system

Abstract

We provide the system. [Solution] A means for receiving environmental data acquired from information gathering equipment, A means for analyzing the aforementioned environmental data to evaluate the state of agricultural targets, A means of proposing agricultural actions based on the evaluations created, Means for displaying proposed agricultural actions, Means for collecting and analyzing market information, A system that includes a means of receiving and learning from user feedback.

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, 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 modern agriculture, due to climate change, market price fluctuations, limited labor force, etc., the coexistence of production optimization and sustainability is required. To address such issues, efficient resource utilization and quick decision-making are essential, but it is difficult to achieve these with conventional agricultural methods. Furthermore, agricultural workers have limited opportunities to fully utilize advanced technologies and information, resulting in a situation where it is difficult to formulate an optimal agricultural plan.

Means for Solving the Problems

[0005] This invention solves the above problems by providing a system that aggregates and analyzes environmental data acquired from information gathering devices. Using information gathering devices such as soil sensors and drones, the system monitors the crop growing environment in real time and analyzes the data to predict the health of the crops and the impact of future climate change. Furthermore, it provides farmers with action suggestions based on the analysis results and advises them on specific measures. Through its market information analysis function, it supports the formulation of optimal sales strategies and aims to maximize profits. This system utilizes machine learning models to collect feedback from users and continuously improve the accuracy of its suggestions. As a result, farmers can achieve more efficient and sustainable agriculture.

[0006] The following are definitions of key terms included in the claims.

[0007] "Information gathering equipment" refers to devices used to acquire environmental data from agricultural sites, including soil sensors and drones.

[0008] "Environmental data" refers to data that indicates the state of the agricultural environment, such as soil moisture, temperature, pH value, nutrient content, weather information, and crop image data.

[0009] "Means of analysis" refers to the technologies and methods used to process collected environmental data and evaluate soil conditions and crop health.

[0010] "Means of proposing agricultural actions" refers to the process of presenting optimal agricultural policies and actions based on analyzed data.

[0011] "Means of display" refers to interface technologies for visually or audibly communicating analysis results and suggestions to agricultural workers.

[0012] "Market information" refers to information that includes data on price trends and demand for agricultural products, and is used to formulate sales strategies for maximizing profits.

[0013] A "machine learning model" refers to an algorithm and its implementation used to analyze data and perform predictions or classifications.

[0014] "Feedback" refers to information obtained from agricultural workers, such as implementation results and opinions, which are incorporated into the system to help improve future processes. [Brief explanation of the drawing]

[0015] [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 the emotion engine is combined. [Figure 14] It is a sequence diagram showing the processing flow of the data processing system in Application Example 2 when the emotion engine is combined.

Mode for Carrying Out the Invention

[0016] 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.

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

[0018] In the following embodiments, the numbered processor (hereinafter simply referred to as "processor") may be one arithmetic unit or a combination of multiple arithmetic units. Also, the processor may be one type of arithmetic unit or a combination of multiple types of arithmetic units. Examples of arithmetic units include CPU (Central Processing Unit), GPU (Graphics Processing Unit), GPGPU (General-Purpose computing on Graphics Processing Units), APU (Accelerated Processing Unit), etc.

[0019] In the following embodiments, the numbered RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a work memory by the processor.

[0020] In the following embodiments, the numbered 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 disk (e.g., hard disk), or magnetic tape, etc.

[0021] 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).

[0022] 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."

[0023] [First Embodiment]

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

[0025] 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.

[0026] 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).

[0027] 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.

[0028] 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.

[0029] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user 20 by outputting the data in a form 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.

[0030] 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.

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

[0032] 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.

[0033] 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.

[0034] 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.

[0035] 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".

[0036] As an embodiment of the present invention, a system for optimizing agriculture using environmental data is provided. This system analyzes data obtained from information collection devices and proposes agricultural actions to realize sustainable and efficient agriculture.

[0037] First, the server receives environmental data from information gathering devices. This includes soil moisture, temperature, and nutrient content acquired by soil sensors and drones, as well as weather information and crop image data. The collected data is pre-processed and cleaned. The server then analyzes the environmental data using machine learning models. This analysis evaluates soil conditions and crop health, and makes predictions, including the impact of future climate change.

[0038] Next, the server generates specific agricultural action suggestions for farmers based on the analysis results. These suggestions include appropriate fertilizer types and amounts, watering timing, pest and disease control measures, and sales strategies based on market trends.

[0039] The terminal presents the user with suggested information sent from the server, either visually or audibly. The suggestions are processed using natural language to ensure intuitive understanding, allowing agricultural workers to select appropriate actions based on them.

[0040] For example, if a user wants to check the soil condition of their farm, the server analyzes data collected from soil sensors to identify deficient nutrients. The terminal then suggests and displays the necessary fertilization methods to the user. As another example, when determining a sales strategy, the server refers to market information, forecasts supply and demand, and suggests the optimal shipping time and pricing strategy.

[0041] This system deepens its learning based on user feedback. The server records actual results in a database, improving the accuracy of its suggestions. This enables continuous improvement, making agriculture increasingly efficient and sustainable.

[0042] The following describes the processing flow.

[0043] Step 1:

[0044] The server receives environmental data in real time from soil sensors, weather sensors, and drones. This data includes soil moisture, temperature, pH value, nutrient content, crop images, and weather information.

[0045] Step 2:

[0046] The server preprocesses the received environmental data. Specifically, it detects missing and outlier values ​​and performs interpolation or deletion as needed. It also performs noise reduction processing.

[0047] Step 3:

[0048] The server applies machine learning models to analyze pre-processed data. This analysis assesses soil conditions and crop health, and also makes predictions based on future weather patterns.

[0049] Step 4:

[0050] The server generates action suggestions based on the analysis results. These include fertilizer application amounts and timing, irrigation schedules, pest and disease outbreak predictions and control measures, and sales strategies based on market trends.

[0051] Step 5:

[0052] The terminal receives suggestions from the server and displays them in an easy-to-understand format for the user using natural language processing technology. The visualized suggestions, presented in a user-understandable format, are shown on the user interface.

[0053] Step 6:

[0054] The user implements the agricultural plan based on the provided suggestions. This includes fertilizing, watering, pest and disease control, and determining the timing of shipments based on market trends.

[0055] Step 7:

[0056] Users provide the system with implementation results and feedback.

[0057] The server collects this feedback and uses it to tune the machine learning model in order to improve the accuracy of future analyses and suggestions.

[0058] This series of processes allows farmers to improve production efficiency and quality while being mindful of the environment.

[0059] (Example 1)

[0060] 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."

[0061] In modern agriculture, it is difficult to propose accurate agricultural practices that effectively utilize environmental information. Traditional methods involve a high diversity of collected data, requiring significant time and effort for data preprocessing and analysis. Furthermore, it is challenging to make highly accurate proposals that take future climate change into account, and obtaining appropriate feedback to improve proposals is a significant issue.

[0062] 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.

[0063] In this invention, the server includes means for receiving and preprocessing environmental information acquired from a data collection device, means for analyzing the preprocessed environmental information using a generating AI model and evaluating the state of agricultural targets, and means for proposing agricultural work based on the analysis results and presenting it via display or audio output. This enables agricultural workers to quickly receive data-driven, advanced agricultural suggestions, thereby realizing efficient and sustainable agricultural activities.

[0064] A "data acquisition device" is a device used to acquire environmental information, and includes equipment such as soil measurement devices and aerial devices.

[0065] "Environmental information" refers to physical and chemical data related to agriculture, such as soil moisture, temperature, nutrient content, weather information, and crop image data.

[0066] "Preprocessing" is a process that removes outliers from data and fills in incomplete data, ensuring data consistency before analysis.

[0067] A "generative AI model" is an algorithm built on machine learning technology that analyzes environmental information to evaluate the condition of agricultural targets and make suggestions.

[0068] "Agricultural work" refers to a series of activities performed by agricultural workers, including practical actions such as fertilizing, watering, pest and disease control, and market delivery.

[0069] "Feedback" refers to information about the results of actions taken based on suggestions provided by users, and is used as learning material to improve the accuracy of the system's suggestions.

[0070] To implement this invention, a server plays a central role. The server first receives environmental information from a data collection device. This data collection device may include a soil measuring device or an aerial measuring device. The data obtained from these devices includes soil moisture, temperature, nutrient content, weather information, and image data showing the growth status of crops.

[0071] The received data is preprocessed on the server. During this preprocessing stage, outliers are removed, and incomplete data is supplemented. This consistent data is then analyzed using a generative AI model implemented on the server. Based on environmental information, the model evaluates the state of agricultural targets and predicts the impact of future climate change on crops.

[0072] Based on the analysis results, the server generates suggestions for agricultural work. These suggestions include appropriate fertilizer application amounts, watering timing, pest and disease control measures, and even sales strategies that take market trends into account. These suggestions are presented to the user visually or audibly via the terminal. The information presented is in an intuitively understandable format using natural language processing technology.

[0073] For example, when a user makes a request such as the prompt "Please tell me the optimal timing and amount for the next fertilization," the server analyzes the necessary data and identifies the optimal timing and amount. As a result, the terminal presents an appropriate suggestion to the user.

[0074] In this way, the system can improve the accuracy of its suggestions based on user feedback. The server records the results of actual actions in a database and deepens the learning of the generating AI model. This continuously improves the accuracy of the system's suggestions.

[0075] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0076] Step 1:

[0077] The server receives environmental information from data collection devices. Inputs include humidity, temperature, and nutrient content data from soil measurement devices, crop image data from aerial equipment, and weather information. This yields multidimensional data about the agricultural environment. The output is to send this data to the next preprocessing step.

[0078] Step 2:

[0079] The server preprocesses the received environmental information. The input for this step is the raw data obtained from step 1. Specifically, it removes outliers from the data and imputes missing data points. It also standardizes the data format and prepares it for analysis. The output is a preprocessed, consistent dataset.

[0080] Step 3:

[0081] The server analyzes the pre-processed data using a generating AI model. The input is the data prepared in step 2. This analysis evaluates the state of agricultural targets from environmental information and models the impact of future climate change. Specifically, it uses machine learning algorithms to perform pattern recognition and outputs predictions of soil nutrient deficiencies and crop health as a result.

[0082] Step 4:

[0083] The server generates agricultural work suggestions based on the analysis results. The input is the evaluation results obtained in step 3. In this step, appropriate fertilizer amounts, irrigation schedules, and pest and disease control measures are listed as specific actions. A pricing strategy that takes market information into account is also included in the suggestions. The output is a specific agricultural work suggestion for the user.

[0084] Step 5:

[0085] The terminal presents the proposed agricultural tasks to the user. The input is the suggestions generated in step 4. In terms of specific actions, information is displayed on the screen through a visual user interface, or notified to the user via audio output. The output is information provided in a format that the user can immediately implement.

[0086] Step 6:

[0087] The user takes action based on the suggestions and sends the results as feedback to the server. The input is the result of the agricultural work actually performed by the user. This feedback is recorded in a database. This allows the generative AI model to learn more, improving the accuracy of future suggestions. The output is an improved suggestion model.

[0088] (Application Example 1)

[0089] 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."

[0090] Conventional agricultural optimization systems are designed for vast farmlands and have the problem of not being able to fully demonstrate their effectiveness in small-scale urban farms and community gardens. Urban farmland is limited, and environmental conditions are diverse, so there is a need for a system that provides appropriate agricultural planning that takes these factors into account. Furthermore, detailed, data-driven support is necessary to quickly respond to market trends and changes in weather conditions that urban farmers face.

[0091] 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.

[0092] In this invention, the server includes means for receiving environmental data acquired from an information gathering device, means for analyzing the environmental data to evaluate the state of agricultural targets, and means for proposing agricultural actions based on the created evaluation. This makes it possible to quickly provide customized agricultural plans and actions to small urban farms.

[0093] An "information gathering device" refers to a device used to acquire various types of environmental data, and includes devices such as sensors and aircraft.

[0094] "Environmental data" refers to information that quantifies external conditions related to agriculture, such as soil moisture, temperature, nutrient content, and weather information.

[0095] "Means for evaluating the state" refers to a method or apparatus for analyzing received environmental data and determining the current state of agricultural land and crops.

[0096] "Means for proposing agricultural actions" refers to methods or devices for determining and providing the optimal actions that agricultural workers should take, based on the results of evaluation.

[0097] "Market information" refers to data that includes demand and supply for agricultural products, price trends, and other commercial information.

[0098] "Means for receiving and learning from feedback" refers to methods or devices for collecting user usage results and opinions and incorporating them into future analyses and suggestions.

[0099] An "agricultural plan adapted to the urban environment" is a plan for agricultural activities that takes into account the unique environmental conditions and constraints of urban areas.

[0100] "Agricultural practices customized for small-scale farms" refers to proposals for agricultural activities that are optimized for the specific conditions and requirements of small-scale farmland.

[0101] The system implementing this invention provides customized agricultural planning for small urban farms and community gardens. A server plays a central role.

[0102] The server receives data from information gathering devices such as soil sensors, weather sensors, and drones installed to collect environmental data. This data includes soil moisture, temperature, nutrient content, and weather information. This environmental data is analyzed using machine learning models (such as Scikit-learn and TENSORFLOW®) based on Python. This analysis allows for the evaluation of soil conditions and crop health at each farm and the prediction of the impact of future climate change.

[0103] Based on the analysis results, the server proposes optimal agricultural actions. These proposals include appropriate fertilizer types and amounts, watering timing, and pest and disease control measures. For example, in a community garden within a city, the analysis can determine the timing and amount of irrigation and implement necessary pest and disease control measures.

[0104] Users view these suggestions on a smartphone app. The app uses natural language processing technology to display the suggestions in an intuitive way, allowing farmers to quickly choose actions based on the suggestions.

[0105] Furthermore, the server collects and analyzes relevant market information in order to propose sales strategies that take market trends into account. This allows the user to receive appropriate shipping timing and pricing strategies.

[0106] For example, a user can send a prompt such as, "Please generate an optimal fertilization schedule based on the soil data collected today," and receive suggestions regarding agricultural actions. This can significantly improve the efficiency and sustainability of urban agriculture.

[0107] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0108] Step 1:

[0109] The server receives environmental data from soil sensors, weather sensors, and drones. Inputs include soil moisture, temperature, nutrient content, and weather information. The server stores this data in a database and outputs cleaned data after handling missing values ​​and removing noise.

[0110] Step 2:

[0111] The server uses a machine learning model to analyze the cleaned environmental data. The input is the clean data obtained in Step 1. The data analysis predicts soil conditions, crop health, and future climate change. Based on this, it outputs soil evaluation results and weather forecasts.

[0112] Step 3:

[0113] The server generates agricultural action suggestions based on the analysis results. The input is the evaluation results from step 2. These suggestions include the type and amount of fertilizer to apply, the timing of watering, and pest and disease control measures. An optimized agricultural action plan is generated as output and saved to the database.

[0114] Step 4:

[0115] The server collects market information and develops sales strategies. It receives market trends and price information as input and forecasts future supply and demand. The resulting sales strategies are then stored in a database along with agricultural action plans as output.

[0116] Step 5:

[0117] Users receive suggestion information from the server via a smartphone app. Inputs are queries or requests related to specific agricultural actions that the user is interested in. Outputs are suggestions for agricultural actions expressed in intuitively understandable natural language.

[0118] Step 6:

[0119] The user selects and executes agricultural actions based on the information provided in the app. The input is the suggested information received in step 5. By executing this, the optimal agricultural action is carried out on-site.

[0120] Step 7:

[0121] User feedback is sent to the server. The input consists of the results and opinions of the agricultural actions performed by the user. The server records this as learning material in a database and uses it to improve the accuracy of future analyses and suggestions.

[0122] These steps enable urban small-scale farmers to achieve data-driven, efficient, and sustainable agriculture.

[0123] 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.

[0124] As an embodiment of the present invention, a system is presented that supports the development of efficient and sustainable plans in agriculture. This system provides suggestions that take into account not only environmental data obtained from information gathering devices, but also the emotions of the user.

[0125] This system begins with the server acquiring environmental data from soil sensors, weather sensors, and drones. This data includes soil conditions, crop growth status, and weather information such as temperature and humidity. This data is transformed into useful information through a data cleaning process, and the server uses machine learning models to analyze this data and evaluate the condition of agricultural targets.

[0126] Next, the emotion engine receives user input and responses, and recognizes and analyzes the user's emotions. For example, it determines whether the user is stressed based on their response time and the words they use when asked questions from their device. It also helps to understand how the user receives suggestions, enabling more effective communication. This emotion information is used in suggestion generation, which will be discussed later.

[0127] The server integrates the results of environmental data analysis with information based on the user's emotions to generate suggestions for appropriate agricultural actions. For example, if the user is feeling stressed, it might prioritize presenting simpler and easier-to-implement suggestions.

[0128] The device receives the generated suggestions and presents them to the user in an easily understandable format using natural language processing technology. In doing so, it can optimize user interaction by considering the user's emotional state. The suggestions cover a wide range of topics, from appropriate fertilization and irrigation plans to pest and disease control, and even sales strategies based on market trends.

[0129] For example, if a user asks a question about market trends, the server analyzes market information to forecast demand and proposes the optimal shipping time and pricing. If the user reacts negatively to the information presented, the emotion engine detects this and the server provides alternative solutions, ensuring a highly satisfying information experience.

[0130] This allows agricultural workers to simultaneously improve production efficiency and reduce their workload while maintaining environmental considerations.

[0131] The following describes the processing flow.

[0132] Step 1:

[0133] The server acquires environmental data in real time from soil sensors, weather sensors, and drones. This data includes soil moisture and nutrients, crop images, and temperature fluctuations.

[0134] Step 2:

[0135] The server preprocesses the received environmental data. It ensures data quality by supplementing missing data and removing outliers. It also formats this data into a format that can be input into machine learning models.

[0136] Step 3:

[0137] The server runs machine learning models using pre-processed data to analyze soil conditions and crop health. The models predict the risk of pest and disease outbreaks and the optimal cultivation schedule.

[0138] Step 4:

[0139] The emotion engine monitors user input and responses, analyzing emotional information from factors such as voice tone and response time. This information is then sent to the server.

[0140] Step 5:

[0141] The server integrates the results of environmental data analysis with the user's emotional state to propose personalized agricultural actions. For example, if the user is feeling stressed, it will generate suggestions to prioritize simpler tasks.

[0142] Step 6:

[0143] The device presents generated suggestions to the user using natural language processing technology. The display is intuitive and easy to understand, and the interaction is emotionally conscious.

[0144] Step 7:

[0145] Users carry out agricultural activities based on the suggestions provided. They provide feedback to the system on the status of their fertilization, irrigation, and pest control measures.

[0146] Step 8:

[0147] The server updates its machine learning model based on user feedback and field data, improving the accuracy of future suggestions. This continuously improves the accuracy and effectiveness of agricultural planning.

[0148] (Example 2)

[0149] 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".

[0150] In agriculture, it is essential to accurately understand environmental changes and crop health, and to select appropriate actions based on that understanding. However, conventional methods present challenges such as the cumbersome process of acquiring and analyzing environmental information, and the difficulty in providing information that takes into account the user's emotional state. Furthermore, effective utilization of user feedback is not being achieved. By solving these problems, we aim to improve the efficiency and sustainability of agriculture.

[0151] 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.

[0152] In this invention, the server includes means for receiving environmental information acquired from an information gathering device, means for data cleaning the environmental information and converting it into useful information, and means for using an artificial intelligence model generated to analyze the useful information and evaluate the state of agricultural targets. This enables accurate acquisition and analysis of environmental information, and by generating suggestions that take user emotions into consideration and learning through feedback, it becomes possible to improve efficiency in agriculture and formulate sustainable plans.

[0153] "Information gathering equipment" refers to all hardware used for monitoring soil and weather conditions, including soil monitoring sensors and aerial equipment.

[0154] "Environmental information" refers to data representing external factors related to crops and farmland, specifically including soil conditions, weather conditions, and crop growth status.

[0155] "Data cleaning" refers to the process of removing noise and outliers from collected data and converting it into useful information.

[0156] A "generated artificial intelligence model" is a machine learning algorithm trained to analyze environmental information and used for prediction and evaluation.

[0157] "Emotional state" refers to the psychological reactions and emotions a user exhibits in response to information, and includes indicators such as stress levels and comprehension.

[0158] "User interface" refers to the display screens and operating methods that users use to interact with a system.

[0159] A "prompt message" refers to a sentence displayed in a user interface that presents information or choices to the user.

[0160] The embodiments for carrying out the present invention will now be described. This system aims to improve efficiency and sustainability in agriculture and performs a comprehensive process from acquiring environmental information to generating proposals.

[0161] First, the server uses information gathering equipment consisting of soil monitoring sensors and aerial devices to receive environmental information such as soil conditions and weather conditions in real time. This makes it possible to instantly grasp crop growth and environmental changes.

[0162] Next, the server performs data cleaning on the received environmental information, transforming it into useful information. The data cleaning process removes noise and imputes missing values, preparing the information for analysis.

[0163] Next, the server uses the generated artificial intelligence model to analyze the cleaned environmental information. This AI model is based on machine learning algorithms and is optimized for predicting crop growth and detecting anomalies. This analysis evaluates the condition of agricultural targets and provides foundational data for determining appropriate agricultural actions.

[0164] When a user asks a question to the system through their terminal, for example, by entering a prompt such as "Please tell me the crop growth forecast for next month," the server generates the corresponding information according to that instruction. The user's emotional state is then analyzed, and suggestions are created accordingly. For example, if the user is feeling stressed, the suggestions will be made concise and actionable.

[0165] Finally, the terminal presents the generated suggestions to the user using natural language processing technology. The terminal not only conveys information in an easy-to-understand way through the user interface, but also collects user feedback and sends it to the server. In this way, the system accumulates user sentiment information and feedback and learns to further optimize future suggestions.

[0166] This system not only significantly improves agricultural efficiency but also reduces the burden on users while considering sustainability.

[0167] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0168] Step 1:

[0169] The server receives environmental information from the information gathering device. The information gathering device uses soil monitoring sensors and aerial equipment to collect data such as soil moisture, temperature, and plant health in real time. Raw environmental data is used as input, and this data is aggregated by the server.

[0170] Step 2:

[0171] The server performs data cleaning on the received environmental information. Since the raw input environmental information contains noise and missing values, the server performs processes to remove and impute them. This process yields clean data suitable for analysis.

[0172] Step 3:

[0173] The server uses the generated artificial intelligence model to analyze clean environmental data. Specifically, it processes the data using machine learning algorithms to predict crop growth and detect potential anomalies. The output provides evaluation results indicating the state of agricultural targets and guidance for future actions.

[0174] Step 4:

[0175] The user enters a prompt message via a terminal. For example, the user might instruct the terminal, "Please tell me the crop growth forecast for next month." This message is entered into the system as an instruction, and the server performs appropriate information retrieval and analysis.

[0176] Step 5:

[0177] The server analyzes the user's emotional state. Based on the user's input speed received from the terminal and past responses, the emotion engine evaluates the stress level and comprehension level. This generates information indicating the user's emotional state as output.

[0178] Step 6:

[0179] The server integrates analysis results with user sentiment information to generate suggestions for optimal agricultural actions. The suggestions are tailored to the user's emotional state. The output is a concrete action suggestion presented in the user interface.

[0180] Step 7:

[0181] The device displays the generated suggestions using natural language processing technology. The device organizes the information to aid user understanding and presents the suggestions in easy-to-understand language. These suggestions are designed to be easily implemented by the user.

[0182] (Application Example 2)

[0183] 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".

[0184] Conventional agricultural support systems are limited to suggestions based on environmental data and have the problem of not being able to provide flexible plans that take into account the user's emotions and stress levels. Furthermore, in urban agriculture management, there is a lack of sufficient means to utilize smart devices to provide appropriate farm work suggestions in real time.

[0185] 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.

[0186] In this invention, the server includes means for receiving environmental data acquired from an information gathering device, means for analyzing the environmental data and evaluating the state of agricultural targets, and means for analyzing the user's emotional data and adjusting the suggested content based on the emotional state. This makes it possible to propose customizable agricultural work plans tailored to the user's situation based on the environmental data and emotional data.

[0187] "Information gathering devices" are instruments used to acquire environmental data related to agriculture. These include soil detection devices and unmanned aerial vehicles.

[0188] "Environmental data" refers to information necessary to achieve agricultural objectives, and this information includes temperature, humidity, precipitation, and soil nutrient status.

[0189] "Analysis methods" refer to technologies and algorithms used to evaluate the condition of agricultural targets based on acquired environmental data.

[0190] "Emotional data" refers to information that reflects a user's emotions and mental state, and is used to analyze this data in order to understand the user's stress level and how they perceive things.

[0191] "Means for adjusting proposed content" refers to methods for changing agricultural behaviors and information presented to users based on emotional data, depending on the situation.

[0192] A "smart device" is an electronic device equipped with internet connectivity and various sensors, offering multiple functions and flexible operation; smartphones are an example of such devices.

[0193] The system for implementing this invention operates in conjunction with an information gathering device, a server, and a smart device. The server acquires environmental data from the information gathering device via sensors and unmanned aerial vehicles. This data is transformed into useful information through a data cleaning process. Based on the environmental data, the server can use machine learning algorithms to evaluate the condition of agricultural targets.

[0194] Furthermore, the server collects user emotional data from smart devices and analyzes it using an emotional analysis engine. Based on the analysis results, it proposes the most suitable agricultural actions for the user. The proposals are flexibly adjusted according to the user's emotional state and real-time environmental conditions.

[0195] The smart device displays suggestions provided by the server to the user. The interface is designed to be intuitive and easy to use, ensuring the user readily accepts the suggestions. The user provides feedback through the smart device, and the system uses this feedback to continue learning.

[0196] For example, when a user asks about a work plan for an urban farm, the server analyzes the latest weather and soil data and proposes a simple work plan to minimize stress based on emotional data. At this time, it provides specific instructions such as, "Today the temperature and humidity are high, so it would be best to water the plants in the evening."

[0197] An example of a prompt message would be: "When the user is feeling stressed, suggest the simplest possible farming task. To do this, if the user inputs 'I'm tired today,' then provide a solution." This is how you would instruct the generative AI model.

[0198] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0199] Step 1:

[0200] The server acquires environmental data from information gathering devices. Sensors and unmanned aerial vehicles are used to collect information such as temperature, humidity, and soil nutrient status. The input is environmental data, and the output is cleaned-up data. This data undergoes initial data processing to remove noise.

[0201] Step 2:

[0202] The server evaluates the state of agricultural targets by running machine learning algorithms using the acquired clean environmental data. Data processing involves data analysis to model the state of agriculture. The input is clean environmental data, and the output is evaluation results such as crop growth status and detailed soil conditions.

[0203] Step 3:

[0204] The server collects user emotion data via smart devices. It uses user-submitted text and response times for analysis. Input is user text data and response speed, while output is an evaluation of the emotional state. Natural language processing techniques are used for data calculation.

[0205] Step 4:

[0206] The server generates optimal agricultural actions based on evaluated environmental conditions and emotional data. It utilizes a generative AI model and prompts to suggest a plan. The input consists of the environmental condition evaluation results and the emotional condition evaluation results, while the output is the proposed agricultural action. The suggestions are adjusted to take the user's stress level into consideration.

[0207] Step 5:

[0208] The terminal displays the suggestions received from the server to the user, presenting them in an easy-to-understand format through an interface. The display includes an intuitive UI design. The input is the suggested agricultural action, and the output is a visual display of the suggestions to the user.

[0209] Step 6:

[0210] Users provide feedback on suggested actions via their devices. This feedback is used to train the system. The input is user feedback data, and the output is updated information for the server's trained model. This improves the accuracy of future suggestions.

[0211] 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.

[0212] 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.

[0213] 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.

[0214] [Second Embodiment]

[0215] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0216] 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.

[0217] 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).

[0218] 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.

[0219] 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.

[0220] 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).

[0221] 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.

[0222] 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.

[0223] 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.

[0224] 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.

[0225] 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.

[0226] 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".

[0227] As an embodiment of the present invention, a system for optimizing agriculture using environmental data is provided. This system analyzes data obtained from information collection devices and proposes agricultural actions to realize sustainable and efficient agriculture.

[0228] First, the server receives environmental data from information gathering devices. This includes soil moisture, temperature, and nutrient content acquired by soil sensors and drones, as well as weather information and crop image data. The collected data is pre-processed and cleaned. The server then analyzes the environmental data using machine learning models. This analysis evaluates soil conditions and crop health, and makes predictions, including the impact of future climate change.

[0229] Next, the server generates specific agricultural action suggestions for farmers based on the analysis results. These suggestions include appropriate fertilizer types and amounts, watering timing, pest and disease control measures, and sales strategies based on market trends.

[0230] The terminal presents the user with suggested information sent from the server, either visually or audibly. The suggestions are processed using natural language to ensure intuitive understanding, allowing agricultural workers to select appropriate actions based on them.

[0231] For example, if a user wants to check the soil condition of their farm, the server analyzes data collected from soil sensors to identify deficient nutrients. The terminal then suggests and displays the necessary fertilization methods to the user. As another example, when determining a sales strategy, the server refers to market information, forecasts supply and demand, and suggests the optimal shipping time and pricing strategy.

[0232] This system deepens its learning based on user feedback. The server records actual results in a database, improving the accuracy of its suggestions. This enables continuous improvement, making agriculture increasingly efficient and sustainable.

[0233] The following describes the processing flow.

[0234] Step 1:

[0235] The server receives environmental data in real time from soil sensors, weather sensors, and drones. This data includes soil moisture, temperature, pH value, nutrient content, crop images, and weather information.

[0236] Step 2:

[0237] The server preprocesses the received environmental data. Specifically, it detects missing and outlier values ​​and performs interpolation or deletion as needed. It also performs noise reduction processing.

[0238] Step 3:

[0239] The server applies machine learning models to analyze pre-processed data. This analysis assesses soil conditions and crop health, and also makes predictions based on future weather patterns.

[0240] Step 4:

[0241] The server generates action suggestions based on the analysis results. These include fertilizer application amounts and timing, irrigation schedules, pest and disease outbreak predictions and control measures, and sales strategies based on market trends.

[0242] Step 5:

[0243] The terminal receives suggestions from the server and displays them in an easy-to-understand format for the user using natural language processing technology. The visualized suggestions, presented in a user-understandable format, are shown on the user interface.

[0244] Step 6:

[0245] The user implements the agricultural plan based on the provided suggestions. This includes fertilizing, watering, pest and disease control, and determining the timing of shipments based on market trends.

[0246] Step 7:

[0247] Users provide the system with implementation results and feedback.

[0248] The server collects this feedback and uses it to tune the machine learning model in order to improve the accuracy of future analyses and suggestions.

[0249] This series of processes allows farmers to improve production efficiency and quality while being mindful of the environment.

[0250] (Example 1)

[0251] 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."

[0252] In modern agriculture, it is difficult to propose accurate agricultural practices that effectively utilize environmental information. Traditional methods involve a high diversity of collected data, requiring significant time and effort for data preprocessing and analysis. Furthermore, it is challenging to make highly accurate proposals that take future climate change into account, and obtaining appropriate feedback to improve proposals is a significant issue.

[0253] 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.

[0254] In this invention, the server includes means for receiving and preprocessing environmental information acquired from a data collection device, means for analyzing the preprocessed environmental information using a generating AI model and evaluating the state of agricultural targets, and means for proposing agricultural work based on the analysis results and presenting it via display or audio output. This enables agricultural workers to quickly receive data-driven, advanced agricultural suggestions, thereby realizing efficient and sustainable agricultural activities.

[0255] A "data acquisition device" is a device used to acquire environmental information, and includes equipment such as soil measurement devices and aerial devices.

[0256] "Environmental information" refers to physical and chemical data related to agriculture, such as soil moisture, temperature, nutrient content, weather information, and crop image data.

[0257] "Preprocessing" is a process that removes outliers from data and fills in incomplete data, ensuring data consistency before analysis.

[0258] A "generative AI model" is an algorithm built on machine learning technology that analyzes environmental information to evaluate the condition of agricultural targets and make suggestions.

[0259] "Agricultural work" refers to a series of activities performed by agricultural workers, including practical actions such as fertilizing, watering, pest and disease control, and market delivery.

[0260] "Feedback" refers to information about the results of actions taken based on suggestions provided by users, and is used as learning material to improve the accuracy of the system's suggestions.

[0261] To implement this invention, a server plays a central role. The server first receives environmental information from a data collection device. This data collection device may include a soil measuring device or an aerial measuring device. The data obtained from these devices includes soil moisture, temperature, nutrient content, weather information, and image data showing the growth status of crops.

[0262] The received data is preprocessed on the server. During this preprocessing stage, outliers are removed, and incomplete data is supplemented. This consistent data is then analyzed using a generative AI model implemented on the server. Based on environmental information, the model evaluates the state of agricultural targets and predicts the impact of future climate change on crops.

[0263] Based on the analysis results, the server generates suggestions for agricultural work. These suggestions include appropriate fertilizer application amounts, watering timing, pest and disease control measures, and even sales strategies that take market trends into account. These suggestions are presented to the user visually or audibly via the terminal. The information presented is in an intuitively understandable format using natural language processing technology.

[0264] For example, when a user makes a request such as the prompt "Please tell me the optimal timing and amount for the next fertilization," the server analyzes the necessary data and identifies the optimal timing and amount. As a result, the terminal presents an appropriate suggestion to the user.

[0265] In this way, the system can improve the accuracy of its suggestions based on user feedback. The server records the results of actual actions in a database and deepens the learning of the generating AI model. This continuously improves the accuracy of the system's suggestions.

[0266] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0267] Step 1:

[0268] The server receives environmental information from data collection devices. Inputs include humidity, temperature, and nutrient content data from soil measurement devices, crop image data from aerial equipment, and weather information. This yields multidimensional data about the agricultural environment. The output is to send this data to the next preprocessing step.

[0269] Step 2:

[0270] The server preprocesses the received environmental information. The input for this step is the raw data obtained from step 1. Specifically, it removes outliers from the data and imputes missing data points. It also standardizes the data format and prepares it for analysis. The output is a preprocessed, consistent dataset.

[0271] Step 3:

[0272] The server analyzes the pre-processed data using a generating AI model. The input is the data prepared in step 2. This analysis evaluates the state of agricultural targets from environmental information and models the impact of future climate change. Specifically, it uses machine learning algorithms to perform pattern recognition and outputs predictions of soil nutrient deficiencies and crop health as a result.

[0273] Step 4:

[0274] The server generates agricultural work suggestions based on the analysis results. The input is the evaluation results obtained in step 3. In this step, appropriate fertilizer amounts, irrigation schedules, and pest and disease control measures are listed as specific actions. A pricing strategy that takes market information into account is also included in the suggestions. The output is a specific agricultural work suggestion for the user.

[0275] Step 5:

[0276] The terminal presents the proposed agricultural tasks to the user. The input is the suggestions generated in step 4. In terms of specific actions, information is displayed on the screen through a visual user interface, or notified to the user via audio output. The output is information provided in a format that the user can immediately implement.

[0277] Step 6:

[0278] The user takes action based on the suggestions and sends the results as feedback to the server. The input is the result of the agricultural work actually performed by the user. This feedback is recorded in a database. This allows the generative AI model to learn more, improving the accuracy of future suggestions. The output is an improved suggestion model.

[0279] (Application Example 1)

[0280] Next, Application Example 1 will be described. In the following description, the data processing device 12 is referred to as a "server", and the smart glasses 214 are referred to as a "terminal".

[0281] Conventional agricultural optimization systems target vast farmlands and have the problem that their effects cannot be fully exerted in small-scale farms or community gardens in urban areas. Agricultural land in cities is limited and environmental conditions are diverse, so there is a need for a system that provides an appropriate agricultural plan considering these factors. Furthermore, in order to quickly respond to changes in market trends and weather conditions faced by urban farmers, detailed support based on data is required.

[0282] The specific processing by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0283] In this invention, the server includes means for receiving environmental data acquired from an information collection device, means for analyzing the environmental data to evaluate the state of the agricultural target, and means for proposing an agricultural action based on the created evaluation. This makes it possible to quickly provide agricultural plans and actions customized for small-scale farms in urban areas.

[0284] The "information collection device" is a device for acquiring various data related to the environment and refers to a device including sensors, aircraft, etc.

[0285] The "environmental data" is information obtained by quantifying external conditions related to agriculture, such as soil humidity, temperature, nutrient content, and weather information.

[0286] The "means for evaluating the state" is a method or device for analyzing the received environmental data and determining the current state of the agricultural land or crops that are the agricultural target.

[0287] The "means for proposing an agricultural action" is a method or device for determining and providing the optimal actions to be executed by agricultural practitioners based on the evaluated results.

[0288] "Market information" refers to data that includes demand and supply for agricultural products, price trends, and other commercial information.

[0289] "Means for receiving and learning from feedback" refers to methods or devices for collecting user usage results and opinions and incorporating them into future analyses and suggestions.

[0290] An "agricultural plan adapted to the urban environment" is a plan for agricultural activities that takes into account the unique environmental conditions and constraints of urban areas.

[0291] "Agricultural practices customized for small-scale farms" refers to proposals for agricultural activities that are optimized for the specific conditions and requirements of small-scale farmland.

[0292] The system implementing this invention provides customized agricultural planning for small urban farms and community gardens. A server plays a central role.

[0293] The server receives data from information gathering devices such as soil sensors, weather sensors, and drones installed to collect environmental data. This data includes soil moisture, temperature, nutrient content, and weather information. This environmental data is analyzed using machine learning models (such as Scikit-learn and TensorFlow) written in Python. This analysis allows for the evaluation of soil conditions and crop health at each farm and the prediction of the impact of future climate change.

[0294] Based on the analysis results, the server proposes optimal agricultural actions. These proposals include appropriate fertilizer types and amounts, watering timing, and pest and disease control measures. For example, in a community garden within a city, the analysis can determine the timing and amount of irrigation and implement necessary pest and disease control measures.

[0295] Users view these suggestions on a smartphone app. The app uses natural language processing technology to display the suggestions in an intuitive way, allowing farmers to quickly choose actions based on the suggestions.

[0296] Furthermore, the server collects and analyzes relevant market information in order to propose sales strategies that take market trends into account. This allows the user to receive appropriate shipping timing and pricing strategies.

[0297] For example, a user can send a prompt such as, "Please generate an optimal fertilization schedule based on the soil data collected today," and receive suggestions regarding agricultural actions. This can significantly improve the efficiency and sustainability of urban agriculture.

[0298] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0299] Step 1:

[0300] The server receives environmental data from soil sensors, weather sensors, and drones. Inputs include soil moisture, temperature, nutrient content, and weather information. The server stores this data in a database and outputs cleaned data after handling missing values ​​and removing noise.

[0301] Step 2:

[0302] The server uses a machine learning model to analyze the cleaned environmental data. The input is the clean data obtained in Step 1. The data analysis predicts soil conditions, crop health, and future climate change. Based on this, it outputs soil evaluation results and weather forecasts.

[0303] Step 3:

[0304] Based on the analysis results, the server generates proposals for agricultural actions. The input is the evaluation results of Step 2. These proposals include the type and amount of fertilizers, the timing of irrigation, and pest control measures. An optimized agricultural action plan is generated as the output and saved in the database.

[0305] Step 4:

[0306] The server collects market information and formulates sales strategies. As input, it receives market trends and price information and predicts future demand and supply. The resulting sales strategies are saved in the database as the output, together with the agricultural action plan.

[0307] Step 5:

[0308] The user receives proposal information from the server through the smartphone app. The input is queries or requests regarding specific agricultural actions that the user is interested in. The output is a proposal for agricultural actions expressed in natural language that is intuitive and easy to understand.

[0309] Step 6:

[0310] The user selects and executes agricultural actions based on the information provided on the app. The input is the proposal information received in Step 5. By executing this, optimal agricultural actions are carried out on-site.

[0311] Step 7:

[0312] Feedback from the user is sent to the server. The input is the results and opinions of the agricultural actions executed by the user. The server records this as learning material in the database and uses it to improve the next analysis and proposal accuracy.

[0313] Through these steps, small-scale urban farmers can achieve data-driven, efficient, and sustainable agriculture.

[0314] 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.

[0315] As an embodiment of the present invention, a system is presented that supports the development of efficient and sustainable plans in agriculture. This system provides suggestions that take into account not only environmental data obtained from information gathering devices, but also the emotions of the user.

[0316] This system begins with the server acquiring environmental data from soil sensors, weather sensors, and drones. This data includes soil conditions, crop growth status, and weather information such as temperature and humidity. This data is transformed into useful information through a data cleaning process, and the server uses machine learning models to analyze this data and evaluate the condition of agricultural targets.

[0317] Next, the emotion engine receives user input and responses, and recognizes and analyzes the user's emotions. For example, it determines whether the user is stressed based on their response time and the words they use when asked questions from their device. It also helps to understand how the user receives suggestions, enabling more effective communication. This emotion information is used in suggestion generation, which will be discussed later.

[0318] The server integrates the results of environmental data analysis with information based on the user's emotions to generate suggestions for appropriate agricultural actions. For example, if the user is feeling stressed, it might prioritize presenting simpler and easier-to-implement suggestions.

[0319] The device receives the generated suggestions and presents them to the user in an easily understandable format using natural language processing technology. In doing so, it can optimize user interaction by considering the user's emotional state. The suggestions cover a wide range of topics, from appropriate fertilization and irrigation plans to pest and disease control, and even sales strategies based on market trends.

[0320] For example, if a user asks a question about market trends, the server analyzes market information to forecast demand and proposes the optimal shipping time and pricing. If the user reacts negatively to the information presented, the emotion engine detects this and the server provides alternative solutions, ensuring a highly satisfying information experience.

[0321] This allows agricultural workers to simultaneously improve production efficiency and reduce their workload while maintaining environmental considerations.

[0322] The following describes the processing flow.

[0323] Step 1:

[0324] The server acquires environmental data in real time from soil sensors, weather sensors, and drones. This data includes soil moisture and nutrients, crop images, and temperature fluctuations.

[0325] Step 2:

[0326] The server preprocesses the received environmental data. It ensures data quality by supplementing missing data and removing outliers. It also formats this data into a format that can be input into machine learning models.

[0327] Step 3:

[0328] The server runs machine learning models using pre-processed data to analyze soil conditions and crop health. The models predict the risk of pest and disease outbreaks and the optimal cultivation schedule.

[0329] Step 4:

[0330] The emotion engine monitors user input and responses, analyzing emotional information from factors such as voice tone and response time. This information is then sent to the server.

[0331] Step 5:

[0332] The server integrates the results of environmental data analysis with the user's emotional state to propose personalized agricultural actions. For example, if the user is feeling stressed, it will generate suggestions to prioritize simpler tasks.

[0333] Step 6:

[0334] The device presents generated suggestions to the user using natural language processing technology. The display is intuitive and easy to understand, and the interaction is emotionally conscious.

[0335] Step 7:

[0336] Users carry out agricultural activities based on the suggestions provided. They provide feedback to the system on the status of their fertilization, irrigation, and pest control measures.

[0337] Step 8:

[0338] The server updates its machine learning model based on user feedback and field data, improving the accuracy of future suggestions. This continuously improves the accuracy and effectiveness of agricultural planning.

[0339] (Example 2)

[0340] 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".

[0341] In agriculture, it is essential to accurately understand environmental changes and crop health, and to select appropriate actions based on that understanding. However, conventional methods present challenges such as the cumbersome process of acquiring and analyzing environmental information, and the difficulty in providing information that takes into account the user's emotional state. Furthermore, effective utilization of user feedback is not being achieved. By solving these problems, we aim to improve the efficiency and sustainability of agriculture.

[0342] 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.

[0343] In this invention, the server includes means for receiving environmental information acquired from an information gathering device, means for data cleaning the environmental information and converting it into useful information, and means for using an artificial intelligence model generated to analyze the useful information and evaluate the state of agricultural targets. This enables accurate acquisition and analysis of environmental information, and by generating suggestions that take user emotions into consideration and learning through feedback, it becomes possible to improve efficiency in agriculture and formulate sustainable plans.

[0344] "Information gathering equipment" refers to all hardware used for monitoring soil and weather conditions, including soil monitoring sensors and aerial equipment.

[0345] "Environmental information" refers to data representing external factors related to crops and farmland, specifically including soil conditions, weather conditions, and crop growth status.

[0346] "Data cleaning" refers to the process of removing noise and outliers from collected data and converting it into useful information.

[0347] A "generated artificial intelligence model" is a machine learning algorithm trained to analyze environmental information and used for prediction and evaluation.

[0348] "Emotional state" refers to the psychological reactions and emotions a user exhibits in response to information, and includes indicators such as stress levels and comprehension.

[0349] "User interface" refers to the display screens and operating methods that users use to interact with a system.

[0350] A "prompt message" refers to a sentence displayed in a user interface that presents information or choices to the user.

[0351] The embodiments for carrying out the present invention will now be described. This system aims to improve efficiency and sustainability in agriculture and performs a comprehensive process from acquiring environmental information to generating proposals.

[0352] First, the server uses information gathering equipment consisting of soil monitoring sensors and aerial devices to receive environmental information such as soil conditions and weather conditions in real time. This makes it possible to instantly grasp crop growth and environmental changes.

[0353] Next, the server performs data cleaning on the received environmental information, transforming it into useful information. The data cleaning process removes noise and imputes missing values, preparing the information for analysis.

[0354] Next, the server uses the generated artificial intelligence model to analyze the cleaned environmental information. This AI model is based on machine learning algorithms and is optimized for predicting crop growth and detecting anomalies. This analysis evaluates the condition of agricultural targets and provides foundational data for determining appropriate agricultural actions.

[0355] When a user asks a question to the system through their terminal, for example, by entering a prompt such as "Please tell me the crop growth forecast for next month," the server generates the corresponding information according to that instruction. The user's emotional state is then analyzed, and suggestions are created accordingly. For example, if the user is feeling stressed, the suggestions will be made concise and actionable.

[0356] Finally, the terminal presents the generated suggestions to the user using natural language processing technology. The terminal not only conveys information in an easy-to-understand way through the user interface, but also collects user feedback and sends it to the server. In this way, the system accumulates user sentiment information and feedback and learns to further optimize future suggestions.

[0357] This system not only significantly improves agricultural efficiency but also reduces the burden on users while considering sustainability.

[0358] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0359] Step 1:

[0360] The server receives environmental information from the information gathering device. The information gathering device uses soil monitoring sensors and aerial equipment to collect data such as soil moisture, temperature, and plant health in real time. Raw environmental data is used as input, and this data is aggregated by the server.

[0361] Step 2:

[0362] The server performs data cleaning on the received environmental information. Since the raw input environmental information contains noise and missing values, the server performs processes to remove and impute them. This process yields clean data suitable for analysis.

[0363] Step 3:

[0364] The server uses the generated artificial intelligence model to analyze clean environmental data. Specifically, it processes the data using machine learning algorithms to predict crop growth and detect potential anomalies. The output provides evaluation results indicating the state of agricultural targets and guidance for future actions.

[0365] Step 4:

[0366] The user enters a prompt message via a terminal. For example, the user might instruct the terminal, "Please tell me the crop growth forecast for next month." This message is entered into the system as an instruction, and the server performs appropriate information retrieval and analysis.

[0367] Step 5:

[0368] The server analyzes the user's emotional state. Based on the user's input speed received from the terminal and past responses, the emotion engine evaluates the stress level and comprehension level. This generates information indicating the user's emotional state as output.

[0369] Step 6:

[0370] The server integrates analysis results with user sentiment information to generate suggestions for optimal agricultural actions. The suggestions are tailored to the user's emotional state. The output is a concrete action suggestion presented in the user interface.

[0371] Step 7:

[0372] The device displays the generated suggestions using natural language processing technology. The device organizes the information to aid user understanding and presents the suggestions in easy-to-understand language. These suggestions are designed to be easily implemented by the user.

[0373] (Application Example 2)

[0374] 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."

[0375] Conventional agricultural support systems are limited to suggestions based on environmental data and have the problem of not being able to provide flexible plans that take into account the user's emotions and stress levels. Furthermore, in urban agriculture management, there is a lack of sufficient means to utilize smart devices to provide appropriate farm work suggestions in real time.

[0376] 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.

[0377] In this invention, the server includes means for receiving environmental data acquired from an information gathering device, means for analyzing the environmental data and evaluating the state of agricultural targets, and means for analyzing the user's emotional data and adjusting the suggested content based on the emotional state. This makes it possible to propose customizable agricultural work plans tailored to the user's situation based on the environmental data and emotional data.

[0378] "Information gathering devices" are instruments used to acquire environmental data related to agriculture. These include soil detection devices and unmanned aerial vehicles.

[0379] "Environmental data" refers to information necessary to achieve agricultural objectives, and this information includes temperature, humidity, precipitation, and soil nutrient status.

[0380] "Analysis methods" refer to technologies and algorithms used to evaluate the condition of agricultural targets based on acquired environmental data.

[0381] "Emotional data" refers to information that reflects a user's emotions and mental state, and is used to analyze this data in order to understand the user's stress level and how they perceive things.

[0382] "Means for adjusting proposed content" refers to methods for changing agricultural behaviors and information presented to users based on emotional data, depending on the situation.

[0383] A "smart device" is an electronic device equipped with internet connectivity and various sensors, offering multiple functions and flexible operation; smartphones are an example of such devices.

[0384] The system for implementing this invention operates in conjunction with an information gathering device, a server, and a smart device. The server acquires environmental data from the information gathering device via sensors and unmanned aerial vehicles. This data is transformed into useful information through a data cleaning process. Based on the environmental data, the server can use machine learning algorithms to evaluate the condition of agricultural targets.

[0385] Furthermore, the server collects user emotional data from smart devices and analyzes it using an emotional analysis engine. Based on the analysis results, it proposes the most suitable agricultural actions for the user. The proposals are flexibly adjusted according to the user's emotional state and real-time environmental conditions.

[0386] The smart device displays suggestions provided by the server to the user. The interface is designed to be intuitive and easy to use, ensuring the user readily accepts the suggestions. The user provides feedback through the smart device, and the system uses this feedback to continue learning.

[0387] For example, when a user asks about a work plan for an urban farm, the server analyzes the latest weather and soil data and proposes a simple work plan to minimize stress based on emotional data. At this time, it provides specific instructions such as, "Today the temperature and humidity are high, so it would be best to water the plants in the evening."

[0388] An example of a prompt message would be: "When the user is feeling stressed, suggest the simplest possible farming task. To do this, if the user inputs 'I'm tired today,' then provide a solution." This is how you would instruct the generative AI model.

[0389] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0390] Step 1:

[0391] The server acquires environmental data from information gathering devices. Sensors and unmanned aerial vehicles are used to collect information such as temperature, humidity, and soil nutrient status. The input is environmental data, and the output is cleaned-up data. This data undergoes initial data processing to remove noise.

[0392] Step 2:

[0393] The server evaluates the state of agricultural targets by running machine learning algorithms using the acquired clean environmental data. Data processing involves data analysis to model the state of agriculture. The input is clean environmental data, and the output is evaluation results such as crop growth status and detailed soil conditions.

[0394] Step 3:

[0395] The server collects user emotion data via smart devices. It uses user-submitted text and response times for analysis. Input is user text data and response speed, while output is an evaluation of the emotional state. Natural language processing techniques are used for data calculation.

[0396] Step 4:

[0397] The server generates optimal agricultural actions based on evaluated environmental conditions and emotional data. It utilizes a generative AI model and prompts to suggest a plan. The input consists of the environmental condition evaluation results and the emotional condition evaluation results, while the output is the proposed agricultural action. The suggestions are adjusted to take the user's stress level into consideration.

[0398] Step 5:

[0399] The terminal displays the suggestions received from the server to the user, presenting them in an easy-to-understand format through an interface. The display includes an intuitive UI design. The input is the suggested agricultural action, and the output is a visual display of the suggestions to the user.

[0400] Step 6:

[0401] Users provide feedback on suggested actions via their devices. This feedback is used to train the system. The input is user feedback data, and the output is updated information for the server's trained model. This improves the accuracy of future suggestions.

[0402] 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.

[0403] 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.

[0404] 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.

[0405] [Third Embodiment]

[0406] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0407] 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.

[0408] 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).

[0409] 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.

[0410] 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.

[0411] 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).

[0412] 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.

[0413] 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.

[0414] 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.

[0415] 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.

[0416] 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.

[0417] 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".

[0418] As an embodiment of the present invention, a system for optimizing agriculture using environmental data is provided. This system analyzes data obtained from information collection devices and proposes agricultural actions to realize sustainable and efficient agriculture.

[0419] First, the server receives environmental data from information gathering devices. This includes soil moisture, temperature, and nutrient content acquired by soil sensors and drones, as well as weather information and crop image data. The collected data is pre-processed and cleaned. The server then analyzes the environmental data using machine learning models. This analysis evaluates soil conditions and crop health, and makes predictions, including the impact of future climate change.

[0420] Next, the server generates specific agricultural action suggestions for farmers based on the analysis results. These suggestions include appropriate fertilizer types and amounts, watering timing, pest and disease control measures, and sales strategies based on market trends.

[0421] The terminal presents the user with suggested information sent from the server, either visually or audibly. The suggestions are processed using natural language to ensure intuitive understanding, allowing agricultural workers to select appropriate actions based on them.

[0422] For example, if a user wants to check the soil condition of their farm, the server analyzes data collected from soil sensors to identify deficient nutrients. The terminal then suggests and displays the necessary fertilization methods to the user. As another example, when determining a sales strategy, the server refers to market information, forecasts supply and demand, and suggests the optimal shipping time and pricing strategy.

[0423] This system deepens its learning based on user feedback. The server records actual results in a database, improving the accuracy of its suggestions. This enables continuous improvement, making agriculture increasingly efficient and sustainable.

[0424] The following describes the processing flow.

[0425] Step 1:

[0426] The server receives environmental data in real time from soil sensors, weather sensors, and drones. This data includes soil moisture, temperature, pH value, nutrient content, crop images, and weather information.

[0427] Step 2:

[0428] The server preprocesses the received environmental data. Specifically, it detects missing and outlier values ​​and performs interpolation or deletion as needed. It also performs noise reduction processing.

[0429] Step 3:

[0430] The server applies machine learning models to analyze pre-processed data. This analysis assesses soil conditions and crop health, and also makes predictions based on future weather patterns.

[0431] Step 4:

[0432] The server generates action suggestions based on the analysis results. These include fertilizer application amounts and timing, irrigation schedules, pest and disease outbreak predictions and control measures, and sales strategies based on market trends.

[0433] Step 5:

[0434] The terminal receives suggestions from the server and displays them in an easy-to-understand format for the user using natural language processing technology. The visualized suggestions, presented in a user-understandable format, are shown on the user interface.

[0435] Step 6:

[0436] The user implements the agricultural plan based on the provided suggestions. This includes fertilizing, watering, pest and disease control, and determining the timing of shipments based on market trends.

[0437] Step 7:

[0438] Users provide the system with implementation results and feedback.

[0439] The server collects this feedback and uses it to tune the machine learning model in order to improve the accuracy of future analyses and suggestions.

[0440] This series of processes allows farmers to improve production efficiency and quality while being mindful of the environment.

[0441] (Example 1)

[0442] 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."

[0443] In modern agriculture, it is difficult to propose accurate agricultural practices that effectively utilize environmental information. Traditional methods involve a high diversity of collected data, requiring significant time and effort for data preprocessing and analysis. Furthermore, it is challenging to make highly accurate proposals that take future climate change into account, and obtaining appropriate feedback to improve proposals is a significant issue.

[0444] 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.

[0445] In this invention, the server includes means for receiving and preprocessing environmental information acquired from a data collection device, means for analyzing the preprocessed environmental information using a generating AI model and evaluating the state of agricultural targets, and means for proposing agricultural work based on the analysis results and presenting it via display or audio output. This enables agricultural workers to quickly receive data-driven, advanced agricultural suggestions, thereby realizing efficient and sustainable agricultural activities.

[0446] A "data acquisition device" is a device used to acquire environmental information, and includes equipment such as soil measurement devices and aerial devices.

[0447] "Environmental information" refers to physical and chemical data related to agriculture, such as soil moisture, temperature, nutrient content, weather information, and crop image data.

[0448] "Preprocessing" is a process that removes outliers from data and fills in incomplete data, ensuring data consistency before analysis.

[0449] A "generative AI model" is an algorithm built on machine learning technology that analyzes environmental information to evaluate the condition of agricultural targets and make suggestions.

[0450] "Agricultural work" refers to a series of activities performed by agricultural workers, including practical actions such as fertilizing, watering, pest and disease control, and market delivery.

[0451] "Feedback" refers to information about the results of actions taken based on suggestions provided by users, and is used as learning material to improve the accuracy of the system's suggestions.

[0452] To implement this invention, a server plays a central role. The server first receives environmental information from a data collection device. This data collection device may include a soil measuring device or an aerial measuring device. The data obtained from these devices includes soil moisture, temperature, nutrient content, weather information, and image data showing the growth status of crops.

[0453] The received data is preprocessed on the server. During this preprocessing stage, outliers are removed, and incomplete data is supplemented. This consistent data is then analyzed using a generative AI model implemented on the server. Based on environmental information, the model evaluates the state of agricultural targets and predicts the impact of future climate change on crops.

[0454] Based on the analysis results, the server generates suggestions for agricultural work. These suggestions include appropriate fertilizer application amounts, watering timing, pest and disease control measures, and even sales strategies that take market trends into account. These suggestions are presented to the user visually or audibly via the terminal. The information presented is in an intuitively understandable format using natural language processing technology.

[0455] For example, when a user makes a request such as the prompt "Please tell me the optimal timing and amount for the next fertilization," the server analyzes the necessary data and identifies the optimal timing and amount. As a result, the terminal presents an appropriate suggestion to the user.

[0456] In this way, the system can improve the accuracy of its suggestions based on user feedback. The server records the results of actual actions in a database and deepens the learning of the generating AI model. This continuously improves the accuracy of the system's suggestions.

[0457] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0458] Step 1:

[0459] The server receives environmental information from data collection devices. Inputs include humidity, temperature, and nutrient content data from soil measurement devices, crop image data from aerial equipment, and weather information. This yields multidimensional data about the agricultural environment. The output is to send this data to the next preprocessing step.

[0460] Step 2:

[0461] The server preprocesses the received environmental information. The input for this step is the raw data obtained from step 1. Specifically, it removes outliers from the data and imputes missing data points. It also standardizes the data format and prepares it for analysis. The output is a preprocessed, consistent dataset.

[0462] Step 3:

[0463] The server analyzes the pre-processed data using a generating AI model. The input is the data prepared in step 2. This analysis evaluates the state of agricultural targets from environmental information and models the impact of future climate change. Specifically, it uses machine learning algorithms to perform pattern recognition and outputs predictions of soil nutrient deficiencies and crop health as a result.

[0464] Step 4:

[0465] The server generates agricultural work suggestions based on the analysis results. The input is the evaluation results obtained in step 3. In this step, appropriate fertilizer amounts, irrigation schedules, and pest and disease control measures are listed as specific actions. A pricing strategy that takes market information into account is also included in the suggestions. The output is a specific agricultural work suggestion for the user.

[0466] Step 5:

[0467] The terminal presents the proposed agricultural tasks to the user. The input is the suggestions generated in step 4. In terms of specific actions, information is displayed on the screen through a visual user interface, or notified to the user via audio output. The output is information provided in a format that the user can immediately implement.

[0468] Step 6:

[0469] The user takes action based on the suggestions and sends the results as feedback to the server. The input is the result of the agricultural work actually performed by the user. This feedback is recorded in a database. This allows the generative AI model to learn more, improving the accuracy of future suggestions. The output is an improved suggestion model.

[0470] (Application Example 1)

[0471] 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."

[0472] Conventional agricultural optimization systems are designed for vast farmlands and have the problem of not being able to fully demonstrate their effectiveness in small-scale urban farms and community gardens. Urban farmland is limited, and environmental conditions are diverse, so there is a need for a system that provides appropriate agricultural planning that takes these factors into account. Furthermore, detailed, data-driven support is necessary to quickly respond to market trends and changes in weather conditions that urban farmers face.

[0473] 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.

[0474] In this invention, the server includes means for receiving environmental data acquired from an information gathering device, means for analyzing the environmental data to evaluate the state of agricultural targets, and means for proposing agricultural actions based on the created evaluation. This makes it possible to quickly provide customized agricultural plans and actions to small urban farms.

[0475] An "information gathering device" refers to a device used to acquire various types of environmental data, and includes devices such as sensors and aircraft.

[0476] "Environmental data" refers to information that quantifies external conditions related to agriculture, such as soil moisture, temperature, nutrient content, and weather information.

[0477] "Means for evaluating the state" refers to a method or apparatus for analyzing received environmental data and determining the current state of agricultural land and crops.

[0478] "Means for proposing agricultural actions" refers to methods or devices for determining and providing the optimal actions that agricultural workers should take, based on the results of evaluation.

[0479] "Market information" refers to data that includes demand and supply for agricultural products, price trends, and other commercial information.

[0480] "Means for receiving and learning from feedback" refers to methods or devices for collecting user usage results and opinions and incorporating them into future analyses and suggestions.

[0481] An "agricultural plan adapted to the urban environment" is a plan for agricultural activities that takes into account the unique environmental conditions and constraints of urban areas.

[0482] "Agricultural practices customized for small-scale farms" refers to proposals for agricultural activities that are optimized for the specific conditions and requirements of small-scale farmland.

[0483] The system implementing this invention provides customized agricultural planning for small urban farms and community gardens. A server plays a central role.

[0484] The server receives data from information gathering devices such as soil sensors, weather sensors, and drones installed to collect environmental data. This data includes soil moisture, temperature, nutrient content, and weather information. This environmental data is analyzed using machine learning models (such as Scikit-learn and TensorFlow) written in Python. This analysis allows for the evaluation of soil conditions and crop health at each farm and the prediction of the impact of future climate change.

[0485] Based on the analysis results, the server proposes optimal agricultural actions. These proposals include appropriate fertilizer types and amounts, watering timing, and pest and disease control measures. For example, in a community garden within a city, the analysis can determine the timing and amount of irrigation and implement necessary pest and disease control measures.

[0486] Users view these suggestions on a smartphone app. The app uses natural language processing technology to display the suggestions in an intuitive way, allowing farmers to quickly choose actions based on the suggestions.

[0487] Furthermore, the server collects and analyzes relevant market information in order to propose sales strategies that take market trends into account. This allows the user to receive appropriate shipping timing and pricing strategies.

[0488] For example, a user can send a prompt such as, "Please generate an optimal fertilization schedule based on the soil data collected today," and receive suggestions regarding agricultural actions. This can significantly improve the efficiency and sustainability of urban agriculture.

[0489] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0490] Step 1:

[0491] The server receives environmental data from soil sensors, weather sensors, and drones. Inputs include soil moisture, temperature, nutrient content, and weather information. The server stores this data in a database and outputs cleaned data after handling missing values ​​and removing noise.

[0492] Step 2:

[0493] The server uses a machine learning model to analyze the cleaned environmental data. The input is the clean data obtained in Step 1. The data analysis predicts soil conditions, crop health, and future climate change. Based on this, it outputs soil evaluation results and weather forecasts.

[0494] Step 3:

[0495] The server generates agricultural action suggestions based on the analysis results. The input is the evaluation results from step 2. These suggestions include the type and amount of fertilizer to apply, the timing of watering, and pest and disease control measures. An optimized agricultural action plan is generated as output and saved to the database.

[0496] Step 4:

[0497] The server collects market information and develops sales strategies. It receives market trends and price information as input and forecasts future supply and demand. The resulting sales strategies are then stored in a database along with agricultural action plans as output.

[0498] Step 5:

[0499] Users receive suggestion information from the server via a smartphone app. Inputs are queries or requests related to specific agricultural actions that the user is interested in. Outputs are suggestions for agricultural actions expressed in intuitively understandable natural language.

[0500] Step 6:

[0501] The user selects and executes agricultural actions based on the information provided in the app. The input is the suggested information received in step 5. By executing this, the optimal agricultural action is carried out on-site.

[0502] Step 7:

[0503] User feedback is sent to the server. The input consists of the results and opinions of the agricultural actions performed by the user. The server records this as learning material in a database and uses it to improve the accuracy of future analyses and suggestions.

[0504] These steps enable urban small-scale farmers to achieve data-driven, efficient, and sustainable agriculture.

[0505] 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.

[0506] As an embodiment of the present invention, a system is presented that supports the development of efficient and sustainable plans in agriculture. This system provides suggestions that take into account not only environmental data obtained from information gathering devices, but also the emotions of the user.

[0507] This system begins with the server acquiring environmental data from soil sensors, weather sensors, and drones. This data includes soil conditions, crop growth status, and weather information such as temperature and humidity. This data is transformed into useful information through a data cleaning process, and the server uses machine learning models to analyze this data and evaluate the condition of agricultural targets.

[0508] Next, the emotion engine receives user input and responses, and recognizes and analyzes the user's emotions. For example, it determines whether the user is stressed based on their response time and the words they use when asked questions from their device. It also helps to understand how the user receives suggestions, enabling more effective communication. This emotion information is used in suggestion generation, which will be discussed later.

[0509] The server integrates the results of environmental data analysis with information based on the user's emotions to generate suggestions for appropriate agricultural actions. For example, if the user is feeling stressed, it might prioritize presenting simpler and easier-to-implement suggestions.

[0510] The device receives the generated suggestions and presents them to the user in an easily understandable format using natural language processing technology. In doing so, it can optimize user interaction by considering the user's emotional state. The suggestions cover a wide range of topics, from appropriate fertilization and irrigation plans to pest and disease control, and even sales strategies based on market trends.

[0511] For example, if a user asks a question about market trends, the server analyzes market information to forecast demand and proposes the optimal shipping time and pricing. If the user reacts negatively to the information presented, the emotion engine detects this and the server provides alternative solutions, ensuring a highly satisfying information experience.

[0512] This allows agricultural workers to simultaneously improve production efficiency and reduce their workload while maintaining environmental considerations.

[0513] The following describes the processing flow.

[0514] Step 1:

[0515] The server acquires environmental data in real time from soil sensors, weather sensors, and drones. This data includes soil moisture and nutrients, crop images, and temperature fluctuations.

[0516] Step 2:

[0517] The server preprocesses the received environmental data. It ensures data quality by supplementing missing data and removing outliers. It also formats this data into a format that can be input into machine learning models.

[0518] Step 3:

[0519] The server runs machine learning models using pre-processed data to analyze soil conditions and crop health. The models predict the risk of pest and disease outbreaks and the optimal cultivation schedule.

[0520] Step 4:

[0521] The emotion engine monitors user input and responses, analyzing emotional information from factors such as voice tone and response time. This information is then sent to the server.

[0522] Step 5:

[0523] The server integrates the results of environmental data analysis with the user's emotional state to propose personalized agricultural actions. For example, if the user is feeling stressed, it will generate suggestions to prioritize simpler tasks.

[0524] Step 6:

[0525] The device presents generated suggestions to the user using natural language processing technology. The display is intuitive and easy to understand, and the interaction is emotionally conscious.

[0526] Step 7:

[0527] Users carry out agricultural activities based on the suggestions provided. They provide feedback to the system on the status of their fertilization, irrigation, and pest control measures.

[0528] Step 8:

[0529] The server updates its machine learning model based on user feedback and field data, improving the accuracy of future suggestions. This continuously improves the accuracy and effectiveness of agricultural planning.

[0530] (Example 2)

[0531] 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."

[0532] In agriculture, it is essential to accurately understand environmental changes and crop health, and to select appropriate actions based on that understanding. However, conventional methods present challenges such as the cumbersome process of acquiring and analyzing environmental information, and the difficulty in providing information that takes into account the user's emotional state. Furthermore, effective utilization of user feedback is not being achieved. By solving these problems, we aim to improve the efficiency and sustainability of agriculture.

[0533] 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.

[0534] In this invention, the server includes means for receiving environmental information acquired from an information gathering device, means for data cleaning the environmental information and converting it into useful information, and means for using an artificial intelligence model generated to analyze the useful information and evaluate the state of agricultural targets. This enables accurate acquisition and analysis of environmental information, and by generating suggestions that take user emotions into consideration and learning through feedback, it becomes possible to improve efficiency in agriculture and formulate sustainable plans.

[0535] "Information gathering equipment" refers to all hardware used for monitoring soil and weather conditions, including soil monitoring sensors and aerial equipment.

[0536] "Environmental information" refers to data representing external factors related to crops and farmland, specifically including soil conditions, weather conditions, and crop growth status.

[0537] "Data cleaning" refers to the process of removing noise and outliers from collected data and converting it into useful information.

[0538] A "generated artificial intelligence model" is a machine learning algorithm trained to analyze environmental information and used for prediction and evaluation.

[0539] "Emotional state" refers to the psychological reactions and emotions a user exhibits in response to information, and includes indicators such as stress levels and comprehension.

[0540] "User interface" refers to the display screens and operating methods that users use to interact with a system.

[0541] A "prompt message" refers to a sentence displayed in a user interface that presents information or choices to the user.

[0542] The embodiments for carrying out the present invention will now be described. This system aims to improve efficiency and sustainability in agriculture and performs a comprehensive process from acquiring environmental information to generating proposals.

[0543] First, the server uses information gathering equipment consisting of soil monitoring sensors and aerial devices to receive environmental information such as soil conditions and weather conditions in real time. This makes it possible to instantly grasp crop growth and environmental changes.

[0544] Next, the server performs data cleaning on the received environmental information, transforming it into useful information. The data cleaning process removes noise and imputes missing values, preparing the information for analysis.

[0545] Next, the server uses the generated artificial intelligence model to analyze the cleaned environmental information. This AI model is based on machine learning algorithms and is optimized for predicting crop growth and detecting anomalies. This analysis evaluates the condition of agricultural targets and provides foundational data for determining appropriate agricultural actions.

[0546] When a user asks a question to the system through their terminal, for example, by entering a prompt such as "Please tell me the crop growth forecast for next month," the server generates the corresponding information according to that instruction. The user's emotional state is then analyzed, and suggestions are created accordingly. For example, if the user is feeling stressed, the suggestions will be made concise and actionable.

[0547] Finally, the terminal presents the generated suggestions to the user using natural language processing technology. The terminal not only conveys information in an easy-to-understand way through the user interface, but also collects user feedback and sends it to the server. In this way, the system accumulates user sentiment information and feedback and learns to further optimize future suggestions.

[0548] This system not only significantly improves agricultural efficiency but also reduces the burden on users while considering sustainability.

[0549] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0550] Step 1:

[0551] The server receives environmental information from the information gathering device. The information gathering device uses soil monitoring sensors and aerial equipment to collect data such as soil moisture, temperature, and plant health in real time. Raw environmental data is used as input, and this data is aggregated by the server.

[0552] Step 2:

[0553] The server performs data cleaning on the received environmental information. Since the raw input environmental information contains noise and missing values, the server performs processes to remove and impute them. This process yields clean data suitable for analysis.

[0554] Step 3:

[0555] The server uses the generated artificial intelligence model to analyze clean environmental data. Specifically, it processes the data using machine learning algorithms to predict crop growth and detect potential anomalies. The output provides evaluation results indicating the state of agricultural targets and guidance for future actions.

[0556] Step 4:

[0557] The user enters a prompt message via a terminal. For example, the user might instruct the terminal, "Please tell me the crop growth forecast for next month." This message is entered into the system as an instruction, and the server performs appropriate information retrieval and analysis.

[0558] Step 5:

[0559] The server analyzes the user's emotional state. Based on the user's input speed received from the terminal and past responses, the emotion engine evaluates the stress level and comprehension level. This generates information indicating the user's emotional state as output.

[0560] Step 6:

[0561] The server integrates analysis results with user sentiment information to generate suggestions for optimal agricultural actions. The suggestions are tailored to the user's emotional state. The output is a concrete action suggestion presented in the user interface.

[0562] Step 7:

[0563] The device displays the generated suggestions using natural language processing technology. The device organizes the information to aid user understanding and presents the suggestions in easy-to-understand language. These suggestions are designed to be easily implemented by the user.

[0564] (Application Example 2)

[0565] 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."

[0566] Conventional agricultural support systems are limited to suggestions based on environmental data and have the problem of not being able to provide flexible plans that take into account the user's emotions and stress levels. Furthermore, in urban agriculture management, there is a lack of sufficient means to utilize smart devices to provide appropriate farm work suggestions in real time.

[0567] 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.

[0568] In this invention, the server includes means for receiving environmental data acquired from an information gathering device, means for analyzing the environmental data and evaluating the state of agricultural targets, and means for analyzing the user's emotional data and adjusting the suggested content based on the emotional state. This makes it possible to propose customizable agricultural work plans tailored to the user's situation based on the environmental data and emotional data.

[0569] "Information gathering devices" are instruments used to acquire environmental data related to agriculture. These include soil detection devices and unmanned aerial vehicles.

[0570] "Environmental data" refers to information necessary to achieve agricultural objectives, and this information includes temperature, humidity, precipitation, and soil nutrient status.

[0571] "Analysis methods" refer to technologies and algorithms used to evaluate the condition of agricultural targets based on acquired environmental data.

[0572] "Emotional data" refers to information that reflects a user's emotions and mental state, and is used to analyze this data in order to understand the user's stress level and how they perceive things.

[0573] "Means for adjusting proposed content" refers to methods for changing agricultural behaviors and information presented to users based on emotional data, depending on the situation.

[0574] A "smart device" is an electronic device equipped with internet connectivity and various sensors, offering multiple functions and flexible operation; smartphones are an example of such devices.

[0575] The system for implementing this invention operates in conjunction with an information gathering device, a server, and a smart device. The server acquires environmental data from the information gathering device via sensors and unmanned aerial vehicles. This data is transformed into useful information through a data cleaning process. Based on the environmental data, the server can use machine learning algorithms to evaluate the condition of agricultural targets.

[0576] Furthermore, the server collects user emotional data from smart devices and analyzes it using an emotional analysis engine. Based on the analysis results, it proposes the most suitable agricultural actions for the user. The proposals are flexibly adjusted according to the user's emotional state and real-time environmental conditions.

[0577] The smart device displays suggestions provided by the server to the user. The interface is designed to be intuitive and easy to use, ensuring the user readily accepts the suggestions. The user provides feedback through the smart device, and the system uses this feedback to continue learning.

[0578] For example, when a user asks about a work plan for an urban farm, the server analyzes the latest weather and soil data and proposes a simple work plan to minimize stress based on emotional data. At this time, it provides specific instructions such as, "Today the temperature and humidity are high, so it would be best to water the plants in the evening."

[0579] An example of a prompt message would be: "When the user is feeling stressed, suggest the simplest possible farming task. To do this, if the user inputs 'I'm tired today,' then provide a solution." This is how you would instruct the generative AI model.

[0580] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0581] Step 1:

[0582] The server acquires environmental data from information gathering devices. Sensors and unmanned aerial vehicles are used to collect information such as temperature, humidity, and soil nutrient status. The input is environmental data, and the output is cleaned-up data. This data undergoes initial data processing to remove noise.

[0583] Step 2:

[0584] The server evaluates the state of agricultural targets by running machine learning algorithms using the acquired clean environmental data. Data processing involves data analysis to model the state of agriculture. The input is clean environmental data, and the output is evaluation results such as crop growth status and detailed soil conditions.

[0585] Step 3:

[0586] The server collects user emotion data via smart devices. It uses user-submitted text and response times for analysis. Input is user text data and response speed, while output is an evaluation of the emotional state. Natural language processing techniques are used for data calculation.

[0587] Step 4:

[0588] The server generates optimal agricultural actions based on evaluated environmental conditions and emotional data. It utilizes a generative AI model and prompts to suggest a plan. The input consists of the environmental condition evaluation results and the emotional condition evaluation results, while the output is the proposed agricultural action. The suggestions are adjusted to take the user's stress level into consideration.

[0589] Step 5:

[0590] The terminal displays the suggestions received from the server to the user, presenting them in an easy-to-understand format through an interface. The display includes an intuitive UI design. The input is the suggested agricultural action, and the output is a visual display of the suggestions to the user.

[0591] Step 6:

[0592] Users provide feedback on suggested actions via their devices. This feedback is used to train the system. The input is user feedback data, and the output is updated information for the server's trained model. This improves the accuracy of future suggestions.

[0593] 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.

[0594] 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.

[0595] 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.

[0596] [Fourth Embodiment]

[0597] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0598] 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.

[0599] 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).

[0600] 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.

[0601] 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.

[0602] 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).

[0603] 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.

[0604] 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.

[0605] 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.

[0606] 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.

[0607] 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.

[0608] 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.

[0609] 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".

[0610] As an embodiment of the present invention, a system for optimizing agriculture using environmental data is provided. This system analyzes data obtained from information collection devices and proposes agricultural actions to realize sustainable and efficient agriculture.

[0611] First, the server receives environmental data from information gathering devices. This includes soil moisture, temperature, and nutrient content acquired by soil sensors and drones, as well as weather information and crop image data. The collected data is pre-processed and cleaned. The server then analyzes the environmental data using machine learning models. This analysis evaluates soil conditions and crop health, and makes predictions, including the impact of future climate change.

[0612] Next, the server generates specific agricultural action suggestions for farmers based on the analysis results. These suggestions include appropriate fertilizer types and amounts, watering timing, pest and disease control measures, and sales strategies based on market trends.

[0613] The terminal presents the user with suggested information sent from the server, either visually or audibly. The suggestions are processed using natural language to ensure intuitive understanding, allowing agricultural workers to select appropriate actions based on them.

[0614] For example, if a user wants to check the soil condition of their farm, the server analyzes data collected from soil sensors to identify deficient nutrients. The terminal then suggests and displays the necessary fertilization methods to the user. As another example, when determining a sales strategy, the server refers to market information, forecasts supply and demand, and suggests the optimal shipping time and pricing strategy.

[0615] This system deepens its learning based on user feedback. The server records actual results in a database, improving the accuracy of its suggestions. This enables continuous improvement, making agriculture increasingly efficient and sustainable.

[0616] The following describes the processing flow.

[0617] Step 1:

[0618] The server receives environmental data in real time from soil sensors, weather sensors, and drones. This data includes soil moisture, temperature, pH value, nutrient content, crop images, and weather information.

[0619] Step 2:

[0620] The server preprocesses the received environmental data. Specifically, it detects missing and outlier values ​​and performs interpolation or deletion as needed. It also performs noise reduction processing.

[0621] Step 3:

[0622] The server applies machine learning models to analyze pre-processed data. This analysis assesses soil conditions and crop health, and also makes predictions based on future weather patterns.

[0623] Step 4:

[0624] The server generates action suggestions based on the analysis results. These include fertilizer application amounts and timing, irrigation schedules, pest and disease outbreak predictions and control measures, and sales strategies based on market trends.

[0625] Step 5:

[0626] The terminal receives suggestions from the server and displays them in an easy-to-understand format for the user using natural language processing technology. The visualized suggestions, presented in a user-understandable format, are shown on the user interface.

[0627] Step 6:

[0628] The user implements the agricultural plan based on the provided suggestions. This includes fertilizing, watering, pest and disease control, and determining the timing of shipments based on market trends.

[0629] Step 7:

[0630] Users provide the system with implementation results and feedback.

[0631] The server collects this feedback and uses it to tune the machine learning model in order to improve the accuracy of future analyses and suggestions.

[0632] This series of processes allows farmers to improve production efficiency and quality while being mindful of the environment.

[0633] (Example 1)

[0634] 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".

[0635] In modern agriculture, it is difficult to propose accurate agricultural practices that effectively utilize environmental information. Traditional methods involve a high diversity of collected data, requiring significant time and effort for data preprocessing and analysis. Furthermore, it is challenging to make highly accurate proposals that take future climate change into account, and obtaining appropriate feedback to improve proposals is a significant issue.

[0636] 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.

[0637] In this invention, the server includes means for receiving and preprocessing environmental information acquired from a data collection device, means for analyzing the preprocessed environmental information using a generating AI model and evaluating the state of agricultural targets, and means for proposing agricultural work based on the analysis results and presenting it via display or audio output. This enables agricultural workers to quickly receive data-driven, advanced agricultural suggestions, thereby realizing efficient and sustainable agricultural activities.

[0638] A "data acquisition device" is a device used to acquire environmental information, and includes equipment such as soil measurement devices and aerial devices.

[0639] "Environmental information" refers to physical and chemical data related to agriculture, such as soil moisture, temperature, nutrient content, weather information, and crop image data.

[0640] "Preprocessing" is a process that removes outliers from data and fills in incomplete data, ensuring data consistency before analysis.

[0641] A "generative AI model" is an algorithm built on machine learning technology that analyzes environmental information to evaluate the condition of agricultural targets and make suggestions.

[0642] "Agricultural work" refers to a series of activities performed by agricultural workers, including practical actions such as fertilizing, watering, pest and disease control, and market delivery.

[0643] "Feedback" refers to information about the results of actions taken based on suggestions provided by users, and is used as learning material to improve the accuracy of the system's suggestions.

[0644] To implement this invention, a server plays a central role. The server first receives environmental information from a data collection device. This data collection device may include a soil measuring device or an aerial measuring device. The data obtained from these devices includes soil moisture, temperature, nutrient content, weather information, and image data showing the growth status of crops.

[0645] The received data is preprocessed on the server. During this preprocessing stage, outliers are removed, and incomplete data is supplemented. This consistent data is then analyzed using a generative AI model implemented on the server. Based on environmental information, the model evaluates the state of agricultural targets and predicts the impact of future climate change on crops.

[0646] Based on the analysis results, the server generates suggestions for agricultural work. These suggestions include appropriate fertilizer application amounts, watering timing, pest and disease control measures, and even sales strategies that take market trends into account. These suggestions are presented to the user visually or audibly via the terminal. The information presented is in an intuitively understandable format using natural language processing technology.

[0647] For example, when a user makes a request such as the prompt "Please tell me the optimal timing and amount for the next fertilization," the server analyzes the necessary data and identifies the optimal timing and amount. As a result, the terminal presents an appropriate suggestion to the user.

[0648] In this way, the system can improve the accuracy of its suggestions based on user feedback. The server records the results of actual actions in a database and deepens the learning of the generating AI model. This continuously improves the accuracy of the system's suggestions.

[0649] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0650] Step 1:

[0651] The server receives environmental information from data collection devices. Inputs include humidity, temperature, and nutrient content data from soil measurement devices, crop image data from aerial equipment, and weather information. This yields multidimensional data about the agricultural environment. The output is to send this data to the next preprocessing step.

[0652] Step 2:

[0653] The server preprocesses the received environmental information. The input for this step is the raw data obtained from step 1. Specifically, it removes outliers from the data and imputes missing data points. It also standardizes the data format and prepares it for analysis. The output is a preprocessed, consistent dataset.

[0654] Step 3:

[0655] The server analyzes the pre-processed data using a generating AI model. The input is the data prepared in step 2. This analysis evaluates the state of agricultural targets from environmental information and models the impact of future climate change. Specifically, it uses machine learning algorithms to perform pattern recognition and outputs predictions of soil nutrient deficiencies and crop health as a result.

[0656] Step 4:

[0657] The server generates agricultural work suggestions based on the analysis results. The input is the evaluation results obtained in step 3. In this step, appropriate fertilizer amounts, irrigation schedules, and pest and disease control measures are listed as specific actions. A pricing strategy that takes market information into account is also included in the suggestions. The output is a specific agricultural work suggestion for the user.

[0658] Step 5:

[0659] The terminal presents the proposed agricultural tasks to the user. The input is the suggestions generated in step 4. In terms of specific actions, information is displayed on the screen through a visual user interface, or notified to the user via audio output. The output is information provided in a format that the user can immediately implement.

[0660] Step 6:

[0661] The user takes action based on the suggestions and sends the results as feedback to the server. The input is the result of the agricultural work actually performed by the user. This feedback is recorded in a database. This allows the generative AI model to learn more, improving the accuracy of future suggestions. The output is an improved suggestion model.

[0662] (Application Example 1)

[0663] 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".

[0664] Conventional agricultural optimization systems are designed for vast farmlands and have the problem of not being able to fully demonstrate their effectiveness in small-scale urban farms and community gardens. Urban farmland is limited, and environmental conditions are diverse, so there is a need for a system that provides appropriate agricultural planning that takes these factors into account. Furthermore, detailed, data-driven support is necessary to quickly respond to market trends and changes in weather conditions that urban farmers face.

[0665] 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.

[0666] In this invention, the server includes means for receiving environmental data acquired from an information gathering device, means for analyzing the environmental data to evaluate the state of agricultural targets, and means for proposing agricultural actions based on the created evaluation. This makes it possible to quickly provide customized agricultural plans and actions to small urban farms.

[0667] An "information gathering device" refers to a device used to acquire various types of environmental data, and includes devices such as sensors and aircraft.

[0668] "Environmental data" refers to information that quantifies external conditions related to agriculture, such as soil moisture, temperature, nutrient content, and weather information.

[0669] "Means for evaluating the state" refers to a method or apparatus for analyzing received environmental data and determining the current state of agricultural land and crops.

[0670] "Means for proposing agricultural actions" refers to methods or devices for determining and providing the optimal actions that agricultural workers should take, based on the results of evaluation.

[0671] "Market information" refers to data that includes demand and supply for agricultural products, price trends, and other commercial information.

[0672] "Means for receiving and learning from feedback" refers to methods or devices for collecting user usage results and opinions and incorporating them into future analyses and suggestions.

[0673] An "agricultural plan adapted to the urban environment" is a plan for agricultural activities that takes into account the unique environmental conditions and constraints of urban areas.

[0674] "Agricultural practices customized for small-scale farms" refers to proposals for agricultural activities that are optimized for the specific conditions and requirements of small-scale farmland.

[0675] The system implementing this invention provides customized agricultural planning for small urban farms and community gardens. A server plays a central role.

[0676] The server receives data from information gathering devices such as soil sensors, weather sensors, and drones installed to collect environmental data. This data includes soil moisture, temperature, nutrient content, and weather information. This environmental data is analyzed using machine learning models (such as Scikit-learn and TensorFlow) written in Python. This analysis allows for the evaluation of soil conditions and crop health at each farm and the prediction of the impact of future climate change.

[0677] Based on the analysis results, the server proposes optimal agricultural actions. These proposals include appropriate fertilizer types and amounts, watering timing, and pest and disease control measures. For example, in a community garden within a city, the analysis can determine the timing and amount of irrigation and implement necessary pest and disease control measures.

[0678] Users view these suggestions on a smartphone app. The app uses natural language processing technology to display the suggestions in an intuitive way, allowing farmers to quickly choose actions based on the suggestions.

[0679] Furthermore, the server collects and analyzes relevant market information in order to propose sales strategies that take market trends into account. This allows the user to receive appropriate shipping timing and pricing strategies.

[0680] For example, a user can send a prompt such as, "Please generate an optimal fertilization schedule based on the soil data collected today," and receive suggestions regarding agricultural actions. This can significantly improve the efficiency and sustainability of urban agriculture.

[0681] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0682] Step 1:

[0683] The server receives environmental data from soil sensors, weather sensors, and drones. Inputs include soil moisture, temperature, nutrient content, and weather information. The server stores this data in a database and outputs cleaned data after handling missing values ​​and removing noise.

[0684] Step 2:

[0685] The server uses a machine learning model to analyze the cleaned environmental data. The input is the clean data obtained in Step 1. The data analysis predicts soil conditions, crop health, and future climate change. Based on this, it outputs soil evaluation results and weather forecasts.

[0686] Step 3:

[0687] The server generates agricultural action suggestions based on the analysis results. The input is the evaluation results from step 2. These suggestions include the type and amount of fertilizer to apply, the timing of watering, and pest and disease control measures. An optimized agricultural action plan is generated as output and saved to the database.

[0688] Step 4:

[0689] The server collects market information and develops sales strategies. It receives market trends and price information as input and forecasts future supply and demand. The resulting sales strategies are then stored in a database along with agricultural action plans as output.

[0690] Step 5:

[0691] Users receive suggestion information from the server via a smartphone app. Inputs are queries or requests related to specific agricultural actions that the user is interested in. Outputs are suggestions for agricultural actions expressed in intuitively understandable natural language.

[0692] Step 6:

[0693] The user selects and executes agricultural actions based on the information provided in the app. The input is the suggested information received in step 5. By executing this, the optimal agricultural action is carried out on-site.

[0694] Step 7:

[0695] User feedback is sent to the server. The input consists of the results and opinions of the agricultural actions performed by the user. The server records this as learning material in a database and uses it to improve the accuracy of future analyses and suggestions.

[0696] These steps enable urban small-scale farmers to achieve data-driven, efficient, and sustainable agriculture.

[0697] 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.

[0698] As an embodiment of the present invention, a system is presented that supports the development of efficient and sustainable plans in agriculture. This system provides suggestions that take into account not only environmental data obtained from information gathering devices, but also the emotions of the user.

[0699] This system begins with the server acquiring environmental data from soil sensors, weather sensors, and drones. This data includes soil conditions, crop growth status, and weather information such as temperature and humidity. This data is transformed into useful information through a data cleaning process, and the server uses machine learning models to analyze this data and evaluate the condition of agricultural targets.

[0700] Next, the emotion engine receives user input and responses, and recognizes and analyzes the user's emotions. For example, it determines whether the user is stressed based on their response time and the words they use when asked questions from their device. It also helps to understand how the user receives suggestions, enabling more effective communication. This emotion information is used in suggestion generation, which will be discussed later.

[0701] The server integrates the results of environmental data analysis with information based on the user's emotions to generate suggestions for appropriate agricultural actions. For example, if the user is feeling stressed, it might prioritize presenting simpler and easier-to-implement suggestions.

[0702] The device receives the generated suggestions and presents them to the user in an easily understandable format using natural language processing technology. In doing so, it can optimize user interaction by considering the user's emotional state. The suggestions cover a wide range of topics, from appropriate fertilization and irrigation plans to pest and disease control, and even sales strategies based on market trends.

[0703] For example, if a user asks a question about market trends, the server analyzes market information to forecast demand and proposes the optimal shipping time and pricing. If the user reacts negatively to the information presented, the emotion engine detects this and the server provides alternative solutions, ensuring a highly satisfying information experience.

[0704] This allows agricultural workers to simultaneously improve production efficiency and reduce their workload while maintaining environmental considerations.

[0705] The following describes the processing flow.

[0706] Step 1:

[0707] The server acquires environmental data in real time from soil sensors, weather sensors, and drones. This data includes soil moisture and nutrients, crop images, and temperature fluctuations.

[0708] Step 2:

[0709] The server preprocesses the received environmental data. It ensures data quality by supplementing missing data and removing outliers. It also formats this data into a format that can be input into machine learning models.

[0710] Step 3:

[0711] The server runs machine learning models using pre-processed data to analyze soil conditions and crop health. The models predict the risk of pest and disease outbreaks and the optimal cultivation schedule.

[0712] Step 4:

[0713] The emotion engine monitors user input and responses, analyzing emotional information from factors such as voice tone and response time. This information is then sent to the server.

[0714] Step 5:

[0715] The server integrates the results of environmental data analysis with the user's emotional state to propose personalized agricultural actions. For example, if the user is feeling stressed, it will generate suggestions to prioritize simpler tasks.

[0716] Step 6:

[0717] The device presents generated suggestions to the user using natural language processing technology. The display is intuitive and easy to understand, and the interaction is emotionally conscious.

[0718] Step 7:

[0719] Users carry out agricultural activities based on the suggestions provided. They provide feedback to the system on the status of their fertilization, irrigation, and pest control measures.

[0720] Step 8:

[0721] The server updates its machine learning model based on user feedback and field data, improving the accuracy of future suggestions. This continuously improves the accuracy and effectiveness of agricultural planning.

[0722] (Example 2)

[0723] 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".

[0724] In agriculture, it is essential to accurately understand environmental changes and crop health, and to select appropriate actions based on that understanding. However, conventional methods present challenges such as the cumbersome process of acquiring and analyzing environmental information, and the difficulty in providing information that takes into account the user's emotional state. Furthermore, effective utilization of user feedback is not being achieved. By solving these problems, we aim to improve the efficiency and sustainability of agriculture.

[0725] 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.

[0726] In this invention, the server includes means for receiving environmental information acquired from an information gathering device, means for data cleaning the environmental information and converting it into useful information, and means for using an artificial intelligence model generated to analyze the useful information and evaluate the state of agricultural targets. This enables accurate acquisition and analysis of environmental information, and by generating suggestions that take user emotions into consideration and learning through feedback, it becomes possible to improve efficiency in agriculture and formulate sustainable plans.

[0727] "Information gathering equipment" refers to all hardware used for monitoring soil and weather conditions, including soil monitoring sensors and aerial equipment.

[0728] "Environmental information" refers to data representing external factors related to crops and farmland, specifically including soil conditions, weather conditions, and crop growth status.

[0729] "Data cleaning" refers to the process of removing noise and outliers from collected data and converting it into useful information.

[0730] A "generated artificial intelligence model" is a machine learning algorithm trained to analyze environmental information and used for prediction and evaluation.

[0731] "Emotional state" refers to the psychological reactions and emotions a user exhibits in response to information, and includes indicators such as stress levels and comprehension.

[0732] "User interface" refers to the display screens and operating methods that users use to interact with a system.

[0733] A "prompt message" refers to a sentence displayed in a user interface that presents information or choices to the user.

[0734] The embodiments for carrying out the present invention will now be described. This system aims to improve efficiency and sustainability in agriculture and performs a comprehensive process from acquiring environmental information to generating proposals.

[0735] First, the server uses information gathering equipment consisting of soil monitoring sensors and aerial devices to receive environmental information such as soil conditions and weather conditions in real time. This makes it possible to instantly grasp crop growth and environmental changes.

[0736] Next, the server performs data cleaning on the received environmental information, transforming it into useful information. The data cleaning process removes noise and imputes missing values, preparing the information for analysis.

[0737] Next, the server uses the generated artificial intelligence model to analyze the cleaned environmental information. This AI model is based on machine learning algorithms and is optimized for predicting crop growth and detecting anomalies. This analysis evaluates the condition of agricultural targets and provides foundational data for determining appropriate agricultural actions.

[0738] When a user asks a question to the system through their terminal, for example, by entering a prompt such as "Please tell me the crop growth forecast for next month," the server generates the corresponding information according to that instruction. The user's emotional state is then analyzed, and suggestions are created accordingly. For example, if the user is feeling stressed, the suggestions will be made concise and actionable.

[0739] Finally, the terminal presents the generated suggestions to the user using natural language processing technology. The terminal not only conveys information in an easy-to-understand way through the user interface, but also collects user feedback and sends it to the server. In this way, the system accumulates user sentiment information and feedback and learns to further optimize future suggestions.

[0740] This system not only significantly improves agricultural efficiency but also reduces the burden on users while considering sustainability.

[0741] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0742] Step 1:

[0743] The server receives environmental information from the information gathering device. The information gathering device uses soil monitoring sensors and aerial equipment to collect data such as soil moisture, temperature, and plant health in real time. Raw environmental data is used as input, and this data is aggregated by the server.

[0744] Step 2:

[0745] The server performs data cleaning on the received environmental information. Since the raw input environmental information contains noise and missing values, the server performs processes to remove and impute them. This process yields clean data suitable for analysis.

[0746] Step 3:

[0747] The server uses the generated artificial intelligence model to analyze clean environmental data. Specifically, it processes the data using machine learning algorithms to predict crop growth and detect potential anomalies. The output provides evaluation results indicating the state of agricultural targets and guidance for future actions.

[0748] Step 4:

[0749] The user enters a prompt message via a terminal. For example, the user might instruct the terminal, "Please tell me the crop growth forecast for next month." This message is entered into the system as an instruction, and the server performs appropriate information retrieval and analysis.

[0750] Step 5:

[0751] The server analyzes the user's emotional state. Based on the user's input speed received from the terminal and past responses, the emotion engine evaluates the stress level and comprehension level. This generates information indicating the user's emotional state as output.

[0752] Step 6:

[0753] The server integrates analysis results with user sentiment information to generate suggestions for optimal agricultural actions. The suggestions are tailored to the user's emotional state. The output is a concrete action suggestion presented in the user interface.

[0754] Step 7:

[0755] The device displays the generated suggestions using natural language processing technology. The device organizes the information to aid user understanding and presents the suggestions in easy-to-understand language. These suggestions are designed to be easily implemented by the user.

[0756] (Application Example 2)

[0757] 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".

[0758] Conventional agricultural support systems are limited to suggestions based on environmental data and have the problem of not being able to provide flexible plans that take into account the user's emotions and stress levels. Furthermore, in urban agriculture management, there is a lack of sufficient means to utilize smart devices to provide appropriate farm work suggestions in real time.

[0759] 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.

[0760] In this invention, the server includes means for receiving environmental data acquired from an information gathering device, means for analyzing the environmental data and evaluating the state of agricultural targets, and means for analyzing the user's emotional data and adjusting the suggested content based on the emotional state. This makes it possible to propose customizable agricultural work plans tailored to the user's situation based on the environmental data and emotional data.

[0761] "Information gathering devices" are instruments used to acquire environmental data related to agriculture. These include soil detection devices and unmanned aerial vehicles.

[0762] "Environmental data" refers to information necessary to achieve agricultural objectives, and this information includes temperature, humidity, precipitation, and soil nutrient status.

[0763] "Analysis methods" refer to technologies and algorithms used to evaluate the condition of agricultural targets based on acquired environmental data.

[0764] "Emotional data" refers to information that reflects a user's emotions and mental state, and is used to analyze this data in order to understand the user's stress level and how they perceive things.

[0765] "Means for adjusting proposed content" refers to methods for changing agricultural behaviors and information presented to users based on emotional data, depending on the situation.

[0766] A "smart device" is an electronic device equipped with internet connectivity and various sensors, offering multiple functions and flexible operation; smartphones are an example of such devices.

[0767] The system for implementing this invention operates in conjunction with an information gathering device, a server, and a smart device. The server acquires environmental data from the information gathering device via sensors and unmanned aerial vehicles. This data is transformed into useful information through a data cleaning process. Based on the environmental data, the server can use machine learning algorithms to evaluate the condition of agricultural targets.

[0768] Furthermore, the server collects user emotional data from smart devices and analyzes it using an emotional analysis engine. Based on the analysis results, it proposes the most suitable agricultural actions for the user. The proposals are flexibly adjusted according to the user's emotional state and real-time environmental conditions.

[0769] The smart device displays suggestions provided by the server to the user. The interface is designed to be intuitive and easy to use, ensuring the user readily accepts the suggestions. The user provides feedback through the smart device, and the system uses this feedback to continue learning.

[0770] For example, when a user asks about a work plan for an urban farm, the server analyzes the latest weather and soil data and proposes a simple work plan to minimize stress based on emotional data. At this time, it provides specific instructions such as, "Today the temperature and humidity are high, so it would be best to water the plants in the evening."

[0771] An example of a prompt message would be: "When the user is feeling stressed, suggest the simplest possible farming task. To do this, if the user inputs 'I'm tired today,' then provide a solution." This is how you would instruct the generative AI model.

[0772] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0773] Step 1:

[0774] The server acquires environmental data from information gathering devices. Sensors and unmanned aerial vehicles are used to collect information such as temperature, humidity, and soil nutrient status. The input is environmental data, and the output is cleaned-up data. This data undergoes initial data processing to remove noise.

[0775] Step 2:

[0776] The server evaluates the state of agricultural targets by running machine learning algorithms using the acquired clean environmental data. Data processing involves data analysis to model the state of agriculture. The input is clean environmental data, and the output is evaluation results such as crop growth status and detailed soil conditions.

[0777] Step 3:

[0778] The server collects user emotion data via smart devices. It uses user-submitted text and response times for analysis. Input is user text data and response speed, while output is an evaluation of the emotional state. Natural language processing techniques are used for data calculation.

[0779] Step 4:

[0780] The server generates optimal agricultural actions based on evaluated environmental conditions and emotional data. It utilizes a generative AI model and prompts to suggest a plan. The input consists of the environmental condition evaluation results and the emotional condition evaluation results, while the output is the proposed agricultural action. The suggestions are adjusted to take the user's stress level into consideration.

[0781] Step 5:

[0782] The terminal displays the suggestions received from the server to the user, presenting them in an easy-to-understand format through an interface. The display includes an intuitive UI design. The input is the suggested agricultural action, and the output is a visual display of the suggestions to the user.

[0783] Step 6:

[0784] Users provide feedback on suggested actions via their devices. This feedback is used to train the system. The input is user feedback data, and the output is updated information for the server's trained model. This improves the accuracy of future suggestions.

[0785] 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.

[0786] 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.

[0787] In the above embodiment, an example was given in which the specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the robot 414.

[0788] 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.

[0789] 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.

[0790] 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.

[0791] 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.

[0792] 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.

[0793] 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."

[0794] 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.

[0795] 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.

[0796] 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.

[0797] 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.

[0798] 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.

[0799] 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.

[0800] 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.

[0801] 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.

[0802] 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.

[0803] 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.

[0804] 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.

[0805] 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.

[0806] The following is further disclosed regarding the embodiments described above.

[0807] (Claim 1)

[0808] A means for receiving environmental data acquired from information gathering equipment,

[0809] A means for analyzing the aforementioned environmental data to evaluate the state of agricultural targets,

[0810] A means of proposing agricultural actions based on the evaluations created,

[0811] Means for displaying proposed agricultural actions,

[0812] Means for collecting and analyzing market information,

[0813] A system that includes a means of receiving and learning from user feedback.

[0814] (Claim 2)

[0815] The system according to claim 1, wherein the information gathering equipment includes at least a soil sensor and a drone.

[0816] (Claim 3)

[0817] The system according to claim 1, wherein the analysis means processes environmental data using a machine learning model.

[0818] "Example 1"

[0819] (Claim 1)

[0820] A means for receiving environmental information acquired from a data collection device,

[0821] A means for preprocessing the aforementioned environmental information to remove outliers and perform data interpolation,

[0822] A means of analyzing preprocessed environmental information using a generative AI model to evaluate the state of agricultural targets and the impact of future climate change,

[0823] A means of proposing agricultural work based on the analysis results,

[0824] The proposed means for displaying or outputting audio of the agricultural work,

[0825] A means of collecting and analyzing market information to propose the optimal shipping timing and pricing strategy,

[0826] A system that includes means for receiving user feedback, recording it in a database, and improving the accuracy of suggestions.

[0827] (Claim 2)

[0828] The system according to claim 1, wherein the data acquisition device includes at least a soil measuring device and an aerator.

[0829] (Claim 3)

[0830] The system according to claim 1, wherein the analysis means processes environmental information using a generated AI model.

[0831] "Application Example 1"

[0832] (Claim 1)

[0833] A means for receiving environmental data acquired from an information gathering device,

[0834] A means for analyzing the aforementioned environmental data to evaluate the state of agricultural targets,

[0835] A means of proposing agricultural actions based on the evaluations created,

[0836] Means for displaying proposed agricultural actions,

[0837] Means for collecting and analyzing market information,

[0838] A means of receiving and learning from user feedback,

[0839] Means for providing agricultural plans adapted to urban environments,

[0840] Means for deriving customized agricultural practices for small urban farms,

[0841] A system that includes this.

[0842] (Claim 2)

[0843] The system according to claim 1, wherein the information gathering device includes at least an environmental sensor and an airplane.

[0844] (Claim 3)

[0845] The system according to claim 1, wherein the analysis means processes environmental data using a learning model.

[0846] "Example 2 of combining an emotion engine"

[0847] (Claim 1)

[0848] A means for receiving environmental information acquired from an information gathering device,

[0849] A means for data cleaning the aforementioned environmental information and converting it into useful information,

[0850] A means of using an artificial intelligence model generated to analyze the aforementioned useful information and evaluate the state of agricultural targets,

[0851] A means of analyzing the user's emotional state and generating emotional information,

[0852] A means of proposing agricultural actions based on the aforementioned evaluation and the aforementioned emotional information,

[0853] A method for presenting proposed agricultural behaviors using natural language processing,

[0854] Means for collecting and analyzing market information,

[0855] A means of receiving and learning from user feedback,

[0856] A means of displaying prompt messages in the user interface,

[0857] A system that includes this.

[0858] (Claim 2)

[0859] The system according to claim 1, wherein the information collection device includes at least a soil monitoring sensor and an aerial device.

[0860] (Claim 3)

[0861] The system according to claim 1, wherein the analysis means processes environmental information using the generated artificial intelligence model.

[0862] "Application example 2 when combining with an emotional engine"

[0863] (Claim 1)

[0864] A means for receiving environmental data acquired from an information gathering device,

[0865] A means for analyzing the aforementioned environmental data to evaluate the state of agricultural targets,

[0866] A means of proposing agricultural actions based on the evaluations created,

[0867] Means for displaying proposed agricultural actions,

[0868] Means for collecting and analyzing market information,

[0869] A means of receiving and learning from user feedback,

[0870] A means of analyzing user emotional data and adjusting suggestions based on their emotional state,

[0871] A means of displaying the results of integrating environmental data and emotional data on a smart device,

[0872] A system that includes this.

[0873] (Claim 2)

[0874] The system according to claim 1, wherein the information gathering device includes at least a soil detection device and an unmanned aerial vehicle.

[0875] (Claim 3)

[0876] The system according to claim 1, wherein the analysis means processes environmental data using a machine learning algorithm. [Explanation of Symbols]

[0877] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots< / url:> < / url:> < / url:> < / url:>

Claims

1. A means for receiving environmental data acquired from information gathering equipment, A means for analyzing the aforementioned environmental data to evaluate the state of agricultural targets, A means of proposing agricultural actions based on the evaluations created, Means for displaying proposed agricultural actions, Means for collecting and analyzing market information, A system that includes a means of receiving and learning from user feedback.

2. The system according to claim 1, wherein the information gathering equipment includes at least a soil sensor and a drone.

3. The system according to claim 1, wherein the analysis means processes environmental data using a machine learning model.

Images