The terminal displays these suggestions on the user's smartphone app, notifying the user visually and audibly. The user can then review these notifications and perform tasks according to the optimized plan in real time.

[0772] 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 controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0773] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0774] 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 this disclosure is not limited thereto, and the specific processing may also be performed by the robot 414.

[0775] Furthermore, the emotion identification model 59, acting 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 a specific mapping, which is an emotion map (see Figure 9). Similarly, the emotion identification model 59 may also determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0776] Figure 9 shows an emotion map 400 in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[0777] These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression.

[0778] The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become.

[0779] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, motorcycles, etc., emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[0780] The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it is 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 the emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more."

[0781] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​representing each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple training data sets, which are combinations of user input and emotion values ​​representing each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions located close together have similar values, as shown in the emotion map 900 in Figure 10. Figure 10 shows an example where multiple emotions such as "reassured," "calm," and "confident" have similar emotion values.

[0782] The above description primarily focuses on the functions of the data processing device 12 in relation to this disclosure. However, the system related to this disclosure is not necessarily implemented on a server. The system related to this disclosure may be implemented as a general information processing system. This disclosure may be implemented, for example, as a software program that runs on a personal computer or as an application that runs on a smartphone. The method related to this disclosure may be provided to users in SaaS (Software as a Service) format.

[0783] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing of the specific process may be performed by multiple computers, including computer 22. For example, a data generation model 58 may be provided in an external device of the data processing device 12, and the external device may generate data according to the input data.

[0784] In the above embodiment, an example was given in which the specific processing program 56 is stored in the storage 32, but the technology of this disclosure is not limited thereto. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-temporary storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-temporary storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes specific processing according to the specific processing program 56.

[0785] 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.

[0786] Furthermore, it is not necessary to store the entirety 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 the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

[0787] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

[0788] The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of 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). Alternatively, the hardware resource that performs a specific process may consist of a single processor.

[0789] Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources.

[0790] Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose.

[0791] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and the like that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[0792] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[0793] The following is further disclosed regarding the embodiments described above.

[0794] (Claim 1)

[0795] A sensor for acquiring environmental data and a terminal for communication with it,

[0796] A server equipped with an artificial intelligence module for analyzing environmental data received from the aforementioned terminal and generating a crop cultivation plan,

[0797] A means for notifying the user of suggestions for agricultural work based on the aforementioned cultivation plan,

[0798] A system that includes this.

[0799] (Claim 2)

[0800] The system according to claim 1, wherein the artificial intelligence module uses a machine learning algorithm to predict the growth of crops.

[0801] (Claim 3)

[0802] The system according to claim 1, wherein the environmental data includes temperature, humidity, and soil conditions.

[0803] "Example 1"

[0804] (Claim 1)

[0805] A measuring device for acquiring environmental information and an information device for communication,

[0806] A computing device equipped with an intelligent module for analyzing environmental information received from the aforementioned information device and generating a crop growth plan,

[0807] A means for notifying users of instructions for agricultural work based on the aforementioned growth plan,

[0808] A means of transmitting data using wireless communication technology,

[0809] The aforementioned computing device has means for referencing past weather information and comparing it with real-time environmental information,

[0810] The means by which the user receives notification and plans specific farm work,

[0811] A system that includes this.

[0812] (Claim 2)

[0813] The system according to claim 1, wherein the intelligent module uses a machine learning algorithm to predict the growth of crops.

[0814] (Claim 3)

[0815] The system according to claim 1, wherein the environmental information includes temperature, humidity, and soil conditions.

[0816] "Application Example 1"

[0817] (Claim 1)

[0818] A measuring device for acquiring environmental information and an information processing device that communicates with it,

[0819] A data processing device equipped with an artificial intelligence module for analyzing environmental information received from the aforementioned information processing device and generating a plant cultivation plan,

[0820] A means for notifying the user of agricultural activity proposals based on the aforementioned cultivation plan,

[0821] The above proposal provides means for optimizing cultivation activities in limited spaces within urban environments,

[0822] A system that includes this.

[0823] (Claim 2)

[0824] The system according to claim 1, wherein the artificial intelligence module uses a data analysis algorithm to predict plant growth.

[0825] (Claim 3)

[0826] The system according to claim 1, wherein the environmental information includes weather data, humidity, and soil conditions.

[0827] "Example 2 of combining an emotion engine"

[0828] (Claim 1)

[0829] A measuring device for acquiring environmental information and a mobile terminal for communication,

[0830] A computer equipped with an automated learning model for analyzing environmental information received from the aforementioned mobile terminal and generating a plant cultivation plan,

[0831] A means for notifying the user of recommended work based on the aforementioned cultivation plan,

[0832] A means for collecting emotional information and adjusting the cultivation plan based on the user's emotional state,

[0833] A system that includes this.

[0834] (Claim 2)

[0835] The system according to claim 1, wherein the automated learning model uses a machine learning process to predict plant growth.

[0836] (Claim 3)

[0837] The system according to claim 1, wherein the environmental information includes temperature, humidity, and soil conditions.

[0838] "Application example 2 when combining with an emotional engine"

[0839] (Claim 1)

[0840] A detection device and a communication device for acquiring environmental information,

[0841] A computing device equipped with an intelligent module for analyzing environmental information received from the aforementioned device and generating a cultivation plan,

[0842] An emotion recognition means for recognizing the user's emotional state,

[0843] A means for notifying the user of farm work suggestions optimized according to the aforementioned emotional state,

[0844] A system that includes this.

[0845] (Claim 2)

[0846] The system according to claim 1, wherein the intelligent module uses a learning algorithm to predict crop growth.

[0847] (Claim 3)

[0848] The system according to claim 1, wherein the environmental information includes temperature, humidity, and soil characteristics. [Explanation of Symbols]

[0849] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots< / url:> < / url:> < / url:> < / url:>

Claims

1. A measuring device for acquiring environmental information and an information processing device that communicates with it, A data processing device equipped with an artificial intelligence module for analyzing environmental information received from the aforementioned information processing device and generating a plant cultivation plan, A means for notifying the user of agricultural activity proposals based on the aforementioned cultivation plan, The above proposal provides means for optimizing cultivation activities in limited spaces within urban environments, A system that includes this.

2. The system according to claim 1, wherein the artificial intelligence module uses a data analysis algorithm to predict plant growth.

3. The system according to claim 1, wherein the environmental information includes weather data, humidity, and soil conditions.

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