system

Abstract

We provide the system. [Solution] As a means of data collection, it has the function of receiving remote sensing data from Earth observation instruments, A function to acquire weather data from weather information providers, It has a function to collect soil data from soil condition detectors installed on the ground, A method for integrating collected data and performing data cleaning, A means of analyzing data using a generative model to generate optimized agricultural management guidelines based on crop growth and environmental changes, A system that includes means for notifying users of the above guidelines.

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, the method including 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 as a 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, performing optimal crop management according to fluctuations in weather conditions and soil conditions is important for achieving high-quality crop production and stable yields. However, in conventional methods, it is necessary to individually obtain and judge satellite data, weather data, and soil data, making it difficult to make quick and accurate decisions. In addition, technical knowledge for integrating and analyzing these data is required, which poses a problem of being inaccessible to many agricultural workers. The purpose of this invention is to provide a platform that integrates such data in real time and supports the determination of optimal agricultural management according to the growth state of crops.

Means for Solving the Problems

[0005] This invention provides a data collection means that integrates remote sensing data from Earth observation instruments, meteorological data from weather information providers, and soil data obtained from ground-based soil condition detectors. Furthermore, the collected data is integrated, data cleaned, and analyzed using a generative model. Based on the analysis results, optimized agricultural management guidelines that respond to crop growth and environmental changes are generated and notified to the user. This enables farmers to take immediate action on crop management, thereby stabilizing quality and yield. The system also includes a function to reflect user feedback in the generative model, allowing for continuous system improvement. Moreover, notifications to users are made timely and reliably using smartphones and tablet-type mobile information terminals.

[0006] "Data collection means" refers to the function of a system that acquires data from Earth observation instruments, weather information providers, and soil condition detectors installed on the ground, and integrates and manages that data.

[0007] "Earth observation instruments" are devices used to acquire information such as images of the Earth's surface and vegetation indices through remote sensing.

[0008] A "weather information provider" is an organization or system that provides data, including weather conditions and forecasts, to external parties.

[0009] A "soil condition detector" is a device placed on the ground that acquires information such as soil moisture, temperature, and pH value through sensors.

[0010] A "generative model" is an artificial intelligence technique used to analyze collected data and generate agricultural management guidelines based on specific algorithms.

[0011] A "user" is an agricultural worker who uses the system to receive and implement agricultural management advice.

[0012] A "portable information terminal" is an electronic device that a user can carry and use, such as a smartphone or tablet, and that has a notification function.

[0013] "Feedback" refers to advice, evaluations, and opinions from users regarding the system's operation. [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] This is a conceptual diagram showing an example of the configuration of a data processing system according to the third embodiment. [Figure 6] This 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] This is a conceptual diagram showing an example of the configuration of a data processing system according to the fourth embodiment. [Figure 8] This 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] This shows an emotion map where multiple emotions are mapped. [Figure 10] This shows an emotion map where multiple emotions are mapped. [Figure 11] This is a sequence diagram showing the processing flow of the data processing system in Example 1. [Figure 12] This 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 Embodiment 2 when combined with an emotion engine. [Figure 14] It is a sequence diagram showing the processing flow of the data processing system in Application Example 2 when combined with an emotion engine.

Modes for Carrying Out the Invention

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

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

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

[0018] In the following embodiments, the labeled RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a work memory by the processor.

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

[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 relates to a system for assisting farmers in optimizing crop management in real time. The system of this invention combines data collection means, generative models, and notification means to provide comprehensive agricultural management advice.

[0036] Server Embodiment

[0037] The server receives remote sensing data from Earth observation instruments and acquires weather data from weather information providers. It also collects soil data from soil condition detectors. This data is integrated and managed, and data cleaning is performed. The server uses generative models to analyze the data and generate agricultural management guidelines based on weather conditions, soil conditions, and crop growth. These guidelines include specific advice for adjusting the timing and amount of irrigation and fertilization according to crop health and predicted weather conditions.

[0038] Terminal embodiment

[0039] The device receives notifications sent from the server and displays them to the user. These notifications are designed to be immediately useful for the user's activities on the farm. The device is a smartphone or tablet-type personal information terminal with an intuitive user interface.

[0040] User Embodiment

[0041] Users manage their farms based on notifications from their devices. For example, they can adjust irrigation schedules based on crop health predictions and plan fertilizer application in response to changing weather conditions. Users can also send feedback on the advice provided to the server via their devices, which allows the system to continuously improve.

[0042] Specific example

[0043] For example, if a user manages a wheat field, the server can use satellite data to detect changes in the leaf color of growing wheat, indicating a possible nitrogen deficiency. If weather data predicts upcoming rain, the server will generate advice to apply nitrogen fertilizer before the rain. The user can receive this notification on their device and carry out the necessary farm work based on the instructions.

[0044] The following describes the processing flow.

[0045] Step 1:

[0046] The server receives remote sensing data from Earth observation instruments. This includes image data showing crop growth conditions and NDVI (Non-Digitally Neutralized Vegetation Index).

[0047] Step 2:

[0048] The server obtains weather data in real time via APIs from weather information providers. This includes information such as precipitation, temperature, humidity, and wind speed.

[0049] Step 3:

[0050] The server collects soil data in real time from soil condition detectors installed on the ground. This data includes soil moisture content, temperature, pH value, and other parameters.

[0051] Step 4:

[0052] The server integrates the collected data, performs data cleaning by imputing missing values ​​and removing outliers.

[0053] Step 5:

[0054] The server uses generative models to analyze integrated data and assess crop environmental stress and growth conditions. For example, it can detect nitrogen deficiency from changes in leaf color.

[0055] Step 6:

[0056] Based on the analysis results, the server generates agricultural management advice. This advice includes specific instructions on irrigation, fertilization, and pest and disease control.

[0057] Step 7:

[0058] The terminal receives agricultural management advice generated from the server and notifies the user. The advice is displayed in an easy-to-read format.

[0059] Step 8:

[0060] The user checks the notifications from their device and prepares to carry out appropriate farm work based on the advice provided.

[0061] Step 9:

[0062] Users can send feedback to the server via their device regarding the results of the farm work they have performed and the advice they have received.

[0063] Step 10:

[0064] The server collects user feedback, analyzes that data to improve the accuracy of the generative model, and updates the model as needed.

[0065] (Example 1)

[0066] 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."

[0067] In agriculture, it is crucial to understand weather changes, soil conditions, and crop growth in real time and to manage them efficiently and optimally based on that information. However, conventional methods suffer from insufficient data collection and a lack of integrated management capabilities, making it difficult to optimize agricultural management. There was also a need for a method to efficiently incorporate user feedback and continuously improve the system.

[0068] 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.

[0069] In this invention, the server includes, as data acquisition means, means for receiving distance exploration data from an observation device, means for acquiring environmental data from a weather information source, and means for accumulating geological data from a soil condition measuring device placed on the ground. This enables the generation of advanced agricultural management guidelines based on real-time integrated data.

[0070] "Data acquisition means" refers to a system or process for receiving and integrating necessary information from observation devices or information sources.

[0071] An "observation device" is an instrument used to acquire data on the state of the Earth and environmental changes as distance-based survey data.

[0072] "Distance survey data" refers to data on environmental conditions and surface changes acquired from remote locations using remote sensing technology.

[0073] A "weather information provider" refers to an external organization or platform that provides environmental data.

[0074] "Environmental data" refers to weather-related data such as temperature, humidity, and precipitation.

[0075] A "soil condition measuring device" is a device placed on the ground to collect geological data.

[0076] "Geological data" refers to data about the condition of the ground, including information on soil moisture content and nutrients.

[0077] A "generative artificial intelligence model" is an AI technology used to analyze data and generate specific guidelines or results.

[0078] "Guidelines for agricultural use management" are specific advice and plans for carrying out optimal agricultural activities according to crop growth and environmental conditions.

[0079] "Users" refer to agricultural workers who receive information through this system and carry out agricultural work.

[0080] The system of this invention is designed to optimize agricultural management in real time, and combines data acquisition means, generation AI models, and notification means to provide comprehensive agricultural management advice.

[0081] Server Embodiment

[0082] The server receives distance survey data from observation devices and acquires environmental data from weather information sources. Furthermore, it collects geological data from soil condition measurement devices. Data acquisition is automated using APIs, and the dataset is formatted and purified using Python's Pandas and NumPy libraries. The server analyzes the integrated data using generative AI models such as TENSORFLOW® and PyTorch to generate agricultural management guidelines based on crop growth and environmental conditions. Specifically, this includes identifying the optimal timing and amount of irrigation and fertilization.

[0083] Terminal embodiment

[0084] The device receives notifications from the server and displays them to the user. The device is a smartphone or tablet-type personal information terminal equipped with an intuitive user interface. Notifications are delivered via a push notification system and displayed visually in a GUI format, allowing the user to immediately decide on the necessary action.

[0085] User Embodiment

[0086] Users manage their farms based on notifications from their devices. For example, they can adjust irrigation schedules based on crop health predictions and plan fertilizer application in response to changes in environmental conditions. Furthermore, they can send feedback on the results of their actions from their devices to the server. This allows the system to continuously improve its generated AI model based on the provided data.

[0087] Specific example

[0088] For example, if you are managing a wheat farm, the server can use remote sensing data to detect changes in leaf color and suggest a nitrogen deficiency. Furthermore, if rainfall is predicted, the server will suggest applying nitrogen fertilizer before the rain. The user receives this notification on their terminal and can quickly adjust farm work. A concrete example of a prompt message would be, "Generate specific advice on the optimal timing for fertilizer application based on wheat field leaf color change data and future weather forecasts."

[0089] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0090] Step 1:

[0091] The server receives distance survey data from observation devices and acquires environmental data from weather information sources. Furthermore, it collects geological data from soil condition measurement devices. It takes remote sensing data, weather data, and soil data as input and integrates them. Specifically, it downloads data via API requests and converts it to a specified format using a Python script. The output is a collection of raw data, which is saved for later processing.

[0092] Step 2:

[0093] The server performs data cleansing on the collected data. It uses the integrated raw data as input. It detects missing values ​​and outliers from this input data and performs imputation or deletion as necessary. Specifically, it uses libraries such as Pandas and NumPy to format the data and create a clean dataset. The output is a clean dataset, ready to proceed to the subsequent analysis steps.

[0094] Step 3:

[0095] The server takes a clean dataset as input and performs analysis using a generated AI model. Specifically, it applies machine learning models such as TensorFlow and PyTorch to extract useful patterns and relationships from the data. The output is agricultural management guidance based on the analysis. This guidance includes appropriate timing for irrigation and fertilization determined from the health of the crops.

[0096] Step 4:

[0097] The server generates agricultural management guidelines obtained from the analysis results as a notification and sends it to the terminal. The input uses the analysis results data and the notification format. Specifically, it sends data to the terminal using the HTTP protocol and delivers notifications to the user in real time via a push notification system. The output is a notification message displayed to the user on the terminal.

[0098] Step 5:

[0099] The terminal receives notifications from the server as input and displays them visually to the user. Specifically, the mobile device's application receives the notification and presents the information to the user in a GUI format. The output is notification information presented in a format that is easy for the user to understand.

[0100] Step 6:

[0101] Users manage their farms using notifications from their devices as input, planning and executing specific tasks. These tasks include operating irrigation equipment and applying fertilizer. Specifically, they perform necessary actions in the field based on the notifications from their devices. The output is the result of the completed farm work, which is returned to the server as feedback via the device after the work is completed.

[0102] Step 7:

[0103] The server uses user feedback as input to improve its generated AI model. Specifically, it collects feedback data and uses it to retrain the generated AI model. The output is an improved AI model, which helps provide more accurate guidance in the future.

[0104] (Application Example 1)

[0105] 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."

[0106] Farmers face the challenge of not being able to grasp crop growth conditions and environmental changes in real time and manage them optimally using conventional methods. Furthermore, there is a demand for efficient farming through automated systems. However, conventional methods lack adequate support for workers on the farm, making efficient work management difficult.

[0107] 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.

[0108] In this invention, the server includes means for receiving remote sensing data from Earth observation instruments as a data collection means, means for acquiring weather data from weather information providers, means for accumulating soil data from soil condition detectors installed on the ground, means for integrating the collected data and performing data cleaning, means for analyzing the data using a generative model and generating optimized agricultural management guidelines based on crop growth and environmental changes, means for performing automated patrols of the farm using management equipment to monitor the condition of targets and perform tasks, and means for notifying workers equipped with visual devices. This enables agricultural workers to receive information in real time and efficiently manage and operate their farms.

[0109] "Data collection means" refers to a system that acquires and integrates data from Earth observation instruments, weather information providers, and soil condition detectors.

[0110] "Earth observation equipment" refers to observation instruments used to acquire remote sensing data from above a farm.

[0111] "Weather information provider" refers to an organization or service that provides weather data.

[0112] A "soil condition detector" is a device installed on the ground to detect the condition of the soil.

[0113] "Data cleaning" is the process of organizing and integrating collected data to prepare it for analysis.

[0114] A "generative model" is a mathematical or machine learning model used to analyze data and generate guidelines or predictions tailored to a specific purpose.

[0115] "Agricultural management guidelines" are instructions and advice that show the optimal management methods in response to crop growth and environmental changes.

[0116] "Management equipment" refers to devices that automatically patrol the farm, collecting data and performing tasks.

[0117] A "visual device" is a device that displays information and provides visual notification to workers.

[0118] The system that realizes this invention uses the following hardware and software to collect and analyze various data. The server integrates remote sensing data acquired for Earth observation, meteorological data acquired from weather information providers, and soil data from soil condition detectors. This information is prepared using data cleaning techniques and analyzed using generative models, such as machine learning frameworks like TensorFlow.

[0119] The agricultural management guidelines generated by the analysis specifically indicate the timing and methods of irrigation and fertilization according to the health of the crops and environmental conditions. The management equipment automatically patrols the farm and performs the necessary tasks. This makes it possible to operate the farm efficiently without human intervention.

[0120] The terminal displays information on smartphones, tablet-type information processing devices, or visual devices, notifying workers of the situation in real time. This information includes detailed guidelines for daily farm work, such as the timing of starting irrigation or applying fertilizer.

[0121] By using smart devices, users can instantly receive these notifications and send feedback back to the system. This allows the generative model to continuously learn and improve the accuracy of agricultural management. Furthermore, providing instructions to workers through visual devices enables rapid response in the field.

[0122] For example, if the server detects a change in the color of tomato leaves and indicates a nitrogen deficiency, a worker wearing a visual device will receive a notification saying, "There are signs of nitrogen deficiency on the tomato leaves. Please fertilize immediately." An example of a prompt message would be, "Anomaly detected during tomato field inspection. What is the next recommended action?" This prompt allows the system to generate and provide appropriate countermeasures.

[0123] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0124] Step 1:

[0125] The server acquires remote sensing data from Earth observation instruments. The input is remote sensing data, and the output generates initial growth data that is stored in a database. This data includes information about crop growth and is verified for consistency for use in subsequent analysis.

[0126] Step 2:

[0127] The server retrieves weather data from weather information providers. The input is predicted weather data, and the output is managed within the server as integrated data. This data includes temperature, humidity, precipitation, etc., and after verifying that each variable is within normal limits, it is reflected in future forecasts.

[0128] Step 3:

[0129] The server collects soil data from soil condition detectors. Inputs include soil moisture and nutrient status data, and output is a success notification indicating that this data was successfully captured. Soil pH and moisture content are considered important in this process, and an alert is generated if any inconsistencies are found.

[0130] Step 4:

[0131] The server integrates the collected data and performs data cleaning. Remote sensing data, weather data, and soil data are used as inputs. Unnecessary data and missing values ​​are removed, and a dataset optimized for analysis is output. The completeness and consistency of the data are verified, and it is ready for the next analysis stage.

[0132] Step 5:

[0133] The server performs data analysis using a generative model. Integrated data, after data cleaning, is the input, and crop growth predictions and guidelines for optimal agricultural management are output. The generative AI model used here interprets the data using statistical methods and machine learning algorithms to formulate future farming plans.

[0134] Step 6:

[0135] The terminal notifies the user based on agricultural management guidelines obtained from the server. The input is the analysis results, and the output is specific work instructions displayed on the user's terminal. At this stage, action-based prompts are also included, allowing the user to immediately understand what to do.

[0136] Step 7:

[0137] Users perform actual farm work based on notifications displayed on their devices. The input is the guidance from the device, and the output is the agricultural action performed. This enables timely decision-making and improves the efficiency of farm work.

[0138] Step 8:

[0139] Users send feedback to the server regarding the status of completed tasks and areas for improvement. The input is the user's feedback information, and the output is training data used in a generative model. Through this feedback loop, the entire system evolves, leading to smarter agricultural management.

[0140] 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.

[0141] This invention relates to a system that helps farmers optimize crop management in real time, incorporating an emotion engine that recognizes user emotions to provide more adaptive advice. The system comprises data collection means, a generative model, an emotion engine, and notification means, and provides comprehensive agricultural management advice.

[0142] Server Embodiment

[0143] The server receives remote sensing data from Earth observation instruments and acquires weather data from weather information providers. It also collects soil data from soil condition detectors installed on the ground. This data is integrated and data cleaning is performed. Subsequently, the collected data is analyzed using a generative model to generate agricultural management guidelines in response to crop growth conditions and environmental changes.

[0144] Emotional Engine Implementation

[0145] The server has a built-in emotion engine that analyzes the user's voice and text input to recognize their emotions. This emotion recognition allows the server to adjust the content and method of advice based on the user's mental state. For example, if the user is feeling stressed, it will provide more concise and easy-to-follow advice.

[0146] Terminal embodiment

[0147] The device receives agricultural management advice and emotion-based adjustment information transmitted from the server and notifies the user. Notifications are customized according to the user's emotional state to ensure a comfortable experience. The device is a smartphone or tablet-type mobile information terminal, and its user interface is intuitive and easy to operate.

[0148] User Embodiment

[0149] Users perform agricultural management based on notifications sent from their devices. For example, the system provides specific examples of how to adjust field irrigation before predicted rainfall. Users can also send feedback to the server regarding their thoughts on agricultural management and the results after implementation. The sentiment engine analyzes the user's feedback and uses the results to improve the generative model.

[0150] Specific example

[0151] For example, if a user managing a wheat field asks "What's the weather like next week?" via voice command on their smartphone, the server performs remote sensing and weather data analysis, and the emotion engine detects that the user is feeling anxious. The server then provides calm and specific advice, such as "Rainfall is expected to be light next week, but temperatures will be high, so it would be a good idea to increase irrigation frequency." Based on this information, the user can plan their work and perform farming efficiently while reducing anxiety.

[0152] The following describes the processing flow.

[0153] Step 1:

[0154] The server receives remote sensing data from Earth observation instruments. This includes image data related to vegetation indices in agricultural land and crop growth conditions.

[0155] Step 2:

[0156] The server obtains real-time weather data via APIs from weather information providers. This includes information such as predicted precipitation, temperature, and humidity.

[0157] Step 3:

[0158] The server collects and integrates data such as soil moisture, temperature, and pH values ​​in real time from soil condition detectors installed on the ground.

[0159] Step 4:

[0160] The server integrates all collected data and performs data cleaning. It corrects any outliers or missing values, preparing the data for analysis.

[0161] Step 5:

[0162] The server uses a generative model to analyze the cleaned data. Based on the analysis results, it evaluates crop growth conditions and environmental stress, and generates optimal agricultural management guidelines.

[0163] Step 6:

[0164] When a user enters a request via voice or text into their device, that information is sent from the device to the server.

[0165] Step 7:

[0166] The server uses an emotion engine to recognize emotions from user input. It analyzes the type and intensity of the emotion to determine the user's psychological state.

[0167] Step 8:

[0168] The server takes the results of sentiment analysis into account and adjusts agricultural management guidelines according to the user's emotions. For example, it generates more concise and reassuring advice for users who are feeling stressed.

[0169] Step 9:

[0170] The device receives and notifies the user of tailored agricultural management advice. The notification is displayed in a format that takes the user's emotional state into consideration.

[0171] Step 10:

[0172] Users check notifications from their devices and perform farm work based on the instructions. After completion, users can send feedback on the results and advice to the server from their devices.

[0173] Step 11:

[0174] The server analyzes user feedback, performs sentiment analysis using an emotion engine, and then uses the feedback to improve the generative model and the way advice is provided.

[0175] (Example 2)

[0176] 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 as the "terminal".

[0177] In modern agriculture, it is difficult to manage crops stably without being affected by climate change and environmental shifts, and the mental burden on farmers is increasing. Therefore, there is a need to provide agricultural management guidelines that utilize real-time environmental data while being tailored to the emotional state of individual users. However, existing systems lack the means to provide individually optimized advice that takes emotions into account, so this problem needs to be solved.

[0178] 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.

[0179] In this invention, the server includes means for receiving remote detection information from Earth observation instruments as a data collection means, means for analyzing the user's emotions using an emotion engine and adjusting advice based on the results, and means for notifying the user using a mobile information terminal. This makes it possible to provide optimized agricultural management guidelines based on real-time, highly accurate environmental data, while taking into account the user's emotional state.

[0180] A "data collection means" is a means that has the function of receiving information from Earth observation instruments and weather information providers, and accumulating soil information from soil condition detectors.

[0181] "Remote detection information" refers to data acquired remotely from geographically distant locations using Earth observation equipment, etc.

[0182] "Weather information" refers to data that shows environmental conditions such as precipitation, temperature, and wind speed, and provides the necessary information for making decisions in agricultural management.

[0183] "Soil information" refers to data that indicates the health of the soil, such as its moisture content and nutrients.

[0184] A "generative model" refers to a mathematical or computational algorithm used to analyze collected data and perform pattern recognition or prediction.

[0185] An "emotion engine" is a technology that analyzes user voice and text input to recognize emotions and generates analysis results as feedback.

[0186] A "notification method" is a mechanism that provides information sent from a server to a user via a mobile device.

[0187] "Personal information terminals" refer to electronic devices such as smartphones and tablets that are easy to carry and have an intuitive user interface.

[0188] This invention is an information system for agricultural workers to efficiently manage crops. The system comprises data collection means, a generative model, an emotion engine, and notification means, and provides comprehensive agricultural management advice.

[0189] The server receives remote detection information from Earth observation instruments and obtains weather information from weather information providers. It also collects soil information from soil condition detectors installed on the ground. This data is integrated and cleaned, and then analyzed by a generative AI model. The generative model learns agriculture-related patterns and generates agricultural management guidelines that respond to crop growth conditions and environmental changes based on the collected data.

[0190] Furthermore, the server uses an emotion engine to analyze the user's voice and text input and recognize their emotions. Based on this information, advice is adjusted according to the user's mental state. For example, if the user is feeling anxious, more concise and actionable advice will be provided.

[0191] The terminal receives agricultural management advice and emotion-based adjustment information transmitted from the server and notifies the user. The terminals used are smartphones and tablet-type mobile devices, and the user interface is intuitive and easy to operate.

[0192] Users perform agricultural management based on notifications from their devices. This system allows users to perform efficient and adaptive agricultural work and reduce mental stress. For example, when managing crop watering, users can receive a notification from the server saying, "It is expected to be dry next week, so please increase the frequency of irrigation," and then create an optimal work plan.

[0193] An example prompt might be, "The user wants to know the optimal agricultural management methods based on remote sensing data and weather data." This allows the server to perform appropriate data analysis and generate advice.

[0194] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0195] Step 1:

[0196] The server receives remote detection information from Earth observation instruments. The input at this stage includes data such as temperature, humidity, and vegetation index acquired via satellites and drones. The server temporarily stores this data in preparation for the next data processing step.

[0197] Step 2:

[0198] The server retrieves weather information provided by weather information providers. The input consists of forecast data such as regional precipitation, temperature, and wind speed. This allows the server to understand future weather conditions and accumulate data for use in crop management planning.

[0199] Step 3:

[0200] The server collects soil information received from soil condition detectors. This input includes data on soil moisture content and nutrients. Based on this, the server obtains basic data for evaluating soil health.

[0201] Step 4:

[0202] The server integrates all the collected data and performs data cleaning. The input here is a wide range of data collected so far, including temperature, humidity, weather conditions, and soil information. The server removes outliers from these datasets and interpolates missing data to prepare them for analysis.

[0203] Step 5:

[0204] The server applies regression analysis and machine learning algorithms using a generative AI model based on the prepared data. The input here is cleaned, integrated data, and the output is crop growth predictions and guidelines for necessary agricultural management. Based on these results, the server determines the optimal agricultural management method.

[0205] Step 6:

[0206] The server uses an emotion engine to recognize emotions from the user's voice and text. Input is user questions and feedback, and the server adjusts the content and wording of advice based on this analysis. The output is optimized advice tailored to the user's mental state.

[0207] Step 7:

[0208] The terminal receives agricultural management guidelines and emotion-based advice sent from the server. The input is this notification data, which the terminal displays to the user. The output is visual or auditory feedback to the user, delivered through the user interface.

[0209] Step 8:

[0210] Users perform agricultural management based on information received from their devices. Input is the notified advice, which users utilize to carry out tasks such as irrigation and fertilization. Output is the actual management activities on the farmland and their results.

[0211] Step 9:

[0212] Users send management results and feedback to the server. The server then analyzes the collected feedback using an emotion engine and uses it to improve the accuracy of future advice and train generative models.

[0213] (Application Example 2)

[0214] 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".

[0215] Agricultural workers and distributors need to operate efficiently and effectively while adapting to environmental changes and diversifying consumer needs. However, conventional agricultural and distribution management methods have struggled to provide real-time data analysis and adaptive guidance tailored to the emotional state of users. Furthermore, optimizing delivery routes while considering emotions has been insufficient to enhance user satisfaction. Effective solutions to these challenges are needed.

[0216] 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.

[0217] In this invention, the server includes, as a data collection means, a function to receive remote sensing data from a global monitoring device, a function to acquire weather data from a weather information provider, and a function to collect soil data from a ground-based soil condition detection device. This enables agricultural workers and distributors to receive adaptive support that is sensitive to their needs based on the data. Furthermore, it can improve demand forecasting and optimize delivery routes in the distribution of agricultural products, thereby increasing user satisfaction.

[0218] "Data collection means" refers to functions for acquiring various environmental information, such as remote sensing data, weather data, and soil data.

[0219] A "global monitoring device" is a device that enables observations on a global scale and provides remote sensing data.

[0220] "Remote sensing data" refers to observational data obtained without direct contact with the object or region, and is collected by satellites or aircraft.

[0221] A "weather information provider" is an organization that conducts weather observations and provides the resulting weather data.

[0222] A "soil condition detection device" is a device used to detect the condition of the soil, such as its moisture and nutrients.

[0223] "Emotion analysis means" refers to a function that recognizes emotions from the user's voice or text and changes the way information is provided accordingly.

[0224] A "generative model" is an analytical model used to optimize agricultural management and logistics based on collected data.

[0225] "Demand forecasting in distribution" is an analysis aimed at ensuring appropriate supply by predicting how much agricultural products will be consumed.

[0226] "Optimizing delivery routes" refers to calculating the shortest and most optimal routes to deliver goods efficiently and quickly.

[0227] "Data preparation" is the process of integrating collected data and processing it into an analyzable format.

[0228] "Users" refers to agricultural workers and distributors who use this system.

[0229] A "mobile information and communication terminal" is a portable device, such as a smartphone or tablet, that has the function of processing information and communicating with others.

[0230] The system for implementing this invention will achieve efficiency and optimization in agriculture and distribution through data collection, analysis, and information provision.

[0231] The server receives remote sensing data through a global monitoring device and acquires weather data from weather information providers. Furthermore, it collects soil data using ground-based soil condition detection devices. This data is converted into a format suitable for analysis through a processing stage. Finally, a generative AI model is used to analyze the collected data and generate agricultural management guidelines. This model utilizes deep learning libraries such as TensorFlow and PyTorch.

[0232] The server also analyzes voice or text input from the user and recognizes their emotional state using sentiment analysis tools. It utilizes Google® Cloud Speech-to-Text for speech recognition and Natural Language Toolkit (NLTK) for natural language processing. This enables the provision of adaptive advice tailored to the user's mental state and needs.

[0233] The terminal receives the analysis results and notifies the user. Mobile information and communication devices, particularly smartphones and tablets, are used for communication. The notifications are adjusted according to the user's emotional state; for example, in the case of an urgent order, a delivery route that allows for quick response is suggested. For this purpose, map APIs capable of handling geographic information in GeoJSON format are used.

[0234] Users develop agricultural management and distribution plans based on the information provided. For example, if a user asks, "What's the weather like next week?", the system analyzes weather data and provides advice such as, "Rainfall is expected to be low next week. High temperatures are anticipated, so increase irrigation." Furthermore, an example of a prompt could be, "Create a prompt to check next week's weather and generate calm, emotionally resonant advice. The target audience is consumers who are anxious about urgent distribution needs."

[0235] In this way, the system can solve various challenges in agriculture and distribution, and improve user satisfaction and efficiency.

[0236] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0237] Step 1:

[0238] The server receives remote sensing data from a global monitoring device. The input is remote sensing data, and by receiving this data, global environmental information can be obtained. The server stores this data in a storage database.

[0239] Step 2:

[0240] The server retrieves weather data from weather information providers. Input data comes from weather information APIs, receiving information such as temperature and precipitation. The received data is automatically stored in the database in real time.

[0241] Step 3:

[0242] The server collects soil data from soil condition detection devices installed on the ground. The input here includes soil moisture and nutrient data. The server then performs a preparation process to format this data for analysis.

[0243] Step 4:

[0244] The server integrates and processes all collected data. Inputs include remote sensing data, weather data, and soil data. The server cleans these data and converts them into an analyzable format.

[0245] Step 5:

[0246] The server uses an AI model to analyze data and generate agricultural management guidelines. The input is a pre-configured dataset, and a machine learning algorithm is applied to predict crop growth and output specific guidelines.

[0247] Step 6:

[0248] The server receives voice or text input from the user and analyzes the emotional state using emotion analysis tools. The input is voice or text data, and an emotion recognition algorithm is used to analyze the emotion and identify the emotional state as output.

[0249] Step 7:

[0250] The server adjusts the guidelines derived from the generative model based on the sentiment analysis results, generating advice optimized for the user. The adjusted advice becomes the output. It provides users with flexible guidelines that respond to their emotions.

[0251] Step 8:

[0252] The device receives tailored advice and notifies the user. The input is the tailored advice, which the device receives and displays to the user at the appropriate time and in the appropriate format. Specifically, the priority and content of the notification change depending on the urgency.

[0253] Step 9:

[0254] Based on the information provided by the user, agricultural management and distribution plans are implemented. The information is used to develop specific farming tasks and distribution procedures. At this time, the implementation plan is revised based on the advice received.

[0255] 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.

[0256] 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.

[0257] 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.

[0258] [Second Embodiment]

[0259] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0260] 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.

[0261] 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).

[0262] 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.

[0263] 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.

[0264] 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).

[0265] 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.

[0266] 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.

[0267] 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.

[0268] 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.

[0269] 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.

[0270] 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".

[0271] This invention relates to a system for assisting farmers in optimizing crop management in real time. The system of this invention combines data collection means, generative models, and notification means to provide comprehensive agricultural management advice.

[0272] Server Embodiment

[0273] The server receives remote sensing data from Earth observation instruments and acquires weather data from weather information providers. It also collects soil data from soil condition detectors. This data is integrated and managed, and data cleaning is performed. The server uses generative models to analyze the data and generate agricultural management guidelines based on weather conditions, soil conditions, and crop growth. These guidelines include specific advice for adjusting the timing and amount of irrigation and fertilization according to crop health and predicted weather conditions.

[0274] Terminal embodiment

[0275] The device receives notifications sent from the server and displays them to the user. These notifications are designed to be immediately useful for the user's activities on the farm. The device is a smartphone or tablet-type personal information terminal with an intuitive user interface.

[0276] User Embodiment

[0277] Users manage their farms based on notifications from their devices. For example, they can adjust irrigation schedules based on crop health predictions and plan fertilizer application in response to changing weather conditions. Users can also send feedback on the advice provided to the server via their devices, which allows the system to continuously improve.

[0278] Specific example

[0279] For example, if a user manages a wheat field, the server can use satellite data to detect changes in the leaf color of growing wheat, indicating a possible nitrogen deficiency. If weather data predicts upcoming rain, the server will generate advice to apply nitrogen fertilizer before the rain. The user can receive this notification on their device and carry out the necessary farm work based on the instructions.

[0280] The following describes the processing flow.

[0281] Step 1:

[0282] The server receives remote sensing data from earth observation instruments. This includes image data indicating the growth state of crops and NDVI (Normalized Difference Vegetation Index), etc.

[0283] Step 2:

[0284] The server obtains real-time weather data via the API of the weather information provider. This includes information such as precipitation, temperature, humidity, wind speed, etc.

[0285] Step 3:

[0286] The server collects real-time soil data from soil condition detectors installed on the ground. This includes the moisture content, temperature, pH value of the soil, etc.

[0287] Step 4:

[0288] The server integrates the collected data, performs imputation of missing values and removal of outliers, and conducts data cleaning.

[0289] Step 5:

[0290] The server uses a generation model to analyze the integrated data and evaluate the environmental stress and growth status of crops. For example, it detects nitrogen deficiency from the color change of leaves.

[0291] Step 6:

[0292] Based on the analysis results, the server generates agricultural management advice. This advice includes specific instructions on irrigation, fertilization, and pest control.

[0293] Step 7:

[0294] The terminal receives agricultural management advice generated from the server and notifies the user. The advice is displayed in an easy-to-read format.

[0295] Step 8:

[0296] The user checks the notifications from their device and prepares to carry out appropriate farm work based on the advice provided.

[0297] Step 9:

[0298] Users can send feedback to the server via their device regarding the results of the farm work they have performed and the advice they have received.

[0299] Step 10:

[0300] The server collects user feedback, analyzes that data to improve the accuracy of the generative model, and updates the model as needed.

[0301] (Example 1)

[0302] 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."

[0303] In agriculture, it is crucial to understand weather changes, soil conditions, and crop growth in real time and to manage them efficiently and optimally based on that information. However, conventional methods suffer from insufficient data collection and a lack of integrated management capabilities, making it difficult to optimize agricultural management. There was also a need for a method to efficiently incorporate user feedback and continuously improve the system.

[0304] 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.

[0305] In this invention, the server includes, as data acquisition means, means for receiving distance exploration data from an observation device, means for acquiring environmental data from a weather information provider, and means for integrating geological data from a soil condition measuring device arranged on the ground. This enables the generation of advanced agricultural management guidelines based on real-time integrated data.

[0306] The "data acquisition means" is a system or process for receiving and integrating necessary information from an observation device or an information provider.

[0307] The "observation device" is a device for acquiring data on the state of the earth and environmental changes as distance exploration data.

[0308] The "distance exploration data" is data on environmental conditions and surface changes acquired from a remote location using remote sensing technology.

[0309] The "weather information provider" is an external organization or platform that provides environmental data.

[0310] The "environmental data" refers to data related to weather such as temperature, humidity, and precipitation.

[0311] The "soil condition measuring device" is a device arranged on the ground for collecting geological data.

[0312] The "geological data" is data on the state of the ground including information on soil moisture content and nutrients.

[0313] The "generative artificial intelligence model" is an AI technology used to analyze data and generate specific guidelines or results.

[0314] The "guidelines for agricultural utilization management" are specific advice or plans for carrying out optimal agricultural activities according to crop growth and environmental conditions.

[0315] "Users" refer to agricultural workers who receive information through this system and carry out agricultural work.

[0316] The system of this invention is designed to optimize agricultural management in real time, and combines data acquisition means, generation AI models, and notification means to provide comprehensive agricultural management advice.

[0317] Server Embodiment

[0318] The server receives distance survey data from observation devices and acquires environmental data from weather information sources. Furthermore, it collects geological data from soil condition measurement devices. Data acquisition is automated using APIs, and the dataset is formatted and purified using Python libraries such as Pandas and NumPy. The server analyzes the integrated data using generative AI models such as TensorFlow and PyTorch to generate agricultural management guidelines based on crop growth and environmental conditions. Specifically, this includes identifying the optimal timing and amount of irrigation and fertilization.

[0319] Terminal embodiment

[0320] The device receives notifications from the server and displays them to the user. The device is a smartphone or tablet-type personal information terminal equipped with an intuitive user interface. Notifications are delivered via a push notification system and displayed visually in a GUI format, allowing the user to immediately decide on the necessary action.

[0321] User Embodiment

[0322] Users manage their farms based on notifications from their devices. For example, they can adjust irrigation schedules based on crop health predictions and plan fertilizer application in response to changes in environmental conditions. Furthermore, they can send feedback on the results of their actions from their devices to the server. This allows the system to continuously improve its generated AI model based on the provided data.

[0323] Specific example

[0324] For example, if you are managing a wheat farm, the server can use remote sensing data to detect changes in leaf color and suggest a nitrogen deficiency. Furthermore, if rainfall is predicted, the server will suggest applying nitrogen fertilizer before the rain. The user receives this notification on their terminal and can quickly adjust farm work. A concrete example of a prompt message would be, "Generate specific advice on the optimal timing for fertilizer application based on wheat field leaf color change data and future weather forecasts."

[0325] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0326] Step 1:

[0327] The server receives distance survey data from observation devices and acquires environmental data from weather information sources. Furthermore, it collects geological data from soil condition measurement devices. It takes remote sensing data, weather data, and soil data as input and integrates them. Specifically, it downloads data via API requests and converts it to a specified format using a Python script. The output is a collection of raw data, which is saved for later processing.

[0328] Step 2:

[0329] The server performs data cleansing on the collected data. It uses the integrated raw data as input. It detects missing values ​​and outliers from this input data and performs imputation or deletion as necessary. Specifically, it uses libraries such as Pandas and NumPy to format the data and create a clean dataset. The output is a clean dataset, ready to proceed to the subsequent analysis steps.

[0330] Step 3:

[0331] The server takes a clean dataset as input and performs analysis using a generated AI model. Specifically, it applies machine learning models such as TensorFlow and PyTorch to extract useful patterns and relationships from the data. The output is agricultural management guidance based on the analysis. This guidance includes appropriate timing for irrigation and fertilization determined from the health of the crops.

[0332] Step 4:

[0333] The server generates agricultural management guidelines obtained from the analysis results as a notification and sends it to the terminal. The input uses the analysis results data and the notification format. Specifically, it sends data to the terminal using the HTTP protocol and delivers notifications to the user in real time via a push notification system. The output is a notification message displayed to the user on the terminal.

[0334] Step 5:

[0335] The terminal receives notifications from the server as input and displays them visually to the user. Specifically, the mobile device's application receives the notification and presents the information to the user in a GUI format. The output is notification information presented in a format that is easy for the user to understand.

[0336] Step 6:

[0337] Users manage their farms using notifications from their devices as input, planning and executing specific tasks. These tasks include operating irrigation equipment and applying fertilizer. Specifically, they perform necessary actions in the field based on the notifications from their devices. The output is the result of the completed farm work, which is returned to the server as feedback via the device after the work is completed.

[0338] Step 7:

[0339] The server uses user feedback as input to improve its generated AI model. Specifically, it collects feedback data and uses it to retrain the generated AI model. The output is an improved AI model, which helps provide more accurate guidance in the future.

[0340] (Application Example 1)

[0341] 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."

[0342] Farmers face the challenge of not being able to grasp crop growth conditions and environmental changes in real time and manage them optimally using conventional methods. Furthermore, there is a demand for efficient farming through automated systems. However, conventional methods lack adequate support for workers on the farm, making efficient work management difficult.

[0343] 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.

[0344] In this invention, the server includes means for receiving remote sensing data from Earth observation instruments as a data collection means, means for acquiring weather data from weather information providers, means for accumulating soil data from soil condition detectors installed on the ground, means for integrating the collected data and performing data cleaning, means for analyzing the data using a generative model and generating optimized agricultural management guidelines based on crop growth and environmental changes, means for performing automated patrols of the farm using management equipment to monitor the condition of targets and perform tasks, and means for notifying workers equipped with visual devices. This enables agricultural workers to receive information in real time and efficiently manage and operate their farms.

[0345] "Data collection means" refers to a system that acquires and integrates data from Earth observation instruments, weather information providers, and soil condition detectors.

[0346] "Earth observation equipment" refers to observation instruments used to acquire remote sensing data from above a farm.

[0347] "Weather information provider" refers to an organization or service that provides weather data.

[0348] A "soil condition detector" is a device installed on the ground to detect the condition of the soil.

[0349] "Data cleaning" is the process of organizing and integrating collected data to prepare it for analysis.

[0350] A "generative model" is a mathematical or machine learning model used to analyze data and generate guidelines or predictions tailored to a specific purpose.

[0351] "Agricultural management guidelines" are instructions and advice that show the optimal management methods in response to crop growth and environmental changes.

[0352] "Management equipment" refers to devices that automatically patrol the farm, collecting data and performing tasks.

[0353] A "visual device" is a device that displays information and provides visual notification to workers.

[0354] The system that realizes this invention uses the following hardware and software to collect and analyze various data. The server integrates remote sensing data acquired for Earth observation, meteorological data acquired from weather information providers, and soil data from soil condition detectors. This information is prepared using data cleaning techniques and analyzed using generative models, such as machine learning frameworks like TensorFlow.

[0355] The agricultural management guidelines generated by the analysis specifically indicate the timing and methods of irrigation and fertilization according to the health of the crops and environmental conditions. The management equipment automatically patrols the farm and performs the necessary tasks. This makes it possible to operate the farm efficiently without human intervention.

[0356] The terminal displays information on smartphones, tablet-type information processing devices, or visual devices, notifying workers of the situation in real time. This information includes detailed guidelines for daily farm work, such as the timing of starting irrigation or applying fertilizer.

[0357] By using smart devices, users can instantly receive these notifications and send feedback back to the system. This allows the generative model to continuously learn and improve the accuracy of agricultural management. Furthermore, providing instructions to workers through visual devices enables rapid response in the field.

[0358] For example, if the server detects a change in the color of tomato leaves and indicates a nitrogen deficiency, a worker wearing a visual device will receive a notification saying, "There are signs of nitrogen deficiency on the tomato leaves. Please fertilize immediately." An example of a prompt message would be, "Anomaly detected during tomato field inspection. What is the next recommended action?" This prompt allows the system to generate and provide appropriate countermeasures.

[0359] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0360] Step 1:

[0361] The server acquires remote sensing data from Earth observation instruments. The input is remote sensing data, and the output generates initial growth data that is stored in a database. This data includes information about crop growth and is verified for consistency for use in subsequent analysis.

[0362] Step 2:

[0363] The server retrieves weather data from weather information providers. The input is predicted weather data, and the output is managed within the server as integrated data. This data includes temperature, humidity, precipitation, etc., and after verifying that each variable is within normal limits, it is reflected in future forecasts.

[0364] Step 3:

[0365] The server collects soil data from soil condition detectors. Inputs include soil moisture and nutrient status data, and output is a success notification indicating that this data was successfully captured. Soil pH and moisture content are considered important in this process, and an alert is generated if any inconsistencies are found.

[0366] Step 4:

[0367] The server integrates the collected data and performs data cleaning. Remote sensing data, weather data, and soil data are used as inputs. Unnecessary data and missing values ​​are removed, and a dataset optimized for analysis is output. The completeness and consistency of the data are verified, and it is ready for the next analysis stage.

[0368] Step 5:

[0369] The server performs data analysis using a generative model. Integrated data, after data cleaning, is the input, and crop growth predictions and guidelines for optimal agricultural management are output. The generative AI model used here interprets the data using statistical methods and machine learning algorithms to formulate future farming plans.

[0370] Step 6:

[0371] The terminal notifies the user based on agricultural management guidelines obtained from the server. The input is the analysis results, and the output is specific work instructions displayed on the user's terminal. At this stage, action-based prompts are also included, allowing the user to immediately understand what to do.

[0372] Step 7:

[0373] Users perform actual farm work based on notifications displayed on their devices. The input is the guidance from the device, and the output is the agricultural action performed. This enables timely decision-making and improves the efficiency of farm work.

[0374] Step 8:

[0375] Users send feedback to the server regarding the status of completed tasks and areas for improvement. The input is the user's feedback information, and the output is training data used in a generative model. Through this feedback loop, the entire system evolves, leading to smarter agricultural management.

[0376] 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.

[0377] This invention relates to a system that helps farmers optimize crop management in real time, incorporating an emotion engine that recognizes user emotions to provide more adaptive advice. The system comprises data collection means, a generative model, an emotion engine, and notification means, and provides comprehensive agricultural management advice.

[0378] Server Embodiment

[0379] The server receives remote sensing data from Earth observation instruments and acquires weather data from weather information providers. It also collects soil data from soil condition detectors installed on the ground. This data is integrated and data cleaning is performed. Subsequently, the collected data is analyzed using a generative model to generate agricultural management guidelines in response to crop growth conditions and environmental changes.

[0380] Emotional Engine Implementation

[0381] The server has a built-in emotion engine that analyzes the user's voice and text input to recognize their emotions. This emotion recognition allows the server to adjust the content and method of advice based on the user's mental state. For example, if the user is feeling stressed, it will provide more concise and easy-to-follow advice.

[0382] Terminal embodiment

[0383] The device receives agricultural management advice and emotion-based adjustment information transmitted from the server and notifies the user. Notifications are customized according to the user's emotional state to ensure a comfortable experience. The device is a smartphone or tablet-type mobile information terminal, and its user interface is intuitive and easy to operate.

[0384] User Embodiment

[0385] Users perform agricultural management based on notifications sent from their devices. For example, the system provides specific examples of how to adjust field irrigation before predicted rainfall. Users can also send feedback to the server regarding their thoughts on agricultural management and the results after implementation. The sentiment engine analyzes the user's feedback and uses the results to improve the generative model.

[0386] Specific example

[0387] For example, if a user managing a wheat field asks "What's the weather like next week?" via voice command on their smartphone, the server performs remote sensing and weather data analysis, and the emotion engine detects that the user is feeling anxious. The server then provides calm and specific advice, such as "Rainfall is expected to be light next week, but temperatures will be high, so it would be a good idea to increase irrigation frequency." Based on this information, the user can plan their work and perform farming efficiently while reducing anxiety.

[0388] The following describes the processing flow.

[0389] Step 1:

[0390] The server receives remote sensing data from Earth observation instruments. This includes image data related to vegetation indices in agricultural land and crop growth conditions.

[0391] Step 2:

[0392] The server obtains real-time weather data via APIs from weather information providers. This includes information such as predicted precipitation, temperature, and humidity.

[0393] Step 3:

[0394] The server collects and integrates data such as soil moisture, temperature, and pH values ​​in real time from soil condition detectors installed on the ground.

[0395] Step 4:

[0396] The server integrates all collected data and performs data cleaning. It corrects any outliers or missing values, preparing the data for analysis.

[0397] Step 5:

[0398] The server uses a generative model to analyze the cleaned data. Based on the analysis results, it evaluates crop growth conditions and environmental stress, and generates optimal agricultural management guidelines.

[0399] Step 6:

[0400] When a user enters a request via voice or text into their device, that information is sent from the device to the server.

[0401] Step 7:

[0402] The server uses an emotion engine to recognize emotions from user input. It analyzes the type and intensity of the emotion to determine the user's psychological state.

[0403] Step 8:

[0404] The server takes the results of sentiment analysis into account and adjusts agricultural management guidelines according to the user's emotions. For example, it generates more concise and reassuring advice for users who are feeling stressed.

[0405] Step 9:

[0406] The device receives and notifies the user of tailored agricultural management advice. The notification is displayed in a format that takes the user's emotional state into consideration.

[0407] Step 10:

[0408] Users check notifications from their devices and perform farm work based on the instructions. After completion, users can send feedback on the results and advice to the server from their devices.

[0409] Step 11:

[0410] The server analyzes user feedback, performs sentiment analysis using an emotion engine, and then uses the feedback to improve the generative model and the way advice is provided.

[0411] (Example 2)

[0412] 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".

[0413] In modern agriculture, it is difficult to manage crops stably without being affected by climate change and environmental shifts, and the mental burden on farmers is increasing. Therefore, there is a need to provide agricultural management guidelines that utilize real-time environmental data while being tailored to the emotional state of individual users. However, existing systems lack the means to provide individually optimized advice that takes emotions into account, so this problem needs to be solved.

[0414] 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.

[0415] In this invention, the server includes means for receiving remote detection information from Earth observation instruments as a data collection means, means for analyzing the user's emotions using an emotion engine and adjusting advice based on the results, and means for notifying the user using a mobile information terminal. This makes it possible to provide optimized agricultural management guidelines based on real-time, highly accurate environmental data, while taking into account the user's emotional state.

[0416] A "data collection means" is a means that has the function of receiving information from Earth observation instruments and weather information providers, and accumulating soil information from soil condition detectors.

[0417] "Remote detection information" refers to data acquired remotely from geographically distant locations using Earth observation equipment, etc.

[0418] "Weather information" refers to data that shows environmental conditions such as precipitation, temperature, and wind speed, and provides the necessary information for making decisions in agricultural management.

[0419] "Soil information" refers to data that indicates the health of the soil, such as its moisture content and nutrients.

[0420] A "generative model" refers to a mathematical or computational algorithm used to analyze collected data and perform pattern recognition or prediction.

[0421] An "emotion engine" is a technology that analyzes user voice and text input to recognize emotions and generates analysis results as feedback.

[0422] A "notification method" is a mechanism that provides information sent from a server to a user via a mobile device.

[0423] "Personal information terminals" refer to electronic devices such as smartphones and tablets that are easy to carry and have an intuitive user interface.

[0424] This invention is an information system for agricultural workers to efficiently manage crops. The system comprises data collection means, a generative model, an emotion engine, and notification means, and provides comprehensive agricultural management advice.

[0425] The server receives remote detection information from Earth observation instruments and obtains weather information from weather information providers. It also collects soil information from soil condition detectors installed on the ground. This data is integrated and cleaned, and then analyzed by a generative AI model. The generative model learns agriculture-related patterns and generates agricultural management guidelines that respond to crop growth conditions and environmental changes based on the collected data.

[0426] Furthermore, the server uses an emotion engine to analyze the user's voice and text input and recognize their emotions. Based on this information, advice is adjusted according to the user's mental state. For example, if the user is feeling anxious, more concise and actionable advice will be provided.

[0427] The terminal receives agricultural management advice and emotion-based adjustment information transmitted from the server and notifies the user. The terminals used are smartphones and tablet-type mobile devices, and the user interface is intuitive and easy to operate.

[0428] Users perform agricultural management based on notifications from their devices. This system allows users to perform efficient and adaptive agricultural work and reduce mental stress. For example, when managing crop watering, users can receive a notification from the server saying, "It is expected to be dry next week, so please increase the frequency of irrigation," and then create an optimal work plan.

[0429] An example prompt might be, "The user wants to know the optimal agricultural management methods based on remote sensing data and weather data." This allows the server to perform appropriate data analysis and generate advice.

[0430] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0431] Step 1:

[0432] The server receives remote detection information from Earth observation instruments. The input at this stage includes data such as temperature, humidity, and vegetation index acquired via satellites and drones. The server temporarily stores this data in preparation for the next data processing step.

[0433] Step 2:

[0434] The server retrieves weather information provided by weather information providers. The input consists of forecast data such as regional precipitation, temperature, and wind speed. This allows the server to understand future weather conditions and accumulate data for use in crop management planning.

[0435] Step 3:

[0436] The server collects soil information received from soil condition detectors. This input includes data on soil moisture content and nutrients. Based on this, the server obtains basic data for evaluating soil health.

[0437] Step 4:

[0438] The server integrates all the collected data and performs data cleaning. The input here is a wide range of data collected so far, including temperature, humidity, weather conditions, and soil information. The server removes outliers from these datasets and interpolates missing data to prepare them for analysis.

[0439] Step 5:

[0440] The server applies regression analysis and machine learning algorithms using a generative AI model based on the prepared data. The input here is cleaned, integrated data, and the output is crop growth predictions and guidelines for necessary agricultural management. Based on these results, the server determines the optimal agricultural management method.

[0441] Step 6:

[0442] The server uses an emotion engine to recognize emotions from the user's voice and text. Input is user questions and feedback, and the server adjusts the content and wording of advice based on this analysis. The output is optimized advice tailored to the user's mental state.

[0443] Step 7:

[0444] The terminal receives agricultural management guidelines and emotion-based advice sent from the server. The input is this notification data, which the terminal displays to the user. The output is visual or auditory feedback to the user, delivered through the user interface.

[0445] Step 8:

[0446] Users perform agricultural management based on information received from their devices. Input is the notified advice, which users utilize to carry out tasks such as irrigation and fertilization. Output is the actual management activities on the farmland and their results.

[0447] Step 9:

[0448] Users send management results and feedback to the server. The server then analyzes the collected feedback using an emotion engine and uses it to improve the accuracy of future advice and train generative models.

[0449] (Application Example 2)

[0450] 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."

[0451] Agricultural workers and distributors need to operate efficiently and effectively while adapting to environmental changes and diversifying consumer needs. However, conventional agricultural and distribution management methods have struggled to provide real-time data analysis and adaptive guidance tailored to the emotional state of users. Furthermore, optimizing delivery routes while considering emotions has been insufficient to enhance user satisfaction. Effective solutions to these challenges are needed.

[0452] 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.

[0453] In this invention, the server includes, as a data collection means, a function to receive remote sensing data from a global monitoring device, a function to acquire weather data from a weather information provider, and a function to collect soil data from a ground-based soil condition detection device. This enables agricultural workers and distributors to receive adaptive support that is sensitive to their needs based on the data. Furthermore, it can improve demand forecasting and optimize delivery routes in the distribution of agricultural products, thereby increasing user satisfaction.

[0454] "Data collection means" refers to functions for acquiring various environmental information, such as remote sensing data, weather data, and soil data.

[0455] A "global monitoring device" is a device that enables observations on a global scale and provides remote sensing data.

[0456] "Remote sensing data" refers to observational data obtained without direct contact with the object or region, and is collected by satellites or aircraft.

[0457] A "weather information provider" is an organization that conducts weather observations and provides the resulting weather data.

[0458] A "soil condition detection device" is a device used to detect the condition of the soil, such as its moisture and nutrients.

[0459] "Emotion analysis means" refers to a function that recognizes emotions from the user's voice or text and changes the way information is provided accordingly.

[0460] A "generative model" is an analytical model used to optimize agricultural management and logistics based on collected data.

[0461] "Demand forecasting in distribution" is an analysis aimed at ensuring appropriate supply by predicting how much agricultural products will be consumed.

[0462] "Optimizing delivery routes" refers to calculating the shortest and most optimal routes to deliver goods efficiently and quickly.

[0463] "Data preparation" is the process of integrating collected data and processing it into an analyzable format.

[0464] "Users" refers to agricultural workers and distributors who use this system.

[0465] A "mobile information and communication terminal" is a portable device, such as a smartphone or tablet, that has the function of processing information and communicating with others.

[0466] The system for implementing this invention will achieve efficiency and optimization in agriculture and distribution through data collection, analysis, and information provision.

[0467] The server receives remote sensing data through a global monitoring device and acquires weather data from weather information providers. Furthermore, it collects soil data using ground-based soil condition detection devices. This data is converted into a format suitable for analysis through a processing stage. Finally, a generative AI model is used to analyze the collected data and generate agricultural management guidelines. This model utilizes deep learning libraries such as TensorFlow and PyTorch.

[0468] The server also analyzes voice or text input from the user and recognizes their emotional state using sentiment analysis tools. It utilizes Google Cloud Speech-to-Text for speech recognition and Natural Language Toolkit (NLTK) for natural language processing. This enables the server to provide adaptive advice tailored to the user's mental state and needs.

[0469] The terminal receives the analysis results and notifies the user. Mobile information and communication devices, particularly smartphones and tablets, are used for communication. The notifications are adjusted according to the user's emotional state; for example, in the case of an urgent order, a delivery route that allows for quick response is suggested. For this purpose, map APIs capable of handling geographic information in GeoJSON format are used.

[0470] Users develop agricultural management and distribution plans based on the information provided. For example, if a user asks, "What's the weather like next week?", the system analyzes weather data and provides advice such as, "Rainfall is expected to be low next week. High temperatures are anticipated, so increase irrigation." Furthermore, an example of a prompt could be, "Create a prompt to check next week's weather and generate calm, emotionally resonant advice. The target audience is consumers who are anxious about urgent distribution needs."

[0471] In this way, the system can solve various challenges in agriculture and distribution, and improve user satisfaction and efficiency.

[0472] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0473] Step 1:

[0474] The server receives remote sensing data from a global monitoring device. The input is remote sensing data, and by receiving this data, global environmental information can be obtained. The server stores this data in a storage database.

[0475] Step 2:

[0476] The server retrieves weather data from weather information providers. Input data comes from weather information APIs, receiving information such as temperature and precipitation. The received data is automatically stored in the database in real time.

[0477] Step 3:

[0478] The server collects soil data from soil condition detection devices installed on the ground. The input here includes soil moisture and nutrient data. The server then performs a preparation process to format this data for analysis.

[0479] Step 4:

[0480] The server integrates and processes all collected data. Inputs include remote sensing data, weather data, and soil data. The server cleans these data and converts them into an analyzable format.

[0481] Step 5:

[0482] The server uses an AI model to analyze data and generate agricultural management guidelines. The input is a pre-configured dataset, and a machine learning algorithm is applied to predict crop growth and output specific guidelines.

[0483] Step 6:

[0484] The server receives voice or text input from the user and analyzes the emotional state using emotion analysis tools. The input is voice or text data, and an emotion recognition algorithm is used to analyze the emotion and identify the emotional state as output.

[0485] Step 7:

[0486] The server adjusts the guidelines derived from the generative model based on the sentiment analysis results, generating advice optimized for the user. The adjusted advice becomes the output. It provides users with flexible guidelines that respond to their emotions.

[0487] Step 8:

[0488] The device receives tailored advice and notifies the user. The input is the tailored advice, which the device receives and displays to the user at the appropriate time and in the appropriate format. Specifically, the priority and content of the notification change depending on the urgency.

[0489] Step 9:

[0490] Based on the information provided by the user, agricultural management and distribution plans are implemented. The information is used to develop specific farming tasks and distribution procedures. At this time, the implementation plan is revised based on the advice received.

[0491] 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.

[0492] 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.

[0493] 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.

[0494] [Third Embodiment]

[0495] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0496] 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.

[0497] 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).

[0498] 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.

[0499] 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.

[0500] 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).

[0501] 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.

[0502] 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.

[0503] 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.

[0504] 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.

[0505] 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.

[0506] 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".

[0507] This invention relates to a system for assisting farmers in optimizing crop management in real time. The system of this invention combines data collection means, generative models, and notification means to provide comprehensive agricultural management advice.

[0508] Server Embodiment

[0509] The server receives remote sensing data from Earth observation instruments and acquires weather data from weather information providers. It also collects soil data from soil condition detectors. This data is integrated and managed, and data cleaning is performed. The server uses generative models to analyze the data and generate agricultural management guidelines based on weather conditions, soil conditions, and crop growth. These guidelines include specific advice for adjusting the timing and amount of irrigation and fertilization according to crop health and predicted weather conditions.

[0510] Terminal embodiment

[0511] The device receives notifications sent from the server and displays them to the user. These notifications are designed to be immediately useful for the user's activities on the farm. The device is a smartphone or tablet-type personal information terminal with an intuitive user interface.

[0512] User Embodiment

[0513] Users manage their farms based on notifications from their devices. For example, they can adjust irrigation schedules based on crop health predictions and plan fertilizer application in response to changing weather conditions. Users can also send feedback on the advice provided to the server via their devices, which allows the system to continuously improve.

[0514] Specific example

[0515] For example, if a user manages a wheat field, the server can use satellite data to detect changes in the leaf color of growing wheat, indicating a possible nitrogen deficiency. If weather data predicts upcoming rain, the server will generate advice to apply nitrogen fertilizer before the rain. The user can receive this notification on their device and carry out the necessary farm work based on the instructions.

[0516] The following describes the processing flow.

[0517] Step 1:

[0518] The server receives remote sensing data from Earth observation instruments. This includes image data showing crop growth conditions and NDVI (Non-Digitally Neutralized Vegetation Index).

[0519] Step 2:

[0520] The server obtains weather data in real time via APIs from weather information providers. This includes information such as precipitation, temperature, humidity, and wind speed.

[0521] Step 3:

[0522] The server collects soil data in real time from soil condition detectors installed on the ground. This data includes soil moisture content, temperature, pH value, and other parameters.

[0523] Step 4:

[0524] The server integrates the collected data, performs data cleaning by imputing missing values ​​and removing outliers.

[0525] Step 5:

[0526] The server uses generative models to analyze integrated data and assess crop environmental stress and growth conditions. For example, it can detect nitrogen deficiency from changes in leaf color.

[0527] Step 6:

[0528] Based on the analysis results, the server generates agricultural management advice. This advice includes specific instructions on irrigation, fertilization, and pest and disease control.

[0529] Step 7:

[0530] The terminal receives agricultural management advice generated from the server and notifies the user. The advice is displayed in an easy-to-read format.

[0531] Step 8:

[0532] The user checks the notifications from their device and prepares to carry out appropriate farm work based on the advice provided.

[0533] Step 9:

[0534] Users can send feedback to the server via their device regarding the results of the farm work they have performed and the advice they have received.

[0535] Step 10:

[0536] The server collects user feedback, analyzes that data to improve the accuracy of the generative model, and updates the model as needed.

[0537] (Example 1)

[0538] 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."

[0539] In agriculture, it is crucial to understand weather changes, soil conditions, and crop growth in real time and to manage them efficiently and optimally based on that information. However, conventional methods suffer from insufficient data collection and a lack of integrated management capabilities, making it difficult to optimize agricultural management. There was also a need for a method to efficiently incorporate user feedback and continuously improve the system.

[0540] 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.

[0541] In this invention, the server includes, as data acquisition means, means for receiving distance exploration data from an observation device, means for acquiring environmental data from a weather information source, and means for accumulating geological data from a soil condition measuring device placed on the ground. This enables the generation of advanced agricultural management guidelines based on real-time integrated data.

[0542] "Data acquisition means" refers to a system or process for receiving and integrating necessary information from observation devices or information sources.

[0543] An "observation device" is an instrument used to acquire data on the state of the Earth and environmental changes as distance-based survey data.

[0544] "Distance survey data" refers to data on environmental conditions and surface changes acquired from remote locations using remote sensing technology.

[0545] A "weather information provider" refers to an external organization or platform that provides environmental data.

[0546] "Environmental data" refers to weather-related data such as temperature, humidity, and precipitation.

[0547] A "soil condition measuring device" is a device placed on the ground to collect geological data.

[0548] "Geological data" refers to data about the condition of the ground, including information on soil moisture content and nutrients.

[0549] A "generative artificial intelligence model" is an AI technology used to analyze data and generate specific guidelines or results.

[0550] "Guidelines for agricultural use management" are specific advice and plans for carrying out optimal agricultural activities according to crop growth and environmental conditions.

[0551] "Users" refer to agricultural workers who receive information through this system and carry out agricultural work.

[0552] The system of this invention is designed to optimize agricultural management in real time, and combines data acquisition means, generation AI models, and notification means to provide comprehensive agricultural management advice.

[0553] Server Embodiment

[0554] The server receives distance survey data from observation devices and acquires environmental data from weather information sources. Furthermore, it collects geological data from soil condition measurement devices. Data acquisition is automated using APIs, and the dataset is formatted and purified using Python libraries such as Pandas and NumPy. The server analyzes the integrated data using generative AI models such as TensorFlow and PyTorch to generate agricultural management guidelines based on crop growth and environmental conditions. Specifically, this includes identifying the optimal timing and amount of irrigation and fertilization.

[0555] Terminal embodiment

[0556] The device receives notifications from the server and displays them to the user. The device is a smartphone or tablet-type personal information terminal equipped with an intuitive user interface. Notifications are delivered via a push notification system and displayed visually in a GUI format, allowing the user to immediately decide on the necessary action.

[0557] User Embodiment

[0558] Users manage their farms based on notifications from their devices. For example, they can adjust irrigation schedules based on crop health predictions and plan fertilizer application in response to changes in environmental conditions. Furthermore, they can send feedback on the results of their actions from their devices to the server. This allows the system to continuously improve its generated AI model based on the provided data.

[0559] Specific example

[0560] For example, if you are managing a wheat farm, the server can use remote sensing data to detect changes in leaf color and suggest a nitrogen deficiency. Furthermore, if rainfall is predicted, the server will suggest applying nitrogen fertilizer before the rain. The user receives this notification on their terminal and can quickly adjust farm work. A concrete example of a prompt message would be, "Generate specific advice on the optimal timing for fertilizer application based on wheat field leaf color change data and future weather forecasts."

[0561] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0562] Step 1:

[0563] The server receives distance survey data from observation devices and acquires environmental data from weather information sources. Furthermore, it collects geological data from soil condition measurement devices. It takes remote sensing data, weather data, and soil data as input and integrates them. Specifically, it downloads data via API requests and converts it to a specified format using a Python script. The output is a collection of raw data, which is saved for later processing.

[0564] Step 2:

[0565] The server performs data cleansing on the collected data. It uses the integrated raw data as input. It detects missing values ​​and outliers from this input data and performs imputation or deletion as necessary. Specifically, it uses libraries such as Pandas and NumPy to format the data and create a clean dataset. The output is a clean dataset, ready to proceed to the subsequent analysis steps.

[0566] Step 3:

[0567] The server takes a clean dataset as input and performs analysis using a generated AI model. Specifically, it applies machine learning models such as TensorFlow and PyTorch to extract useful patterns and relationships from the data. The output is agricultural management guidance based on the analysis. This guidance includes appropriate timing for irrigation and fertilization determined from the health of the crops.

[0568] Step 4:

[0569] The server generates agricultural management guidelines obtained from the analysis results as a notification and sends it to the terminal. The input uses the analysis results data and the notification format. Specifically, it sends data to the terminal using the HTTP protocol and delivers notifications to the user in real time via a push notification system. The output is a notification message displayed to the user on the terminal.

[0570] Step 5:

[0571] The terminal receives notifications from the server as input and displays them visually to the user. Specifically, the mobile device's application receives the notification and presents the information to the user in a GUI format. The output is notification information presented in a format that is easy for the user to understand.

[0572] Step 6:

[0573] Users manage their farms using notifications from their devices as input, planning and executing specific tasks. These tasks include operating irrigation equipment and applying fertilizer. Specifically, they perform necessary actions in the field based on the notifications from their devices. The output is the result of the completed farm work, which is returned to the server as feedback via the device after the work is completed.

[0574] Step 7:

[0575] The server uses user feedback as input to improve its generated AI model. Specifically, it collects feedback data and uses it to retrain the generated AI model. The output is an improved AI model, which helps provide more accurate guidance in the future.

[0576] (Application Example 1)

[0577] 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."

[0578] Farmers face the challenge of not being able to grasp crop growth conditions and environmental changes in real time and manage them optimally using conventional methods. Furthermore, there is a demand for efficient farming through automated systems. However, conventional methods lack adequate support for workers on the farm, making efficient work management difficult.

[0579] 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.

[0580] In this invention, the server includes means for receiving remote sensing data from Earth observation instruments as a data collection means, means for acquiring weather data from weather information providers, means for accumulating soil data from soil condition detectors installed on the ground, means for integrating the collected data and performing data cleaning, means for analyzing the data using a generative model and generating optimized agricultural management guidelines based on crop growth and environmental changes, means for performing automated patrols of the farm using management equipment to monitor the condition of targets and perform tasks, and means for notifying workers equipped with visual devices. This enables agricultural workers to receive information in real time and efficiently manage and operate their farms.

[0581] "Data collection means" refers to a system that acquires and integrates data from Earth observation instruments, weather information providers, and soil condition detectors.

[0582] "Earth observation equipment" refers to observation instruments used to acquire remote sensing data from above a farm.

[0583] "Weather information provider" refers to an organization or service that provides weather data.

[0584] A "soil condition detector" is a device installed on the ground to detect the condition of the soil.

[0585] "Data cleaning" is the process of organizing and integrating collected data to prepare it for analysis.

[0586] A "generative model" is a mathematical or machine learning model used to analyze data and generate guidelines or predictions tailored to a specific purpose.

[0587] "Agricultural management guidelines" are instructions and advice that show the optimal management methods in response to crop growth and environmental changes.

[0588] "Management equipment" refers to devices that automatically patrol the farm, collecting data and performing tasks.

[0589] A "visual device" is a device that displays information and provides visual notification to workers.

[0590] The system that realizes this invention uses the following hardware and software to collect and analyze various data. The server integrates remote sensing data acquired for Earth observation, meteorological data acquired from weather information providers, and soil data from soil condition detectors. This information is prepared using data cleaning techniques and analyzed using generative models, such as machine learning frameworks like TensorFlow.

[0591] The agricultural management guidelines generated by the analysis specifically indicate the timing and methods of irrigation and fertilization according to the health of the crops and environmental conditions. The management equipment automatically patrols the farm and performs the necessary tasks. This makes it possible to operate the farm efficiently without human intervention.

[0592] The terminal displays information on smartphones, tablet-type information processing devices, or visual devices, notifying workers of the situation in real time. This information includes detailed guidelines for daily farm work, such as the timing of starting irrigation or applying fertilizer.

[0593] By using smart devices, users can instantly receive these notifications and send feedback back to the system. This allows the generative model to continuously learn and improve the accuracy of agricultural management. Furthermore, providing instructions to workers through visual devices enables rapid response in the field.

[0594] For example, if the server detects a change in the color of tomato leaves and indicates a nitrogen deficiency, a worker wearing a visual device will receive a notification saying, "There are signs of nitrogen deficiency on the tomato leaves. Please fertilize immediately." An example of a prompt message would be, "Anomaly detected during tomato field inspection. What is the next recommended action?" This prompt allows the system to generate and provide appropriate countermeasures.

[0595] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0596] Step 1:

[0597] The server acquires remote sensing data from Earth observation instruments. The input is remote sensing data, and the output generates initial growth data that is stored in a database. This data includes information about crop growth and is verified for consistency for use in subsequent analysis.

[0598] Step 2:

[0599] The server retrieves weather data from weather information providers. The input is predicted weather data, and the output is managed within the server as integrated data. This data includes temperature, humidity, precipitation, etc., and after verifying that each variable is within normal limits, it is reflected in future forecasts.

[0600] Step 3:

[0601] The server collects soil data from soil condition detectors. Inputs include soil moisture and nutrient status data, and output is a success notification indicating that this data was successfully captured. Soil pH and moisture content are considered important in this process, and an alert is generated if any inconsistencies are found.

[0602] Step 4:

[0603] The server integrates the collected data and performs data cleaning. Remote sensing data, weather data, and soil data are used as inputs. Unnecessary data and missing values ​​are removed, and a dataset optimized for analysis is output. The completeness and consistency of the data are verified, and it is ready for the next analysis stage.

[0604] Step 5:

[0605] The server performs data analysis using a generative model. Integrated data, after data cleaning, is the input, and crop growth predictions and guidelines for optimal agricultural management are output. The generative AI model used here interprets the data using statistical methods and machine learning algorithms to formulate future farming plans.

[0606] Step 6:

[0607] The terminal notifies the user based on agricultural management guidelines obtained from the server. The input is the analysis results, and the output is specific work instructions displayed on the user's terminal. At this stage, action-based prompts are also included, allowing the user to immediately understand what to do.

[0608] Step 7:

[0609] Users perform actual farm work based on notifications displayed on their devices. The input is the guidance from the device, and the output is the agricultural action performed. This enables timely decision-making and improves the efficiency of farm work.

[0610] Step 8:

[0611] Users send feedback to the server regarding the status of completed tasks and areas for improvement. The input is the user's feedback information, and the output is training data used in a generative model. Through this feedback loop, the entire system evolves, leading to smarter agricultural management.

[0612] 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.

[0613] This invention relates to a system that helps farmers optimize crop management in real time, incorporating an emotion engine that recognizes user emotions to provide more adaptive advice. The system comprises data collection means, a generative model, an emotion engine, and notification means, and provides comprehensive agricultural management advice.

[0614] Server Embodiment

[0615] The server receives remote sensing data from Earth observation instruments and acquires weather data from weather information providers. It also collects soil data from soil condition detectors installed on the ground. This data is integrated and data cleaning is performed. Subsequently, the collected data is analyzed using a generative model to generate agricultural management guidelines in response to crop growth conditions and environmental changes.

[0616] Emotional Engine Implementation

[0617] The server has a built-in emotion engine that analyzes the user's voice and text input to recognize their emotions. This emotion recognition allows the server to adjust the content and method of advice based on the user's mental state. For example, if the user is feeling stressed, it will provide more concise and easy-to-follow advice.

[0618] Terminal embodiment

[0619] The device receives agricultural management advice and emotion-based adjustment information transmitted from the server and notifies the user. Notifications are customized according to the user's emotional state to ensure a comfortable experience. The device is a smartphone or tablet-type mobile information terminal, and its user interface is intuitive and easy to operate.

[0620] User Embodiment

[0621] Users perform agricultural management based on notifications sent from their devices. For example, the system provides specific examples of how to adjust field irrigation before predicted rainfall. Users can also send feedback to the server regarding their thoughts on agricultural management and the results after implementation. The sentiment engine analyzes the user's feedback and uses the results to improve the generative model.

[0622] Specific example

[0623] For example, if a user managing a wheat field asks "What's the weather like next week?" via voice command on their smartphone, the server performs remote sensing and weather data analysis, and the emotion engine detects that the user is feeling anxious. The server then provides calm and specific advice, such as "Rainfall is expected to be light next week, but temperatures will be high, so it would be a good idea to increase irrigation frequency." Based on this information, the user can plan their work and perform farming efficiently while reducing anxiety.

[0624] The following describes the processing flow.

[0625] Step 1:

[0626] The server receives remote sensing data from Earth observation instruments. This includes image data related to vegetation indices in agricultural land and crop growth conditions.

[0627] Step 2:

[0628] The server obtains real-time weather data via APIs from weather information providers. This includes information such as predicted precipitation, temperature, and humidity.

[0629] Step 3:

[0630] The server collects and integrates data such as soil moisture, temperature, and pH values ​​in real time from soil condition detectors installed on the ground.

[0631] Step 4:

[0632] The server integrates all collected data and performs data cleaning. It corrects any outliers or missing values, preparing the data for analysis.

[0633] Step 5:

[0634] The server uses a generative model to analyze the cleaned data. Based on the analysis results, it evaluates crop growth conditions and environmental stress, and generates optimal agricultural management guidelines.

[0635] Step 6:

[0636] When a user enters a request via voice or text into their device, that information is sent from the device to the server.

[0637] Step 7:

[0638] The server uses an emotion engine to recognize emotions from user input. It analyzes the type and intensity of the emotion to determine the user's psychological state.

[0639] Step 8:

[0640] The server takes the results of sentiment analysis into account and adjusts agricultural management guidelines according to the user's emotions. For example, it generates more concise and reassuring advice for users who are feeling stressed.

[0641] Step 9:

[0642] The device receives and notifies the user of tailored agricultural management advice. The notification is displayed in a format that takes the user's emotional state into consideration.

[0643] Step 10:

[0644] Users check notifications from their devices and perform farm work based on the instructions. After completion, users can send feedback on the results and advice to the server from their devices.

[0645] Step 11:

[0646] The server analyzes user feedback, performs sentiment analysis using an emotion engine, and then uses the feedback to improve the generative model and the way advice is provided.

[0647] (Example 2)

[0648] 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."

[0649] In modern agriculture, it is difficult to manage crops stably without being affected by climate change and environmental shifts, and the mental burden on farmers is increasing. Therefore, there is a need to provide agricultural management guidelines that utilize real-time environmental data while being tailored to the emotional state of individual users. However, existing systems lack the means to provide individually optimized advice that takes emotions into account, so this problem needs to be solved.

[0650] 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.

[0651] In this invention, the server includes means for receiving remote detection information from Earth observation instruments as a data collection means, means for analyzing the user's emotions using an emotion engine and adjusting advice based on the results, and means for notifying the user using a mobile information terminal. This makes it possible to provide optimized agricultural management guidelines based on real-time, highly accurate environmental data, while taking into account the user's emotional state.

[0652] A "data collection means" is a means that has the function of receiving information from Earth observation instruments and weather information providers, and accumulating soil information from soil condition detectors.

[0653] "Remote detection information" refers to data acquired remotely from geographically distant locations using Earth observation equipment, etc.

[0654] "Weather information" refers to data that shows environmental conditions such as precipitation, temperature, and wind speed, and provides the necessary information for making decisions in agricultural management.

[0655] "Soil information" refers to data that indicates the health of the soil, such as its moisture content and nutrients.

[0656] A "generative model" refers to a mathematical or computational algorithm used to analyze collected data and perform pattern recognition or prediction.

[0657] An "emotion engine" is a technology that analyzes user voice and text input to recognize emotions and generates analysis results as feedback.

[0658] A "notification method" is a mechanism that provides information sent from a server to a user via a mobile device.

[0659] "Personal information terminals" refer to electronic devices such as smartphones and tablets that are easy to carry and have an intuitive user interface.

[0660] This invention is an information system for agricultural workers to efficiently manage crops. The system comprises data collection means, a generative model, an emotion engine, and notification means, and provides comprehensive agricultural management advice.

[0661] The server receives remote detection information from Earth observation instruments and obtains weather information from weather information providers. It also collects soil information from soil condition detectors installed on the ground. This data is integrated and cleaned, and then analyzed by a generative AI model. The generative model learns agriculture-related patterns and generates agricultural management guidelines that respond to crop growth conditions and environmental changes based on the collected data.

[0662] Furthermore, the server uses an emotion engine to analyze the user's voice and text input and recognize their emotions. Based on this information, advice is adjusted according to the user's mental state. For example, if the user is feeling anxious, more concise and actionable advice will be provided.

[0663] The terminal receives agricultural management advice and emotion-based adjustment information transmitted from the server and notifies the user. The terminals used are smartphones and tablet-type mobile devices, and the user interface is intuitive and easy to operate.

[0664] Users perform agricultural management based on notifications from their devices. This system allows users to perform efficient and adaptive agricultural work and reduce mental stress. For example, when managing crop watering, users can receive a notification from the server saying, "It is expected to be dry next week, so please increase the frequency of irrigation," and then create an optimal work plan.

[0665] An example prompt might be, "The user wants to know the optimal agricultural management methods based on remote sensing data and weather data." This allows the server to perform appropriate data analysis and generate advice.

[0666] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0667] Step 1:

[0668] The server receives remote detection information from Earth observation instruments. The input at this stage includes data such as temperature, humidity, and vegetation index acquired via satellites and drones. The server temporarily stores this data in preparation for the next data processing step.

[0669] Step 2:

[0670] The server retrieves weather information provided by weather information providers. The input consists of forecast data such as regional precipitation, temperature, and wind speed. This allows the server to understand future weather conditions and accumulate data for use in crop management planning.

[0671] Step 3:

[0672] The server collects soil information received from soil condition detectors. This input includes data on soil moisture content and nutrients. Based on this, the server obtains basic data for evaluating soil health.

[0673] Step 4:

[0674] The server integrates all the collected data and performs data cleaning. The input here is a wide range of data collected so far, including temperature, humidity, weather conditions, and soil information. The server removes outliers from these datasets and interpolates missing data to prepare them for analysis.

[0675] Step 5:

[0676] The server applies regression analysis and machine learning algorithms using a generative AI model based on the prepared data. The input here is cleaned, integrated data, and the output is crop growth predictions and guidelines for necessary agricultural management. Based on these results, the server determines the optimal agricultural management method.

[0677] Step 6:

[0678] The server uses an emotion engine to recognize emotions from the user's voice and text. Input is user questions and feedback, and the server adjusts the content and wording of advice based on this analysis. The output is optimized advice tailored to the user's mental state.

[0679] Step 7:

[0680] The terminal receives agricultural management guidelines and emotion-based advice sent from the server. The input is this notification data, which the terminal displays to the user. The output is visual or auditory feedback to the user, delivered through the user interface.

[0681] Step 8:

[0682] Users perform agricultural management based on information received from their devices. Input is the notified advice, which users utilize to carry out tasks such as irrigation and fertilization. Output is the actual management activities on the farmland and their results.

[0683] Step 9:

[0684] Users send management results and feedback to the server. The server then analyzes the collected feedback using an emotion engine and uses it to improve the accuracy of future advice and train generative models.

[0685] (Application Example 2)

[0686] 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."

[0687] Agricultural workers and distributors need to operate efficiently and effectively while adapting to environmental changes and diversifying consumer needs. However, conventional agricultural and distribution management methods have struggled to provide real-time data analysis and adaptive guidance tailored to the emotional state of users. Furthermore, optimizing delivery routes while considering emotions has been insufficient to enhance user satisfaction. Effective solutions to these challenges are needed.

[0688] 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.

[0689] In this invention, the server includes, as a data collection means, a function to receive remote sensing data from a global monitoring device, a function to acquire weather data from a weather information provider, and a function to collect soil data from a ground-based soil condition detection device. This enables agricultural workers and distributors to receive adaptive support that is sensitive to their needs based on the data. Furthermore, it can improve demand forecasting and optimize delivery routes in the distribution of agricultural products, thereby increasing user satisfaction.

[0690] "Data collection means" refers to functions for acquiring various environmental information, such as remote sensing data, weather data, and soil data.

[0691] A "global monitoring device" is a device that enables observations on a global scale and provides remote sensing data.

[0692] "Remote sensing data" refers to observational data obtained without direct contact with the object or region, and is collected by satellites or aircraft.

[0693] A "weather information provider" is an organization that conducts weather observations and provides the resulting weather data.

[0694] A "soil condition detection device" is a device used to detect the condition of the soil, such as its moisture and nutrients.

[0695] "Emotion analysis means" refers to a function that recognizes emotions from the user's voice or text and changes the way information is provided accordingly.

[0696] A "generative model" is an analytical model used to optimize agricultural management and logistics based on collected data.

[0697] "Demand forecasting in distribution" is an analysis aimed at ensuring appropriate supply by predicting how much agricultural products will be consumed.

[0698] "Optimizing delivery routes" refers to calculating the shortest and most optimal routes to deliver goods efficiently and quickly.

[0699] "Data preparation" is the process of integrating collected data and processing it into an analyzable format.

[0700] "Users" refers to agricultural workers and distributors who use this system.

[0701] A "mobile information and communication terminal" is a portable device, such as a smartphone or tablet, that has the function of processing information and communicating with others.

[0702] The system for implementing this invention will achieve efficiency and optimization in agriculture and distribution through data collection, analysis, and information provision.

[0703] The server receives remote sensing data through a global monitoring device and acquires weather data from weather information providers. Furthermore, it collects soil data using ground-based soil condition detection devices. This data is converted into a format suitable for analysis through a processing stage. Finally, a generative AI model is used to analyze the collected data and generate agricultural management guidelines. This model utilizes deep learning libraries such as TensorFlow and PyTorch.

[0704] The server also analyzes voice or text input from the user and recognizes their emotional state using sentiment analysis tools. It utilizes Google Cloud Speech-to-Text for speech recognition and Natural Language Toolkit (NLTK) for natural language processing. This enables the server to provide adaptive advice tailored to the user's mental state and needs.

[0705] The terminal receives the analysis results and notifies the user. Mobile information and communication devices, particularly smartphones and tablets, are used for communication. The notifications are adjusted according to the user's emotional state; for example, in the case of an urgent order, a delivery route that allows for quick response is suggested. For this purpose, map APIs capable of handling geographic information in GeoJSON format are used.

[0706] Users develop agricultural management and distribution plans based on the information provided. For example, if a user asks, "What's the weather like next week?", the system analyzes weather data and provides advice such as, "Rainfall is expected to be low next week. High temperatures are anticipated, so increase irrigation." Furthermore, an example of a prompt could be, "Create a prompt to check next week's weather and generate calm, emotionally resonant advice. The target audience is consumers who are anxious about urgent distribution needs."

[0707] In this way, the system can solve various challenges in agriculture and distribution, and improve user satisfaction and efficiency.

[0708] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0709] Step 1:

[0710] The server receives remote sensing data from a global monitoring device. The input is remote sensing data, and by receiving this data, global environmental information can be obtained. The server stores this data in a storage database.

[0711] Step 2:

[0712] The server retrieves weather data from weather information providers. Input data comes from weather information APIs, receiving information such as temperature and precipitation. The received data is automatically stored in the database in real time.

[0713] Step 3:

[0714] The server collects soil data from soil condition detection devices installed on the ground. The input here includes soil moisture and nutrient data. The server then performs a preparation process to format this data for analysis.

[0715] Step 4:

[0716] The server integrates and processes all collected data. Inputs include remote sensing data, weather data, and soil data. The server cleans these data and converts them into an analyzable format.

[0717] Step 5:

[0718] The server uses an AI model to analyze data and generate agricultural management guidelines. The input is a pre-configured dataset, and a machine learning algorithm is applied to predict crop growth and output specific guidelines.

[0719] Step 6:

[0720] The server receives voice or text input from the user and analyzes the emotional state using emotion analysis tools. The input is voice or text data, and an emotion recognition algorithm is used to analyze the emotion and identify the emotional state as output.

[0721] Step 7:

[0722] The server adjusts the guidelines derived from the generative model based on the sentiment analysis results, generating advice optimized for the user. The adjusted advice becomes the output. It provides users with flexible guidelines that respond to their emotions.

[0723] Step 8:

[0724] The device receives tailored advice and notifies the user. The input is the tailored advice, which the device receives and displays to the user at the appropriate time and in the appropriate format. Specifically, the priority and content of the notification change depending on the urgency.

[0725] Step 9:

[0726] Based on the information provided by the user, agricultural management and distribution plans are implemented. The information is used to develop specific farming tasks and distribution procedures. At this time, the implementation plan is revised based on the advice received.

[0727] 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.

[0728] 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.

[0729] 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.

[0730] [Fourth Embodiment]

[0731] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0732] 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.

[0733] 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).

[0734] 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.

[0735] 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.

[0736] 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).

[0737] 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.

[0738] 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.

[0739] 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.

[0740] 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.

[0741] 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.

[0742] 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.

[0743] 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".

[0744] This invention relates to a system for assisting farmers in optimizing crop management in real time. The system of this invention combines data collection means, generative models, and notification means to provide comprehensive agricultural management advice.

[0745] Server Embodiment

[0746] The server receives remote sensing data from Earth observation instruments and acquires weather data from weather information providers. It also collects soil data from soil condition detectors. This data is integrated and managed, and data cleaning is performed. The server uses generative models to analyze the data and generate agricultural management guidelines based on weather conditions, soil conditions, and crop growth. These guidelines include specific advice for adjusting the timing and amount of irrigation and fertilization according to crop health and predicted weather conditions.

[0747] Terminal embodiment

[0748] The device receives notifications sent from the server and displays them to the user. These notifications are designed to be immediately useful for the user's activities on the farm. The device is a smartphone or tablet-type personal information terminal with an intuitive user interface.

[0749] User Embodiment

[0750] Users manage their farms based on notifications from their devices. For example, they can adjust irrigation schedules based on crop health predictions and plan fertilizer application in response to changing weather conditions. Users can also send feedback on the advice provided to the server via their devices, which allows the system to continuously improve.

[0751] Specific example

[0752] For example, if a user manages a wheat field, the server can use satellite data to detect changes in the leaf color of growing wheat, indicating a possible nitrogen deficiency. If weather data predicts upcoming rain, the server will generate advice to apply nitrogen fertilizer before the rain. The user can receive this notification on their device and carry out the necessary farm work based on the instructions.

[0753] The following describes the processing flow.

[0754] Step 1:

[0755] The server receives remote sensing data from Earth observation instruments. This includes image data showing crop growth conditions and NDVI (Non-Digitally Neutralized Vegetation Index).

[0756] Step 2:

[0757] The server obtains weather data in real time via APIs from weather information providers. This includes information such as precipitation, temperature, humidity, and wind speed.

[0758] Step 3:

[0759] The server collects soil data in real time from soil condition detectors installed on the ground. This data includes soil moisture content, temperature, pH value, and other parameters.

[0760] Step 4:

[0761] The server integrates the collected data, performs data cleaning by imputing missing values ​​and removing outliers.

[0762] Step 5:

[0763] The server uses generative models to analyze integrated data and assess crop environmental stress and growth conditions. For example, it can detect nitrogen deficiency from changes in leaf color.

[0764] Step 6:

[0765] Based on the analysis results, the server generates agricultural management advice. This advice includes specific instructions on irrigation, fertilization, and pest and disease control.

[0766] Step 7:

[0767] The terminal receives agricultural management advice generated from the server and notifies the user. The advice is displayed in an easy-to-read format.

[0768] Step 8:

[0769] The user checks the notifications from their device and prepares to carry out appropriate farm work based on the advice provided.

[0770] Step 9:

[0771] Users can send feedback to the server via their device regarding the results of the farm work they have performed and the advice they have received.

[0772] Step 10:

[0773] The server collects user feedback, analyzes that data to improve the accuracy of the generative model, and updates the model as needed.

[0774] (Example 1)

[0775] 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".

[0776] In agriculture, it is crucial to understand weather changes, soil conditions, and crop growth in real time and to manage them efficiently and optimally based on that information. However, conventional methods suffer from insufficient data collection and a lack of integrated management capabilities, making it difficult to optimize agricultural management. There was also a need for a method to efficiently incorporate user feedback and continuously improve the system.

[0777] 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.

[0778] In this invention, the server includes, as data acquisition means, means for receiving distance exploration data from an observation device, means for acquiring environmental data from a weather information source, and means for accumulating geological data from a soil condition measuring device placed on the ground. This enables the generation of advanced agricultural management guidelines based on real-time integrated data.

[0779] "Data acquisition means" refers to a system or process for receiving and integrating necessary information from observation devices or information sources.

[0780] An "observation device" is an instrument used to acquire data on the state of the Earth and environmental changes as distance-based survey data.

[0781] "Distance survey data" refers to data on environmental conditions and surface changes acquired from remote locations using remote sensing technology.

[0782] A "weather information provider" refers to an external organization or platform that provides environmental data.

[0783] "Environmental data" refers to weather-related data such as temperature, humidity, and precipitation.

[0784] A "soil condition measuring device" is a device placed on the ground to collect geological data.

[0785] "Geological data" refers to data about the condition of the ground, including information on soil moisture content and nutrients.

[0786] A "generative artificial intelligence model" is an AI technology used to analyze data and generate specific guidelines or results.

[0787] "Guidelines for agricultural use management" are specific advice and plans for carrying out optimal agricultural activities according to crop growth and environmental conditions.

[0788] "Users" refer to agricultural workers who receive information through this system and carry out agricultural work.

[0789] The system of this invention is designed to optimize agricultural management in real time, and combines data acquisition means, generation AI models, and notification means to provide comprehensive agricultural management advice.

[0790] Server Embodiment

[0791] The server receives distance survey data from observation devices and acquires environmental data from weather information sources. Furthermore, it collects geological data from soil condition measurement devices. Data acquisition is automated using APIs, and the dataset is formatted and purified using Python libraries such as Pandas and NumPy. The server analyzes the integrated data using generative AI models such as TensorFlow and PyTorch to generate agricultural management guidelines based on crop growth and environmental conditions. Specifically, this includes identifying the optimal timing and amount of irrigation and fertilization.

[0792] Terminal embodiment

[0793] The device receives notifications from the server and displays them to the user. The device is a smartphone or tablet-type personal information terminal equipped with an intuitive user interface. Notifications are delivered via a push notification system and displayed visually in a GUI format, allowing the user to immediately decide on the necessary action.

[0794] User Embodiment

[0795] Users manage their farms based on notifications from their devices. For example, they can adjust irrigation schedules based on crop health predictions and plan fertilizer application in response to changes in environmental conditions. Furthermore, they can send feedback on the results of their actions from their devices to the server. This allows the system to continuously improve its generated AI model based on the provided data.

[0796] Specific example

[0797] For example, if you are managing a wheat farm, the server can use remote sensing data to detect changes in leaf color and suggest a nitrogen deficiency. Furthermore, if rainfall is predicted, the server will suggest applying nitrogen fertilizer before the rain. The user receives this notification on their terminal and can quickly adjust farm work. A concrete example of a prompt message would be, "Generate specific advice on the optimal timing for fertilizer application based on wheat field leaf color change data and future weather forecasts."

[0798] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0799] Step 1:

[0800] The server receives distance survey data from observation devices and acquires environmental data from weather information sources. Furthermore, it collects geological data from soil condition measurement devices. It takes remote sensing data, weather data, and soil data as input and integrates them. Specifically, it downloads data via API requests and converts it to a specified format using a Python script. The output is a collection of raw data, which is saved for later processing.

[0801] Step 2:

[0802] The server performs data cleansing on the collected data. It uses the integrated raw data as input. It detects missing values ​​and outliers from this input data and performs imputation or deletion as necessary. Specifically, it uses libraries such as Pandas and NumPy to format the data and create a clean dataset. The output is a clean dataset, ready to proceed to the subsequent analysis steps.

[0803] Step 3:

[0804] The server takes a clean dataset as input and performs analysis using a generated AI model. Specifically, it applies machine learning models such as TensorFlow and PyTorch to extract useful patterns and relationships from the data. The output is agricultural management guidance based on the analysis. This guidance includes appropriate timing for irrigation and fertilization determined from the health of the crops.

[0805] Step 4:

[0806] The server generates agricultural management guidelines obtained from the analysis results as a notification and sends it to the terminal. The input uses the analysis results data and the notification format. Specifically, it sends data to the terminal using the HTTP protocol and delivers notifications to the user in real time via a push notification system. The output is a notification message displayed to the user on the terminal.

[0807] Step 5:

[0808] The terminal receives notifications from the server as input and displays them visually to the user. Specifically, the mobile device's application receives the notification and presents the information to the user in a GUI format. The output is notification information presented in a format that is easy for the user to understand.

[0809] Step 6:

[0810] Users manage their farms using notifications from their devices as input, planning and executing specific tasks. These tasks include operating irrigation equipment and applying fertilizer. Specifically, they perform necessary actions in the field based on the notifications from their devices. The output is the result of the completed farm work, which is returned to the server as feedback via the device after the work is completed.

[0811] Step 7:

[0812] The server uses user feedback as input to improve its generated AI model. Specifically, it collects feedback data and uses it to retrain the generated AI model. The output is an improved AI model, which helps provide more accurate guidance in the future.

[0813] (Application Example 1)

[0814] 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".

[0815] Farmers face the challenge of not being able to grasp crop growth conditions and environmental changes in real time and manage them optimally using conventional methods. Furthermore, there is a demand for efficient farming through automated systems. However, conventional methods lack adequate support for workers on the farm, making efficient work management difficult.

[0816] 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.

[0817] In this invention, the server includes means for receiving remote sensing data from Earth observation instruments as a data collection means, means for acquiring weather data from weather information providers, means for accumulating soil data from soil condition detectors installed on the ground, means for integrating the collected data and performing data cleaning, means for analyzing the data using a generative model and generating optimized agricultural management guidelines based on crop growth and environmental changes, means for performing automated patrols of the farm using management equipment to monitor the condition of targets and perform tasks, and means for notifying workers equipped with visual devices. This enables agricultural workers to receive information in real time and efficiently manage and operate their farms.

[0818] "Data collection means" refers to a system that acquires and integrates data from Earth observation instruments, weather information providers, and soil condition detectors.

[0819] "Earth observation equipment" refers to observation instruments used to acquire remote sensing data from above a farm.

[0820] "Weather information provider" refers to an organization or service that provides weather data.

[0821] A "soil condition detector" is a device installed on the ground to detect the condition of the soil.

[0822] "Data cleaning" is the process of organizing and integrating collected data to prepare it for analysis.

[0823] A "generative model" is a mathematical or machine learning model used to analyze data and generate guidelines or predictions tailored to a specific purpose.

[0824] "Agricultural management guidelines" are instructions and advice that show the optimal management methods in response to crop growth and environmental changes.

[0825] "Management equipment" refers to devices that automatically patrol the farm, collecting data and performing tasks.

[0826] A "visual device" is a device that displays information and provides visual notification to workers.

[0827] The system that realizes this invention uses the following hardware and software to collect and analyze various data. The server integrates remote sensing data acquired for Earth observation, meteorological data acquired from weather information providers, and soil data from soil condition detectors. This information is prepared using data cleaning techniques and analyzed using generative models, such as machine learning frameworks like TensorFlow.

[0828] The agricultural management guidelines generated by the analysis specifically indicate the timing and methods of irrigation and fertilization according to the health of the crops and environmental conditions. The management equipment automatically patrols the farm and performs the necessary tasks. This makes it possible to operate the farm efficiently without human intervention.

[0829] The terminal displays information on smartphones, tablet-type information processing devices, or visual devices, notifying workers of the situation in real time. This information includes detailed guidelines for daily farm work, such as the timing of starting irrigation or applying fertilizer.

[0830] By using smart devices, users can instantly receive these notifications and send feedback back to the system. This allows the generative model to continuously learn and improve the accuracy of agricultural management. Furthermore, providing instructions to workers through visual devices enables rapid response in the field.

[0831] For example, if the server detects a change in the color of tomato leaves and indicates a nitrogen deficiency, a worker wearing a visual device will receive a notification saying, "There are signs of nitrogen deficiency on the tomato leaves. Please fertilize immediately." An example of a prompt message would be, "Anomaly detected during tomato field inspection. What is the next recommended action?" This prompt allows the system to generate and provide appropriate countermeasures.

[0832] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0833] Step 1:

[0834] The server acquires remote sensing data from Earth observation instruments. The input is remote sensing data, and the output generates initial growth data that is stored in a database. This data includes information about crop growth and is verified for consistency for use in subsequent analysis.

[0835] Step 2:

[0836] The server retrieves weather data from weather information providers. The input is predicted weather data, and the output is managed within the server as integrated data. This data includes temperature, humidity, precipitation, etc., and after verifying that each variable is within normal limits, it is reflected in future forecasts.

[0837] Step 3:

[0838] The server collects soil data from soil condition detectors. Inputs include soil moisture and nutrient status data, and output is a success notification indicating that this data was successfully captured. Soil pH and moisture content are considered important in this process, and an alert is generated if any inconsistencies are found.

[0839] Step 4:

[0840] The server integrates the collected data and performs data cleaning. Remote sensing data, weather data, and soil data are used as inputs. Unnecessary data and missing values ​​are removed, and a dataset optimized for analysis is output. The completeness and consistency of the data are verified, and it is ready for the next analysis stage.

[0841] Step 5:

[0842] The server performs data analysis using a generative model. Integrated data, after data cleaning, is the input, and crop growth predictions and guidelines for optimal agricultural management are output. The generative AI model used here interprets the data using statistical methods and machine learning algorithms to formulate future farming plans.

[0843] Step 6:

[0844] The terminal notifies the user based on agricultural management guidelines obtained from the server. The input is the analysis results, and the output is specific work instructions displayed on the user's terminal. At this stage, action-based prompts are also included, allowing the user to immediately understand what to do.

[0845] Step 7:

[0846] Users perform actual farm work based on notifications displayed on their devices. The input is the guidance from the device, and the output is the agricultural action performed. This enables timely decision-making and improves the efficiency of farm work.

[0847] Step 8:

[0848] Users send feedback to the server regarding the status of completed tasks and areas for improvement. The input is the user's feedback information, and the output is training data used in a generative model. Through this feedback loop, the entire system evolves, leading to smarter agricultural management.

[0849] 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.

[0850] This invention relates to a system that helps farmers optimize crop management in real time, incorporating an emotion engine that recognizes user emotions to provide more adaptive advice. The system comprises data collection means, a generative model, an emotion engine, and notification means, and provides comprehensive agricultural management advice.

[0851] Server Embodiment

[0852] The server receives remote sensing data from Earth observation instruments and acquires weather data from weather information providers. It also collects soil data from soil condition detectors installed on the ground. This data is integrated and data cleaning is performed. Subsequently, the collected data is analyzed using a generative model to generate agricultural management guidelines in response to crop growth conditions and environmental changes.

[0853] Emotional Engine Implementation

[0854] The server has a built-in emotion engine that analyzes the user's voice and text input to recognize their emotions. This emotion recognition allows the server to adjust the content and method of advice based on the user's mental state. For example, if the user is feeling stressed, it will provide more concise and easy-to-follow advice.

[0855] Terminal embodiment

[0856] The device receives agricultural management advice and emotion-based adjustment information transmitted from the server and notifies the user. Notifications are customized according to the user's emotional state to ensure a comfortable experience. The device is a smartphone or tablet-type mobile information terminal, and its user interface is intuitive and easy to operate.

[0857] User Embodiment

[0858] Users perform agricultural management based on notifications sent from their devices. For example, the system provides specific examples of how to adjust field irrigation before predicted rainfall. Users can also send feedback to the server regarding their thoughts on agricultural management and the results after implementation. The sentiment engine analyzes the user's feedback and uses the results to improve the generative model.

[0859] Specific example

[0860] For example, if a user managing a wheat field asks "What's the weather like next week?" via voice command on their smartphone, the server performs remote sensing and weather data analysis, and the emotion engine detects that the user is feeling anxious. The server then provides calm and specific advice, such as "Rainfall is expected to be light next week, but temperatures will be high, so it would be a good idea to increase irrigation frequency." Based on this information, the user can plan their work and perform farming efficiently while reducing anxiety.

[0861] The following describes the processing flow.

[0862] Step 1:

[0863] The server receives remote sensing data from Earth observation instruments. This includes image data related to vegetation indices in agricultural land and crop growth conditions.

[0864] Step 2:

[0865] The server obtains real-time weather data via APIs from weather information providers. This includes information such as predicted precipitation, temperature, and humidity.

[0866] Step 3:

[0867] The server collects and integrates data such as soil moisture, temperature, and pH values ​​in real time from soil condition detectors installed on the ground.

[0868] Step 4:

[0869] The server integrates all collected data and performs data cleaning. It corrects any outliers or missing values, preparing the data for analysis.

[0870] Step 5:

[0871] The server uses a generative model to analyze the cleaned data. Based on the analysis results, it evaluates crop growth conditions and environmental stress, and generates optimal agricultural management guidelines.

[0872] Step 6:

[0873] When a user enters a request via voice or text into their device, that information is sent from the device to the server.

[0874] Step 7:

[0875] The server uses an emotion engine to recognize emotions from user input. It analyzes the type and intensity of the emotion to determine the user's psychological state.

[0876] Step 8:

[0877] The server takes the results of sentiment analysis into account and adjusts agricultural management guidelines according to the user's emotions. For example, it generates more concise and reassuring advice for users who are feeling stressed.

[0878] Step 9:

[0879] The device receives and notifies the user of tailored agricultural management advice. The notification is displayed in a format that takes the user's emotional state into consideration.

[0880] Step 10:

[0881] Users check notifications from their devices and perform farm work based on the instructions. After completion, users can send feedback on the results and advice to the server from their devices.

[0882] Step 11:

[0883] The server analyzes user feedback, performs sentiment analysis using an emotion engine, and then uses the feedback to improve the generative model and the way advice is provided.

[0884] (Example 2)

[0885] 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".

[0886] In modern agriculture, it is difficult to manage crops stably without being affected by climate change and environmental shifts, and the mental burden on farmers is increasing. Therefore, there is a need to provide agricultural management guidelines that utilize real-time environmental data while being tailored to the emotional state of individual users. However, existing systems lack the means to provide individually optimized advice that takes emotions into account, so this problem needs to be solved.

[0887] 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.

[0888] In this invention, the server includes means for receiving remote detection information from Earth observation instruments as a data collection means, means for analyzing the user's emotions using an emotion engine and adjusting advice based on the results, and means for notifying the user using a mobile information terminal. This makes it possible to provide optimized agricultural management guidelines based on real-time, highly accurate environmental data, while taking into account the user's emotional state.

[0889] A "data collection means" is a means that has the function of receiving information from Earth observation instruments and weather information providers, and accumulating soil information from soil condition detectors.

[0890] "Remote detection information" refers to data acquired remotely from geographically distant locations using Earth observation equipment, etc.

[0891] "Weather information" refers to data that shows environmental conditions such as precipitation, temperature, and wind speed, and provides the necessary information for making decisions in agricultural management.

[0892] "Soil information" refers to data that indicates the health of the soil, such as its moisture content and nutrients.

[0893] A "generative model" refers to a mathematical or computational algorithm used to analyze collected data and perform pattern recognition or prediction.

[0894] An "emotion engine" is a technology that analyzes user voice and text input to recognize emotions and generates analysis results as feedback.

[0895] A "notification method" is a mechanism that provides information sent from a server to a user via a mobile device.

[0896] "Personal information terminals" refer to electronic devices such as smartphones and tablets that are easy to carry and have an intuitive user interface.

[0897] This invention is an information system for agricultural workers to efficiently manage crops. The system comprises data collection means, a generative model, an emotion engine, and notification means, and provides comprehensive agricultural management advice.

[0898] The server receives remote detection information from Earth observation instruments and obtains weather information from weather information providers. It also collects soil information from soil condition detectors installed on the ground. This data is integrated and cleaned, and then analyzed by a generative AI model. The generative model learns agriculture-related patterns and generates agricultural management guidelines that respond to crop growth conditions and environmental changes based on the collected data.

[0899] Furthermore, the server uses an emotion engine to analyze the user's voice and text input and recognize their emotions. Based on this information, advice is adjusted according to the user's mental state. For example, if the user is feeling anxious, more concise and actionable advice will be provided.

[0900] The terminal receives agricultural management advice and emotion-based adjustment information transmitted from the server and notifies the user. The terminals used are smartphones and tablet-type mobile devices, and the user interface is intuitive and easy to operate.

[0901] Users perform agricultural management based on notifications from their devices. This system allows users to perform efficient and adaptive agricultural work and reduce mental stress. For example, when managing crop watering, users can receive a notification from the server saying, "It is expected to be dry next week, so please increase the frequency of irrigation," and then create an optimal work plan.

[0902] An example prompt might be, "The user wants to know the optimal agricultural management methods based on remote sensing data and weather data." This allows the server to perform appropriate data analysis and generate advice.

[0903] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0904] Step 1:

[0905] The server receives remote detection information from Earth observation instruments. The input at this stage includes data such as temperature, humidity, and vegetation index acquired via satellites and drones. The server temporarily stores this data in preparation for the next data processing step.

[0906] Step 2:

[0907] The server retrieves weather information provided by weather information providers. The input consists of forecast data such as regional precipitation, temperature, and wind speed. This allows the server to understand future weather conditions and accumulate data for use in crop management planning.

[0908] Step 3:

[0909] The server collects soil information received from soil condition detectors. This input includes data on soil moisture content and nutrients. Based on this, the server obtains basic data for evaluating soil health.

[0910] Step 4:

[0911] The server integrates all the collected data and performs data cleaning. The input here is a wide range of data collected so far, including temperature, humidity, weather conditions, and soil information. The server removes outliers from these datasets and interpolates missing data to prepare them for analysis.

[0912] Step 5:

[0913] The server applies regression analysis and machine learning algorithms using a generative AI model based on the prepared data. The input here is cleaned, integrated data, and the output is crop growth predictions and guidelines for necessary agricultural management. Based on these results, the server determines the optimal agricultural management method.

[0914] Step 6:

[0915] The server uses an emotion engine to recognize emotions from the user's voice and text. Input is user questions and feedback, and the server adjusts the content and wording of advice based on this analysis. The output is optimized advice tailored to the user's mental state.

[0916] Step 7:

[0917] The terminal receives agricultural management guidelines and emotion-based advice sent from the server. The input is this notification data, which the terminal displays to the user. The output is visual or auditory feedback to the user, delivered through the user interface.

[0918] Step 8:

[0919] Users perform agricultural management based on information received from their devices. Input is the notified advice, which users utilize to carry out tasks such as irrigation and fertilization. Output is the actual management activities on the farmland and their results.

[0920] Step 9:

[0921] Users send management results and feedback to the server. The server then analyzes the collected feedback using an emotion engine and uses it to improve the accuracy of future advice and train generative models.

[0922] (Application Example 2)

[0923] 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".

[0924] Agricultural workers and distributors need to operate efficiently and effectively while adapting to environmental changes and diversifying consumer needs. However, conventional agricultural and distribution management methods have struggled to provide real-time data analysis and adaptive guidance tailored to the emotional state of users. Furthermore, optimizing delivery routes while considering emotions has been insufficient to enhance user satisfaction. Effective solutions to these challenges are needed.

[0925] 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.

[0926] In this invention, the server includes, as a data collection means, a function to receive remote sensing data from a global monitoring device, a function to acquire weather data from a weather information provider, and a function to collect soil data from a ground-based soil condition detection device. This enables agricultural workers and distributors to receive adaptive support that is sensitive to their needs based on the data. Furthermore, it can improve demand forecasting and optimize delivery routes in the distribution of agricultural products, thereby increasing user satisfaction.

[0927] "Data collection means" refers to functions for acquiring various environmental information, such as remote sensing data, weather data, and soil data.

[0928] A "global monitoring device" is a device that enables observations on a global scale and provides remote sensing data.

[0929] "Remote sensing data" refers to observational data obtained without direct contact with the object or region, and is collected by satellites or aircraft.

[0930] A "weather information provider" is an organization that conducts weather observations and provides the resulting weather data.

[0931] A "soil condition detection device" is a device used to detect the condition of the soil, such as its moisture and nutrients.

[0932] "Emotion analysis means" refers to a function that recognizes emotions from the user's voice or text and changes the way information is provided accordingly.

[0933] A "generative model" is an analytical model used to optimize agricultural management and logistics based on collected data.

[0934] "Demand forecasting in distribution" is an analysis aimed at ensuring appropriate supply by predicting how much agricultural products will be consumed.

[0935] "Optimizing delivery routes" refers to calculating the shortest and most optimal routes to deliver goods efficiently and quickly.

[0936] "Data preparation" is the process of integrating collected data and processing it into an analyzable format.

[0937] "Users" refers to agricultural workers and distributors who use this system.

[0938] A "mobile information and communication terminal" is a portable device, such as a smartphone or tablet, that has the function of processing information and communicating with others.

[0939] The system for implementing this invention will achieve efficiency and optimization in agriculture and distribution through data collection, analysis, and information provision.

[0940] The server receives remote sensing data through a global monitoring device and acquires weather data from weather information providers. Furthermore, it collects soil data using ground-based soil condition detection devices. This data is converted into a format suitable for analysis through a processing stage. Finally, a generative AI model is used to analyze the collected data and generate agricultural management guidelines. This model utilizes deep learning libraries such as TensorFlow and PyTorch.

[0941] The server also analyzes voice or text input from the user and recognizes their emotional state using sentiment analysis tools. It utilizes Google Cloud Speech-to-Text for speech recognition and Natural Language Toolkit (NLTK) for natural language processing. This enables the server to provide adaptive advice tailored to the user's mental state and needs.

[0942] The terminal receives the analysis results and notifies the user. Mobile information and communication devices, particularly smartphones and tablets, are used for communication. The notifications are adjusted according to the user's emotional state; for example, in the case of an urgent order, a delivery route that allows for quick response is suggested. For this purpose, map APIs capable of handling geographic information in GeoJSON format are used.

[0943] Users develop agricultural management and distribution plans based on the information provided. For example, if a user asks, "What's the weather like next week?", the system analyzes weather data and provides advice such as, "Rainfall is expected to be low next week. High temperatures are anticipated, so increase irrigation." Furthermore, an example of a prompt could be, "Create a prompt to check next week's weather and generate calm, emotionally resonant advice. The target audience is consumers who are anxious about urgent distribution needs."

[0944] In this way, the system can solve various challenges in agriculture and distribution, and improve user satisfaction and efficiency.

[0945] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0946] Step 1:

[0947] The server receives remote sensing data from a global monitoring device. The input is remote sensing data, and by receiving this data, global environmental information can be obtained. The server stores this data in a storage database.

[0948] Step 2:

[0949] The server retrieves weather data from weather information providers. Input data comes from weather information APIs, receiving information such as temperature and precipitation. The received data is automatically stored in the database in real time.

[0950] Step 3:

[0951] The server collects soil data from soil condition detection devices installed on the ground. The input here includes soil moisture and nutrient data. The server then performs a preparation process to format this data for analysis.

[0952] Step 4:

[0953] The server integrates and processes all collected data. Inputs include remote sensing data, weather data, and soil data. The server cleans these data and converts them into an analyzable format.

[0954] Step 5:

[0955] The server uses an AI model to analyze data and generate agricultural management guidelines. The input is a pre-configured dataset, and a machine learning algorithm is applied to predict crop growth and output specific guidelines.

[0956] Step 6:

[0957] The server receives voice or text input from the user and analyzes the emotional state using emotion analysis tools. The input is voice or text data, and an emotion recognition algorithm is used to analyze the emotion and identify the emotional state as output.

[0958] Step 7:

[0959] The server adjusts the guidelines derived from the generative model based on the sentiment analysis results, generating advice optimized for the user. The adjusted advice becomes the output. It provides users with flexible guidelines that respond to their emotions.

[0960] Step 8:

[0961] The device receives tailored advice and notifies the user. The input is the tailored advice, which the device receives and displays to the user at the appropriate time and in the appropriate format. Specifically, the priority and content of the notification change depending on the urgency.

[0962] Step 9:

[0963] Based on the information provided by the user, agricultural management and distribution plans are implemented. The information is used to develop specific farming tasks and distribution procedures. At this time, the implementation plan is revised based on the advice received.

[0964] 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.

[0965] 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.

[0966] 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 robot 414.

[0967] 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.

[0968] 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.

[0969] 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.

[0970] 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.

[0971] 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.

[0972] 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."

[0973] 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.

[0974] 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.

[0975] 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.

[0976] 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.

[0977] 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.

[0978] 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.

[0979] 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.

[0980] 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.

[0981] 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.

[0982] 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.

[0983] 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.

[0984] 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.

[0985] The following is further disclosed regarding the embodiments described above.

[0986] (Claim 1)

[0987] As a means of data collection, it has the function of receiving remote sensing data from Earth observation instruments,

[0988] A function to acquire weather data from weather information providers,

[0989] It has a function to collect soil data from soil condition detectors installed on the ground,

[0990] A method for integrating collected data and performing data cleaning,

[0991] A means of analyzing data using a generative model to generate optimized agricultural management guidelines based on crop growth and environmental changes,

[0992] A system that includes means for notifying users of the above guidelines.

[0993] (Claim 2)

[0994] The system according to claim 1, further comprising a learning support function for receiving user feedback and incorporating it into the generative model.

[0995] (Claim 3)

[0996] The system according to claim 1, which provides notifications to a user using a smartphone or tablet-type mobile information terminal.

[0997] "Example 1"

[0998] (Claim 1)

[0999] As a means of acquiring data, it has a function to receive distance survey data from observation equipment,

[1000] A function to acquire environmental data from weather information providers,

[1001] It has a function to collect geological data from soil condition measuring devices placed on the ground,

[1002] A means of integrating and purifying the collected data,

[1003] A means of analyzing information using generative artificial intelligence models to generate guidelines for optimized agricultural use management based on plant growth conditions and environmental changes,

[1004] A system that includes means of informing users of the above guidelines.

[1005] (Claim 2)

[1006] The system according to claim 1, which includes a learning support function for receiving user feedback and reflecting it in a generative artificial intelligence model.

[1007] (Claim 3)

[1008] The system according to claim 1, which provides notifications to users using a mobile information terminal.

[1009] "Application Example 1"

[1010] (Claim 1)

[1011] As a means of data collection, there is a means for receiving remote sensing data from Earth observation instruments,

[1012] Means for obtaining weather data from weather information providers,

[1013] A means for collecting soil data from soil condition detectors installed on the ground,

[1014] A method for integrating collected data and performing data cleaning,

[1015] A means of analyzing data using a generative model to generate optimized agricultural management guidelines based on crop growth and environmental changes,

[1016] Means for notifying users of the above guidelines,

[1017] A means of performing automated patrols within the farm using management equipment to monitor the condition of the target and execute tasks,

[1018] A system that includes means for notifying workers wearing visual devices.

[1019] (Claim 2)

[1020] The system according to claim 1, further comprising a learning support function for receiving user feedback and incorporating it into the generative model.

[1021] (Claim 3)

[1022] The system according to claim 1, which provides notifications to a user using a portable information processing device or a visual assistance device.

[1023] "Example 2 of combining an emotion engine"

[1024] (Claim 1)

[1025] As a means of data collection, it has the function of receiving remote detection information from Earth observation instruments,

[1026] A function to obtain weather information from weather information providers,

[1027] It has a function to collect soil information from soil condition detectors installed on the ground,

[1028] A means of integrating the collected information and organizing the data,

[1029] A means of analyzing information using a generative model to generate guidelines for optimized agricultural management based on crop growth and environmental changes,

[1030] A means of analyzing a user's emotions using an emotion engine and adjusting advice based on the results,

[1031] A system that includes means of notifying users of the above guidelines and coordinated advice.

[1032] (Claim 2)

[1033] The system according to claim 1, which includes a learning support function for receiving user feedback, performing sentiment analysis, and reflecting it in a generative model.

[1034] (Claim 3)

[1035] The system according to claim 1, which provides notifications to a user using a mobile information terminal.

[1036] "Application example 2 when combining with an emotional engine"

[1037] (Claim 1)

[1038] As a means of data collection, it has a function to receive remote sensing data from a large-scale monitoring device,

[1039] The function to acquire weather data from weather information providers,

[1040] It has a function to collect soil data from a soil condition detection device installed on the ground,

[1041] Methods for integrating and organizing collected data,

[1042] A means of analyzing data using a generative model to generate guidelines for optimized agricultural management based on crop growth and environmental changes,

[1043] Means of notifying users of the above guidelines,

[1044] An emotion analysis means that analyzes the user's voice or text input, recognizes their emotional state, and adaptively adjusts the guidelines;

[1045] A means of forecasting demand and optimizing delivery routes in the distribution of agricultural products,

[1046] A system that includes this.

[1047] (Claim 2)

[1048] The system according to claim 1, comprising a learning support function for receiving feedback and reflecting it in a generative model, and a function for adjusting the delivery method and notification content considering the user's emotional state.

[1049] (Claim 3)

[1050] The system according to claim 1, which uses a mobile information and communication terminal to notify users and provides information optimized for urgent distribution needs. [Explanation of Symbols]

[1051] 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. As a means of data collection, it has the function of receiving remote sensing data from Earth observation instruments, A function to acquire weather data from weather information providers, It has a function to collect soil data from soil condition detectors installed on the ground, A method for integrating collected data and performing data cleaning, A means of analyzing data using a generative model to generate optimized agricultural management guidelines based on crop growth and environmental changes, A system that includes means for notifying users of the above guidelines.

2. The system according to claim 1, further comprising a learning support function for receiving user feedback and reflecting it in the generative model.

3. The system according to claim 1, which notifies the user using a smartphone or tablet-type mobile information terminal.

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