System

Abstract

An object of a system according to an embodiment is to efficiently monitor a growth condition of a crop and propose an optimal cultivation method.SOLUTION: A system according to the embodiment includes a AI technology, an IoT technology, and a 5G technology. In the AI art, the growth conditions of crops are monitored, and an optimal cultivation method is proposed. The IoT technology collects and manages various data of farmland in real time. The 5G technology realizes high-speed and large-capacity communication.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] Conventional techniques have had the problem of making it difficult to efficiently monitor the growth status of agricultural crops and propose optimal cultivation methods.

[0005] The system according to the embodiment aims to efficiently monitor the growth status of agricultural crops and propose optimal cultivation methods. [Means for solving the problem]

[0006] The system according to the embodiment incorporates AI technology, IoT technology, and 5G technology. The AI ​​technology monitors the growth status of agricultural crops and proposes optimal cultivation methods. The IoT technology collects and manages various farmland data in real time. The 5G technology enables high-speed, large-capacity data communication. [Effects of the Invention]

[0007] The system according to the embodiment can efficiently monitor the growth status of agricultural crops and propose optimal cultivation methods. [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 a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[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 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[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 support system according to an embodiment of the present invention is a system that utilizes AI technology, IoT technology, and 5G technology to improve agricultural productivity, realize sustainable agriculture, and support the stabilization of food supply. As a result, the agricultural support system can improve the productivity of farmers, realize sustainable agriculture, and support the stabilization of food supply.

[0029] An agricultural support system according to an embodiment incorporates AI technology, IoT technology, and 5G technology. The AI ​​technology monitors the growth status of agricultural crops and proposes optimal cultivation methods. For example, the AI ​​technology analyzes weather and soil data to determine optimal irrigation and fertilization timing. The AI ​​technology can also use generative AI (e.g., text generation AI or multimodal generation AI) to create 3D models of agricultural crop growth status in real time, allowing visual confirmation. The AI ​​technology can also perform early detection of agricultural pests and diseases and propose appropriate countermeasures. The IoT technology collects and manages various farmland data in real time. For example, sensors can be used to monitor soil humidity, temperature, and nutrient status and transmit the data to the cloud. The IoT technology can also monitor the operating status of agricultural machinery in real time and predict malfunctions. The IoT technology can also monitor the post-harvest storage environment of agricultural crops to support quality maintenance. 5G technology enables high-speed, large-capacity data communication. For example, 5G technology can use drones to capture images of the entire farmland and upload the video data to the cloud in real time. 5G technology can also integrate data from a wide range of sensors across farmland in real time to enable comprehensive farmland management. Furthermore, 5G technology can also use emotion estimation to monitor farmers' emotions in real time while they work and provide suggestions for stress reduction. This allows the agricultural support system according to the embodiment to improve farm productivity, realize sustainable agriculture, and support the stabilization of food supplies.

[0030] AI technology can analyze weather data or soil data and suggest cultivation methods. For example, AI technology can analyze weather data and suggest the optimal timing for irrigation. AI technology can also analyze soil data and suggest the optimal timing for fertilization. Furthermore, AI technology can combine and analyze weather data and soil data to suggest comprehensive cultivation methods. This makes it possible to suggest optimal cultivation methods by analyzing weather data and soil data.

[0031] IoT technology can use sensors to monitor the humidity, temperature, and nutrient status of soil and send the data to the cloud. For example, IoT technology can use a temperature sensor to monitor the temperature of the soil and send the data to the cloud. IoT technology can also use a humidity sensor to monitor the humidity of the soil and send the data to the cloud. IoT technology can also use a nutrient sensor to monitor the nutrient status of the soil and send the data to the cloud. This allows the status of farmland to be understood by monitoring the soil condition in real time using sensors and sending the data to the cloud.

[0032] 5G technology can be used to capture images of the entire farmland using drones and upload the video data to the cloud in real time. For example, 5G technology can be used to capture images of the entire farmland in high resolution and upload the video data to the cloud in real time. 5G technology can also be used to capture images of the entire farmland using drones and analyze the video data in real time. 5G technology can also be used to capture images of the entire farmland using drones and upload the video data to the cloud in real time to identify abnormalities. This makes it possible to quickly grasp the status of a wide area of ​​farmland by using drones to capture images of the entire farmland and upload the video data to the cloud in real time.

[0033] AI technology can monitor the growth status of agricultural crops and optimize resource usage. For example, AI technology can monitor the growth status of agricultural crops and optimize water usage. AI technology can also monitor the growth status of agricultural crops and optimize fertilizer usage. Furthermore, AI technology can monitor the growth status of agricultural crops and optimize energy usage. In this way, sustainable agriculture can be achieved by monitoring the growth status of agricultural crops and optimizing resource usage.

[0034] AI technology can predict harvest yields and harvest at the right time. For example, AI technology can predict harvest yields and suggest the optimal harvest timing. AI technology can also predict harvest yields and optimize harvesting schedules. Furthermore, AI technology can predict harvest yields and make plans for post-harvest storage and transportation. This makes it possible to stabilize the food supply by predicting harvest yields and harvesting at the right time.

[0035] AI technology can create 3D models of the growth status of agricultural crops in real time, allowing them to be visually confirmed. For example, AI technology can use generative AI to create 3D models of the growth status of agricultural crops in real time. AI technology can also use generative AI to create 3D models of the growth status of agricultural crops in real time, allowing farmers to visually check the status on their smartphones or tablets. AI technology can also use generative AI to create 3D models of the growth status of agricultural crops in real time, allowing farmers to visually check the status on their smartphones or tablets. AI technology can also use generative AI to create 3D models of the growth status of agricultural crops in real time, allowing farmers to visually check the status. This makes it easier for farmers to understand the growth process of their crops.

[0036] AI technology can detect pests and diseases in agricultural crops early and suggest countermeasures. For example, AI technology uses image recognition technology to detect pests and diseases in agricultural crops early. AI technology can also suggest how to use pesticides and preventive measures depending on the type of pest or disease. Furthermore, AI technology can detect pests and diseases in agricultural crops early and notify farmers in real time. This allows for early detection of pests and diseases in agricultural crops and suggests appropriate countermeasures, thereby maintaining the health of crops.

[0037] AI technology can not only monitor the growth of crops, but also the health of livestock and suggest breeding methods. For example, AI technology can simultaneously monitor the growth of crops and the health of livestock. AI technology can also suggest the type and amount of feed based on the health of livestock. Furthermore, AI technology can monitor the growth of crops and the health of livestock and notify in real time if an abnormality occurs. This makes it possible to simultaneously monitor the growth of crops and the health of livestock and suggest optimal breeding methods, thereby improving the efficiency of agriculture as a whole.

[0038] When monitoring the growth status of agricultural crops, AI technology can suggest the amount of pesticide to use from an environmental protection perspective. For example, AI technology monitors the growth status of agricultural crops and calculates the minimum amount of pesticide needed based on the occurrence of pests and diseases. AI technology can also suggest the optimal timing for use based on pesticide use history and environmental data. Furthermore, AI technology can adjust the type and concentration of pesticides to minimize the impact on the environment. This reduces the burden on the environment by monitoring the growth status of agricultural crops and suggesting the optimal amount of pesticide to use from an environmental protection perspective.

[0039] IoT technology can use IoT sensors to collect detailed microclimate data on farmland and propose cultivation methods for each region. IoT technology, for example, can use IoT sensors to collect detailed microclimate data on farmland. IoT technology can also propose optimal crop selection and cultivation methods based on weather data for each region. Furthermore, IoT technology can notify farmers in real time of abnormal weather and issue alerts. This allows detailed collection of microclimate data on farmland and proposals for optimal cultivation methods for each region, improving the quality and yield of agricultural crops.

[0040] IoT technology can monitor the operating status of agricultural machinery in real time using IoT devices and predict failures. IoT technology can also use vibration sensors and temperature sensors to detect abnormal behavior and identify signs of failure. IoT technology can also analyze past operating data, learn failure patterns, and create predictive models. This makes it possible to monitor the operating status of agricultural machinery in real time and predict failures, thereby improving the efficiency of agricultural work.

[0041] IoT technology can collect and manage environmental data not only from farmland but also from greenhouses and hydroponic cultivation facilities. IoT technology can also collect and manage environmental data from greenhouses and hydroponic cultivation facilities. IoT technology can also suggest optimal cultivation conditions based on environmental data. Furthermore, IoT technology can notify farmers in real time if an abnormality occurs and issue an alert. This allows for the optimization of the growing environment for agricultural crops by collecting and managing environmental data from not only farmland but also from greenhouses and hydroponic cultivation facilities.

[0042] IoT technology can monitor the post-harvest storage environment of agricultural produce using IoT sensors and support quality maintenance. IoT technology, for example, uses IoT sensors to monitor the post-harvest storage environment of agricultural produce. IoT technology can also propose optimal storage conditions based on data on the storage environment. Furthermore, IoT technology can notify farmers in real time if an abnormality occurs and issue an alert. This helps maintain the quality of agricultural produce by monitoring the post-harvest storage environment and supporting quality maintenance.

[0043] 5G technology can analyze drone footage of the entire farmland in real time and instantly identify any abnormalities. For example, 5G technology can send high-resolution video data to the cloud, where AI can identify any abnormalities. 5G technology can also identify areas of pest infestation or nutrient deficiencies and notify farmers. 5G technology can also map abnormalities based on the video data, allowing farmers to visually confirm them. This allows drone footage of the entire farmland to be analyzed in real time and any abnormalities to be immediately identified, enabling rapid response.

[0044] 5G technology can be used to integrate data from sensors spread across a wide range of farmland in real time, enabling comprehensive farmland management. 5G technology can transmit data such as temperature, humidity, and soil nutrients to the cloud for comprehensive management. 5G technology can also provide immediate notifications and suggest countermeasures when abnormalities occur. 5G technology can also display data visually, allowing farmers to easily understand the situation. This allows data from sensors spread across a wide range of farmland to be integrated in real time, enabling comprehensive farmland management, enabling a unified understanding of the status of the farmland.

[0045] 5G technology can be used to manage infrastructure data not only for farmland but for the entire rural area in real time, improving efficiency throughout the region. 5G technology can send data such as road conditions and power supply status to the cloud for comprehensive management. 5G technology can also provide immediate notification in the event of an abnormality and suggest countermeasures. 5G technology can also display data visually, allowing local residents to easily understand the situation. This will enable real-time management of infrastructure data not only for farmland but for the entire rural area, improving efficiency throughout the region and supporting the sustainable development of rural areas.

[0046] 5G technology can be used to monitor the status of agricultural products during transportation in real time, helping to maintain quality. 5G technology can send data such as temperature, humidity, and vibration to the cloud to monitor quality during transportation. 5G technology can also provide immediate notification and suggest countermeasures if an abnormality occurs. 5G technology can also visually display data during transportation, making it easier for transporters to understand the situation. This allows for real-time monitoring of the status of agricultural products during transportation and helps to maintain quality, thereby maintaining the quality of agricultural products.

[0047] AI technology can use generative AI to optimize the motion plans of robots to advance the automation of agricultural work. For example, AI technology can use generative AI to optimize the motion patterns of a harvesting robot. AI technology can also use generative AI to optimize the motion patterns of a planting robot. Furthermore, AI technology can also use generative AI to optimize the motion patterns of a weeding robot. In this way, by optimizing the motion plans of robots to advance the automation of agricultural work, the efficiency of agricultural work is improved.

[0048] AI technology can support the optimization of not only farm work but also farm management, improving profitability. For example, AI technology can support the optimization of not only farm work but also farm management. AI technology can also select highly profitable crops and propose sales strategies. Furthermore, AI technology can also propose efficient resource management and optimal labor allocation to reduce costs. This will support the optimization of not only farm work but also farm management, improving profitability and improving the efficiency of agriculture as a whole.

[0049] AI technology can not only improve the efficiency of agricultural work but also support environmental conservation activities on farmland. For example, AI technology can not only improve the efficiency of agricultural work but also support environmental conservation activities on farmland. AI technology can also make suggestions for the use of environmentally friendly pesticides and soil improvement. Furthermore, AI technology can also make suggestions for the introduction of sustainable agricultural techniques and the protection of biodiversity. This not only improves the efficiency of agricultural work but also supports environmental conservation activities on farmland, thereby realizing sustainable agriculture.

[0050] AI technology can use generative AI to simulate the optimal natural environment for growing crops and propose sustainable cultivation methods. For example, AI technology can use generative AI to simulate the optimal natural environment for growing crops. AI technology can also use generative AI to simulate the optimal natural environment for growing crops and propose optimal cultivation conditions. AI technology can also use generative AI to simulate the optimal natural environment for growing crops and propose sustainable cultivation methods. In this way, sustainable agriculture can be achieved by simulating the optimal natural environment for growing crops and proposing sustainable cultivation methods.

[0051] AI technology can monitor the growth status of agricultural crops and propose resource usage plans to minimize environmental impact. For example, AI technology can monitor the growth status of agricultural crops and optimize the amount of water and fertilizer used. AI technology can also propose optimal irrigation schedules and fertilization plans based on environmental data. Furthermore, AI technology can also propose plans to adjust resource usage in the event of abnormal weather. In this way, sustainable agriculture can be achieved by monitoring the growth status of agricultural crops and proposing resource usage plans to minimize environmental impact.

[0052] AI technology can monitor not only crops but also livestock rearing environments, supporting sustainable livestock farming. For example, AI technology can monitor not only crops but also livestock rearing environments. AI technology can also monitor livestock body temperature and activity levels, and monitor their health in real time. Furthermore, AI technology can also suggest optimization of feed usage and rearing environments. This will enable monitoring not only crops but also livestock rearing environments, supporting sustainable livestock farming and achieving sustainable agriculture.

[0053] When monitoring the growth status of agricultural crops, AI technology can make suggestions that take into account the protection of the local ecosystem. For example, AI technology can monitor the growth status of agricultural crops and make suggestions for the use of ecosystem-friendly pesticides and soil improvement. AI technology can also make suggestions for protecting biodiversity and introducing environmentally friendly agricultural techniques. Furthermore, AI technology can also suggest optimal resource management and environmental protection activities based on environmental data. This will enable sustainable agriculture by making suggestions that take into account the protection of the local ecosystem when monitoring the growth status of agricultural crops.

[0054] AI technology can integrate multiple data sources to improve yield forecast accuracy using generative AI. For example, AI technology can integrate multiple data sources to improve yield forecast accuracy using generative AI. AI technology can also integrate multiple data sources to improve yield forecast accuracy and make predictions in real time using generative AI. Furthermore, AI technology can also integrate multiple data sources to improve yield forecast accuracy using generative AI and allow farmers to check the forecast results on their smartphones or tablets. This allows harvest planning to be optimized by integrating multiple data sources to improve yield forecast accuracy.

[0055] AI technology can monitor the post-harvest storage environment in real time and suggest the optimal storage method to maintain quality. For example, AI technology can monitor the post-harvest storage environment in real time. AI technology can also monitor the post-harvest storage environment in real time and suggest the optimal storage conditions. Furthermore, AI technology can monitor the post-harvest storage environment in real time, notify in real time if an abnormality occurs, and suggest countermeasures. In this way, the quality of agricultural products can be maintained by monitoring the post-harvest storage environment in real time and suggesting the optimal storage method to maintain quality.

[0056] AI technology can not only predict harvest yields but also support the optimization of distribution routes, reducing food waste. For example, AI technology can not only predict harvest yields but also support the optimization of distribution routes. AI technology can also suggest optimal transportation routes and storage locations, reducing food waste. Furthermore, AI technology can also suggest distribution plans that take into account the balance between supply and demand. This not only predicts harvest yields but also supports the optimization of distribution routes, reducing food waste.

[0057] AI technology can monitor not only the storage environment after harvest but also the environment during transportation, supporting quality maintenance. For example, AI technology can monitor not only the storage environment after harvest but also the environment during transportation. AI technology can also visually display data during transportation, making it easy for transporters to understand the situation. Furthermore, AI technology can immediately notify if an abnormality occurs and suggest countermeasures. This allows the quality of agricultural products to be maintained by monitoring not only the storage environment after harvest but also the environment during transportation, supporting quality maintenance.

[0058] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0059] The agricultural support system can also collect detailed microclimate data on farmland and propose cultivation methods for each region. For example, IoT sensors can be used to collect detailed microclimate data on farmland and propose optimal crop selection and cultivation methods based on weather data for each region. It can also provide real-time notifications and alerts to farmers in the event of abnormal weather. This allows for detailed collection of microclimate data on farmland and proposals for optimal cultivation methods for each region, improving the quality and yield of agricultural crops.

[0060] The agricultural support system can also monitor the status of agricultural products during transport in real time and support quality maintenance. For example, data such as temperature, humidity, and vibration can be sent to the cloud to monitor quality during transport. It can also immediately notify customers if an abnormality occurs and suggest countermeasures. It can also visually display data during transport, allowing transport companies to easily understand the situation. This allows for real-time monitoring of the status of agricultural products during transport and supports quality maintenance, thereby maintaining the quality of agricultural products.

[0061] The agricultural support system can also monitor the growth status of crops and propose resource usage plans to minimize environmental impact. For example, it can monitor the growth status of crops and optimize the amount of water and fertilizer used. It can also propose optimal irrigation schedules and fertilization plans based on environmental data. It can also propose plans to adjust resource usage in the event of abnormal weather. In this way, sustainable agriculture can be achieved by monitoring the growth status of crops and proposing resource usage plans to minimize environmental impact.

[0062] The agricultural support system can also make suggestions that take into account the protection of the local ecosystem when monitoring the growth of agricultural crops. For example, it can monitor the growth of agricultural crops and make suggestions for the use of ecosystem-friendly pesticides and soil improvement. It can also make suggestions for protecting biodiversity and introducing environmentally friendly agricultural techniques. It can also make suggestions for optimal resource management and environmental protection activities based on environmental data. This allows for sustainable agriculture to be achieved by making suggestions that take into account the protection of the local ecosystem when monitoring the growth of agricultural crops.

[0063] The agricultural support system can also monitor the growth status of agricultural products, monitor the post-harvest storage environment in real time, and propose optimal storage methods to maintain quality. For example, it can monitor the post-harvest storage environment in real time and propose optimal storage conditions. It can also notify in real time if an abnormality occurs and propose countermeasures. This allows the quality of agricultural products to be maintained by monitoring the post-harvest storage environment in real time and proposing optimal storage methods to maintain quality.

[0064] The processing flow of the first embodiment will be briefly explained below.

[0065] Step 1: AI technology monitors the growth status of agricultural crops and proposes optimal cultivation methods. Specifically, it analyzes weather and soil data to suggest optimal irrigation and fertilization timing. Generative AI can also be used to create 3D models of crop growth status in real time, allowing for visual confirmation. It can also detect crop diseases and pests early and propose appropriate countermeasures. Step 2: IoT technology collects and manages various farmland data in real time. Specifically, sensors are used to monitor soil humidity, temperature, and nutrient levels, and this data is sent to the cloud. It can also monitor the operating status of agricultural machinery in real time and predict malfunctions. It can also monitor the storage environment of agricultural products after harvesting, helping to maintain quality. Step 3: 5G technology will enable high-speed, high-capacity data communication. Specifically, drones will be used to capture images of the entire farmland and upload the video data to the cloud in real time. Data from sensors across the farmland will be integrated in real time to enable comprehensive farmland management. Furthermore, emotion estimation functionality will be used to monitor farmers' emotions in real time while they work, and suggestions for stress reduction will be made.

[0066] (Example 2) The agricultural support system according to an embodiment of the present invention is a system that utilizes AI technology, IoT technology, and 5G technology to improve agricultural productivity, realize sustainable agriculture, and support the stabilization of food supply. As a result, the agricultural support system can improve the productivity of farmers, realize sustainable agriculture, and support the stabilization of food supply.

[0067] An agricultural support system according to an embodiment incorporates AI technology, IoT technology, and 5G technology. The AI ​​technology monitors the growth status of agricultural crops and proposes optimal cultivation methods. For example, the AI ​​technology analyzes weather and soil data to determine optimal irrigation and fertilization timing. The AI ​​technology can also use generative AI (e.g., text generation AI or multimodal generation AI) to create 3D models of agricultural crop growth status in real time, allowing visual confirmation. The AI ​​technology can also perform early detection of agricultural pests and diseases and propose appropriate countermeasures. The IoT technology collects and manages various farmland data in real time. For example, sensors can be used to monitor soil humidity, temperature, and nutrient status and transmit the data to the cloud. The IoT technology can also monitor the operating status of agricultural machinery in real time and predict malfunctions. The IoT technology can also monitor the post-harvest storage environment of agricultural crops to support quality maintenance. 5G technology enables high-speed, large-capacity data communication. For example, 5G technology can use drones to capture images of the entire farmland and upload the video data to the cloud in real time. 5G technology can also integrate data from a wide range of sensors across farmland in real time to enable comprehensive farmland management. Furthermore, 5G technology can also use emotion estimation to monitor farmers' emotions in real time while they work and provide suggestions for stress reduction. This allows the agricultural support system according to the embodiment to improve farm productivity, realize sustainable agriculture, and support the stabilization of food supplies.

[0068] AI technology can analyze weather data or soil data and suggest cultivation methods. For example, AI technology can analyze weather data and suggest the optimal timing for irrigation. AI technology can also analyze soil data and suggest the optimal timing for fertilization. Furthermore, AI technology can combine and analyze weather data and soil data to suggest comprehensive cultivation methods. This makes it possible to suggest optimal cultivation methods by analyzing weather data and soil data.

[0069] IoT technology can use sensors to monitor the humidity, temperature, and nutrient status of soil and send the data to the cloud. For example, IoT technology can use a temperature sensor to monitor the temperature of the soil and send the data to the cloud. IoT technology can also use a humidity sensor to monitor the humidity of the soil and send the data to the cloud. IoT technology can also use a nutrient sensor to monitor the nutrient status of the soil and send the data to the cloud. This allows the status of farmland to be understood by monitoring the soil condition in real time using sensors and sending the data to the cloud.

[0070] 5G technology can be used to capture images of the entire farmland using drones and upload the video data to the cloud in real time. For example, 5G technology can be used to capture images of the entire farmland in high resolution and upload the video data to the cloud in real time. 5G technology can also be used to capture images of the entire farmland using drones and analyze the video data in real time. 5G technology can also be used to capture images of the entire farmland using drones and upload the video data to the cloud in real time to identify abnormalities. This makes it possible to quickly grasp the status of a wide area of ​​farmland by using drones to capture images of the entire farmland and upload the video data to the cloud in real time.

[0071] AI technology can monitor the growth status of agricultural crops and optimize resource usage. For example, AI technology can monitor the growth status of agricultural crops and optimize water usage. AI technology can also monitor the growth status of agricultural crops and optimize fertilizer usage. Furthermore, AI technology can monitor the growth status of agricultural crops and optimize energy usage. In this way, sustainable agriculture can be achieved by monitoring the growth status of agricultural crops and optimizing resource usage.

[0072] AI technology can predict harvest yields and harvest at the right time. For example, AI technology can predict harvest yields and suggest the optimal harvest timing. AI technology can also predict harvest yields and optimize harvesting schedules. Furthermore, AI technology can predict harvest yields and make plans for post-harvest storage and transportation. This makes it possible to stabilize the food supply by predicting harvest yields and harvesting at the right time.

[0073] AI technology can create 3D models of the growth status of agricultural crops in real time, allowing them to be visually confirmed. For example, AI technology can use generative AI to create 3D models of the growth status of agricultural crops in real time. AI technology can also use generative AI to create 3D models of the growth status of agricultural crops in real time, allowing farmers to visually check the status on their smartphones or tablets. AI technology can also use generative AI to create 3D models of the growth status of agricultural crops in real time, allowing farmers to visually check the status on their smartphones or tablets. AI technology can also use generative AI to create 3D models of the growth status of agricultural crops in real time, allowing farmers to visually check the status. This makes it easier for farmers to understand the growth process of their crops.

[0074] AI technology can detect pests and diseases in agricultural crops early and suggest countermeasures. For example, AI technology uses image recognition technology to detect pests and diseases in agricultural crops early. AI technology can also suggest how to use pesticides and preventive measures depending on the type of pest or disease. Furthermore, AI technology can detect pests and diseases in agricultural crops early and notify farmers in real time. This allows for early detection of pests and diseases in agricultural crops and suggests appropriate countermeasures, thereby maintaining the health of crops.

[0075] AI technology can use emotion estimation to monitor farmers' stress levels and propose work schedules. For example, AI technology can use emotion estimation to monitor farmers' stress levels and propose optimal work schedules. Furthermore, AI technology can use emotion estimation to monitor farmers' stress levels and propose optimal work schedules. Furthermore, AI technology can use emotion estimation to monitor farmers' stress levels and propose relaxation methods to reduce stress. In this way, by monitoring farmers' stress levels and proposing optimal work schedules, farmers' health and work efficiency can be improved.

[0076] AI technology can not only monitor the growth of crops, but also the health of livestock and suggest breeding methods. For example, AI technology can simultaneously monitor the growth of crops and the health of livestock. AI technology can also suggest the type and amount of feed based on the health of livestock. Furthermore, AI technology can monitor the growth of crops and the health of livestock and notify in real time if an abnormality occurs. This makes it possible to simultaneously monitor the growth of crops and the health of livestock and suggest optimal breeding methods, thereby improving the efficiency of agriculture as a whole.

[0077] When monitoring the growth status of agricultural crops, AI technology can suggest the amount of pesticide to use from an environmental protection perspective. For example, AI technology monitors the growth status of agricultural crops and calculates the minimum amount of pesticide needed based on the occurrence of pests and diseases. AI technology can also suggest the optimal timing for use based on pesticide use history and environmental data. Furthermore, AI technology can adjust the type and concentration of pesticides to minimize the impact on the environment. This reduces the burden on the environment by monitoring the growth status of agricultural crops and suggesting the optimal amount of pesticide to use from an environmental protection perspective.

[0078] IoT technology can use IoT sensors to collect detailed microclimate data on farmland and propose cultivation methods for each region. IoT technology, for example, can use IoT sensors to collect detailed microclimate data on farmland. IoT technology can also propose optimal crop selection and cultivation methods based on weather data for each region. Furthermore, IoT technology can notify farmers in real time of abnormal weather and issue alerts. This allows detailed collection of microclimate data on farmland and proposals for optimal cultivation methods for each region, improving the quality and yield of agricultural crops.

[0079] IoT technology can monitor the operating status of agricultural machinery in real time using IoT devices and predict failures. IoT technology can also use vibration sensors and temperature sensors to detect abnormal behavior and identify signs of failure. IoT technology can also analyze past operating data, learn failure patterns, and create predictive models. This makes it possible to monitor the operating status of agricultural machinery in real time and predict failures, thereby improving the efficiency of agricultural work.

[0080] IoT technology can use an emotion estimation function to monitor farmers' emotions while they work and make suggestions to improve work efficiency. IoT technology, for example, uses the emotion estimation function to monitor farmers' emotions while they work. IoT technology can also use the emotion estimation function to monitor farmers' emotions while they work and suggest optimal work schedules. IoT technology can also use the emotion estimation function to monitor farmers' emotions while they work and suggest relaxation methods to reduce stress. In this way, by monitoring farmers' emotions while they work and making suggestions to improve work efficiency, farmers' work efficiency is improved.

[0081] IoT technology can collect and manage environmental data not only from farmland but also from greenhouses and hydroponic cultivation facilities. IoT technology can also collect and manage environmental data from greenhouses and hydroponic cultivation facilities. IoT technology can also suggest optimal cultivation conditions based on environmental data. Furthermore, IoT technology can notify farmers in real time if an abnormality occurs and issue an alert. This allows for the optimization of the growing environment for agricultural crops by collecting and managing environmental data from not only farmland but also from greenhouses and hydroponic cultivation facilities.

[0082] IoT technology can monitor the post-harvest storage environment of agricultural produce using IoT sensors and support quality maintenance. IoT technology, for example, uses IoT sensors to monitor the post-harvest storage environment of agricultural produce. IoT technology can also propose optimal storage conditions based on data on the storage environment. Furthermore, IoT technology can notify farmers in real time if an abnormality occurs and issue an alert. This helps maintain the quality of agricultural produce by monitoring the post-harvest storage environment and supporting quality maintenance.

[0083] IoT technology can use an emotion estimation function to evaluate farmers' work satisfaction in real time and make suggestions for improvement. IoT technology, for example, can use an emotion estimation function to evaluate farmers' work satisfaction in real time. IoT technology can also use an emotion estimation function to evaluate farmers' work satisfaction in real time and suggest optimal work environments and work contents. IoT technology can also use an emotion estimation function to evaluate farmers' work satisfaction in real time and suggest relaxation methods to reduce stress. In this way, by evaluating farmers' work satisfaction in real time and making suggestions for improvement, the working environment for farmers can be improved.

[0084] 5G technology can analyze drone footage of the entire farmland in real time and instantly identify any abnormalities. For example, 5G technology can send high-resolution video data to the cloud, where AI can identify any abnormalities. 5G technology can also identify areas of pest infestation or nutrient deficiencies and notify farmers. 5G technology can also map abnormalities based on the video data, allowing farmers to visually confirm them. This allows drone footage of the entire farmland to be analyzed in real time and any abnormalities to be immediately identified, enabling rapid response.

[0085] 5G technology can be used to integrate data from sensors spread across a wide range of farmland in real time, enabling comprehensive farmland management. 5G technology can transmit data such as temperature, humidity, and soil nutrients to the cloud for comprehensive management. 5G technology can also provide immediate notifications and suggest countermeasures when abnormalities occur. 5G technology can also display data visually, allowing farmers to easily understand the situation. This allows data from sensors spread across a wide range of farmland to be integrated in real time, enabling comprehensive farmland management, enabling a unified understanding of the status of the farmland.

[0086] 5G technology can use emotion estimation to monitor farmers' emotions while working in real time and make suggestions to reduce stress. 5G technology can, for example, use emotion estimation to monitor farmers' emotions while working in real time. 5G technology can also use emotion estimation to monitor farmers' emotions while working in real time and make suggestions to reduce stress. 5G technology can also use emotion estimation to monitor farmers' emotions while working in real time and make suggestions to reduce stress. 5G technology can also use emotion estimation to monitor farmers' emotions while working in real time and make suggestions to reduce stress, improving farmers' health and work efficiency.

[0087] 5G technology can be used to manage infrastructure data not only for farmland but for the entire rural area in real time, improving efficiency throughout the region. 5G technology can send data such as road conditions and power supply status to the cloud for comprehensive management. 5G technology can also provide immediate notification in the event of an abnormality and suggest countermeasures. 5G technology can also display data visually, allowing local residents to easily understand the situation. This will enable real-time management of infrastructure data not only for farmland but for the entire rural area, improving efficiency throughout the region and supporting the sustainable development of rural areas.

[0088] 5G technology can be used to monitor the status of agricultural products during transportation in real time, helping to maintain quality. 5G technology can send data such as temperature, humidity, and vibration to the cloud to monitor quality during transportation. 5G technology can also provide immediate notification and suggest countermeasures if an abnormality occurs. 5G technology can also visually display data during transportation, making it easier for transporters to understand the situation. This allows for real-time monitoring of the status of agricultural products during transportation and helps to maintain quality, thereby maintaining the quality of agricultural products.

[0089] 5G technology can use emotion estimation to analyze consumer purchasing behavior in real time and optimize marketing strategies. 5G technology can, for example, use emotion estimation to analyze consumer purchasing behavior in real time. 5G technology can also use emotion estimation to analyze consumer purchasing behavior in real time and propose optimal marketing strategies. 5G technology can also use emotion estimation to analyze consumer purchasing behavior in real time and propose optimal advertising timing and content. This allows for real-time analysis of consumer purchasing behavior and optimization of marketing strategies, improving sales efficiency.

[0090] AI technology can use generative AI to optimize the motion plans of robots to advance the automation of agricultural work. For example, AI technology can use generative AI to optimize the motion patterns of a harvesting robot. AI technology can also use generative AI to optimize the motion patterns of a planting robot. Furthermore, AI technology can also use generative AI to optimize the motion patterns of a weeding robot. In this way, by optimizing the motion plans of robots to advance the automation of agricultural work, the efficiency of agricultural work is improved.

[0091] AI technology can use its emotion estimation function to propose work schedules that will increase farmers' motivation. For example, AI technology can use its emotion estimation function to propose work schedules that will increase farmers' motivation. AI technology can also use its emotion estimation function to propose work schedules that will increase farmers' motivation, and suggest intensive work when farmers are highly motivated. AI technology can also use its emotion estimation function to propose work schedules that will increase farmers' motivation, and suggest time for rest and refreshment when farmers are low in motivation. In this way, proposing work schedules that will increase farmers' motivation improves the efficiency of agricultural work.

[0092] AI technology can support the optimization of not only farm work but also farm management, improving profitability. For example, AI technology can support the optimization of not only farm work but also farm management. AI technology can also select highly profitable crops and propose sales strategies. Furthermore, AI technology can also propose efficient resource management and optimal labor allocation to reduce costs. This will support the optimization of not only farm work but also farm management, improving profitability and improving the efficiency of agriculture as a whole.

[0093] AI technology can not only improve the efficiency of agricultural work but also support environmental conservation activities on farmland. For example, AI technology can not only improve the efficiency of agricultural work but also support environmental conservation activities on farmland. AI technology can also make suggestions for the use of environmentally friendly pesticides and soil improvement. Furthermore, AI technology can also make suggestions for the introduction of sustainable agricultural techniques and the protection of biodiversity. This not only improves the efficiency of agricultural work but also supports environmental conservation activities on farmland, thereby realizing sustainable agriculture.

[0094] AI technology can use the emotion estimation function to suggest community activities to improve farmers' work satisfaction. For example, AI technology can use the emotion estimation function to suggest community activities to improve farmers' work satisfaction. AI technology can also use the emotion estimation function to suggest community activities to improve farmers' work satisfaction and suggest events and workshops for farmers to interact with each other. AI technology can also use the emotion estimation function to suggest community activities to improve farmers' work satisfaction and suggest activities to reduce farmers' stress and increase their motivation. In this way, suggesting community activities to improve farmers' work satisfaction improves the working environment for farmers.

[0095] AI technology can use generative AI to simulate the optimal natural environment for growing crops and propose sustainable cultivation methods. For example, AI technology can use generative AI to simulate the optimal natural environment for growing crops. AI technology can also use generative AI to simulate the optimal natural environment for growing crops and propose optimal cultivation conditions. AI technology can also use generative AI to simulate the optimal natural environment for growing crops and propose sustainable cultivation methods. In this way, sustainable agriculture can be achieved by simulating the optimal natural environment for growing crops and proposing sustainable cultivation methods.

[0096] AI technology can monitor the growth status of agricultural crops and propose resource usage plans to minimize environmental impact. For example, AI technology can monitor the growth status of agricultural crops and optimize the amount of water and fertilizer used. AI technology can also propose optimal irrigation schedules and fertilization plans based on environmental data. Furthermore, AI technology can also propose plans to adjust resource usage in the event of abnormal weather. In this way, sustainable agriculture can be achieved by monitoring the growth status of agricultural crops and proposing resource usage plans to minimize environmental impact.

[0097] AI technology can use its emotion estimation function to suggest educational programs to raise farmers' environmental awareness. For example, AI technology can use its emotion estimation function to suggest educational programs to raise farmers' environmental awareness. AI technology can also use its emotion estimation function to suggest educational programs to raise farmers' environmental awareness and suggest online courses or workshops. AI technology can also use its emotion estimation function to suggest educational programs to raise farmers' environmental awareness and provide videos and materials that convey the importance of environmental conservation activities. In this way, sustainable agriculture can be achieved by suggesting educational programs to raise farmers' environmental awareness.

[0098] AI technology can monitor not only crops but also livestock rearing environments, supporting sustainable livestock farming. For example, AI technology can monitor not only crops but also livestock rearing environments. AI technology can also monitor livestock body temperature and activity levels, and monitor their health in real time. Furthermore, AI technology can also suggest optimization of feed usage and rearing environments. This will enable monitoring not only crops but also livestock rearing environments, supporting sustainable livestock farming and achieving sustainable agriculture.

[0099] When monitoring the growth status of agricultural crops, AI technology can make suggestions that take into account the protection of the local ecosystem. For example, AI technology can monitor the growth status of agricultural crops and make suggestions for the use of ecosystem-friendly pesticides and soil improvement. AI technology can also make suggestions for protecting biodiversity and introducing environmentally friendly agricultural techniques. Furthermore, AI technology can also suggest optimal resource management and environmental protection activities based on environmental data. This will enable sustainable agriculture by making suggestions that take into account the protection of the local ecosystem when monitoring the growth status of agricultural crops.

[0100] AI technology can use emotion estimation to analyze consumers' environmental awareness and optimize marketing strategies for sustainable agricultural products. For example, AI technology can use emotion estimation to analyze consumers' environmental awareness. AI technology can also use emotion estimation to analyze consumers' environmental awareness and optimize marketing strategies for sustainable agricultural products. Furthermore, AI technology can also use emotion estimation to analyze consumers' environmental awareness and suggest optimal advertising timing and content. In this way, analyzing consumers' environmental awareness and optimizing marketing strategies for sustainable agricultural products will promote sustainable agriculture.

[0101] AI technology can integrate multiple data sources to improve yield forecast accuracy using generative AI. For example, AI technology can integrate multiple data sources to improve yield forecast accuracy using generative AI. AI technology can also integrate multiple data sources to improve yield forecast accuracy and make predictions in real time using generative AI. Furthermore, AI technology can also integrate multiple data sources to improve yield forecast accuracy using generative AI and allow farmers to check the forecast results on their smartphones or tablets. This allows harvest planning to be optimized by integrating multiple data sources to improve yield forecast accuracy.

[0102] AI technology can monitor the post-harvest storage environment in real time and suggest the optimal storage method to maintain quality. For example, AI technology can monitor the post-harvest storage environment in real time. AI technology can also monitor the post-harvest storage environment in real time and suggest the optimal storage conditions. Furthermore, AI technology can monitor the post-harvest storage environment in real time, notify in real time if an abnormality occurs, and suggest countermeasures. In this way, the quality of agricultural products can be maintained by monitoring the post-harvest storage environment in real time and suggesting the optimal storage method to maintain quality.

[0103] AI technology can not only predict harvest yields but also support the optimization of distribution routes, reducing food waste. For example, AI technology can not only predict harvest yields but also support the optimization of distribution routes. AI technology can also suggest optimal transportation routes and storage locations, reducing food waste. Furthermore, AI technology can also suggest distribution plans that take into account the balance between supply and demand. This not only predicts harvest yields but also supports the optimization of distribution routes, reducing food waste.

[0104] AI technology can monitor not only the storage environment after harvest but also the environment during transportation, supporting quality maintenance. For example, AI technology can monitor not only the storage environment after harvest but also the environment during transportation. AI technology can also visually display data during transportation, making it easy for transporters to understand the situation. Furthermore, AI technology can immediately notify if an abnormality occurs and suggest countermeasures. This allows the quality of agricultural products to be maintained by monitoring not only the storage environment after harvest but also the environment during transportation, supporting quality maintenance.

[0105] AI technology can use emotion estimation to analyze consumer purchasing behavior and propose production plans according to demand. For example, AI technology can use emotion estimation to analyze consumer purchasing behavior. AI technology can also use emotion estimation to analyze consumer purchasing behavior and propose production plans according to demand. AI technology can also use emotion estimation to analyze consumer purchasing behavior and propose optimal production schedules and production volumes. In this way, by analyzing consumer purchasing behavior and proposing production plans according to demand, it is possible to stabilize the food supply.

[0106] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0107] The agricultural support system can also collect detailed microclimate data on farmland and propose cultivation methods for each region. For example, IoT sensors can be used to collect detailed microclimate data on farmland and propose optimal crop selection and cultivation methods based on weather data for each region. It can also provide real-time notifications and alerts to farmers in the event of abnormal weather. This allows for detailed collection of microclimate data on farmland and proposals for optimal cultivation methods for each region, improving the quality and yield of agricultural crops.

[0108] The agricultural support system can also monitor the status of agricultural products during transport in real time and support quality maintenance. For example, data such as temperature, humidity, and vibration can be sent to the cloud to monitor quality during transport. It can also immediately notify customers if an abnormality occurs and suggest countermeasures. It can also visually display data during transport, allowing transport companies to easily understand the situation. This allows for real-time monitoring of the status of agricultural products during transport and supports quality maintenance, thereby maintaining the quality of agricultural products.

[0109] The agricultural support system can also monitor the growth status of crops and propose resource usage plans to minimize environmental impact. For example, it can monitor the growth status of crops and optimize the amount of water and fertilizer used. It can also propose optimal irrigation schedules and fertilization plans based on environmental data. It can also propose plans to adjust resource usage in the event of abnormal weather. In this way, sustainable agriculture can be achieved by monitoring the growth status of crops and proposing resource usage plans to minimize environmental impact.

[0110] The agricultural support system can also make suggestions that take into account the protection of the local ecosystem when monitoring the growth of agricultural crops. For example, it can monitor the growth of agricultural crops and make suggestions for the use of ecosystem-friendly pesticides and soil improvement. It can also make suggestions for protecting biodiversity and introducing environmentally friendly agricultural techniques. It can also make suggestions for optimal resource management and environmental protection activities based on environmental data. This allows for sustainable agriculture to be achieved by making suggestions that take into account the protection of the local ecosystem when monitoring the growth of agricultural crops.

[0111] The agricultural support system can also monitor the growth status of agricultural products, monitor the post-harvest storage environment in real time, and propose optimal storage methods to maintain quality. For example, it can monitor the post-harvest storage environment in real time and propose optimal storage conditions. It can also notify in real time if an abnormality occurs and propose countermeasures. This allows the quality of agricultural products to be maintained by monitoring the post-harvest storage environment in real time and proposing optimal storage methods to maintain quality.

[0112] The agricultural support system can also use an emotion estimation function to monitor farmers' emotions while they work in real time and make suggestions to reduce stress. For example, the emotion estimation function can be used to monitor farmers' emotions while they work in real time. It can also suggest optimal work schedules. It can also suggest relaxation methods to reduce stress. In this way, by monitoring farmers' emotions while they work in real time and making suggestions to reduce stress, it can improve farmers' health and work efficiency.

[0113] The agricultural support system can also use the emotion estimation function to propose work schedules to increase farmers' motivation. For example, the emotion estimation function can be used to propose work schedules to increase farmers' motivation. It can also suggest intensive work when farmers are highly motivated. It can also suggest time for rest and refreshment when farmers are low in motivation. In this way, proposing work schedules to increase farmers' motivation improves the efficiency of agricultural work.

[0114] The agricultural support system can also use the emotion estimation function to suggest community activities to improve farmers' work satisfaction. For example, the emotion estimation function can be used to suggest community activities to improve farmers' work satisfaction. It can also suggest events and workshops for farmers to interact with each other. It can also suggest activities to reduce stress and increase motivation for farmers. In this way, suggesting community activities to improve farmers' work satisfaction improves the working environment for farmers.

[0115] The agricultural support system can also use emotion estimation to analyze consumer purchasing behavior in real time and optimize marketing strategies. For example, the emotion estimation function can be used to analyze consumer purchasing behavior in real time. It can also propose optimal marketing strategies. It can also propose optimal advertising timing and content. This allows for real-time analysis of consumer purchasing behavior and optimization of marketing strategies, improving sales efficiency.

[0116] The agricultural support system can also use the emotion estimation function to analyze consumers' environmental awareness and optimize marketing strategies for sustainable agricultural products. For example, the emotion estimation function can be used to analyze consumers' environmental awareness and optimize marketing strategies for sustainable agricultural products. Furthermore, the system can suggest optimal advertising timing and content. In this way, analyzing consumers' environmental awareness and optimizing marketing strategies for sustainable agricultural products can promote sustainable agriculture.

[0117] The processing flow of the second embodiment will be briefly explained below.

[0118] Step 1: AI technology monitors the growth status of agricultural crops and proposes optimal cultivation methods. Specifically, it analyzes weather and soil data to suggest optimal irrigation and fertilization timing. Generative AI can also be used to create 3D models of crop growth status in real time, allowing for visual confirmation. It can also detect crop diseases and pests early and propose appropriate countermeasures. Step 2: IoT technology collects and manages various farmland data in real time. Specifically, sensors are used to monitor soil humidity, temperature, and nutrient levels, and this data is sent to the cloud. It can also monitor the operating status of agricultural machinery in real time and predict malfunctions. It can also monitor the storage environment of agricultural products after harvesting, helping to maintain quality. Step 3: 5G technology will enable high-speed, high-capacity data communication. Specifically, drones will be used to capture images of the entire farmland and upload the video data to the cloud in real time. Data from sensors across the farmland will be integrated in real time to enable comprehensive farmland management. Furthermore, emotion estimation functionality will be used to monitor farmers' emotions in real time while they work, and suggestions for stress reduction will be made.

[0119] 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.

[0120] 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.

[0121] 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.

[0122] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0123] 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.

[0124] 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.

[0125] 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.

[0126] 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.

[0127] 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).

[0128] 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.

[0129] 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.

[0130] 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.

[0131] 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 a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0132] 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. The smart glasses 214 also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0133] 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.

[0134] 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.

[0135] 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.

[0136] 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.

[0137] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0138] 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.

[0139] 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.

[0140] 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.

[0141] 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.

[0142] 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).

[0143] 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.

[0144] 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.

[0145] 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.

[0146] 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 a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0147] In the headset type terminal 314, 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 headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0148] 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.

[0149] 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.

[0150] 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.

[0151] 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.

[0152] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0153] 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.

[0154] 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.

[0155] 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.

[0156] 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.

[0157] 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).

[0158] 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.

[0159] 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.

[0160] 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.

[0161] 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.

[0162] 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 a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0163] In the robot 414, 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. The robot 414 also has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0164] 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.

[0165] 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.

[0166] 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.

[0167] 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.

[0168] 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.

[0169] 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.

[0170] 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.

[0171] 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).

[0172] 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.

[0173] 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."

[0174] 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.

[0175] 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.

[0176] 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.

[0177] 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.

[0178] 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.

[0179] 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.

[0180] 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.

[0181] 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.

[0182] 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.

[0183] 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.

[0184] 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.

[0185] 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]

[0186] 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. AI technology that uses AI to monitor the growth status of agricultural crops and propose optimal cultivation methods, IoT technology is used to collect and manage various farmland data in real time, Equipped with 5G technology that enables high-speed, large-capacity data communication A system characterized by:

2. The AI ​​technology is Analyzing meteorological or soil data and proposing the cultivation method 2. The system of claim 1.

3. The IoT technology is Sensors are used to monitor soil moisture, temperature, and nutrient status, and the data is sent to the cloud.

2. The system of claim 1.

4. The 5G technology is Drones are used to capture images of the entire farmland, and the video data is uploaded to the cloud in real time.

2. The system of claim 1.

5. The AI ​​technology is Monitor the growth status of the crops and optimize resource usage 2. The system of claim 1.

6. The AI ​​technology is Predict the yield and harvest at the right time 2. The system of claim 1.

7. The AI ​​technology is The growth status of the crops will be visualized in real time using the 3D model.

2. The system of claim 1.

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