System

Abstract

An object of a system according to an embodiment is to propose an optimal cultivation method based on data.SOLUTION: A system includes a data collection unit, an analysis unit, and a proposal unit. The data collection unit collects weather data of farmland, soil data, and growth data of crops. The analysis unit analyzes the weather data, the soil data, and the growth data of the crop collected by the data collection unit. The proposal unit proposes an optimum cultivation method based on the data analyzed by the analysis unit.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

[0002] Patent document 1 discloses a persona chatbot control method performed by at least one processor, the method including the steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to a description of the chatbot character, encoding the prompt, and inputting the encoded prompt into a language model to generate a chatbot utterance in response to the user utterance. [Prior art documents] [Patent documents]

[0003] [Patent Document 1] Japanese Patent Publication No. 2022-180282 Summary of the Invention [Problem to be solved by the invention]

[0004] With conventional technology, crop cultivation methods often relied on the experience and intuition of farmers, and there was a lack of objective, data-based judgment.

[0005] The system according to the embodiment aims to propose an optimal cultivation method based on data. [Means for solving the problem]

[0006] The system according to the embodiment includes a data collection unit, an analysis unit, and a proposal unit. The data collection unit collects meteorological data, soil data, and crop growth data for farmland. The analysis unit analyzes the meteorological data, soil data, and crop growth data collected by the data collection unit. The proposal unit proposes an optimal cultivation method based on the data analyzed by the analysis unit. [Effects of the Invention]

[0007] The system according to the embodiment can propose an optimal cultivation method based on the data. [Brief explanation of the drawings]

[0008] [Figure 1] 1 is a conceptual diagram showing an example of the configuration of a data processing system according to a first embodiment. [Figure 2] 1 is a conceptual diagram showing an example of main functions of a data processing device and a smart device according to a first embodiment. [Figure 3] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a second embodiment. [Figure 4] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and smart glasses according to a second embodiment. [Figure 5] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a third embodiment. [Figure 6] FIG. 11 is a conceptual diagram showing an example of main functions of a data processing device and a headset-type terminal according to a third embodiment. [Figure 7] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a fourth embodiment. [Figure 8] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and a robot according to a fourth embodiment. [Figure 9] 1 shows an emotion map onto which multiple emotions are mapped. [Figure 10] 1 shows an emotion map onto which multiple emotions are mapped. DETAILED DESCRIPTION OF THE INVENTION

[0009] An example of an embodiment of a system according to the technology of the present disclosure will be described below with reference to the accompanying drawings.

[0010] First, the terms used in the following description will be explained.

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

[0012] In the following embodiments, a coded RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a working memory by a processor.

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

[0014] In the following embodiments, a communication I / F (Interface) with a symbol is an interface including a communication processor, an antenna, etc. The communication I / F controls communication between multiple computers. Examples of communication standards applied to the communication I / F include wireless communication standards including 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), and Bluetooth (registered trademark).

[0015] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." In other words, "A and / or B" means that it may be only A, only B, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" is also applied when three or more things are expressed connected by "and / or."

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

[0017] 1, a data processing system 10 includes a data processing device 12 and a smart device 14. An example of the data processing device 12 is a server.

[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, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0019] The smart device 14 includes a computer 36, a reception device 38, an output device 40, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The reception device 38, the output device 40, and the camera 42 are also connected to the bus 52.

[0020] The reception device 38 includes a touch panel 38A and a microphone 38B, and receives user input. The touch panel 38A detects contact with a pointer (for example, a pen or a finger) to receive user input by the touch of the pointer. The microphone 38B detects the user's voice to receive user input by voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 (see FIG. 2) acquires the data indicating the user input.

[0021] Output device 40 includes a display 40A and a speaker 40B, and presents data to a user by outputting the data in a form of expression that the user can perceive (e.g., audio and / or text). Display 40A displays visible information such as text and images in accordance with instructions from processor 46. Speaker 40B outputs audio in accordance with instructions from processor 46. Camera 42 is a compact digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0022] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 control the exchange of various information between the processor 46 and the processor 28 via the network 54.

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

[0024] 2, in the data processing device 12, a specific process is performed by the processor 28. A specific processing program 56 is stored in the storage 32. The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific process is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0025] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0026] In the smart device 14, the specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The specific processing program 60 is used together 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 the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the smart device 14 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0027] Note that a device other than the data processing device 12 may 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 a processing result (prediction result, etc.) using the data generation model 58 by communicating with the server device having the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device owned by a user (e.g., a mobile phone, a robot, a home appliance, etc.). Next, an example of processing by the data processing system 10 according to the first embodiment will be described.

[0028] (Example 1) The agricultural crop production consulting system according to an embodiment of the present invention is a system that makes objective decisions based on data and proposes optimal cultivation methods without relying on the experience or intuition of farmers. As a result, the agricultural crop production consulting system can make objective decisions based on data and propose optimal cultivation methods without relying on the experience or intuition of farmers.

[0029] The agricultural crop production consulting system according to the embodiment includes a data collection unit, an analysis unit, and a proposal unit. The data collection unit collects meteorological data, soil data, and crop growth data for the farmland. For example, the data collection unit collects data such as temperature, precipitation, sunshine hours, and soil nutrient content. The data collection unit can also collect data in real time using sensors. The analysis unit analyzes the meteorological data, soil data, and crop growth data collected by the data collection unit. For example, the analysis unit analyzes the data using a machine learning algorithm to identify optimal conditions for crop growth. The analysis unit can also perform statistical analysis based on past data. The proposal unit proposes an optimal cultivation method based on the data analyzed by the analysis unit. For example, the proposal unit proposes optimal sowing times, irrigation timing, fertilization amounts and timing, pest control measures, etc. for a specific crop. The proposal unit can also detect changes in meteorological conditions and the occurrence of pests and diseases and propose appropriate measures. As a result, the agricultural crop production consulting system according to the embodiment can make objective, data-based decisions in agricultural crop production and propose optimal cultivation methods.

[0030] The data collection unit can collect data on temperature, precipitation, sunshine hours, and soil nutrient content to identify optimal conditions for crop growth. The data collection unit can collect data on, for example, temperature, precipitation, sunshine hours, and soil nutrient content to identify optimal conditions for crop growth. For example, the data collection unit can combine high-resolution cameras and image analysis technology to analyze changes in the color and shape of crop leaves in real time and identify early signs of pests and diseases. The data collection unit can also use soil sensors to measure the nutrient content of the soil to identify optimal conditions for crop growth. This can improve yields and quality by identifying optimal conditions for crop growth.

[0031] The proposal unit can propose the optimal sowing time, irrigation timing, amount and timing of fertilization, and pest and disease control measures for a specific crop. For example, the proposal unit can propose the optimal sowing time, irrigation timing, amount and timing of fertilization, and pest and disease control measures for a specific crop. For example, the proposal unit can use AI to analyze soil samples and evaluate the types and activity levels of microorganisms to assess the health of the soil and propose more precise cultivation methods. The proposal unit can also detect changes in weather conditions and the occurrence of pests and diseases and propose appropriate measures. This makes it possible to propose the optimal cultivation method for a specific crop, thereby improving yield and quality.

[0032] The proposal unit can detect changes in weather conditions or the occurrence of pests and diseases and propose appropriate countermeasures. For example, the proposal unit can use a wearable device to monitor heart rate and electrodermal activity so that the AI ​​can analyze the farmer's stress level and propose harvesting work when stress is low. The proposal unit can also monitor changes in weather conditions in real time and propose appropriate countermeasures. This makes it possible to detect changes in weather conditions and the occurrence of pests and diseases early and take appropriate countermeasures.

[0033] The suggestion unit can analyze past data and suggest cultivation methods that have been successful under similar conditions. For example, the suggestion unit can analyze past data and suggest cultivation methods that have been successful under similar conditions. For example, the suggestion unit can use AI to analyze past data, identify successful cultivation methods, and suggest those methods. The suggestion unit can also perform statistical analysis based on past data and suggest successful cultivation methods. In this way, by analyzing past data and suggesting successful cultivation methods, it is possible to improve yields and quality.

[0034] The proposal unit can provide information on optimal cultivation methods and support farmers in putting those methods into practice. For example, the proposal unit can incorporate the latest agricultural techniques and research results into the educational content provided by the AI, ensuring that farmers always have the latest knowledge. The proposal unit can also provide customized educational programs that take into account regional characteristics. This can improve yields and quality by supporting farmers in putting optimal cultivation methods into practice.

[0035] The data collection unit can analyze changes in the color or shape of crop leaves in real time, enabling early detection of pests and diseases. For example, the data collection unit can combine a high-resolution camera with image analysis technology to detect changes in leaf color and identify early signs of pests and diseases. The data collection unit can also use a soil sensor to measure the nutrient content of the soil and identify optimal conditions for crop growth. This allows early detection of pests and diseases by analyzing changes in the color or shape of crop leaves in real time.

[0036] The data collection unit analyzes microbial activity in the soil and evaluates the health of the soil, allowing it to propose more precise cultivation methods. For example, the data collection unit analyzes microbial activity in the soil and evaluates the health of the soil, allowing it to propose more precise cultivation methods. For example, the data collection unit uses AI to analyze soil samples and evaluate the types and activity levels of microorganisms, thereby assessing the health of the soil and proposing more precise cultivation methods. The data collection unit can also use soil sensors to measure the nutrient content of the soil and identify the optimal conditions for crop growth. This allows it to analyze microbial activity in the soil and evaluate the health of the soil, allowing it to propose more precise cultivation methods.

[0037] The data collection unit can add surrounding ecosystem data and propose cultivation methods that are compatible with environmental protection. For example, the data collection unit can add surrounding ecosystem data and propose cultivation methods that are compatible with environmental protection. For example, the data collection unit can install sensors to monitor the activity of plants and animals so that the AI ​​can collect surrounding ecosystem data, and propose refraining from using pesticides during periods when specific organisms are active. The data collection unit can also collect weather data in real time and propose cultivation methods that respond to weather fluctuations. In this way, sustainable agriculture can be achieved by adding surrounding ecosystem data and proposing cultivation methods that are compatible with environmental protection.

[0038] The data collection unit can add the farmer's work history and compare it with past work patterns to propose an optimal work schedule. The data collection unit, for example, can add the farmer's work history and compare it with past work patterns to propose an optimal work schedule. For example, the data collection unit can use AI to collect the farmer's work history, analyze past work patterns, and propose an optimal work schedule. The data collection unit can also perform statistical analysis based on past data to propose an optimal work schedule. This makes it possible to improve work efficiency by adding the farmer's work history and comparing it with past work patterns to propose an optimal work schedule.

[0039] The proposal unit can analyze the genetic information of crops and propose genetically optimal cultivation conditions. The proposal unit, for example, analyzes the genetic information of crops and proposes genetically optimal cultivation conditions. For example, the proposal unit uses AI to analyze the genetic information of crops, identify the conditions under which specific genes are expressed, and propose a cultivation method based on those conditions. The proposal unit can also perform statistical analysis based on the genetic information and propose genetically optimal cultivation conditions. In this way, by analyzing the genetic information of crops and proposing genetically optimal cultivation conditions, it is possible to improve yield and quality.

[0040] The proposal unit can analyze local market data and propose cultivation methods that meet demand. For example, the proposal unit can analyze local market data and propose cultivation methods that meet demand. For example, the proposal unit can use AI to analyze local market data and propose cultivation methods that are suited to periods when demand for a particular crop is high. The proposal unit can also perform statistical analysis based on market data and propose cultivation methods that meet demand. This makes it possible to maximize profits by analyzing local market data and proposing cultivation methods that meet demand.

[0041] The proposal unit can improve the cultivation techniques of the entire community by analyzing and sharing the success stories of other farmers. For example, the proposal unit can improve the cultivation techniques of the entire community by analyzing and sharing the success stories of other farmers. For example, the proposal unit can use AI to analyze the success stories of other farmers and share that information to improve the cultivation techniques of the entire community. For example, the proposal unit can register successful cultivation methods in a database and provide it to other farmers. The proposal unit can also perform statistical analysis based on the success stories to improve the cultivation techniques of the entire community. In this way, the proposal unit can improve the cultivation techniques of the entire community by analyzing and sharing the success stories of other farmers.

[0042] The suggestion unit can analyze the usage data of agricultural machinery and suggest the optimal way to use the machinery. For example, the suggestion unit can analyze the usage data of agricultural machinery and suggest the optimal way to use the machinery. For example, the suggestion unit can use AI to analyze the usage data of agricultural machinery and suggest the optimal way to use it. For example, the suggestion unit can suggest the efficient way to use tractors and combine harvesters. The suggestion unit can also perform statistical analysis based on the usage data and suggest the optimal way to use the machinery. In this way, by analyzing the usage data of agricultural machinery and suggesting the optimal way to use the machinery, it is possible to improve work efficiency.

[0043] The data collection unit can analyze the growth rate of crops and detect growth abnormalities early. The data collection unit, for example, analyzes the growth rate of crops and detects growth abnormalities early. For example, the data collection unit uses image data taken periodically so that AI can analyze the growth rate of crops in real time. For example, it can identify crops with slow growth rates and detect abnormalities early. The data collection unit can also perform statistical analysis based on the growth rate to detect growth abnormalities early. This allows the analysis of crop growth rate and early detection of growth abnormalities so that appropriate measures can be taken.

[0044] The data collection unit can analyze the moisture content of the soil and propose optimal irrigation timing. The data collection unit, for example, analyzes the moisture content of the soil and proposes optimal irrigation timing. For example, the data collection unit installs a soil sensor so that the AI ​​can analyze the moisture content of the soil in real time, and proposes optimal irrigation timing when the moisture content drops. The data collection unit can also perform statistical analysis based on the moisture content and propose optimal irrigation timing. In this way, crop growth can be optimized by analyzing the moisture content of the soil and proposing optimal irrigation timing.

[0045] The data collection unit can add surrounding weather data and propose cultivation methods that respond to weather fluctuations. For example, the data collection unit can add surrounding weather data and propose cultivation methods that respond to weather fluctuations. For example, the data collection unit can use AI to collect surrounding weather data in real time and propose cultivation methods that respond to weather fluctuations. For example, the data collection unit can propose the timing of irrigation and fertilization in accordance with fluctuations in temperature and precipitation. The data collection unit can also perform statistical analysis based on the weather data and propose cultivation methods that respond to weather fluctuations. This makes it possible to optimize crop growth by adding surrounding weather data and proposing cultivation methods that respond to weather fluctuations.

[0046] The data collection unit can add data on pest and disease occurrence in crops and take countermeasures early. For example, the data collection unit can use AI to collect data on pest and disease occurrence in crops in real time and take countermeasures early. For example, it can detect early signs of pest and disease and suggest the use of appropriate pesticides. The data collection unit can also perform statistical analysis based on pest and disease occurrence data and take countermeasures early. In this way, by adding data on pest and disease occurrence in crops and taking countermeasures early, damage caused by pests can be minimized.

[0047] The proposal unit can take into account local market data when analyzing past data and propose cultivation methods that meet demand. For example, the proposal unit can take into account local market data when analyzing past data and propose cultivation methods that meet demand. For example, the proposal unit can use AI to analyze local market data and propose cultivation methods that are suited to periods of high demand for specific crops. The proposal unit can also perform statistical analysis based on market data and propose cultivation methods that meet demand. This makes it possible to maximize profits by taking into account local market data when analyzing past data and proposing cultivation methods that meet demand.

[0048] The suggestion unit, when analyzing past data, takes into account the success stories of other farmers and shares them, thereby improving the cultivation techniques of the entire community. For example, when analyzing past data, the suggestion unit takes into account the success stories of other farmers and shares them, thereby improving the cultivation techniques of the entire community. For example, the suggestion unit uses AI to analyze the success stories of other farmers and shares that information, thereby improving the cultivation techniques of the entire community. For example, successful cultivation methods can be registered in a database and provided to other farmers. The suggestion unit can also perform statistical analysis based on the success stories to improve the cultivation techniques of the entire community. In this way, when analyzing past data, the suggestion unit takes into account the success stories of other farmers and shares them, thereby improving the cultivation techniques of the entire community.

[0049] The suggestion unit can take into account agricultural machinery usage data when analyzing past data and suggest the optimal way to use the machinery. For example, the suggestion unit can use AI to analyze agricultural machinery usage data and suggest the optimal way to use the machinery. For example, the suggestion unit can use AI to analyze agricultural machinery usage data and suggest the optimal way to use it. For example, the suggestion unit can suggest the efficient way to use a tractor or combine. The suggestion unit can also perform statistical analysis based on usage data and suggest the optimal way to use the machinery. This makes it possible to improve work efficiency by taking into account agricultural machinery usage data when analyzing past data and suggesting the optimal way to use the machinery.

[0050] The proposal department can incorporate the latest agricultural techniques and research results into the educational content for farmers, allowing them to always acquire the latest knowledge. For example, the proposal department can use AI to collect the latest agricultural techniques and research results and create educational content based on them. For example, it can provide new cultivation techniques and methods for controlling pests and diseases in the form of videos and text. The proposal department can also provide customized educational programs that take into account the characteristics of each region. This allows the latest agricultural techniques and research results to be incorporated into the educational content for farmers, allowing them to always acquire the latest knowledge.

[0051] The proposal unit can provide a customized educational program for farmers that takes into account the characteristics of the region. For example, the proposal unit provides a customized educational program for farmers that takes into account the characteristics of the region. For example, the proposal unit provides a customized educational program using AI that takes into account the characteristics of the region. For example, a program is provided that teaches cultivation methods that are suitable for the climate and soil conditions of the region. The proposal unit can also perform statistical analysis based on the characteristics of the region and provide a customized educational program. In this way, by providing a customized educational program for farmers that takes into account the characteristics of the region, it is possible to enable farmers to acquire knowledge that is suitable for their region.

[0052] The Proposal Department can improve the knowledge of the entire community by incorporating and sharing the success stories of other farmers in the educational content for farmers. For example, the Proposal Department can improve the knowledge of the entire community by incorporating and sharing the success stories of other farmers in the educational content for farmers. For example, the Proposal Department can use AI to collect success stories of other farmers and incorporate that information into the educational content. For example, they can share examples of successful cultivation methods and pest and disease control measures. The Proposal Department can also perform statistical analysis based on the success stories to improve the knowledge of the entire community. In this way, the knowledge of the entire community can be improved by incorporating and sharing the success stories of other farmers in the educational content for farmers.

[0053] The proposal unit can incorporate agricultural machinery usage and maintenance methods into the educational content for farmers, supporting optimal use of the machinery. For example, the proposal unit can incorporate agricultural machinery usage and maintenance methods into the educational content for farmers, supporting optimal use of the machinery. For example, the proposal unit can use AI to incorporate agricultural machinery usage and maintenance methods into the educational content. For example, it can provide content that teaches farmers how to efficiently use and maintain tractors and combines. The proposal unit can also perform statistical analysis based on usage and maintenance methods to support optimal use of the machinery. In this way, by incorporating agricultural machinery usage and maintenance methods into the educational content for farmers, it is possible to support optimal use of the machinery.

[0054] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0055] The agricultural production consulting system can further include a health management unit that monitors the health status of farmers. The health management unit collects farmers' health data and proposes appropriate health management methods. For example, the health management unit monitors the farmers' sleep patterns and dietary content and proposes healthy lifestyle habits. The health management unit can also detect farmers' poor health at an early stage and encourage them to visit an appropriate medical institution. This helps maintain the farmers' health and improves work efficiency.

[0056] The agricultural crop production consulting system can further include an ecosystem management unit that collects ecosystem data on farmland and proposes cultivation methods that are compatible with environmental protection. The ecosystem management unit collects ecosystem data on the surrounding area and proposes cultivation methods that are compatible with environmental protection. For example, it may propose refraining from using pesticides during periods when certain organisms are active. The ecosystem management unit can also collect weather data in real time and propose cultivation methods that respond to weather changes. This makes it possible to achieve sustainable agriculture.

[0057] The agricultural production consulting system can further include a work history management unit that adds the farmer's work history and compares it with past work patterns to propose an optimal work schedule. The work history management unit collects the farmer's work history, analyzes past work patterns, and proposes an optimal work schedule. For example, it can perform statistical analysis based on past data to propose an optimal work schedule. This can improve work efficiency.

[0058] The agricultural production consulting system can further include a machinery management unit that analyzes agricultural machinery usage data and proposes optimal ways to use the machinery. The machinery management unit collects agricultural machinery usage data and proposes optimal ways to use the machinery. For example, it proposes efficient ways to use tractors and combine harvesters. The machinery management unit can also perform statistical analysis based on usage data and propose optimal ways to use the machinery. This can improve work efficiency.

[0059] The agricultural crop production consulting system can further include a market analysis unit that analyzes regional market data and proposes cultivation methods according to demand. The market analysis unit collects regional market data and proposes cultivation methods according to demand. For example, it proposes cultivation methods suited to periods when demand for a particular crop is high. The market analysis unit can also perform statistical analysis based on market data and propose cultivation methods according to demand. This can maximize profits.

[0060] The agricultural crop production consulting system can further include a community support department that analyzes and shares the success stories of other farmers, thereby improving the cultivation techniques of the entire community. The community support department collects the success stories of other farmers and shares that information to improve the cultivation techniques of the entire community. For example, it can register successful cultivation methods in a database and provide it to other farmers. The community support department can also perform statistical analysis based on the success stories to improve the cultivation techniques of the entire community. This can improve the cultivation techniques of the entire community.

[0061] The processing flow of the first embodiment will be briefly explained below.

[0062] Step 1: The data collection unit collects meteorological data, soil data, and crop growth data for the farmland, such as temperature, precipitation, sunshine hours, and soil nutrient content. Data can also be collected in real time using sensors. Step 2: The analysis unit analyzes the weather, soil, and crop growth data collected by the data collection unit. For example, it uses machine learning algorithms to analyze the data and identify optimal conditions for crop growth. It can also perform statistical analysis based on past data. Step 3: The proposal unit proposes optimal cultivation methods based on the data analyzed by the analysis unit. For example, it proposes the optimal sowing time for a specific crop, irrigation timing, fertilization amount and timing, and pest control measures. It can also detect changes in weather conditions and the occurrence of pests and diseases and propose appropriate measures.

[0063] (Example 2) The agricultural crop production consulting system according to an embodiment of the present invention is a system that makes objective decisions based on data and proposes optimal cultivation methods without relying on the experience or intuition of farmers. As a result, the agricultural crop production consulting system can make objective decisions based on data and propose optimal cultivation methods without relying on the experience or intuition of farmers.

[0064] The agricultural crop production consulting system according to the embodiment includes a data collection unit, an analysis unit, and a proposal unit. The data collection unit collects meteorological data, soil data, and crop growth data for the farmland. For example, the data collection unit collects data such as temperature, precipitation, sunshine hours, and soil nutrient content. The data collection unit can also collect data in real time using sensors. The analysis unit analyzes the meteorological data, soil data, and crop growth data collected by the data collection unit. For example, the analysis unit analyzes the data using a machine learning algorithm to identify optimal conditions for crop growth. The analysis unit can also perform statistical analysis based on past data. The proposal unit proposes an optimal cultivation method based on the data analyzed by the analysis unit. For example, the proposal unit proposes optimal sowing times, irrigation timing, fertilization amounts and timing, pest control measures, etc. for a specific crop. The proposal unit can also detect changes in meteorological conditions and the occurrence of pests and diseases and propose appropriate measures. As a result, the agricultural crop production consulting system according to the embodiment can make objective, data-based decisions in agricultural crop production and propose optimal cultivation methods.

[0065] The data collection unit can collect data on temperature, precipitation, sunshine hours, and soil nutrient content to identify optimal conditions for crop growth. The data collection unit can collect data on, for example, temperature, precipitation, sunshine hours, and soil nutrient content to identify optimal conditions for crop growth. For example, the data collection unit can combine high-resolution cameras and image analysis technology to analyze changes in the color and shape of crop leaves in real time and identify early signs of pests and diseases. The data collection unit can also use soil sensors to measure the nutrient content of the soil to identify optimal conditions for crop growth. This can improve yields and quality by identifying optimal conditions for crop growth.

[0066] The proposal unit can propose the optimal sowing time, irrigation timing, amount and timing of fertilization, and pest and disease control measures for a specific crop. For example, the proposal unit can propose the optimal sowing time, irrigation timing, amount and timing of fertilization, and pest and disease control measures for a specific crop. For example, the proposal unit can use AI to analyze soil samples and evaluate the types and activity levels of microorganisms to assess the health of the soil and propose more precise cultivation methods. The proposal unit can also detect changes in weather conditions and the occurrence of pests and diseases and propose appropriate measures. This makes it possible to propose the optimal cultivation method for a specific crop, thereby improving yield and quality.

[0067] The proposal unit can detect changes in weather conditions or the occurrence of pests and diseases and propose appropriate countermeasures. For example, the proposal unit can use a wearable device to monitor heart rate and electrodermal activity so that the AI ​​can analyze the farmer's stress level and propose harvesting work when stress is low. The proposal unit can also monitor changes in weather conditions in real time and propose appropriate countermeasures. This makes it possible to detect changes in weather conditions and the occurrence of pests and diseases early and take appropriate countermeasures.

[0068] The suggestion unit can analyze past data and suggest cultivation methods that have been successful under similar conditions. For example, the suggestion unit can analyze past data and suggest cultivation methods that have been successful under similar conditions. For example, the suggestion unit can use AI to analyze past data, identify successful cultivation methods, and suggest those methods. The suggestion unit can also perform statistical analysis based on past data and suggest successful cultivation methods. In this way, by analyzing past data and suggesting successful cultivation methods, it is possible to improve yields and quality.

[0069] The proposal unit can provide information on optimal cultivation methods and support farmers in putting those methods into practice. For example, the proposal unit can incorporate the latest agricultural techniques and research results into the educational content provided by the AI, ensuring that farmers always have the latest knowledge. The proposal unit can also provide customized educational programs that take into account regional characteristics. This can improve yields and quality by supporting farmers in putting optimal cultivation methods into practice.

[0070] The data collection unit can analyze changes in the color or shape of crop leaves in real time, enabling early detection of pests and diseases. For example, the data collection unit can combine a high-resolution camera with image analysis technology to detect changes in leaf color and identify early signs of pests and diseases. The data collection unit can also use a soil sensor to measure the nutrient content of the soil and identify optimal conditions for crop growth. This allows early detection of pests and diseases by analyzing changes in the color or shape of crop leaves in real time.

[0071] The data collection unit analyzes microbial activity in the soil and evaluates the health of the soil, allowing it to propose more precise cultivation methods. For example, the data collection unit analyzes microbial activity in the soil and evaluates the health of the soil, allowing it to propose more precise cultivation methods. For example, the data collection unit uses AI to analyze soil samples and evaluate the types and activity levels of microorganisms, thereby assessing the health of the soil and proposing more precise cultivation methods. The data collection unit can also use soil sensors to measure the nutrient content of the soil and identify the optimal conditions for crop growth. This allows it to analyze microbial activity in the soil and evaluate the health of the soil, allowing it to propose more precise cultivation methods.

[0072] The data collection unit can analyze farmers' stress levels and suggest tasks that are optimal for times when stress is low. For example, the data collection unit can analyze farmers' stress levels and suggest tasks that are optimal for times when stress is low. For example, the data collection unit can use a wearable device to monitor heart rate and electrodermal activity to enable AI to analyze farmers' stress levels and suggest harvesting tasks when stress is low. The data collection unit can also monitor farmers' emotional state in real time and suggest ways to relax when stress increases. This allows the burden on farmers to be reduced by analyzing their stress levels and suggesting tasks that are optimal for times when stress is low.

[0073] The data collection unit can add surrounding ecosystem data and propose cultivation methods that are compatible with environmental protection. For example, the data collection unit can add surrounding ecosystem data and propose cultivation methods that are compatible with environmental protection. For example, the data collection unit can install sensors to monitor the activity of plants and animals so that the AI ​​can collect surrounding ecosystem data, and propose refraining from using pesticides during periods when specific organisms are active. The data collection unit can also collect weather data in real time and propose cultivation methods that respond to weather fluctuations. In this way, sustainable agriculture can be achieved by adding surrounding ecosystem data and proposing cultivation methods that are compatible with environmental protection.

[0074] The data collection unit can add the farmer's work history and compare it with past work patterns to propose an optimal work schedule. The data collection unit, for example, can add the farmer's work history and compare it with past work patterns to propose an optimal work schedule. For example, the data collection unit can use AI to collect the farmer's work history, analyze past work patterns, and propose an optimal work schedule. The data collection unit can also perform statistical analysis based on past data to propose an optimal work schedule. This makes it possible to improve work efficiency by adding the farmer's work history and comparing it with past work patterns to propose an optimal work schedule.

[0075] The proposal unit can analyze the genetic information of crops and propose genetically optimal cultivation conditions. The proposal unit, for example, analyzes the genetic information of crops and proposes genetically optimal cultivation conditions. For example, the proposal unit uses AI to analyze the genetic information of crops, identify the conditions under which specific genes are expressed, and propose a cultivation method based on those conditions. The proposal unit can also perform statistical analysis based on the genetic information and propose genetically optimal cultivation conditions. In this way, by analyzing the genetic information of crops and proposing genetically optimal cultivation conditions, it is possible to improve yield and quality.

[0076] The proposal unit can analyze local market data and propose cultivation methods that meet demand. For example, the proposal unit can analyze local market data and propose cultivation methods that meet demand. For example, the proposal unit can use AI to analyze local market data and propose cultivation methods that are suited to periods when demand for a particular crop is high. The proposal unit can also perform statistical analysis based on market data and propose cultivation methods that meet demand. This makes it possible to maximize profits by analyzing local market data and proposing cultivation methods that meet demand.

[0077] The suggestion unit can analyze the emotional state of the farmer and suggest a cultivation method to reduce stress. The suggestion unit, for example, analyzes the emotional state of the farmer and suggests a cultivation method to reduce stress. For example, the suggestion unit uses an emotion estimation function to analyze the emotional state of the farmer and suggests a cultivation method to reduce stress. For example, it can suggest work that is less stressful. The suggestion unit can also monitor the emotional state of the farmer in real time and suggest relaxation methods when stress increases. In this way, the burden on the farmer can be reduced by analyzing the emotional state of the farmer and suggesting a cultivation method to reduce stress.

[0078] The proposal unit can improve the cultivation techniques of the entire community by analyzing and sharing the success stories of other farmers. For example, the proposal unit can improve the cultivation techniques of the entire community by analyzing and sharing the success stories of other farmers. For example, the proposal unit can use AI to analyze the success stories of other farmers and share that information to improve the cultivation techniques of the entire community. For example, the proposal unit can register successful cultivation methods in a database and provide it to other farmers. The proposal unit can also perform statistical analysis based on the success stories to improve the cultivation techniques of the entire community. In this way, the proposal unit can improve the cultivation techniques of the entire community by analyzing and sharing the success stories of other farmers.

[0079] The suggestion unit can analyze the usage data of agricultural machinery and suggest the optimal way to use the machinery. For example, the suggestion unit can analyze the usage data of agricultural machinery and suggest the optimal way to use the machinery. For example, the suggestion unit can use AI to analyze the usage data of agricultural machinery and suggest the optimal way to use it. For example, the suggestion unit can suggest the efficient way to use tractors and combine harvesters. The suggestion unit can also perform statistical analysis based on the usage data and suggest the optimal way to use the machinery. In this way, by analyzing the usage data of agricultural machinery and suggesting the optimal way to use the machinery, it is possible to improve work efficiency.

[0080] The suggestion unit can analyze the emotional state of the farmer and suggest a cultivation method to elicit positive emotions. The suggestion unit, for example, analyzes the emotional state of the farmer and suggests a cultivation method to elicit positive emotions. For example, the suggestion unit analyzes the emotional state of the farmer using an emotion estimation function and suggests a cultivation method to elicit positive emotions. For example, the suggestion unit can suggest tasks that the farmer can enjoy. The suggestion unit can also monitor the emotional state of the farmer in real time and provide feedback to elicit positive emotions. In this way, the farmer's motivation can be improved by analyzing the emotional state of the farmer and suggesting a cultivation method to elicit positive emotions.

[0081] The data collection unit can analyze the growth rate of crops and detect growth abnormalities early. The data collection unit, for example, analyzes the growth rate of crops and detects growth abnormalities early. For example, the data collection unit uses image data taken periodically so that AI can analyze the growth rate of crops in real time. For example, it can identify crops with slow growth rates and detect abnormalities early. The data collection unit can also perform statistical analysis based on the growth rate to detect growth abnormalities early. This allows the analysis of crop growth rate and early detection of growth abnormalities so that appropriate measures can be taken.

[0082] The data collection unit can analyze the moisture content of the soil and propose optimal irrigation timing. The data collection unit, for example, analyzes the moisture content of the soil and proposes optimal irrigation timing. For example, the data collection unit installs a soil sensor so that the AI ​​can analyze the moisture content of the soil in real time, and proposes optimal irrigation timing when the moisture content drops. The data collection unit can also perform statistical analysis based on the moisture content and propose optimal irrigation timing. In this way, crop growth can be optimized by analyzing the moisture content of the soil and proposing optimal irrigation timing.

[0083] The data collection unit can monitor the emotional state of the farmer in real time and suggest relaxation methods when stress increases. The data collection unit, for example, monitors the emotional state of the farmer in real time and suggests relaxation methods when stress increases. For example, the data collection unit uses an emotion estimation function to monitor the emotional state of the farmer in real time and suggests relaxation methods when stress increases. For example, it can suggest relaxing music or taking a break. The data collection unit can also perform statistical analysis based on the emotional state of the farmer and suggest relaxation methods when stress increases. In this way, by monitoring the emotional state of the farmer in real time and suggesting relaxation methods when stress increases, it is possible to reduce the farmer's stress.

[0084] The data collection unit can add surrounding weather data and propose cultivation methods that respond to weather fluctuations. For example, the data collection unit can add surrounding weather data and propose cultivation methods that respond to weather fluctuations. For example, the data collection unit can use AI to collect surrounding weather data in real time and propose cultivation methods that respond to weather fluctuations. For example, the data collection unit can propose the timing of irrigation and fertilization in accordance with fluctuations in temperature and precipitation. The data collection unit can also perform statistical analysis based on the weather data and propose cultivation methods that respond to weather fluctuations. This makes it possible to optimize crop growth by adding surrounding weather data and proposing cultivation methods that respond to weather fluctuations.

[0085] The data collection unit can add data on pest and disease occurrence in crops and take countermeasures early. For example, the data collection unit can use AI to collect data on pest and disease occurrence in crops in real time and take countermeasures early. For example, it can detect early signs of pest and disease and suggest the use of appropriate pesticides. The data collection unit can also perform statistical analysis based on pest and disease occurrence data and take countermeasures early. In this way, by adding data on pest and disease occurrence in crops and taking countermeasures early, damage caused by pests can be minimized.

[0086] The data collection unit can monitor the emotional state of the farmer in real time and provide feedback to elicit positive emotions. The data collection unit, for example, monitors the emotional state of the farmer in real time and provides feedback to elicit positive emotions. For example, the data collection unit uses an emotion estimation function to monitor the emotional state of the farmer in real time and provides feedback to elicit positive emotions. For example, the data collection unit can present encouraging messages or success stories. The data collection unit can also perform statistical analysis based on the emotional state of the farmer and provide feedback to elicit positive emotions. In this way, by monitoring the emotional state of the farmer in real time and providing feedback to elicit positive emotions, it is possible to improve the motivation of the farmer.

[0087] The proposal unit can take into account local market data when analyzing past data and propose cultivation methods that meet demand. For example, the proposal unit can take into account local market data when analyzing past data and propose cultivation methods that meet demand. For example, the proposal unit can use AI to analyze local market data and propose cultivation methods that are suited to periods of high demand for specific crops. The proposal unit can also perform statistical analysis based on market data and propose cultivation methods that meet demand. This makes it possible to maximize profits by taking into account local market data when analyzing past data and proposing cultivation methods that meet demand.

[0088] The suggestion unit can take into account the emotional state of the farmer when analyzing past data and suggest a cultivation method that reduces stress. For example, the suggestion unit can take into account the emotional state of the farmer when analyzing past data and suggest a cultivation method that reduces stress. For example, the suggestion unit can analyze the emotional state of the farmer using an emotion estimation function and suggest a cultivation method that reduces stress. For example, the suggestion unit can suggest work that is less stressful. The suggestion unit can also perform statistical analysis based on the emotional state of the farmer and suggest a cultivation method that reduces stress. In this way, by taking into account the emotional state of the farmer when analyzing past data and suggesting a cultivation method that reduces stress, the burden on the farmer can be reduced.

[0089] The suggestion unit, when analyzing past data, takes into account the success stories of other farmers and shares them, thereby improving the cultivation techniques of the entire community. For example, when analyzing past data, the suggestion unit takes into account the success stories of other farmers and shares them, thereby improving the cultivation techniques of the entire community. For example, the suggestion unit uses AI to analyze the success stories of other farmers and shares that information, thereby improving the cultivation techniques of the entire community. For example, successful cultivation methods can be registered in a database and provided to other farmers. The suggestion unit can also perform statistical analysis based on the success stories to improve the cultivation techniques of the entire community. In this way, when analyzing past data, the suggestion unit takes into account the success stories of other farmers and shares them, thereby improving the cultivation techniques of the entire community.

[0090] The suggestion unit can take into account agricultural machinery usage data when analyzing past data and suggest the optimal way to use the machinery. For example, the suggestion unit can use AI to analyze agricultural machinery usage data and suggest the optimal way to use the machinery. For example, the suggestion unit can use AI to analyze agricultural machinery usage data and suggest the optimal way to use it. For example, the suggestion unit can suggest the efficient way to use a tractor or combine. The suggestion unit can also perform statistical analysis based on usage data and suggest the optimal way to use the machinery. This makes it possible to improve work efficiency by taking into account agricultural machinery usage data when analyzing past data and suggesting the optimal way to use the machinery.

[0091] The suggestion unit can take into account the emotional state of the farmer when analyzing past data and suggest a cultivation method that will elicit positive emotions. For example, the suggestion unit can take into account the emotional state of the farmer when analyzing past data and suggest a cultivation method that will elicit positive emotions. For example, the suggestion unit can analyze the emotional state of the farmer using an emotion estimation function and suggest a cultivation method that will elicit positive emotions. For example, the suggestion unit can suggest tasks that the farmer can enjoy. The suggestion unit can also perform statistical analysis based on the emotional state of the farmer and suggest a cultivation method that will elicit positive emotions. In this way, by taking into account the emotional state of the farmer when analyzing past data and suggesting a cultivation method that will elicit positive emotions, the motivation of the farmer can be improved.

[0092] The proposal department can incorporate the latest agricultural techniques and research results into the educational content for farmers, allowing them to always acquire the latest knowledge. For example, the proposal department can use AI to collect the latest agricultural techniques and research results and create educational content based on them. For example, it can provide new cultivation techniques and methods for controlling pests and diseases in the form of videos and text. The proposal department can also provide customized educational programs that take into account the characteristics of each region. This allows the latest agricultural techniques and research results to be incorporated into the educational content for farmers, allowing them to always acquire the latest knowledge.

[0093] The proposal unit can provide a customized educational program for farmers that takes into account the characteristics of the region. For example, the proposal unit provides a customized educational program for farmers that takes into account the characteristics of the region. For example, the proposal unit provides a customized educational program using AI that takes into account the characteristics of the region. For example, a program is provided that teaches cultivation methods that are suitable for the climate and soil conditions of the region. The proposal unit can also perform statistical analysis based on the characteristics of the region and provide a customized educational program. In this way, by providing a customized educational program for farmers that takes into account the characteristics of the region, it is possible to enable farmers to acquire knowledge that is suitable for their region.

[0094] The suggestion unit can analyze the emotional state of the farmer and provide educational content to reduce stress. The suggestion unit, for example, analyzes the emotional state of the farmer and provides educational content to reduce stress. For example, the suggestion unit uses an emotion estimation function to analyze the emotional state of the farmer and provides educational content to reduce stress. For example, the suggestion unit provides content that teaches relaxation methods and stress management techniques. The suggestion unit can also perform statistical analysis based on the emotional state of the farmer and provide educational content to reduce stress. In this way, by analyzing the emotional state of the farmer and providing educational content to reduce stress, the burden on the farmer can be reduced.

[0095] The Proposal Department can improve the knowledge of the entire community by incorporating and sharing the success stories of other farmers in the educational content for farmers. For example, the Proposal Department can improve the knowledge of the entire community by incorporating and sharing the success stories of other farmers in the educational content for farmers. For example, the Proposal Department can use AI to collect success stories of other farmers and incorporate that information into the educational content. For example, they can share examples of successful cultivation methods and pest and disease control measures. The Proposal Department can also perform statistical analysis based on the success stories to improve the knowledge of the entire community. In this way, the knowledge of the entire community can be improved by incorporating and sharing the success stories of other farmers in the educational content for farmers.

[0096] The proposal unit can incorporate agricultural machinery usage and maintenance methods into the educational content for farmers, supporting optimal use of the machinery. For example, the proposal unit can incorporate agricultural machinery usage and maintenance methods into the educational content for farmers, supporting optimal use of the machinery. For example, the proposal unit can use AI to incorporate agricultural machinery usage and maintenance methods into the educational content. For example, it can provide content that teaches farmers how to efficiently use and maintain tractors and combines. The proposal unit can also perform statistical analysis based on usage and maintenance methods to support optimal use of the machinery. In this way, by incorporating agricultural machinery usage and maintenance methods into the educational content for farmers, it is possible to support optimal use of the machinery.

[0097] The suggestion unit can analyze the emotional state of the farmer and provide educational content to elicit positive emotions. The suggestion unit, for example, analyzes the emotional state of the farmer and provides educational content to elicit positive emotions. For example, the suggestion unit uses an emotion estimation function to analyze the emotional state of the farmer and provides educational content to elicit positive emotions. For example, the suggestion unit provides content that allows farmers to learn how to work in a way that they can enjoy and how to create a relaxing environment. The suggestion unit can also perform statistical analysis based on the emotional state of the farmer and provide educational content to elicit positive emotions. In this way, by analyzing the emotional state of the farmer and providing educational content to elicit positive emotions, it is possible to improve the motivation of the farmer.

[0098] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0099] The agricultural production consulting system can further include a health management unit that monitors the health status of farmers. The health management unit collects farmers' health data and proposes appropriate health management methods. For example, the health management unit monitors the farmers' sleep patterns and dietary content and proposes healthy lifestyle habits. The health management unit can also detect farmers' poor health at an early stage and encourage them to visit an appropriate medical institution. This helps maintain the farmers' health and improves work efficiency.

[0100] The agricultural crop production consulting system can further include an emotional feedback unit that analyzes the emotional state of the farmer and provides feedback to elicit positive emotions. The emotional feedback unit monitors the emotional state of the farmer in real time and provides feedback to elicit positive emotions. For example, it presents encouraging messages and success stories. The emotional feedback unit can also perform statistical analysis based on the emotional state of the farmer and provide feedback to elicit positive emotions. This can improve the motivation of the farmer.

[0101] The agricultural crop production consulting system can further include an ecosystem management unit that collects ecosystem data on farmland and proposes cultivation methods that are compatible with environmental protection. The ecosystem management unit collects ecosystem data on the surrounding area and proposes cultivation methods that are compatible with environmental protection. For example, it may propose refraining from using pesticides during periods when certain organisms are active. The ecosystem management unit can also collect weather data in real time and propose cultivation methods that respond to weather changes. This makes it possible to achieve sustainable agriculture.

[0102] The agricultural crop production consulting system can further include an emotion management unit that analyzes the emotional state of farmers and suggests cultivation methods to reduce stress. The emotion management unit monitors the emotional state of farmers in real time and suggests cultivation methods to reduce stress. For example, it suggests less stressful work. The emotion management unit can also perform statistical analysis based on the emotional state of farmers and suggest cultivation methods to reduce stress. This can reduce the burden on farmers.

[0103] The agricultural production consulting system can further include a work history management unit that adds the farmer's work history and compares it with past work patterns to propose an optimal work schedule. The work history management unit collects the farmer's work history, analyzes past work patterns, and proposes an optimal work schedule. For example, it can perform statistical analysis based on past data to propose an optimal work schedule. This can improve work efficiency.

[0104] The agricultural crop production consulting system can further include an emotion promotion unit that analyzes the emotional state of the farmer and suggests cultivation methods that will elicit positive emotions. The emotion promotion unit monitors the farmer's emotional state in real time and suggests cultivation methods that will elicit positive emotions. For example, it can suggest tasks that the farmer can enjoy. The emotion promotion unit can also perform statistical analysis based on the farmer's emotional state and suggest cultivation methods that will elicit positive emotions. This can improve the motivation of the farmer.

[0105] The agricultural production consulting system can further include a machinery management unit that analyzes agricultural machinery usage data and proposes optimal ways to use the machinery. The machinery management unit collects agricultural machinery usage data and proposes optimal ways to use the machinery. For example, it proposes efficient ways to use tractors and combine harvesters. The machinery management unit can also perform statistical analysis based on usage data and propose optimal ways to use the machinery. This can improve work efficiency.

[0106] The agricultural crop production consulting system can further include an education support unit that analyzes the emotional state of farmers and provides educational content to reduce stress. The education support unit monitors the emotional state of farmers in real time and provides educational content to reduce stress. For example, it can provide content that teaches relaxation methods and stress management techniques. The education support unit can also perform statistical analysis based on the emotional state of farmers and provide educational content to reduce stress. This can reduce the burden on farmers.

[0107] The agricultural crop production consulting system can further include a market analysis unit that analyzes regional market data and proposes cultivation methods according to demand. The market analysis unit collects regional market data and proposes cultivation methods according to demand. For example, it proposes cultivation methods suited to periods when demand for a particular crop is high. The market analysis unit can also perform statistical analysis based on market data and propose cultivation methods according to demand. This can maximize profits.

[0108] The agricultural crop production consulting system can further include a community support department that analyzes and shares the success stories of other farmers, thereby improving the cultivation techniques of the entire community. The community support department collects the success stories of other farmers and shares that information to improve the cultivation techniques of the entire community. For example, it can register successful cultivation methods in a database and provide it to other farmers. The community support department can also perform statistical analysis based on the success stories to improve the cultivation techniques of the entire community. This can improve the cultivation techniques of the entire community.

[0109] The processing flow of the second embodiment will be briefly explained below.

[0110] Step 1: The data collection unit collects meteorological data, soil data, and crop growth data for the farmland, such as temperature, precipitation, sunshine hours, and soil nutrient content. Data can also be collected in real time using sensors. Step 2: The analysis unit analyzes the weather, soil, and crop growth data collected by the data collection unit. For example, it uses machine learning algorithms to analyze the data and identify optimal conditions for crop growth. It can also perform statistical analysis based on past data. Step 3: The proposal unit proposes optimal cultivation methods based on the data analyzed by the analysis unit. For example, it proposes the optimal sowing time for a specific crop, irrigation timing, fertilization amount and timing, and pest control measures. It can also detect changes in weather conditions and the occurrence of pests and diseases and propose appropriate measures.

[0111] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0112] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0113] Furthermore, the processing by the data processing system 10 described above is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. Furthermore, 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.

[0114] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0115] 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.

[0116] 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, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0117] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0118] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0119] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0120] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0121] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0122] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0123] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0124] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. 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 the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the smart glasses 214 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0125] Note that a device other than the data processing device 12 may 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 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0126] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0127] The data generation model 58 is a so-called generative AI. An example of the 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 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0128] 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 executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart glasses 214 or an external device, etc., and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0129] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0130] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0131] 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, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0132] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[0133] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0134] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0135] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0136] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0137] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0138] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0139] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 may also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0140] Note that a device other than the data processing device 12 may 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 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0141] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0142] The data generation model 58 is a so-called generative AI. An example of the 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 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0143] 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 executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset type terminal 314, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset type terminal 314. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the headset type terminal 314 or an external device, etc., and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0144] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0145] 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.

[0146] 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, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0147] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[0148] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0149] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0150] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0151] The control 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 emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[0152] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0153] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0154] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0155] In the robot 414, the processor 46 performs the identification process. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0156] Note that a device other than the data processing device 12 may 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 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0157] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0158] The data generation model 58 is a so-called generative AI. An example of the 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 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0159] 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 executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the robot 414 or an external device, etc., and the robot 414 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0160] The emotion identification model 59 as an emotion engine may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0161] FIG. 9 illustrates an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and behaviors arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion encompasses both emotions and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0162] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[0163] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0164] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the area called "reaction," where sensation is dominant. The right half of the emotion map lists emotions belonging to the area called "situation," where situational awareness is dominant.

[0165] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[0166] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[0167] In the above embodiment, an example was given in which a specific process is performed by one computer 22, but the technology disclosed herein is not limited to this, and distributed processing of the specific process may be performed by multiple computers including computer 22.

[0168] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[0169] 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.

[0170] It is not necessary to store all of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[0171] The hardware resource for executing a specific process can be any of the following types of processors: A processor, for example, is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A processor also includes a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[0172] The hardware resource that executes the specific process may be configured with one of these various processors, or may be configured with a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific process may be a single processor.

[0173] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[0174] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[0175] In the above example, the first to fourth embodiments have been described separately, but some or all of these embodiments may be combined. The smart device 14, smart glasses 214, headset terminal 314, and robot 414 are merely examples, and they may be combined, or other devices may be used. In the above example, the first and second embodiments have been described separately, but they may be combined.

[0176] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0177] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference. [Explanation of symbols]

[0178] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. a data collection unit that collects meteorological data, soil data, and crop growth data of farmland; an analysis unit that analyzes the meteorological data, the soil data, and the crop growth data collected by the data collection unit; a proposal unit that proposes an optimal cultivation method based on the data analyzed by the analysis unit. A system characterized by:

2. The data collection unit Adding data on the surrounding ecosystem, we will propose the above-mentioned cultivation method that is compatible with environmental protection.

2. The system of claim 1.

3. The proposal unit Analyze the genetic information of the crop and propose genetically optimal cultivation conditions 2. The system of claim 1.

4. The data collection unit Analyze the growth rate of the crop and detect abnormalities in growth at an early stage.

2. The system of claim 1.

5. The proposal unit Incorporate the latest agricultural techniques and research findings into the education provided to farmers, ensuring that they always have the latest knowledge.

2. The system of claim 1.

6. The data collection unit Analyzing farmers' emotional state and making work suggestions to elicit positive emotions 2. The system of claim 1.

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