system

Abstract

The system according to this embodiment aims to propose optimal management methods and cultivars for realizing sustainable agriculture. [Solution] The system according to the embodiment comprises an analysis unit, a proposal unit, an analysis unit, a cultivation proposal unit, an image analysis unit, and a grasping unit. The analysis unit analyzes environmental data. The proposal unit proposes the optimal management method based on the data analyzed by the analysis unit. The analysis unit analyzes data on soil, climate, and crop types and varieties. The cultivation proposal unit proposes the optimal crop varieties and resource allocation based on the data analyzed by the analysis unit. The image analysis unit analyzes image data from satellites and drones. The grasping unit grasps the farmland conditions based on the data analyzed by the image analysis unit.

Description

Technical Field

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

Background Art

[0002] Patent Document 1 discloses a persona chatbot control method 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 chatbot character, encoding the prompt, and inputting the encoded prompt into a language model to generate a chatbot utterance in response to the user utterance.

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the conventional technology, there was a problem that it was difficult to select an optimal management method and cultivation variety for practicing sustainable agriculture.

[0005] The system according to the embodiment aims to propose an optimal management method and cultivation variety for realizing sustainable agriculture.

Means for Solving the Problems

[0006] The system according to this embodiment comprises an analysis unit, a proposal unit, an analysis unit, a cultivation proposal unit, an image analysis unit, and a grasping unit. The analysis unit analyzes environmental data. The proposal unit proposes the optimal management method based on the data analyzed by the analysis unit. The analysis unit analyzes data on soil, climate, and crop types and varieties. The cultivation proposal unit proposes the optimal crop varieties and resource allocation based on the data analyzed by the analysis unit. The image analysis unit analyzes image data from satellites and drones. The grasping unit grasps the farmland conditions based on the data analyzed by the image analysis unit. [Effects of the Invention]

[0007] The system according to this embodiment can propose optimal management methods and cultivars for realizing sustainable agriculture. [Brief explanation of the drawing]

[0008] [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. [Modes for carrying out the invention]

[0009] Hereinafter, an example of an embodiment of the system relating to the technology of this disclosure will be described with reference to the attached drawings.

[0010] First, let's explain the terminology used in the following explanation.

[0011] In the following embodiments, the signed processor (hereinafter simply referred to as "processor") may be a single arithmetic unit or a combination of multiple arithmetic units. Furthermore, the processor may be a single type of arithmetic unit or a combination of multiple types of arithmetic units. Examples of arithmetic units include CPU (Central Processing Unit), GPU (Graphics Processing Unit), GPGPU (General-Purpose computing on Graphics Processing Units), APU (Accelerated Processing Unit), or TPU (Tensor Processing Unit).

[0012] In the following embodiments, signed RAM (Random Access Memory) is a memory that temporarily stores information and is used as work memory by the processor.

[0013] In the following embodiments, the signed storage is one or more non-volatile storage devices that store various programs and various parameters. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), or magnetic tapes.

[0014] In the following embodiments, the numbered communication I / F (Interface) is an interface including a communication processor, an antenna, and the like. The communication I / F controls communication between a plurality of computers. Examples of communication standards applicable to the communication I / F include wireless communication standards such as 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark).

[0015] 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 only A, only B, or a combination of A and B. Also, in this specification, when expressing three or more matters connected by "and / or", the same concept as "A and / or B" is applied.

[0016] [First Embodiment] FIG. 1 shows an example of the configuration of a data processing system 10 according to the first embodiment.

[0017] As shown in FIG. 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.

[0018] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. Also, the database 24 and the communication I / F 26 are connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0019] The smart device 14 comprises a computer 36, a receiving 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 receiving device 38, output device 40, and camera 42 are also connected to the bus 52.

[0020] The reception device 38 is equipped with a touch panel 38A and a microphone 38B, and accepts user input. The touch panel 38A accepts user input via touch by detecting contact with an object (e.g., a pen or finger). The microphone 38B accepts user input via voice 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 unit 12. In the data processing unit 12, the specific processing unit 290 (see Figure 2) acquires the data indicating the user input.

[0021] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user by outputting the data in a form perceptible to the user (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.

[0022] 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.

[0023] Figure 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0024] 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.

[0025] 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. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0026] In the smart device 14, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The specific processing program 60 is used in conjunction with the specific processing program 56 by the data processing system 10. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart device 14 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0027] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device (e.g., a generation server) may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device having the data generation model 58. The data processing device 12 may also be a server device or a terminal device owned by a user (e.g., a mobile phone, robot, home appliance, etc.). Next, an example of processing by the data processing system 10 according to the first embodiment will be described.

[0028] (Example of form 1) The sustainable agricultural system according to an embodiment of the present invention is a system that uses AI to analyze environmental data and improve agricultural productivity. This sustainable agricultural system analyzes environmental and weather data and proposes sustainable management methods. Furthermore, it analyzes data on soil, climate, crop types and varieties, and proposes optimal cultivars and resource allocation considering market food demand and price fluctuations. It also works in conjunction with satellite and drone image data to grasp farmland conditions in real time. This allows producers to quickly identify and respond to problems. For example, the sustainable agricultural system collects detailed data such as temperature, precipitation, humidity, and wind speed, and the AI ​​analyzes it to propose optimal management methods. For example, it can propose optimal irrigation schedules and fertilization plans for specific crops. This enables efficient use of resources. Next, the sustainable agricultural system collects data such as soil pH value, nutrient content, climate conditions, and crop growth characteristics, and the AI ​​analyzes it to propose optimal cultivars and resource allocation. This enables efficient agriculture that takes into account market food demand and price fluctuations. Furthermore, the sustainable agricultural system can analyze satellite images and drone aerial images to grasp crop growth status and pest and disease occurrences. This allows producers to quickly identify and address problems. This enables sustainable agriculture, allowing for efficient resource use and protection of ecosystem balance. Thus, sustainable agricultural systems can achieve efficient resource use and protection of ecosystem balance.

[0029] The sustainable agricultural system according to this embodiment comprises an analysis unit, a proposal unit, an analysis unit, a cultivation proposal unit, an image analysis unit, and a grasping unit. The analysis unit analyzes environmental data. The analysis unit collects environmental data such as temperature, precipitation, humidity, and wind speed, and analyzes it using AI. The analysis unit can, for example, propose an optimal irrigation schedule based on temperature data. The analysis unit can, for example, propose an optimal fertilization plan based on precipitation data. The analysis unit can, for example, propose an optimal cultivation method based on humidity data. The proposal unit proposes an optimal management method based on the data analyzed by the analysis unit. The proposal unit can, for example, propose an optimal irrigation schedule for a specific crop. The proposal unit can, for example, propose an optimal fertilization plan for a specific crop. The proposal unit can, for example, propose an optimal cultivation method for a specific crop. The analysis unit analyzes data on soil, climate, and crop type and variety. The analysis unit analyzes, for example, soil pH values ​​and nutrient content. The analysis unit can, for example, analyze climatic conditions. The analysis unit can, for example, analyze the growth characteristics of crops. The cultivation proposal unit proposes the optimal cultivar and resource allocation based on the data analyzed by the analysis unit. The cultivation proposal unit proposes the optimal cultivar considering market food demand and price fluctuations. The cultivation proposal unit can propose the optimal resource allocation considering market food demand and price fluctuations. The cultivation proposal unit can propose the optimal cultivation method considering market food demand and price fluctuations. The image analysis unit analyzes satellite and drone image data. The image analysis unit can, for example, analyze satellite images to understand the growth status of crops. The image analysis unit can, for example, analyze drone aerial images to understand the occurrence of pests and diseases. The image analysis unit can, for example, analyze satellite images to understand the condition of farmland. The assessment unit understands the condition of farmland based on the data analyzed by the image analysis unit. The assessment unit can, for example, understand the growth status of crops. The assessment unit can, for example, understand the occurrence of pests and diseases. The assessment unit can, for example, understand the condition of farmland.As a result, the sustainable agricultural system according to the embodiment enables the analysis of environmental data, the proposal of optimal management methods, the analysis of soil and climate data, the proposal of optimal crop varieties and resource allocation, the analysis of image data, and the understanding of farmland conditions.

[0030] The analysis unit analyzes environmental data. For example, it collects environmental data such as temperature, precipitation, humidity, and wind speed, and analyzes it using AI. Specifically, it can propose an optimal irrigation schedule based on temperature data. Temperature data is acquired in real time from sensors and weather databases, and the AI ​​calculates the optimal irrigation timing for crop growth by comparing it with past data. For example, on days with high temperatures, evaporation increases, requiring more frequent irrigation; the AI ​​automatically determines this and issues instructions to the irrigation system. It can also propose an optimal fertilization plan based on precipitation data. Precipitation data is acquired from rain gauges and weather databases, and the AI ​​analyzes precipitation patterns. For example, during periods of heavy rainfall, the timing of fertilization is adjusted to prevent fertilizer runoff. Finally, it can propose an optimal cultivation method based on humidity data. Humidity data is acquired from humidity sensors, and the AI ​​proposes the optimal cultivation environment according to the type of crop and its growth stage. For example, high humidity increases the risk of pest and disease outbreaks, so the AI ​​proposes appropriate control methods. This allows the analysis unit to analyze environmental data in detail and provide optimal management methods for crop growth.

[0031] The proposal department proposes optimal management methods based on data analyzed by the analysis department. For example, the proposal department can propose an optimal irrigation schedule for a specific crop. Specifically, based on temperature and precipitation data provided by the analysis department, the AI ​​calculates the crop's water demand and proposes the optimal irrigation timing. For example, by increasing the frequency of irrigation during dry periods and decreasing it during wet periods, the department can efficiently utilize water resources. The proposal department can also propose an optimal fertilization plan for a specific crop. The AI ​​considers soil nutrient data and the crop's growth stage to propose the optimal timing and amount of fertilization. For example, by applying more nitrogen fertilizer in the early stages of crop growth and increasing phosphorus fertilizer in the later stages, the department can promote healthy crop growth. The proposal department can also propose an optimal cultivation method for a specific crop. Based on humidity and temperature data, the AI ​​proposes the optimal cultivation environment. For example, since the risk of pest and disease outbreaks increases during periods of high humidity, the AI ​​proposes appropriate control methods. In this way, the proposal department can provide optimal management methods for crop growth based on data provided by the analysis department.

[0032] The analysis department analyzes data on soil, climate, and crop types and varieties. For example, it analyzes soil pH values ​​and nutrient content. Specifically, it measures soil pH values ​​and nutrient content such as nitrogen, phosphorus, and potassium by collecting soil samples and analyzing them in the laboratory. This allows for the creation of fertilization plans to appropriately supply the nutrients necessary for crops. It can also analyze climate conditions. By collecting meteorological data and having AI analyze past weather patterns, it predicts future weather conditions. For example, it can predict fluctuations in temperature and precipitation and create cultivation plans accordingly. It can also analyze crop growth characteristics. It analyzes growth patterns and pest and disease risk for each crop type and variety and proposes optimal cultivation methods. In this way, the analysis department can analyze soil, climate, and crop data in detail and support the healthy growth of crops.

[0033] The cultivation proposal department proposes optimal crop varieties and resource allocations based on data analyzed by the analysis department. For example, the cultivation proposal department proposes optimal crop varieties considering market food demand and price fluctuations. Specifically, AI analyzes market data and selects crops with high demand and stable prices. This allows farmers to create cultivation plans to maximize profits. It can propose optimal resource allocations considering market food demand and price fluctuations. AI calculates optimal fertilizer and water allocations based on soil and climate data. For example, it proposes a fertilization plan that maximizes crop growth while minimizing fertilizer use. It can propose optimal cultivation methods considering market food demand and price fluctuations. AI proposes optimal cultivation methods based on climate data and crop growth characteristics. For example, it proposes using shade nets to protect crops during periods of high temperatures. This allows the cultivation proposal department to propose optimal crop varieties and resource allocations to farmers based on data provided by the analysis department.

[0034] The image analysis unit analyzes image data from satellites and drones. For example, it analyzes satellite images to understand the growth status of crops. Specifically, AI analyzes satellite images to evaluate the health and growth rate of crops. For example, it uses NDVI (Normalized Density Vegetation Index) to measure the greenness of crops and evaluate their health. It can also analyze aerial images taken by drones to understand the occurrence of pests and diseases. Drones capture high-resolution images, and AI analyzes these images to identify the locations of pest and disease outbreaks. For example, it can detect abnormalities such as leaf discoloration and holes, allowing for early countermeasures. It can also analyze satellite images to understand the condition of farmland. AI analyzes the soil condition and moisture content of farmland and proposes optimal management methods. As a result, the image analysis unit can analyze satellite and drone image data in detail and understand the condition of farmland in real time.

[0035] The assessment unit grasps the conditions of farmland based on data analyzed by the image analysis unit. For example, the assessment unit grasps the growth status of crops. Specifically, it evaluates the health and growth rate of crops based on data provided by the image analysis unit. For example, it measures the greenness of crops using NDVI (Normalized Density Index) to evaluate their health. It can grasp the occurrence of pests and diseases. Based on data provided by the image analysis unit, it identifies the locations of pest and disease outbreaks and takes early countermeasures. For example, it detects abnormalities such as leaf discoloration and holes and proposes appropriate control methods. It can grasp the conditions of farmland. Based on data provided by the image analysis unit, it evaluates the soil condition and moisture content of the farmland and proposes optimal management methods. In this way, the assessment unit can grasp the conditions of farmland in detail based on data provided by the image analysis unit and provide appropriate management methods.

[0036] The analysis unit can analyze environmental data such as temperature, precipitation, humidity, and wind speed. For example, the analysis unit can collect temperature data and analyze it using AI. For example, the analysis unit can collect precipitation data and analyze it using AI. For example, the analysis unit can collect humidity data and analyze it using AI. By doing so, the analysis unit can propose the optimal management method by analyzing the environmental data. Some or all of the above-described processes in the analysis unit may be performed using AI or not. For example, the analysis unit can input temperature data into a generating AI and have the generating AI perform the analysis of the temperature data.

[0037] The proposal unit can propose an optimal irrigation schedule and fertilization plan for a specific crop based on the data analyzed by the analysis unit. For example, the proposal unit can propose an optimal irrigation schedule for a specific crop. For example, the proposal unit can propose an optimal fertilization plan for a specific crop. For example, the proposal unit can propose an optimal cultivation method for a specific crop. In this way, the proposal unit can propose an optimal irrigation schedule and fertilization plan for a specific crop. Some or all of the above processing in the proposal unit may be performed using AI or not. For example, the proposal unit can input the data analyzed by the analysis unit into a generating AI and have the generating AI execute the proposal of an optimal irrigation schedule and fertilization plan.

[0038] The analysis unit can analyze data such as soil pH values, nutrient content, climatic conditions, and crop growth characteristics. For example, the analysis unit can analyze soil pH values. For example, the analysis unit can analyze soil nutrient content. For example, the analysis unit can analyze climatic conditions. This allows the analysis unit to analyze data such as soil pH values, nutrient content, climatic conditions, and crop growth characteristics. Some or all of the above-described processes in the analysis unit may be performed using AI or not. For example, the analysis unit can input soil pH value data into a generating AI and have the generating AI perform an analysis of the soil pH value.

[0039] The cultivation proposal unit can propose optimal cultivars and resource allocations based on data analyzed by the analysis unit, taking into account market food demand and price fluctuations. For example, the cultivation proposal unit can propose optimal cultivars considering market food demand. For example, the cultivation proposal unit can propose optimal resource allocations considering price fluctuations. For example, the cultivation proposal unit can propose optimal cultivation methods considering market food demand and price fluctuations. In this way, the cultivation proposal unit can propose optimal cultivars and resource allocations considering market food demand and price fluctuations. Some or all of the above processing in the cultivation proposal unit may be performed using AI or not. For example, the cultivation proposal unit can input data analyzed by the analysis unit into a generation AI and have the generation AI execute a proposal for optimal cultivars and resource allocations.

[0040] The image analysis unit can analyze satellite images and drone aerial images to understand the growth status of crops and the occurrence of pests and diseases. For example, the image analysis unit can analyze satellite images to understand the growth status of crops. For example, the image analysis unit can analyze drone aerial images to understand the occurrence of pests and diseases. For example, the image analysis unit can analyze satellite images to understand the condition of farmland. In this way, the image analysis unit can analyze satellite images and drone aerial images to understand the growth status of crops and the occurrence of pests and diseases. Some or all of the above processing in the image analysis unit may be performed using AI or not. For example, the image analysis unit can input satellite image data into a generating AI and have the generating AI perform an analysis of the crop growth status.

[0041] The understanding unit can grasp the conditions of farmland based on the data analyzed by the image analysis unit and provide this information to producers. For example, the understanding unit can grasp the growth status of crops. For example, the understanding unit can grasp the occurrence status of pests and diseases. For example, the understanding unit can grasp the conditions of farmland. In this way, the understanding unit can grasp the conditions of farmland and provide this information to producers. Some or all of the above processing in the understanding unit may be performed using AI or not. For example, the understanding unit can input the data analyzed by the image analysis unit into a generating AI and have the generating AI perform the task of grasping the conditions of farmland.

[0042] The analysis unit can be equipped with a function to detect anomalies by comparing environmental data with past data during analysis. For example, the analysis unit can detect abnormally high or low temperatures by comparing them with past temperature data. For example, the analysis unit can detect abnormally heavy rainfall or drought by comparing them with past precipitation data. For example, the analysis unit can detect abnormally dry or wet conditions by comparing them with past humidity data. In this way, the analysis unit can detect anomalies by comparing them with past data. Some or all of the above-described processes in the analysis unit may be performed using AI or not. For example, the analysis unit can input past environmental data into a generating AI and have the generating AI perform the detection of anomalies.

[0043] The analysis unit can improve the accuracy of its analysis by considering climate patterns in specific regions when analyzing environmental data. For example, the analysis unit can perform analysis by considering seasonal temperature fluctuation patterns in specific regions. For example, the analysis unit can optimize irrigation plans by considering precipitation patterns in specific regions. For example, the analysis unit can propose wind damage countermeasures by considering wind speed patterns in specific regions. In this way, the analysis unit can improve the accuracy of its analysis by considering climate patterns in specific regions. Some or all of the above processing in the analysis unit may be performed using AI or not. For example, the analysis unit can input climate data for specific regions into a generating AI and have the generating AI perform the improvement of analysis accuracy.

[0044] The proposal unit can improve the accuracy of its proposals by referring to past proposal history. For example, the proposal unit can analyze past proposal history and prioritize the most effective proposals. For example, the proposal unit can refer to past proposal history to make proposals for similar situations. For example, the proposal unit can customize proposal content based on past proposal history. This allows the proposal unit to improve the accuracy of its proposals by referring to past proposal history. Some or all of the above processes in the proposal unit may be performed using AI or not. For example, the proposal unit can input past proposal history data into a generation AI and have the generation AI perform the task of improving the accuracy of proposals.

[0045] The proposal unit can customize its suggestions according to the growth stage of a specific crop. For example, during the early stages of crop growth, the proposal unit can suggest appropriate irrigation and fertilization. During the mid-stage of crop growth, the proposal unit can suggest pest and disease control measures. During the harvest season, the proposal unit can suggest harvesting timing and methods. In this way, the proposal unit can customize its suggestions according to the growth stage of a specific crop. Some or all of the above processing in the proposal unit may be performed using AI or not. For example, the proposal unit can input growth stage data for a specific crop into a generating AI and have the generating AI perform the customization of the suggestions.

[0046] The proposal unit can adjust its proposals to take into account local agricultural policies and regulations. For example, the proposal unit can make proposals that qualify for subsidies based on local agricultural policies. For example, the proposal unit can make proposals for usable pesticides and fertilizers based on local regulations. For example, the proposal unit can make proposals for sustainable agricultural practices based on local agricultural policies. This allows the proposal unit to adjust its proposals to take into account local agricultural policies and regulations. Some or all of the above processing in the proposal unit may be performed using AI or not. For example, the proposal unit can input local agricultural policy data into a generating AI and have the generating AI perform the adjustment of the proposal content.

[0047] The proposal unit can enhance its proposals by referring to successful case studies of other agricultural producers. For example, the proposal unit can propose effective irrigation methods based on successful case studies of other agricultural producers. For example, the proposal unit can propose pest and disease control measures based on successful case studies of other agricultural producers. For example, the proposal unit can propose harvest timing and methods based on successful case studies of other agricultural producers. In this way, the proposal unit can enhance its proposals by referring to successful case studies of other agricultural producers. Some or all of the above processing in the proposal unit may be performed using AI or not. For example, the proposal unit can input data on successful case studies of other agricultural producers into a generating AI and have the generating AI perform the enhancement of the proposal.

[0048] The analysis unit can improve the accuracy of its analysis by taking into account the microbial activity of the soil during the analysis. For example, the analysis unit can monitor the microbial activity of the soil and reflect it in the analysis results. For example, the analysis unit can propose an optimal fertilization plan by taking into account the microbial activity of the soil. For example, the analysis unit can propose an optimal irrigation schedule by taking into account the microbial activity of the soil. In this way, the analysis unit can improve the accuracy of its analysis by taking into account the microbial activity of the soil. Some or all of the above processes in the analysis unit may be performed using AI or not. For example, the analysis unit can input soil microbial activity data into a generating AI and have the generating AI perform the improvement of analysis accuracy.

[0049] The analysis unit can apply different analytical methods to each stage of crop growth during analysis. For example, during the early stages of crop growth, the analysis unit can focus on analyzing soil nutrients. During the middle stages of crop growth, the analysis unit can focus on analyzing the occurrence of pests and diseases. During the harvest season, the analysis unit can perform analyses to determine the optimal harvest time. This allows the analysis unit to apply the most suitable analytical method for each stage of crop growth. Some or all of the above-described processes in the analysis unit may be performed using AI or not. For example, the analysis unit can input crop growth stage data into a generating AI and have the generating AI execute the application of analytical methods.

[0050] The analysis unit can provide analysis results by combining regional climate change data during the analysis process. For example, the analysis unit can propose an optimal irrigation schedule by combining regional temperature fluctuation data. For example, the analysis unit can propose an optimal fertilization plan by combining regional precipitation fluctuation data. For example, the analysis unit can propose wind damage countermeasures by combining regional wind speed fluctuation data. In this way, the analysis unit can provide analysis results by combining regional climate change data. Some or all of the above processing in the analysis unit may be performed using AI or not. For example, the analysis unit can input regional climate change data into a generating AI and have the generating AI perform the task of providing analysis results.

[0051] The analysis unit can improve the accuracy of its analysis by collaborating with other agricultural databases during the analysis process. For example, the analysis unit can acquire soil nutrient data by collaborating with other agricultural databases and incorporate it into the analysis. For example, the analysis unit can acquire climate data by collaborating with other agricultural databases and incorporate it into the analysis. For example, the analysis unit can acquire crop growth data by collaborating with other agricultural databases and incorporate it into the analysis. In this way, the analysis unit can improve the accuracy of its analysis by collaborating with other agricultural databases. Some or all of the above processes in the analysis unit may be performed using AI or not. For example, the analysis unit can input data from other agricultural databases into a generating AI and have the generating AI perform the task of improving the accuracy of the analysis.

[0052] The cultivation suggestion unit can improve the accuracy of its suggestions by referring to past cultivation history when making suggestions. For example, the cultivation suggestion unit can analyze past cultivation history and prioritize suggesting the most effective cultivation methods. For example, the cultivation suggestion unit can refer to past cultivation history to make cultivation suggestions for similar situations. For example, the cultivation suggestion unit can customize the content of its cultivation suggestions based on past cultivation history. This allows the cultivation suggestion unit to improve the accuracy of its suggestions by referring to past cultivation history. Some or all of the above processes in the cultivation suggestion unit may be performed using AI or not. For example, the cultivation suggestion unit can input past cultivation history data into a generating AI and have the generating AI perform the task of improving the accuracy of its suggestions.

[0053] The cultivation proposal unit can enhance its proposals by considering demand forecast data for specific markets. For example, the cultivation proposal unit can propose the optimal cultivar based on market demand forecast data. For example, the cultivation proposal unit can propose adjusting the harvest time based on market demand forecast data. For example, the cultivation proposal unit can propose optimizing the cultivation area based on market demand forecast data. In this way, the cultivation proposal unit can enhance its proposals by considering demand forecast data for specific markets. Some or all of the above processing in the cultivation proposal unit may be performed using AI or not. For example, the cultivation proposal unit can input demand forecast data for specific markets into a generating AI and have the generating AI perform the enhancement of the proposal.

[0054] The cultivation proposal department can adjust its proposals when submitting them, taking into account local agricultural policies and regulations. For example, the cultivation proposal department can submit cultivation proposals that qualify for subsidies based on local agricultural policies. For example, the cultivation proposal department can submit proposals for usable pesticides and fertilizers based on local regulations. For example, the cultivation proposal department can submit proposals for sustainable agricultural practices based on local agricultural policies. This allows the cultivation proposal department to adjust its proposals to take into account local agricultural policies and regulations. Some or all of the above processing in the cultivation proposal department may be performed using AI or not. For example, the cultivation proposal department can input local agricultural policy data into a generating AI and have the generating AI perform the adjustment of the proposal content.

[0055] The cultivation proposal unit can enhance its proposals by referring to successful case studies from other agricultural producers. For example, the cultivation proposal unit can propose effective cultivation methods based on successful case studies from other agricultural producers. For example, the cultivation proposal unit can propose pest and disease control measures based on successful case studies from other agricultural producers. For example, the cultivation proposal unit can propose harvest timing and methods based on successful case studies from other agricultural producers. In this way, the cultivation proposal unit can enhance its proposals by referring to successful case studies from other agricultural producers. Some or all of the above processes in the cultivation proposal unit may be performed using AI or not. For example, the cultivation proposal unit can input data on successful case studies from other agricultural producers into a generating AI and have the generating AI perform the enhancement of the proposals.

[0056] The image analysis unit can be equipped with a function to detect anomalies by comparing current images with past image data during image analysis. For example, the image analysis unit can detect abnormal growth conditions by comparing current crop images with past crop images. For example, the image analysis unit can detect abnormal pest and disease outbreaks by comparing current pest and disease images with past pest and disease images. For example, the image analysis unit can detect abnormal soil conditions by comparing current soil images with past soil images. In this way, the image analysis unit can detect anomalies by comparing current image data with past image data. Some or all of the above-described processes in the image analysis unit may be performed using AI or not. For example, the image analysis unit can input past image data into a generating AI and have the generating AI perform anomaly detection.

[0057] The image analysis unit can customize its analysis method according to the growth stage of a specific crop during image analysis. For example, during the early stages of crop growth, the image analysis unit can focus on analyzing germination status. During the middle stages of crop growth, the image analysis unit can focus on analyzing the occurrence of pests and diseases. During the harvest season, the image analysis unit can perform analysis to determine the optimal harvest time. In this way, the image analysis unit can customize its analysis method according to the growth stage of a specific crop. Some or all of the above-described processes in the image analysis unit may be performed using AI or not. For example, the image analysis unit can input growth stage data of a specific crop into a generating AI and have the generating AI perform the customization of the analysis method.

[0058] The image analysis unit can combine geographical data during image analysis and display the analysis results separately for each region. For example, the image analysis unit can analyze the crop growth status in each region and display the growth status for each region. For example, the image analysis unit can analyze the pest and disease occurrence status in each region and display the occurrence status for each region. For example, the image analysis unit can analyze the soil conditions in each region and display the soil conditions for each region. In this way, the image analysis unit can combine geographical data and display the analysis results separately for each region. Some or all of the above processing in the image analysis unit may be performed using AI or not. For example, the image analysis unit can input geographical data into a generating AI and have the generating AI perform the display of analysis results by region.

[0059] The image analysis unit can collect data in real time during image analysis and provide analysis results immediately. For example, the image analysis unit can collect crop images in real time and provide analysis results immediately. For example, the image analysis unit can collect pest and disease images in real time and provide analysis results immediately. For example, the image analysis unit can collect soil images in real time and provide analysis results immediately. As a result, the image analysis unit can collect data in real time and provide analysis results immediately. Some or all of the above-described processes in the image analysis unit may be performed using AI or not. For example, the image analysis unit can input data collected in real time into a generating AI and have the generating AI execute a process to provide analysis results immediately.

[0060] The data acquisition unit can be equipped with a function to detect anomalies by comparing current farmland conditions with past data. For example, the data acquisition unit can detect abnormal crop growth by comparing current farmland conditions with past data. For example, the data acquisition unit can detect abnormal pest and disease outbreaks by comparing current pest and disease outbreaks with past data. For example, the data acquisition unit can detect abnormal soil conditions by comparing current soil conditions with past data. In this way, the data acquisition unit can detect anomalies by comparing current data with past data. Some or all of the above processing in the data acquisition unit may be performed using AI or not. For example, the data acquisition unit can input past farmland conditions data into a generating AI and have the generating AI perform anomaly detection.

[0061] The assessment unit can improve the accuracy of its assessment of farmland conditions by considering the climate patterns of a specific region. For example, the assessment unit can assess farmland conditions by considering the seasonal temperature fluctuation patterns of a specific region. For example, the assessment unit can assess farmland conditions by considering the precipitation patterns of a specific region. For example, the assessment unit can assess farmland conditions by considering the wind speed patterns of a specific region. In this way, the assessment unit can improve the accuracy of its assessment by considering the climate patterns of a specific region. Some or all of the above processing in the assessment unit may be performed using AI or not. For example, the assessment unit can input climate data of a specific region into a generating AI and have the generating AI perform the improvement of assessment accuracy.

[0062] The assessment unit can combine geographical data to assess the condition of farmland and display the assessment results separately for each region. For example, the assessment unit can assess the crop growth status in each region and display the growth status for each region. For example, the assessment unit can assess the occurrence of pests and diseases in each region and display the occurrence status for each region. For example, the assessment unit can assess the soil conditions in each region and display the soil conditions for each region. In this way, the assessment unit can combine geographical data to display the assessment results separately for each region. Some or all of the above processing in the assessment unit may be performed using AI or not. For example, the assessment unit can input geographical data into a generating AI and have the generating AI perform the display of assessment results by region.

[0063] The assessment unit can collect data in real time when assessing farmland conditions and provide assessment results immediately. For example, the assessment unit can collect crop growth status data in real time and provide assessment results immediately. For example, the assessment unit can collect pest and disease occurrence data in real time and provide assessment results immediately. For example, the assessment unit can collect soil condition data in real time and provide assessment results immediately. As a result, the assessment unit can collect data in real time and provide assessment results immediately. Some or all of the above processing in the assessment unit may be performed using AI or not. For example, the assessment unit can input data collected in real time into a generating AI and have the generating AI execute a process to provide assessment results immediately.

[0064] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0065] Sustainable agricultural systems can also include a forecasting unit. This unit predicts future weather conditions and market demand based on data obtained from analysis and data processing units. For example, the forecasting unit can combine historical and current weather data to predict future temperature and precipitation fluctuations. It can also combine historical and current market data to predict future food demand and price fluctuations. Furthermore, it can predict harvest time and yield based on crop growth data. This allows the forecasting unit to predict future weather conditions and market demand, enabling agricultural producers to take appropriate measures.

[0066] Sustainable agricultural systems can also include a notification unit. This unit provides timely notifications to agricultural producers based on data from analysis and analytical units, as well as suggestions from a proposal unit. For example, the notification unit can warn agricultural producers when rapid changes in temperature or rainfall are predicted. It can also provide appropriate management methods based on crop growth and pest / disease outbreaks. Furthermore, it can provide optimal harvest times and sales strategies based on market demand and price fluctuations. In this way, the notification unit helps agricultural producers respond quickly.

[0067] Sustainable agricultural systems can also be equipped with a learning unit. The learning unit learns about the behavior and reactions of agricultural producers based on data obtained from the analysis and interpretation units, thereby improving the system's accuracy. For example, the learning unit can record how agricultural producers responded to suggestions from the suggestion unit and incorporate this into future suggestions. The learning unit can also learn how agricultural producers responded to various weather and market conditions and incorporate this into future predictions. Furthermore, it can learn about the management methods adopted by agricultural producers and improve the accuracy of its suggestions for optimal management methods. In this way, the learning unit learns about the behavior and reactions of agricultural producers, thereby improving the system's accuracy.

[0068] Sustainable agricultural systems can also include a communication department. This department facilitates information sharing and cooperation among agricultural producers. For example, it can provide a platform where producers can share their experiences and knowledge. It can also assist producers in scheduling collaborative work with other producers. Furthermore, it can offer a chat function for producers to receive advice and support from other producers. In this way, the communication department promotes information sharing and cooperation among agricultural producers, supporting the realization of sustainable agriculture.

[0069] Sustainable agricultural systems can also include an evaluation unit. The evaluation unit assesses the effectiveness of agricultural producers' activities and the system based on data obtained from the analysis and interpretation units. For example, the evaluation unit can evaluate the results of agricultural producers acting in accordance with proposals from the proposal unit and confirm the effectiveness of the proposals. The evaluation unit can, for example, evaluate the effectiveness of management methods adopted by agricultural producers and provide data to propose optimal management methods. The evaluation unit can, for example, evaluate the effectiveness of the entire system and identify areas for improvement toward achieving sustainable agriculture. In this way, the evaluation unit assesses the effectiveness of agricultural producers' activities and the system, supporting the realization of sustainable agriculture.

[0070] The following briefly describes the processing flow for example form 1.

[0071] Step 1: The analysis unit analyzes environmental data. The analysis unit collects environmental data such as temperature, precipitation, humidity, and wind speed, and analyzes it using AI. For example, it can propose an optimal irrigation schedule based on temperature data, an optimal fertilization plan based on precipitation data, or an optimal cultivation method based on humidity data. Step 2: The proposal unit proposes the optimal management method based on the data analyzed by the analysis unit. For example, it can propose the optimal irrigation schedule, fertilization plan, and cultivation method for a specific crop. Step 3: The analysis unit analyzes data on soil, climate, and crop type and variety. For example, it can analyze soil pH values, nutrient content, climatic conditions, and crop growth characteristics. Step 4: The cultivation proposal department proposes the optimal cultivar and resource allocation based on the data analyzed by the analysis department. For example, it can propose the optimal cultivar, resource allocation, and cultivation method considering market food demand and price fluctuations. Step 5: The image analysis unit analyzes image data from satellites and drones. For example, it can analyze satellite images to understand the growth status of crops and the condition of farmland, or analyze aerial images taken by drones to understand the occurrence of pests and diseases. Step 6: The assessment unit grasps the farmland conditions based on the data analyzed by the image analysis unit. For example, it can grasp the crop growth status, the occurrence of pests and diseases, and the condition of the farmland.

[0072] (Example of form 2) The sustainable agricultural system according to an embodiment of the present invention is a system that uses AI to analyze environmental data and improve agricultural productivity. This sustainable agricultural system analyzes environmental and weather data and proposes sustainable management methods. Furthermore, it analyzes data on soil, climate, crop types and varieties, and proposes optimal cultivars and resource allocation considering market food demand and price fluctuations. It also works in conjunction with satellite and drone image data to grasp farmland conditions in real time. This allows producers to quickly identify and respond to problems. For example, the sustainable agricultural system collects detailed data such as temperature, precipitation, humidity, and wind speed, and the AI ​​analyzes it to propose optimal management methods. For example, it can propose optimal irrigation schedules and fertilization plans for specific crops. This enables efficient use of resources. Next, the sustainable agricultural system collects data such as soil pH value, nutrient content, climate conditions, and crop growth characteristics, and the AI ​​analyzes it to propose optimal cultivars and resource allocation. This enables efficient agriculture that takes into account market food demand and price fluctuations. Furthermore, the sustainable agricultural system can analyze satellite images and drone aerial images to grasp crop growth status and pest and disease occurrences. This allows producers to quickly identify and address problems. This enables sustainable agriculture, allowing for efficient resource use and protection of ecosystem balance. Thus, sustainable agricultural systems can achieve efficient resource use and protection of ecosystem balance.

[0073] The sustainable agricultural system according to this embodiment comprises an analysis unit, a proposal unit, an analysis unit, a cultivation proposal unit, an image analysis unit, and a grasping unit. The analysis unit analyzes environmental data. The analysis unit collects environmental data such as temperature, precipitation, humidity, and wind speed, and analyzes it using AI. The analysis unit can, for example, propose an optimal irrigation schedule based on temperature data. The analysis unit can, for example, propose an optimal fertilization plan based on precipitation data. The analysis unit can, for example, propose an optimal cultivation method based on humidity data. The proposal unit proposes an optimal management method based on the data analyzed by the analysis unit. The proposal unit can, for example, propose an optimal irrigation schedule for a specific crop. The proposal unit can, for example, propose an optimal fertilization plan for a specific crop. The proposal unit can, for example, propose an optimal cultivation method for a specific crop. The analysis unit analyzes data on soil, climate, and crop type and variety. The analysis unit analyzes, for example, soil pH values ​​and nutrient content. The analysis unit can, for example, analyze climatic conditions. The analysis unit can, for example, analyze the growth characteristics of crops. The cultivation proposal unit proposes the optimal cultivar and resource allocation based on the data analyzed by the analysis unit. The cultivation proposal unit proposes the optimal cultivar considering market food demand and price fluctuations. The cultivation proposal unit can propose the optimal resource allocation considering market food demand and price fluctuations. The cultivation proposal unit can propose the optimal cultivation method considering market food demand and price fluctuations. The image analysis unit analyzes satellite and drone image data. The image analysis unit can, for example, analyze satellite images to understand the growth status of crops. The image analysis unit can, for example, analyze drone aerial images to understand the occurrence of pests and diseases. The image analysis unit can, for example, analyze satellite images to understand the condition of farmland. The assessment unit understands the condition of farmland based on the data analyzed by the image analysis unit. The assessment unit can, for example, understand the growth status of crops. The assessment unit can, for example, understand the occurrence of pests and diseases. The assessment unit can, for example, understand the condition of farmland.As a result, the sustainable agricultural system according to the embodiment enables the analysis of environmental data, the proposal of optimal management methods, the analysis of soil and climate data, the proposal of optimal crop varieties and resource allocation, the analysis of image data, and the understanding of farmland conditions.

[0074] The analysis unit analyzes environmental data. For example, it collects environmental data such as temperature, precipitation, humidity, and wind speed, and analyzes it using AI. Specifically, it can propose an optimal irrigation schedule based on temperature data. Temperature data is acquired in real time from sensors and weather databases, and the AI ​​calculates the optimal irrigation timing for crop growth by comparing it with past data. For example, on days with high temperatures, evaporation increases, requiring more frequent irrigation; the AI ​​automatically determines this and issues instructions to the irrigation system. It can also propose an optimal fertilization plan based on precipitation data. Precipitation data is acquired from rain gauges and weather databases, and the AI ​​analyzes precipitation patterns. For example, during periods of heavy rainfall, the timing of fertilization is adjusted to prevent fertilizer runoff. Finally, it can propose an optimal cultivation method based on humidity data. Humidity data is acquired from humidity sensors, and the AI ​​proposes the optimal cultivation environment according to the type of crop and its growth stage. For example, high humidity increases the risk of pest and disease outbreaks, so the AI ​​proposes appropriate control methods. This allows the analysis unit to analyze environmental data in detail and provide optimal management methods for crop growth.

[0075] The proposal department proposes optimal management methods based on data analyzed by the analysis department. For example, the proposal department can propose an optimal irrigation schedule for a specific crop. Specifically, based on temperature and precipitation data provided by the analysis department, the AI ​​calculates the crop's water demand and proposes the optimal irrigation timing. For example, by increasing the frequency of irrigation during dry periods and decreasing it during wet periods, the department can efficiently utilize water resources. The proposal department can also propose an optimal fertilization plan for a specific crop. The AI ​​considers soil nutrient data and the crop's growth stage to propose the optimal timing and amount of fertilization. For example, by applying more nitrogen fertilizer in the early stages of crop growth and increasing phosphorus fertilizer in the later stages, the department can promote healthy crop growth. The proposal department can also propose an optimal cultivation method for a specific crop. Based on humidity and temperature data, the AI ​​proposes the optimal cultivation environment. For example, since the risk of pest and disease outbreaks increases during periods of high humidity, the AI ​​proposes appropriate control methods. In this way, the proposal department can provide optimal management methods for crop growth based on data provided by the analysis department.

[0076] The analysis department analyzes data on soil, climate, and crop types and varieties. For example, it analyzes soil pH values ​​and nutrient content. Specifically, it measures soil pH values ​​and nutrient content such as nitrogen, phosphorus, and potassium by collecting soil samples and analyzing them in the laboratory. This allows for the creation of fertilization plans to appropriately supply the nutrients necessary for crops. It can also analyze climate conditions. By collecting meteorological data and having AI analyze past weather patterns, it predicts future weather conditions. For example, it can predict fluctuations in temperature and precipitation and create cultivation plans accordingly. It can also analyze crop growth characteristics. It analyzes growth patterns and pest and disease risk for each crop type and variety and proposes optimal cultivation methods. In this way, the analysis department can analyze soil, climate, and crop data in detail and support the healthy growth of crops.

[0077] The cultivation proposal department proposes optimal crop varieties and resource allocations based on data analyzed by the analysis department. For example, the cultivation proposal department proposes optimal crop varieties considering market food demand and price fluctuations. Specifically, AI analyzes market data and selects crops with high demand and stable prices. This allows farmers to create cultivation plans to maximize profits. It can propose optimal resource allocations considering market food demand and price fluctuations. AI calculates optimal fertilizer and water allocations based on soil and climate data. For example, it proposes a fertilization plan that maximizes crop growth while minimizing fertilizer use. It can propose optimal cultivation methods considering market food demand and price fluctuations. AI proposes optimal cultivation methods based on climate data and crop growth characteristics. For example, it proposes using shade nets to protect crops during periods of high temperatures. This allows the cultivation proposal department to propose optimal crop varieties and resource allocations to farmers based on data provided by the analysis department.

[0078] The image analysis unit analyzes image data from satellites and drones. For example, it analyzes satellite images to understand the growth status of crops. Specifically, AI analyzes satellite images to evaluate the health and growth rate of crops. For example, it uses NDVI (Normalized Density Vegetation Index) to measure the greenness of crops and evaluate their health. It can also analyze aerial images taken by drones to understand the occurrence of pests and diseases. Drones capture high-resolution images, and AI analyzes these images to identify the locations of pest and disease outbreaks. For example, it can detect abnormalities such as leaf discoloration and holes, allowing for early countermeasures. It can also analyze satellite images to understand the condition of farmland. AI analyzes the soil condition and moisture content of farmland and proposes optimal management methods. As a result, the image analysis unit can analyze satellite and drone image data in detail and understand the condition of farmland in real time.

[0079] The assessment unit grasps the conditions of farmland based on data analyzed by the image analysis unit. For example, the assessment unit grasps the growth status of crops. Specifically, it evaluates the health and growth rate of crops based on data provided by the image analysis unit. For example, it measures the greenness of crops using NDVI (Normalized Density Index) to evaluate their health. It can grasp the occurrence of pests and diseases. Based on data provided by the image analysis unit, it identifies the locations of pest and disease outbreaks and takes early countermeasures. For example, it detects abnormalities such as leaf discoloration and holes and proposes appropriate control methods. It can grasp the conditions of farmland. Based on data provided by the image analysis unit, it evaluates the soil condition and moisture content of the farmland and proposes optimal management methods. In this way, the assessment unit can grasp the conditions of farmland in detail based on data provided by the image analysis unit and provide appropriate management methods.

[0080] The analysis unit can analyze environmental data such as temperature, precipitation, humidity, and wind speed. For example, the analysis unit can collect temperature data and analyze it using AI. For example, the analysis unit can collect precipitation data and analyze it using AI. For example, the analysis unit can collect humidity data and analyze it using AI. By doing so, the analysis unit can propose the optimal management method by analyzing the environmental data. Some or all of the above-described processes in the analysis unit may be performed using AI or not. For example, the analysis unit can input temperature data into a generating AI and have the generating AI perform the analysis of the temperature data.

[0081] The proposal unit can propose an optimal irrigation schedule and fertilization plan for a specific crop based on the data analyzed by the analysis unit. For example, the proposal unit can propose an optimal irrigation schedule for a specific crop. For example, the proposal unit can propose an optimal fertilization plan for a specific crop. For example, the proposal unit can propose an optimal cultivation method for a specific crop. In this way, the proposal unit can propose an optimal irrigation schedule and fertilization plan for a specific crop. Some or all of the above processing in the proposal unit may be performed using AI or not. For example, the proposal unit can input the data analyzed by the analysis unit into a generating AI and have the generating AI execute the proposal of an optimal irrigation schedule and fertilization plan.

[0082] The analysis unit can analyze data such as soil pH values, nutrient content, climatic conditions, and crop growth characteristics. For example, the analysis unit can analyze soil pH values. For example, the analysis unit can analyze soil nutrient content. For example, the analysis unit can analyze climatic conditions. This allows the analysis unit to analyze data such as soil pH values, nutrient content, climatic conditions, and crop growth characteristics. Some or all of the above-described processes in the analysis unit may be performed using AI or not. For example, the analysis unit can input soil pH value data into a generating AI and have the generating AI perform an analysis of the soil pH value.

[0083] The cultivation proposal unit can propose optimal cultivars and resource allocations based on data analyzed by the analysis unit, taking into account market food demand and price fluctuations. For example, the cultivation proposal unit can propose optimal cultivars considering market food demand. For example, the cultivation proposal unit can propose optimal resource allocations considering price fluctuations. For example, the cultivation proposal unit can propose optimal cultivation methods considering market food demand and price fluctuations. In this way, the cultivation proposal unit can propose optimal cultivars and resource allocations considering market food demand and price fluctuations. Some or all of the above processing in the cultivation proposal unit may be performed using AI or not. For example, the cultivation proposal unit can input data analyzed by the analysis unit into a generation AI and have the generation AI execute a proposal for optimal cultivars and resource allocations.

[0084] The image analysis unit can analyze satellite images and drone aerial images to understand the growth status of crops and the occurrence of pests and diseases. For example, the image analysis unit can analyze satellite images to understand the growth status of crops. For example, the image analysis unit can analyze drone aerial images to understand the occurrence of pests and diseases. For example, the image analysis unit can analyze satellite images to understand the condition of farmland. In this way, the image analysis unit can analyze satellite images and drone aerial images to understand the growth status of crops and the occurrence of pests and diseases. Some or all of the above processing in the image analysis unit may be performed using AI or not. For example, the image analysis unit can input satellite image data into a generating AI and have the generating AI perform an analysis of the crop growth status.

[0085] The understanding unit can grasp the conditions of farmland based on the data analyzed by the image analysis unit and provide this information to producers. For example, the understanding unit can grasp the growth status of crops. For example, the understanding unit can grasp the occurrence status of pests and diseases. For example, the understanding unit can grasp the conditions of farmland. In this way, the understanding unit can grasp the conditions of farmland and provide this information to producers. Some or all of the above processing in the understanding unit may be performed using AI or not. For example, the understanding unit can input the data analyzed by the image analysis unit into a generating AI and have the generating AI perform the task of grasping the conditions of farmland.

[0086] The analysis unit can estimate the user's emotions and adjust the method of analyzing environmental data based on the estimated user emotions. For example, if the user is stressed, the analysis unit can summarize the analysis results concisely and provide them in a visually easy-to-understand format. For example, if the user is relaxed, the analysis unit can provide detailed analysis results and explain the background and rationale of the data in detail. For example, if the user is in a hurry, the analysis unit can prioritize displaying the most important analysis results to enable quick decision-making. In this way, the analysis unit can adjust the method of analyzing environmental data based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the analysis unit may be performed using AI or not. For example, the analysis unit can input user emotion data into a generative AI and have the generative AI adjust the method of analyzing environmental data.

[0087] The analysis unit can be equipped with a function to detect anomalies by comparing environmental data with past data during analysis. For example, the analysis unit can detect abnormally high or low temperatures by comparing them with past temperature data. For example, the analysis unit can detect abnormally heavy rainfall or drought by comparing them with past precipitation data. For example, the analysis unit can detect abnormally dry or wet conditions by comparing them with past humidity data. In this way, the analysis unit can detect anomalies by comparing them with past data. Some or all of the above-described processes in the analysis unit may be performed using AI or not. For example, the analysis unit can input past environmental data into a generating AI and have the generating AI perform the detection of anomalies.

[0088] The analysis unit can improve the accuracy of its analysis by considering climate patterns in specific regions when analyzing environmental data. For example, the analysis unit can perform analysis by considering seasonal temperature fluctuation patterns in specific regions. For example, the analysis unit can optimize irrigation plans by considering precipitation patterns in specific regions. For example, the analysis unit can propose wind damage countermeasures by considering wind speed patterns in specific regions. In this way, the analysis unit can improve the accuracy of its analysis by considering climate patterns in specific regions. Some or all of the above processing in the analysis unit may be performed using AI or not. For example, the analysis unit can input climate data for specific regions into a generating AI and have the generating AI perform the improvement of analysis accuracy.

[0089] The suggestion unit can estimate the user's emotions and adjust its suggestions based on those emotions. For example, if the user is stressed, the suggestion unit can make concise and easy-to-implement suggestions. If the user is relaxed, the suggestion unit can make suggestions that include detailed explanations. If the user is in a hurry, the suggestion unit can make suggestions that can be implemented quickly. In this way, the suggestion unit can adjust its suggestions based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the suggestion unit may be performed using AI or not. For example, the suggestion unit can input user emotion data into a generative AI and have the generative AI adjust the suggestions.

[0090] The proposal unit can improve the accuracy of its proposals by referring to past proposal history. For example, the proposal unit can analyze past proposal history and prioritize the most effective proposals. For example, the proposal unit can refer to past proposal history to make proposals for similar situations. For example, the proposal unit can customize proposal content based on past proposal history. This allows the proposal unit to improve the accuracy of its proposals by referring to past proposal history. Some or all of the above processes in the proposal unit may be performed using AI or not. For example, the proposal unit can input past proposal history data into a generation AI and have the generation AI perform the task of improving the accuracy of proposals.

[0091] The proposal unit can customize its suggestions according to the growth stage of a specific crop. For example, during the early stages of crop growth, the proposal unit can suggest appropriate irrigation and fertilization. During the mid-stage of crop growth, the proposal unit can suggest pest and disease control measures. During the harvest season, the proposal unit can suggest harvesting timing and methods. In this way, the proposal unit can customize its suggestions according to the growth stage of a specific crop. Some or all of the above processing in the proposal unit may be performed using AI or not. For example, the proposal unit can input growth stage data for a specific crop into a generating AI and have the generating AI perform the customization of the suggestions.

[0092] The suggestion unit can estimate the user's emotions and determine the priority of suggestions based on the estimated emotions. For example, if the user is stressed, the suggestion unit will prioritize the most important suggestions. For example, if the user is relaxed, the suggestion unit can provide detailed suggestions. For example, if the user is in a hurry, the suggestion unit can prioritize suggestions that can be acted upon quickly. In this way, the suggestion unit can determine the priority of suggestions based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the suggestion unit may be performed using AI or not. For example, the suggestion unit can input user emotion data into a generative AI and have the generative AI determine the priority of suggestions.

[0093] The proposal unit can adjust its proposals to take into account local agricultural policies and regulations. For example, the proposal unit can make proposals that qualify for subsidies based on local agricultural policies. For example, the proposal unit can make proposals for usable pesticides and fertilizers based on local regulations. For example, the proposal unit can make proposals for sustainable agricultural practices based on local agricultural policies. This allows the proposal unit to adjust its proposals to take into account local agricultural policies and regulations. Some or all of the above processing in the proposal unit may be performed using AI or not. For example, the proposal unit can input local agricultural policy data into a generating AI and have the generating AI perform the adjustment of the proposal content.

[0094] The proposal unit can enhance its proposals by referring to successful case studies of other agricultural producers. For example, the proposal unit can propose effective irrigation methods based on successful case studies of other agricultural producers. For example, the proposal unit can propose pest and disease control measures based on successful case studies of other agricultural producers. For example, the proposal unit can propose harvest timing and methods based on successful case studies of other agricultural producers. In this way, the proposal unit can enhance its proposals by referring to successful case studies of other agricultural producers. Some or all of the above processing in the proposal unit may be performed using AI or not. For example, the proposal unit can input data on successful case studies of other agricultural producers into a generating AI and have the generating AI perform the enhancement of the proposal.

[0095] The analysis unit can estimate the user's emotions and adjust the analysis method based on the estimated emotions. For example, if the user is stressed, the analysis unit can provide a concise and easy-to-understand analysis result. For example, if the user is relaxed, the analysis unit can provide a detailed analysis result. For example, if the user is in a hurry, the analysis unit can prioritize providing the most important analysis results. In this way, the analysis unit can adjust the analysis method based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the analysis unit may be performed using AI or not. For example, the analysis unit can input user emotion data into a generative AI and have the generative AI adjust the analysis method.

[0096] The analysis unit can improve the accuracy of its analysis by taking into account the microbial activity of the soil during the analysis. For example, the analysis unit can monitor the microbial activity of the soil and reflect it in the analysis results. For example, the analysis unit can propose an optimal fertilization plan by taking into account the microbial activity of the soil. For example, the analysis unit can propose an optimal irrigation schedule by taking into account the microbial activity of the soil. In this way, the analysis unit can improve the accuracy of its analysis by taking into account the microbial activity of the soil. Some or all of the above processes in the analysis unit may be performed using AI or not. For example, the analysis unit can input soil microbial activity data into a generating AI and have the generating AI perform the improvement of analysis accuracy.

[0097] The analysis unit can apply different analytical methods to each stage of crop growth during analysis. For example, during the early stages of crop growth, the analysis unit can focus on analyzing soil nutrients. During the middle stages of crop growth, the analysis unit can focus on analyzing the occurrence of pests and diseases. During the harvest season, the analysis unit can perform analyses to determine the optimal harvest time. This allows the analysis unit to apply the most suitable analytical method for each stage of crop growth. Some or all of the above-described processes in the analysis unit may be performed using AI or not. For example, the analysis unit can input crop growth stage data into a generating AI and have the generating AI execute the application of analytical methods.

[0098] The analysis unit can estimate the user's emotions and adjust how the analysis results are displayed based on the estimated emotions. For example, if the user is stressed, the analysis unit can provide the analysis results in a concise and visually easy-to-understand format. For example, if the user is relaxed, the analysis unit can provide detailed analysis results and explain the background and rationale of the data in detail. For example, if the user is in a hurry, the analysis unit can prioritize displaying the most important analysis results to enable quick decision-making. This allows the analysis unit to adjust how the analysis results are displayed based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the analysis unit may be performed using AI or not. For example, the analysis unit can input user emotion data into a generative AI and have the generative AI adjust how the analysis results are displayed.

[0099] The analysis unit can provide analysis results by combining regional climate change data during the analysis process. For example, the analysis unit can propose an optimal irrigation schedule by combining regional temperature fluctuation data. For example, the analysis unit can propose an optimal fertilization plan by combining regional precipitation fluctuation data. For example, the analysis unit can propose wind damage countermeasures by combining regional wind speed fluctuation data. In this way, the analysis unit can provide analysis results by combining regional climate change data. Some or all of the above processing in the analysis unit may be performed using AI or not. For example, the analysis unit can input regional climate change data into a generating AI and have the generating AI perform the task of providing analysis results.

[0100] The analysis unit can improve the accuracy of its analysis by collaborating with other agricultural databases during the analysis process. For example, the analysis unit can acquire soil nutrient data by collaborating with other agricultural databases and incorporate it into the analysis. For example, the analysis unit can acquire climate data by collaborating with other agricultural databases and incorporate it into the analysis. For example, the analysis unit can acquire crop growth data by collaborating with other agricultural databases and incorporate it into the analysis. In this way, the analysis unit can improve the accuracy of its analysis by collaborating with other agricultural databases. Some or all of the above processes in the analysis unit may be performed using AI or not. For example, the analysis unit can input data from other agricultural databases into a generating AI and have the generating AI perform the task of improving the accuracy of the analysis.

[0101] The cultivation suggestion unit can estimate the user's emotions and adjust the cultivation suggestions based on those emotions. For example, if the user is stressed, the cultivation suggestion unit can provide concise and easy-to-follow cultivation suggestions. If the user is relaxed, the cultivation suggestion unit can provide cultivation suggestions with detailed explanations. If the user is in a hurry, the cultivation suggestion unit can provide cultivation suggestions that can be implemented quickly. In this way, the cultivation suggestion unit can adjust the cultivation suggestions based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the cultivation suggestion unit may be performed using AI or not. For example, the cultivation suggestion unit can input user emotion data into a generative AI and have the generative AI adjust the cultivation suggestions.

[0102] The cultivation suggestion unit can improve the accuracy of its suggestions by referring to past cultivation history when making suggestions. For example, the cultivation suggestion unit can analyze past cultivation history and prioritize suggesting the most effective cultivation methods. For example, the cultivation suggestion unit can refer to past cultivation history to make cultivation suggestions for similar situations. For example, the cultivation suggestion unit can customize the content of its cultivation suggestions based on past cultivation history. This allows the cultivation suggestion unit to improve the accuracy of its suggestions by referring to past cultivation history. Some or all of the above processes in the cultivation suggestion unit may be performed using AI or not. For example, the cultivation suggestion unit can input past cultivation history data into a generating AI and have the generating AI perform the task of improving the accuracy of its suggestions.

[0103] The cultivation proposal unit can enhance its proposals by considering demand forecast data for specific markets. For example, the cultivation proposal unit can propose the optimal cultivar based on market demand forecast data. For example, the cultivation proposal unit can propose adjusting the harvest time based on market demand forecast data. For example, the cultivation proposal unit can propose optimizing the cultivation area based on market demand forecast data. In this way, the cultivation proposal unit can enhance its proposals by considering demand forecast data for specific markets. Some or all of the above processing in the cultivation proposal unit may be performed using AI or not. For example, the cultivation proposal unit can input demand forecast data for specific markets into a generating AI and have the generating AI perform the enhancement of the proposal.

[0104] The cultivation suggestion unit can estimate the user's emotions and determine the priority of cultivation suggestions based on the estimated emotions. For example, if the user is stressed, the cultivation suggestion unit will prioritize the most important cultivation suggestions. For example, if the user is relaxed, the cultivation suggestion unit can provide detailed cultivation suggestions. For example, if the user is in a hurry, the cultivation suggestion unit can prioritize cultivation suggestions that can be executed quickly. In this way, the cultivation suggestion unit can determine the priority of cultivation suggestions based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the cultivation suggestion unit may be performed using AI or not. For example, the cultivation suggestion unit can input user emotion data into a generative AI and have the generative AI determine the priority of cultivation suggestions.

[0105] The cultivation proposal department can adjust its proposals when submitting them, taking into account local agricultural policies and regulations. For example, the cultivation proposal department can submit cultivation proposals that qualify for subsidies based on local agricultural policies. For example, the cultivation proposal department can submit proposals for usable pesticides and fertilizers based on local regulations. For example, the cultivation proposal department can submit proposals for sustainable agricultural practices based on local agricultural policies. This allows the cultivation proposal department to adjust its proposals to take into account local agricultural policies and regulations. Some or all of the above processing in the cultivation proposal department may be performed using AI or not. For example, the cultivation proposal department can input local agricultural policy data into a generating AI and have the generating AI perform the adjustment of the proposal content.

[0106] The cultivation proposal unit can enhance its proposals by referring to successful case studies from other agricultural producers. For example, the cultivation proposal unit can propose effective cultivation methods based on successful case studies from other agricultural producers. For example, the cultivation proposal unit can propose pest and disease control measures based on successful case studies from other agricultural producers. For example, the cultivation proposal unit can propose harvest timing and methods based on successful case studies from other agricultural producers. In this way, the cultivation proposal unit can enhance its proposals by referring to successful case studies from other agricultural producers. Some or all of the above processes in the cultivation proposal unit may be performed using AI or not. For example, the cultivation proposal unit can input data on successful case studies from other agricultural producers into a generating AI and have the generating AI perform the enhancement of the proposals.

[0107] The image analysis unit can estimate the user's emotions and adjust the image analysis method based on the estimated emotions. For example, if the user is stressed, the image analysis unit can provide concise and easy-to-understand image analysis results. For example, if the user is relaxed, the image analysis unit can provide detailed image analysis results. For example, if the user is in a hurry, the image analysis unit can prioritize providing the most important image analysis results. In this way, the image analysis unit can adjust the image analysis method based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the image analysis unit may be performed using AI or not. For example, the image analysis unit can input user emotion data into the generative AI and have the generative AI adjust the image analysis method.

[0108] The image analysis unit can be equipped with a function to detect anomalies by comparing current images with past image data during image analysis. For example, the image analysis unit can detect abnormal growth conditions by comparing current crop images with past crop images. For example, the image analysis unit can detect abnormal pest and disease outbreaks by comparing current pest and disease images with past pest and disease images. For example, the image analysis unit can detect abnormal soil conditions by comparing current soil images with past soil images. In this way, the image analysis unit can detect anomalies by comparing current image data with past image data. Some or all of the above-described processes in the image analysis unit may be performed using AI or not. For example, the image analysis unit can input past image data into a generating AI and have the generating AI perform anomaly detection.

[0109] The image analysis unit can customize its analysis method according to the growth stage of a specific crop during image analysis. For example, during the early stages of crop growth, the image analysis unit can focus on analyzing germination status. During the middle stages of crop growth, the image analysis unit can focus on analyzing the occurrence of pests and diseases. During the harvest season, the image analysis unit can perform analysis to determine the optimal harvest time. In this way, the image analysis unit can customize its analysis method according to the growth stage of a specific crop. Some or all of the above-described processes in the image analysis unit may be performed using AI or not. For example, the image analysis unit can input growth stage data of a specific crop into a generating AI and have the generating AI perform the customization of the analysis method.

[0110] The image analysis unit can estimate the user's emotions and adjust how the image analysis results are displayed based on the estimated emotions. For example, if the user is stressed, the image analysis unit can provide the image analysis results in a concise and visually easy-to-understand format. For example, if the user is relaxed, the image analysis unit can provide detailed image analysis results and explain the background and rationale of the data in detail. For example, if the user is in a hurry, the image analysis unit can prioritize displaying the most important image analysis results to enable quick decision-making. In this way, the image analysis unit can adjust how the image analysis results are displayed based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the image analysis unit may be performed using AI or not. For example, the image analysis unit can input user emotion data into the generative AI and have the generative AI adjust how the image analysis results are displayed.

[0111] The image analysis unit can combine geographical data during image analysis and display the analysis results separately for each region. For example, the image analysis unit can analyze the crop growth status in each region and display the growth status for each region. For example, the image analysis unit can analyze the pest and disease occurrence status in each region and display the occurrence status for each region. For example, the image analysis unit can analyze the soil conditions in each region and display the soil conditions for each region. In this way, the image analysis unit can combine geographical data and display the analysis results separately for each region. Some or all of the above processing in the image analysis unit may be performed using AI or not. For example, the image analysis unit can input geographical data into a generating AI and have the generating AI perform the display of analysis results by region.

[0112] The image analysis unit can collect data in real time during image analysis and provide analysis results immediately. For example, the image analysis unit can collect crop images in real time and provide analysis results immediately. For example, the image analysis unit can collect pest and disease images in real time and provide analysis results immediately. For example, the image analysis unit can collect soil images in real time and provide analysis results immediately. As a result, the image analysis unit can collect data in real time and provide analysis results immediately. Some or all of the above-described processes in the image analysis unit may be performed using AI or not. For example, the image analysis unit can input data collected in real time into a generating AI and have the generating AI execute a process to provide analysis results immediately.

[0113] The understanding unit can estimate the user's emotions and adjust the method of understanding the farmland conditions based on the estimated user emotions. For example, if the user is stressed, the understanding unit can provide a concise and easy-to-understand farmland condition assessment result. For example, if the user is relaxed, the understanding unit can provide a detailed farmland condition assessment result. For example, if the user is in a hurry, the understanding unit can prioritize providing the most important farmland condition assessment results. In this way, the understanding unit can adjust the method of understanding the farmland conditions based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the understanding unit may be performed using AI or not using AI. For example, the understanding unit can input user emotion data into the generative AI and have the generative AI perform the adjustment of the farmland condition assessment method.

[0114] The data acquisition unit can be equipped with a function to detect anomalies by comparing current farmland conditions with past data. For example, the data acquisition unit can detect abnormal crop growth by comparing current farmland conditions with past data. For example, the data acquisition unit can detect abnormal pest and disease outbreaks by comparing current pest and disease outbreaks with past data. For example, the data acquisition unit can detect abnormal soil conditions by comparing current soil conditions with past data. In this way, the data acquisition unit can detect anomalies by comparing current data with past data. Some or all of the above processing in the data acquisition unit may be performed using AI or not. For example, the data acquisition unit can input past farmland conditions data into a generating AI and have the generating AI perform anomaly detection.

[0115] The assessment unit can improve the accuracy of its assessment of farmland conditions by considering the climate patterns of a specific region. For example, the assessment unit can assess farmland conditions by considering the seasonal temperature fluctuation patterns of a specific region. For example, the assessment unit can assess farmland conditions by considering the precipitation patterns of a specific region. For example, the assessment unit can assess farmland conditions by considering the wind speed patterns of a specific region. In this way, the assessment unit can improve the accuracy of its assessment by considering the climate patterns of a specific region. Some or all of the above processing in the assessment unit may be performed using AI or not. For example, the assessment unit can input climate data of a specific region into a generating AI and have the generating AI perform the improvement of assessment accuracy.

[0116] The understanding unit can estimate the user's emotions and adjust how the understanding results are displayed based on the estimated emotions. For example, if the user is stressed, the understanding unit provides the understanding results in a concise and visually easy-to-understand format. For example, if the user is relaxed, the understanding unit provides detailed understanding results and can explain the background and rationale of the data in detail. For example, if the user is in a hurry, the understanding unit prioritizes displaying the most important understanding results to enable quick decision-making. In this way, the understanding unit can adjust how the understanding results are displayed based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the understanding unit may be performed using AI or not. For example, the understanding unit can input user emotion data into a generative AI and have the generative AI adjust how the understanding results are displayed.

[0117] The assessment unit can combine geographical data to assess the condition of farmland and display the assessment results separately for each region. For example, the assessment unit can assess the crop growth status in each region and display the growth status for each region. For example, the assessment unit can assess the occurrence of pests and diseases in each region and display the occurrence status for each region. For example, the assessment unit can assess the soil conditions in each region and display the soil conditions for each region. In this way, the assessment unit can combine geographical data to display the assessment results separately for each region. Some or all of the above processing in the assessment unit may be performed using AI or not. For example, the assessment unit can input geographical data into a generating AI and have the generating AI perform the display of assessment results by region.

[0118] The assessment unit can collect data in real time when assessing farmland conditions and provide assessment results immediately. For example, the assessment unit can collect crop growth status data in real time and provide assessment results immediately. For example, the assessment unit can collect pest and disease occurrence data in real time and provide assessment results immediately. For example, the assessment unit can collect soil condition data in real time and provide assessment results immediately. As a result, the assessment unit can collect data in real time and provide assessment results immediately. Some or all of the above processing in the assessment unit may be performed using AI or not. For example, the assessment unit can input data collected in real time into a generating AI and have the generating AI execute a process to provide assessment results immediately.

[0119] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0120] Sustainable agricultural systems can also include a forecasting unit. This unit predicts future weather conditions and market demand based on data obtained from analysis and data processing units. For example, the forecasting unit can combine historical and current weather data to predict future temperature and precipitation fluctuations. It can also combine historical and current market data to predict future food demand and price fluctuations. Furthermore, it can predict harvest time and yield based on crop growth data. This allows the forecasting unit to predict future weather conditions and market demand, enabling agricultural producers to take appropriate measures.

[0121] Sustainable agricultural systems can also include a notification unit. This unit provides timely notifications to agricultural producers based on data from analysis and analytical units, as well as suggestions from a proposal unit. For example, the notification unit can warn agricultural producers when rapid changes in temperature or rainfall are predicted. It can also provide appropriate management methods based on crop growth and pest / disease outbreaks. Furthermore, it can provide optimal harvest times and sales strategies based on market demand and price fluctuations. In this way, the notification unit helps agricultural producers respond quickly.

[0122] Sustainable agricultural systems can also be equipped with a learning unit. The learning unit learns about the behavior and reactions of agricultural producers based on data obtained from the analysis and interpretation units, thereby improving the system's accuracy. For example, the learning unit can record how agricultural producers responded to suggestions from the suggestion unit and incorporate this into future suggestions. The learning unit can also learn how agricultural producers responded to various weather and market conditions and incorporate this into future predictions. Furthermore, it can learn about the management methods adopted by agricultural producers and improve the accuracy of its suggestions for optimal management methods. In this way, the learning unit learns about the behavior and reactions of agricultural producers, thereby improving the system's accuracy.

[0123] Sustainable agricultural systems can also include a communication department. This department facilitates information sharing and cooperation among agricultural producers. For example, it can provide a platform where producers can share their experiences and knowledge. It can also assist producers in scheduling collaborative work with other producers. Furthermore, it can offer a chat function for producers to receive advice and support from other producers. In this way, the communication department promotes information sharing and cooperation among agricultural producers, supporting the realization of sustainable agriculture.

[0124] Sustainable agricultural systems can also include an evaluation unit. The evaluation unit assesses the effectiveness of agricultural producers' activities and the system based on data obtained from the analysis and interpretation units. For example, the evaluation unit can evaluate the results of agricultural producers acting in accordance with proposals from the proposal unit and confirm the effectiveness of the proposals. The evaluation unit can, for example, evaluate the effectiveness of management methods adopted by agricultural producers and provide data to propose optimal management methods. The evaluation unit can, for example, evaluate the effectiveness of the entire system and identify areas for improvement toward achieving sustainable agriculture. In this way, the evaluation unit assesses the effectiveness of agricultural producers' activities and the system, supporting the realization of sustainable agriculture.

[0125] Sustainable agricultural systems can also be equipped with an emotion estimation unit. This unit estimates the emotions of agricultural producers and adjusts the system's operation based on the estimated emotions. For example, if an agricultural producer is stressed, the emotion estimation unit can simplify system operation and reduce their burden. If an agricultural producer is relaxed, for example, the emotion estimation unit can provide detailed information to facilitate a deeper understanding. If an agricultural producer is in a hurry, for example, the emotion estimation unit can prioritize displaying the most important information to support quick decision-making. In this way, the emotion estimation unit adjusts the system's operation based on the agricultural producer's emotions, improving usability.

[0126] Sustainable agricultural systems can also incorporate an emotional feedback unit. This unit monitors the emotions of agricultural producers in real time and reflects this in the system's operation. For example, if an agricultural producer is stressed, the emotional feedback unit can simplify system operation and reduce their burden. If an agricultural producer is relaxed, for example, the emotional feedback unit can provide detailed information to facilitate a deeper understanding. If an agricultural producer is in a hurry, for example, the emotional feedback unit can prioritize displaying the most important information to support quick decision-making. In this way, the emotional feedback unit adjusts the system's operation based on the agricultural producer's emotions, improving usability.

[0127] Sustainable agricultural systems can also incorporate an emotion analysis unit. This unit analyzes the emotions of agricultural producers and uses the findings to improve the system. For example, it can analyze the situations in which agricultural producers experience stress and provide data to improve the system's usability. It can also analyze what information agricultural producers are seeking and improve the system's information delivery capabilities. Furthermore, it can analyze when agricultural producers use the system and propose measures to encourage its use. In this way, the emotion analysis unit analyzes the emotions of agricultural producers and uses the findings to improve the system.

[0128] Sustainable agricultural systems can also be equipped with an emotionally adaptive unit. This unit adapts the system's operation according to the farmer's emotions. For example, if the farmer is stressed, the emotionally adaptive unit can simplify system operation and reduce the burden. If the farmer is relaxed, for example, the emotionally adaptive unit can provide detailed information to facilitate a deeper understanding. If the farmer is in a hurry, for example, the emotionally adaptive unit can prioritize displaying the most important information to support quick decision-making. In this way, the emotionally adaptive unit adapts the system's operation according to the farmer's emotions, improving usability.

[0129] Sustainable agricultural systems can also be equipped with an emotion monitoring unit. This unit monitors the emotions of agricultural producers in real time and reflects this in the system's operation. For example, if an agricultural producer is stressed, the emotion monitoring unit can simplify system operation and reduce their burden. If an agricultural producer is relaxed, for example, the emotion monitoring unit can provide detailed information to facilitate a deeper understanding. If an agricultural producer is in a hurry, for example, the emotion monitoring unit can prioritize displaying the most important information to support quick decision-making. In this way, the emotion monitoring unit adjusts the system's operation based on the agricultural producer's emotions, improving usability.

[0130] The following briefly describes the processing flow for example form 2.

[0131] Step 1: The analysis unit analyzes environmental data. The analysis unit collects environmental data such as temperature, precipitation, humidity, and wind speed, and analyzes it using AI. For example, it can propose an optimal irrigation schedule based on temperature data, an optimal fertilization plan based on precipitation data, or an optimal cultivation method based on humidity data. Step 2: The proposal unit proposes the optimal management method based on the data analyzed by the analysis unit. For example, it can propose the optimal irrigation schedule, fertilization plan, and cultivation method for a specific crop. Step 3: The analysis unit analyzes data on soil, climate, and crop type and variety. For example, it can analyze soil pH values, nutrient content, climatic conditions, and crop growth characteristics. Step 4: The cultivation proposal department proposes the optimal cultivar and resource allocation based on the data analyzed by the analysis department. For example, it can propose the optimal cultivar, resource allocation, and cultivation method considering market food demand and price fluctuations. Step 5: The image analysis unit analyzes image data from satellites and drones. For example, it can analyze satellite images to understand the growth status of crops and the condition of farmland, or analyze aerial images taken by drones to understand the occurrence of pests and diseases. Step 6: The assessment unit grasps the farmland conditions based on the data analyzed by the image analysis unit. For example, it can grasp the crop growth status, the occurrence of pests and diseases, and the condition of the farmland.

[0132] 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.

[0133] Data generation model 58 is a form of 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> Examples of generative AI include text generation AI, image generation AI, and multimodal generation AI. 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 (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats from audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each of the above parts is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example.Furthermore, processing performed by AI, including generative AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by AI, including generative AI.

[0134] Furthermore, the processing performed by the data processing system 10 described above is carried out by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but it may also be carried out by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0135] Each of the multiple elements described above, including the analysis unit, proposal unit, analysis unit, cultivation proposal unit, image analysis unit, and understanding unit, is implemented in at least one of the smart device 14 and the data processing unit 12. For example, the analysis unit is implemented by the processor 46 of the smart device 14, which collects environmental data and analyzes it using AI. The proposal unit is implemented by the specific processing unit 290 of the data processing unit 12, which proposes the optimal management method based on the analyzed data. The analysis unit is implemented by the specific processing unit 290 of the data processing unit 12, which analyzes data on soil, climate, crop type, and variety. The cultivation proposal unit is implemented by the specific processing unit 290 of the data processing unit 12, which proposes the optimal crop varieties and resource allocation. The image analysis unit is implemented by the processor 46 of the smart device 14, which analyzes image data from satellites and drones. The understanding unit is implemented by the specific processing unit 290 of the data processing unit 12, which grasps the farmland conditions based on the data analyzed by the image analysis unit. The correspondence between each part and the device or control unit is not limited to the examples described above, and various modifications are possible.

[0136] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0137] 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.

[0138] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. 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 and / or LAN.

[0139] 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.

[0140] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, 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.

[0141] 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, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0142] 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.

[0143] 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 by the processor 28. The storage 32 stores the specific processing program 56.

[0144] The processor 28 reads a 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 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0145] 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. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0146] In the smart glasses 214, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0147] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0148] 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.

[0149] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. 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 inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0150] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart glasses 214 or an external device, and the smart glasses 214 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0151] Each of the multiple elements described above, including the analysis unit, proposal unit, analysis unit, cultivation proposal unit, image analysis unit, and understanding unit, is implemented, for example, in at least one of the smart glasses 214 and the data processing unit 12. For example, the analysis unit is implemented by the processor 46 of the smart glasses 214, which collects environmental data and analyzes it using AI. The proposal unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12, which proposes the optimal management method based on the analyzed data. The analysis unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12, which analyzes data on soil, climate, crop type and variety. The cultivation proposal unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12, which proposes the optimal crop varieties and resource allocation. The image analysis unit is implemented, for example, by the processor 46 of the smart glasses 214, which analyzes image data from satellites and drones. The understanding unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12, which grasps the farmland conditions based on the data analyzed by the image analysis unit. The correspondence between each part and the device or control unit is not limited to the examples described above, and various modifications are possible.

[0152] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0153] 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.

[0154] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. 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 and / or LAN.

[0155] 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.

[0156] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, 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.

[0157] 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, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0158] 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.

[0159] 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.

[0160] The processor 28 reads a 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 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0161] 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. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0162] In the headset terminal 314, specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads the specific program 60 from the storage 50 and executes the read specific program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific program 60 executed on the RAM 48. The headset terminal 314 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0163] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0164] 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.

[0165] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. 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 inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0166] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset terminal 314, but may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset terminal 314. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the headset terminal 314 or an external device, and the headset terminal 314 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0167] Each of the multiple elements described above, including the analysis unit, proposal unit, analysis unit, cultivation proposal unit, image analysis unit, and understanding unit, is implemented in at least one of the headset terminal 314 and the data processing unit 12. For example, the analysis unit is implemented by the processor 46 of the headset terminal 314, which collects environmental data and analyzes it using AI. The proposal unit is implemented by the specific processing unit 290 of the data processing unit 12, which proposes the optimal management method based on the analyzed data. The analysis unit is implemented by the specific processing unit 290 of the data processing unit 12, which analyzes data on soil, climate, crop type, and variety. The cultivation proposal unit is implemented by the specific processing unit 290 of the data processing unit 12, which proposes the optimal crop varieties and resource allocation. The image analysis unit is implemented by the processor 46 of the headset terminal 314, which analyzes image data from satellites and drones. The understanding unit is implemented by the specific processing unit 290 of the data processing unit 12, which grasps the farmland conditions based on the data analyzed by the image analysis unit. The correspondence between each part and the device or control unit is not limited to the examples described above, and various modifications are possible.

[0168] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0169] 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.

[0170] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. 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 and / or LAN.

[0171] 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.

[0172] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, 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.

[0173] 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 image sensor or CCD image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0174] 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.

[0175] 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. The robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0176] 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.

[0177] The processor 28 reads a 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 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0178] 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. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0179] In robot 414, specific processing is performed by processor 46. A specific program 60 is stored in storage 50. Processor 46 reads the specific program 60 from storage 50 and executes it on RAM 48. The specific processing is achieved by processor 46 acting as a control unit 46A according to the specific program 60 executed on RAM 48. Robot 414 also has data generation model 58 and emotion identification model 59, similar to those of the robot, and can perform processing similar to that of the specific processing unit 290 using these models.

[0180] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0181] 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.

[0182] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. 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 inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0183] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the robot 414 or an external device, and the robot 414 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0184] Each of the multiple elements described above, including the analysis unit, proposal unit, analysis unit, cultivation proposal unit, image analysis unit, and grasping unit, is implemented in at least one of the following: the robot 414 and the data processing unit 12. For example, the analysis unit is implemented by the processor 46 of the robot 414, which collects environmental data and analyzes it using AI. The proposal unit is implemented by the specific processing unit 290 of the data processing unit 12, which proposes the optimal management method based on the analyzed data. The analysis unit is implemented by the specific processing unit 290 of the data processing unit 12, which analyzes data on soil, climate, crop type, and variety. The cultivation proposal unit is implemented by the specific processing unit 290 of the data processing unit 12, which proposes the optimal crop varieties and resource allocation. The image analysis unit is implemented by the processor 46 of the robot 414, which analyzes image data from satellites and drones. The grasping unit is implemented by the specific processing unit 290 of the data processing unit 12, which grasps the farmland conditions based on the data analyzed by the image analysis unit. The correspondence between each part and the device or control unit is not limited to the examples described above, and various modifications are possible.

[0185] 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.

[0186] Figure 9 shows the 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.

[0187] 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.

[0188] 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.

[0189] 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, and motorcycles, 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 based, for example, 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.

[0190] 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."

[0191] 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.

[0192] 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 method for the specific process may be used, which includes computer 22 and multiple other computers.

[0193] 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.

[0194] 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.

[0195] 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.

[0196] 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.

[0197] 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.

[0198] 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.

[0199] 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.

[0200] Furthermore, although the above-described examples were divided into four embodiments, some or all of these embodiments may be combined. Also, the smart device 14, smart glasses 214, headset terminal 314, and robot 414 are just examples, and they may be combined, or other devices may be used. Also, although the above-described examples were divided into two embodiments, Embodiment 1 and Embodiment 2, these may be combined.

[0201] 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 other things 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.

[0202] 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.

[0203] (Note 1) An analysis unit that analyzes environmental data, A proposal unit proposes an optimal management method based on the data analyzed by the aforementioned analysis unit, The analysis department analyzes data on soil, climate, and crop types and varieties. Based on the data analyzed by the aforementioned analysis unit, the cultivation proposal unit proposes the optimal cultivar and resource allocation. The image analysis unit analyzes satellite and drone image data, The system includes a unit that grasps the condition of farmland based on the data analyzed by the image analysis unit. A system characterized by the following features. (Note 2) The aforementioned analysis unit, Analyze environmental data such as temperature, precipitation, humidity, and wind speed. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned proposal section is, Based on the data analyzed by the aforementioned analysis unit, the optimal irrigation schedule and fertilization plan are proposed for a specific crop. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned analysis unit is We analyze data such as soil pH levels, nutrient content, climatic conditions, and crop growth characteristics. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned cultivation proposal section, Based on the data analyzed by the aforementioned analysis department, we propose the optimal cultivars and resource allocation, taking into account market food demand and price fluctuations. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned image analysis unit, By analyzing satellite imagery and drone aerial photographs, we can understand the growth status of crops and the occurrence of pests and diseases. The system described in Appendix 1, characterized by the features described herein. (Note 7) The gripping part is, Based on the data analyzed by the aforementioned image analysis unit, the conditions of the farmland are understood and provided to producers. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned analysis unit, We estimate user emotions and adjust the method of analyzing environmental data based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned analysis unit, Add a function to detect anomalies when analyzing environmental data by comparing it with past data. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned analysis unit, When analyzing environmental data, consider climate patterns in specific regions to improve the accuracy of the analysis. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned proposal section is, It estimates the user's emotions and adjusts the suggestions based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned proposal section is, When making a proposal, refer to past proposal history to improve the accuracy of the proposal. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned proposal section is, When making a proposal, customize the proposal content according to the growth stage of the specific crop. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned proposal section is, It estimates the user's emotions and determines the priority of suggestions based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned proposal section is, When making a proposal, adjust the content of the proposal to take into account local agricultural policies and regulations. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned proposal section is, When making a proposal, we will strengthen it by referring to the success stories of other agricultural producers. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned analysis unit is We estimate the user's emotions and adjust the analysis method based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned analysis unit is To improve analytical accuracy, consider soil microbial activity during analysis. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned analysis unit is During analysis, different analytical methods are applied depending on the growth stage of the crop. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned analysis unit is It estimates the user's emotions and adjusts how the analysis results are displayed based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned analysis unit is During the analysis, we combine regional climate change data to provide the analysis results. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned analysis unit is During analysis, we improve the accuracy of the analysis by linking with other agricultural databases. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned cultivation proposal section, The system estimates the user's emotions and adjusts cultivation suggestions based on those emotions. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned cultivation proposal section, When making cultivation suggestions, we improve the accuracy of the suggestions by referring to past cultivation history. The system described in Appendix 1, characterized by the features described herein. (Note 25) The aforementioned cultivation proposal section, When proposing cultivation methods, we will strengthen the proposal by considering demand forecast data for specific markets. The system described in Appendix 1, characterized by the features described herein. (Note 26) The aforementioned cultivation proposal section, The system estimates the user's emotions and prioritizes cultivation suggestions based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 27) The aforementioned cultivation proposal section, When proposing cultivation methods, we adjust the proposal content to take into account local agricultural policies and regulations. The system described in Appendix 1, characterized by the features described herein. (Note 28) The aforementioned cultivation proposal section, When proposing cultivation methods, we will strengthen our proposals by referring to successful case studies from other agricultural producers. The system described in Appendix 1, characterized by the features described herein. (Note 29) The aforementioned image analysis unit, It estimates the user's emotions and adjusts the image analysis method based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 30) The aforementioned image analysis unit, Add a function to detect anomalies during image analysis by comparing the current image with past image data. The system described in Appendix 1, characterized by the features described herein. (Note 31) The aforementioned image analysis unit, During image analysis, the analysis method is customized according to the growth stage of a specific crop. The system described in Appendix 1, characterized by the features described herein. (Note 32) The aforementioned image analysis unit, It estimates the user's emotions and adjusts how the image analysis results are displayed based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 33) The aforementioned image analysis unit, During image analysis, geographical data is combined to display the analysis results separately for each region. The system described in Appendix 1, characterized by the features described herein. (Note 34) The aforementioned image analysis unit, During image analysis, data is collected in real time, and analysis results are provided immediately. The system described in Appendix 1, characterized by the features described herein. (Note 35) The gripping part is, The system estimates user sentiment and adjusts the method of assessing farmland conditions based on the estimated user sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 36) The gripping part is, Add a function to detect anomalies when assessing the condition of farmland by comparing it with past data. The system described in Appendix 1, characterized by the features described herein. (Note 37) The gripping part is, When assessing the condition of agricultural land, consider the climate patterns of specific regions to improve the accuracy of the assessment. The system described in Appendix 1, characterized by the features described herein. (Note 38) The gripping part is, It estimates the user's emotions and adjusts how the results are displayed based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 39) The gripping part is, When assessing the condition of farmland, geographical data is combined to display the assessment results separately for each region. The system described in Appendix 1, characterized by the features described herein. (Note 40) The gripping part is, When assessing the condition of farmland, data is collected in real time and the results are provided immediately. The system described in Appendix 1, characterized by the features described herein. [Explanation of Symbols]

[0204] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots

Claims

1. An analysis unit that analyzes environmental data, A proposal unit proposes an optimal management method based on the data analyzed by the aforementioned analysis unit, The analysis department analyzes data on soil, climate, and crop types and varieties. Based on the data analyzed by the aforementioned analysis unit, the cultivation proposal unit proposes the optimal cultivar and resource allocation. The image analysis unit analyzes satellite and drone image data, The system includes a unit that grasps the condition of farmland based on the data analyzed by the image analysis unit. A system characterized by the following features.

2. The aforementioned analysis unit, Analyze environmental data such as temperature, precipitation, humidity, and wind speed. The system according to feature 1.

3. The aforementioned proposal section is, Based on the data analyzed by the aforementioned analysis unit, the optimal irrigation schedule and fertilization plan are proposed for a specific crop. The system according to feature 1.

4. The aforementioned analysis unit is We analyze data such as soil pH levels, nutrient content, climatic conditions, and crop growth characteristics. The system according to feature 1.

5. The aforementioned cultivation proposal section, Based on the data analyzed by the aforementioned analysis department, we propose the optimal cultivars and resource allocation, taking into account market food demand and price fluctuations. The system according to feature 1.

6. The aforementioned image analysis unit, By analyzing satellite imagery and drone aerial photographs, we can understand the growth status of crops and the occurrence of pests and diseases. The system according to feature 1.

7. The gripping part is, Based on the data analyzed by the aforementioned image analysis unit, the conditions of the farmland are understood and provided to producers. The system according to feature 1.

8. The aforementioned analysis unit, We estimate user emotions and adjust the method of analyzing environmental data based on the estimated user emotions. The system according to feature 1.

9. The aforementioned analysis unit, Add a function to detect anomalies when analyzing environmental data by comparing it with past data. The system according to feature 1.

10. The aforementioned analysis unit, When analyzing environmental data, consider climate patterns in specific regions to improve the accuracy of the analysis. The system according to feature 1.

Images