system

Abstract

The system according to this embodiment aims to improve agricultural productivity by predicting harvest times based on soil conditions and weather forecasts, early detection and treatment suggestions for pests and diseases, and planning the optimal supply of water and fertilizer. [Solution] The system according to the embodiment comprises a measurement unit, a collection unit, a prediction unit, a monitoring unit, a proposal unit, and a planning unit. The measurement unit measures the soil condition. The collection unit collects weather forecast data. The prediction unit analyzes the data obtained by the measurement unit and the collection unit to predict the harvest time. The monitoring unit monitors the condition of the crops. The proposal unit analyzes the data obtained by the monitoring unit to propose methods for early detection and treatment of pests and diseases. The planning unit analyzes data on soil moisture and nutrients to create a plan for water or fertilizer supply.

Description

Technical Field

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

Background Art

[0002] Patent Document 1 discloses a method for controlling a persona chatbot, which is performed by at least one processor, and includes steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of a chatbot character, encoding the prompt, and inputting the encoded prompt into a language model to generate a chatbot utterance in response to the user utterance.

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the conventional technology, there is a problem that it is difficult to efficiently perform prediction of the harvesting time based on the soil condition and weather forecast, early detection of pests and diseases, and proposal of treatment, and optimal supply planning of water and fertilizers.

[0005] The system according to the embodiment aims to perform prediction of the harvesting time based on the soil condition and weather forecast, early detection of pests and diseases, and proposal of treatment, and optimal supply planning of water and fertilizers in order to improve agricultural productivity.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a measurement unit, a collection unit, a prediction unit, a monitoring unit, a proposal unit, and a planning unit. The measurement unit measures the soil condition. The collection unit collects weather forecast data. The prediction unit analyzes the data obtained by the measurement unit and the collection unit to predict the harvest time. The monitoring unit monitors the condition of the crops. The proposal unit analyzes the data obtained by the monitoring unit to propose methods for early detection and treatment of pests and diseases. The planning unit analyzes data on soil moisture and nutrients to create a water or fertilizer supply plan. [Effects of the Invention]

[0007] The system according to this embodiment can improve agricultural productivity by predicting harvest times based on soil conditions and weather forecasts, early detection and treatment suggestions for pests and diseases, and creating optimal water and fertilizer supply plans. [Brief explanation of the drawing]

[0008] [Figure 1] This is a conceptual diagram showing an example of the configuration of a data processing system according to the first embodiment. [Figure 2] This is a conceptual diagram showing an example of the essential functions of a data processing device and a smart device according to the first embodiment. [Figure 3] This is a conceptual diagram showing an example of the configuration of a data processing system according to the second embodiment. [Figure 4] This is a conceptual diagram showing an example of the main functions of a data processing device and smart glasses according to the second embodiment. [Figure 5] This is a conceptual diagram showing an example of the configuration of a data processing system according to the third embodiment. [Figure 6] This is a conceptual diagram showing an example of the main functions of a data processing device and a headset-type terminal according to the third embodiment. [Figure 7] This is a conceptual diagram showing an example of the configuration of a data processing system according to the fourth embodiment. [Figure 8] This is a conceptual diagram showing an example of the main functions of a data processing device and a robot according to the fourth embodiment. [Figure 9]This shows an emotion map where multiple emotions are mapped. [Figure 10] This shows an emotion map where multiple emotions are mapped. [Modes for carrying out the invention]

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

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

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

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

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

[0014] In the following embodiments, the numbered communication I / F (Interface) is an interface including a communication processor, an antenna, and the like. The communication I / F manages communication between a plurality of 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), or Bluetooth (registered trademark).

[0015] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B". That is, "A and / or B" means that it may be only A, only B, or a combination of A and B. Also, in this specification, when expressing three or more matters connected by "and / or", the same concept as "A and / or B" is applied.

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

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

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

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

[0020] The reception device 38 is equipped with a touch panel 38A and a microphone 38B, and accepts user input. The touch panel 38A accepts user input via touch by detecting contact with an object (e.g., a pen or finger). The microphone 38B accepts user input via voice by detecting the user's voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and microphone 38B to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 (see Figure 2) acquires the data indicating the user input.

[0021] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user by outputting the data in a form perceptible to the user (e.g., audio and / or text). The display 40A displays visible information such as text and images according to instructions from the processor 46. The speaker 40B outputs audio according to instructions from the processor 46. The camera 42 is a small digital camera equipped with an optical system such as a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0022] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various types of information between processor 46 and processor 28 via network 54.

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

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

[0025] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

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

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

[0028] (Example of form 1) An AI assistant for improving agricultural productivity according to an embodiment of the present invention is a system that predicts harvest time based on soil conditions and weather forecasts, enables early detection and treatment suggestions for pests and diseases, and creates an optimal water and fertilizer supply plan. The AI ​​assistant for improving agricultural productivity has a measurement unit that measures soil conditions, thereby acquiring data on soil moisture and nutrients. Next, there is a collection unit that collects weather forecast data, thereby acquiring weather forecast data. There is a prediction unit that analyzes this data to predict the harvest time. Furthermore, there is a monitoring unit that monitors the condition of crops, thereby monitoring the health of the crops. There is a proposal unit that analyzes the data obtained by the monitoring unit and proposes early detection and treatment methods for pests and diseases. Finally, there is a planning unit that analyzes soil moisture and nutrient data and creates an appropriate water or fertilizer supply plan. For example, if soil moisture is low, the planning unit will create an appropriate water supply plan. It is also possible to adjust the harvest time based on the growth stage of the crop. Furthermore, it can propose treatment methods according to the type of abnormality. For example, if a specific pest or disease occurs, it will propose a treatment method according to its type. It is also possible to propose a long-term agricultural plan based on the data collected by the AI. For example, it can analyze past data and predict future yields. Furthermore, the collected data can be stored in the cloud and shared with other farmers. This allows farmers to share information and achieve more efficient agriculture. In addition, it is possible to estimate the user's emotions and adjust the frequency of soil measurements based on those emotions. For example, if the user is stressed, the measurement frequency can be reduced to alleviate the user's burden. It is also possible to measure soil conditions at different depths simultaneously. This allows for a more detailed understanding of soil conditions. As a result, the AI ​​assistant for improving agricultural productivity can predict harvest times based on soil conditions and weather forecasts, provide early detection and treatment suggestions for pests and diseases, and create optimal water and fertilizer supply plans.

[0029] The AI ​​assistant for improving agricultural productivity according to this embodiment comprises a measurement unit, a data collection unit, a prediction unit, a monitoring unit, a proposal unit, and a planning unit. The measurement unit measures the condition of the soil. The condition of the soil includes, but is not limited to, humidity, pH, and nutrient concentration. For example, the measurement unit measures the humidity of the soil using a sensor. The measurement unit can also measure the pH of the soil. The measurement unit can also measure the nutrient concentration of the soil. For example, the measurement unit measures the humidity of the soil in real time and collects the data. The measurement unit can also periodically measure the pH of the soil and collect the data. The measurement unit can also periodically measure the nutrient concentration of the soil and collect the data. The data collection unit collects weather forecast data. The weather forecast data includes, but is not limited to, temperature, precipitation, and wind speed. For example, the data collection unit collects temperature data. The data collection unit can also collect precipitation data. The data collection unit can also collect wind speed data. For example, the data collection unit collects temperature data in real time and analyzes the data. The data collection unit can also collect precipitation data periodically and analyze the data. The data collection unit can also collect wind speed data periodically and analyze the data. The forecasting unit analyzes the data obtained by the measurement and data collection units and predicts the harvest time. The forecasting unit predicts the harvest time based on, for example, the growth stage of the crop. The forecasting unit can also predict the harvest time based on weather conditions. The forecasting unit can also predict the harvest time based on past data. For example, the forecasting unit analyzes the growth stage of the crop and predicts the optimal harvest time. The forecasting unit can also analyze weather conditions and predict the optimal harvest time. The forecasting unit can also analyze past data and predict the optimal harvest time. The monitoring unit monitors the condition of the crop. The condition of the crop includes, but is not limited to, leaf color, growth rate, and the presence or absence of pests and diseases. The monitoring unit monitors leaf color, for example. The monitoring unit can also monitor the growth rate. The monitoring unit can also monitor the presence or absence of pests and diseases. For example, the monitoring unit can monitor leaf color in real time and collect data. The monitoring unit can also periodically monitor growth rate and collect data.The monitoring unit can also periodically monitor for the presence of pests and diseases and collect data. The proposal unit analyzes the data obtained by the monitoring unit and proposes methods for early detection and treatment of pests and diseases. For example, the proposal unit proposes treatment methods according to the type of pest or disease. The proposal unit can also propose preventive measures according to the risk of pest and disease outbreaks. The proposal unit can also propose countermeasures according to the current state of pest and disease outbreaks. For example, the proposal unit can identify the type of pest or disease and propose the optimal treatment method. The proposal unit can also analyze the risk of pest and disease outbreaks and propose preventive measures. The proposal unit can also analyze the current state of pest and disease outbreaks and propose countermeasures. The planning unit analyzes data on soil moisture and nutrients and develops plans for water or fertilizer supply. For example, the planning unit develops a water supply plan based on soil moisture. The planning unit can also develop a fertilizer supply plan based on the concentration of nutrients in the soil. The planning unit can also develop water and fertilizer supply plans based on the growth stage of the crop. For example, the planning unit analyzes soil moisture and develops an optimal water supply plan. The planning unit can also analyze the concentration of nutrients in the soil and create an optimal fertilizer supply plan. The planning unit can also analyze the growth stage of crops and create an optimal water and fertilizer supply plan. As a result, the AI ​​assistant for improving agricultural productivity according to this embodiment can predict harvest times based on soil conditions and weather forecasts, provide early detection and treatment suggestions for pests and diseases, and create an optimal water and fertilizer supply plan.

[0030] The measurement unit measures soil conditions. Soil conditions include, but are not limited to, humidity, pH, and nutrient concentrations. For example, the measurement unit measures soil humidity using a sensor. Specifically, a soil humidity sensor detects the amount of moisture in the soil in real time and transmits the data to a central database. This allows farmers to immediately understand whether the soil is dry or wet and to plan appropriate irrigation. The measurement unit can also measure soil pH. A soil pH sensor periodically measures the acidity or alkalinity of the soil and collects the data. This allows farmers to maintain the pH balance of the soil and provide an optimal environment for crop growth. Furthermore, the measurement unit can also measure the concentration of nutrients in the soil. For example, it uses sensors to measure the concentrations of major nutrients such as nitrogen, phosphorus, and potassium in the soil and collects the data. This allows farmers to understand the nutritional status of crops and add fertilizer as needed to maximize crop health and yield. The measurement unit collects this data in real time and transmits it to a central database, enabling farmers to quickly and accurately understand soil conditions and take appropriate measures.

[0031] The data collection unit collects weather forecast data. This data includes, but is not limited to, temperature, precipitation, and wind speed. For example, the unit collects temperature data. Specifically, it obtains temperature data in real time from a weather data service and stores this data in a central database. This allows farmers to plan appropriate agricultural activities in response to temperature fluctuations. The data collection unit can also collect precipitation data. Precipitation data is obtained from rainfall sensors and weather data services and is updated in real time. This allows farmers to adjust irrigation plans based on rainfall forecasts and ensure the efficient use of water resources. Furthermore, the data collection unit can also collect wind speed data. Wind speed data is obtained from wind speed sensors and weather data services and is updated in real time. This allows farmers to plan appropriate agricultural activities in response to wind speed fluctuations, helping to protect crops and prevent the spread of pests and diseases. The data collection unit centrally manages this weather forecast data and can collaborate with other systems and departments as needed. For example, the collected data is stored on a cloud server, making it accessible to the forecasting and planning units. This allows the data collection unit to efficiently and effectively collect weather forecast data, improving the overall system performance.

[0032] The prediction unit analyzes data obtained by the measurement and collection units to predict the harvest time. Specifically, it predicts the harvest time based on the growth stage of the crop. For example, to analyze the growth stage of the crop, it uses image recognition technology to evaluate the condition of the leaves and stems of the crop and understand the progress of growth. The prediction unit can also predict the harvest time based on weather conditions. For example, it analyzes weather data such as temperature, precipitation, and sunshine hours to identify the optimal weather conditions for crop growth. Furthermore, the prediction unit can also predict the harvest time based on historical data. For example, it analyzes past harvest data and weather data to identify the optimal harvest time for a particular crop. The prediction unit integrates this data to predict the optimal harvest time with high accuracy. In addition, the prediction unit uses AI to simulate multiple scenarios and identify the most likely harvest time. This allows farmers to optimize the timing of harvesting and maximize crop quality and yield. The prediction unit can continuously revise its prediction results based on real-time updated data to respond to the latest conditions. As a result, the prediction unit can always provide highly accurate harvest time predictions based on the latest information, supporting farmers' decision-making.

[0033] The monitoring unit monitors the condition of crops. This includes, but is not limited to, leaf color, growth rate, and the presence or absence of pests and diseases. For example, the unit monitors leaf color. Specifically, it uses image recognition technology to analyze the leaf color of crops and assess their health. Changes in leaf color often indicate early signs of nutrient deficiencies or pests and diseases, allowing for early detection and countermeasures. The monitoring unit can also monitor growth rate. Growth rate is evaluated by analyzing regularly captured image data and data from sensors. This allows for early countermeasures if crop growth is slow or abnormal growth patterns are observed. Furthermore, the monitoring unit can also monitor for the presence or absence of pests and diseases. Pest and disease monitoring uses image recognition technology and sensors to analyze the surface of crops and their surrounding environment to detect the presence of pests and diseases. This enables early detection and rapid countermeasures against pests and diseases. The monitoring unit collects this data in real time and transmits it to a central database, enabling farmers to quickly and accurately understand the condition of their crops and take appropriate measures.

[0034] The proposal department analyzes data obtained by the monitoring department and proposes methods for early detection and treatment of pests and diseases. Specifically, it proposes treatment methods tailored to the type of pest or disease. For example, it uses image recognition technology to identify the type of pest or disease and proposes the optimal treatment method for that type. The proposal department can also propose preventive measures based on the risk of pest and disease outbreaks. For example, it analyzes past data and weather conditions to identify the times and conditions under which the risk of a particular pest or disease outbreak increases and proposes preventive measures. Furthermore, the proposal department can propose countermeasures based on the current state of pest and disease outbreaks. For example, if a pest or disease outbreak is confirmed, it evaluates its scope and impact and proposes rapid and effective countermeasures. The proposal department integrates this data and uses AI to propose optimal treatment methods and preventive measures with high accuracy. This allows farmers to detect pests and diseases early and take rapid countermeasures, maximizing crop health and yield. Based on data updated in real time, the proposal department continuously modifies its proposals to respond to the latest situations. This allows the proposal department to always provide highly accurate proposals based on the latest information, supporting farmers' decision-making.

[0035] The planning department analyzes soil moisture and nutrient data to create water and fertilizer supply plans. Specifically, it creates water supply plans based on soil moisture. For example, it analyzes data from soil moisture sensors to create an irrigation schedule that maintains the optimal moisture level for crop growth. The planning department can also create fertilizer supply plans based on soil nutrient concentrations. For example, it analyzes the concentrations of major nutrients such as nitrogen, phosphorus, and potassium in the soil to create a fertilizer schedule that supplies the nutrients necessary for crop growth at the appropriate time. Furthermore, the planning department can create water and fertilizer supply plans based on the crop's growth stage. For example, it analyzes the crop's growth stage and plans the optimal amount of water and fertilizer to supply according to each stage of growth. The planning department can integrate this data and use AI to create highly accurate optimal supply plans. This allows farmers to optimize water and fertilizer supply and maximize crop health and yield. The planning department can continuously revise supply plans based on real-time updated data to respond to the latest conditions. This allows the planning department to provide highly accurate supply plans based on the latest information at all times, supporting the decision-making of agricultural workers.

[0036] The prediction unit can adjust the harvest time based on the growth stage of the crop. For example, the prediction unit can analyze the growth stage of the crop and adjust the optimal harvest time. The prediction unit can also adjust the harvest time based on the growth stage of the crop. The prediction unit can also analyze the growth stage of the crop and adjust the harvest time. This makes it possible to adjust the harvest time according to the growth stage of the crop. Some or all of the above processing in the prediction unit may be performed using, for example, a generating AI, or without using a generating AI. For example, the prediction unit can input crop growth stage data into a generating AI and have the generating AI perform the adjustment of the harvest time.

[0037] The suggestion unit can propose treatment methods according to the type of abnormality. For example, the suggestion unit can propose treatment methods according to the type of pest or disease. The suggestion unit can also propose treatment methods according to the type of abnormality. The suggestion unit can analyze the type of abnormality and propose treatment methods. This allows for the proposal of appropriate treatment methods according to the type of abnormality. Some or all of the above-described processes in the suggestion unit may be performed using, for example, a generative AI, or without using a generative AI. For example, the suggestion unit can input abnormality type data into a generative AI and have the generative AI execute the suggestion of treatment methods.

[0038] The planning unit can create water and fertilizer supply plans according to the type and growth stage of the crops. For example, the planning unit can create water and fertilizer supply plans according to the type of crop. The planning unit can also create water and fertilizer supply plans based on the growth stage of the crops. The planning unit can also analyze the type and growth stage of the crops and create supply plans. This makes it possible to create appropriate supply plans according to the type and growth stage of the crops. Some or all of the above processes in the planning unit may be performed using, for example, a generative AI, or not using a generative AI. For example, the planning unit can input crop type and growth stage data into a generative AI and have the generative AI create a supply plan.

[0039] The proposal unit can propose long-term agricultural plans based on data collected by the AI. For example, the proposal unit can analyze past data and predict future yields. The proposal unit can also propose long-term agricultural plans. The proposal unit can analyze collected data and propose long-term agricultural plans. This allows it to propose long-term agricultural plans. Some or all of the above processing in the proposal unit may be performed using, for example, a generative AI, or without a generative AI. For example, the proposal unit can input collected data into a generative AI and have the generative AI execute a proposal for a long-term agricultural plan.

[0040] The sharing unit can store the collected data in the cloud and share it with other farmers. The sharing unit can, for example, upload the collected data to the cloud. The sharing unit can also share the data stored in the cloud with other farmers. The sharing unit can store the collected data in the cloud and share it with other farmers. This allows the data to be stored in the cloud and shared with other farmers. Some or all of the above processing in the sharing unit may be performed using, for example, a generative AI, or not using a generative AI. For example, the sharing unit can input the collected data into a generative AI and have the generative AI perform the storage and sharing to the cloud.

[0041] The measurement unit can simultaneously measure soil conditions at different depths and integrate and analyze the data from each depth. For example, the measurement unit can simultaneously acquire soil samples at different depths and measure the moisture and nutrients at each depth. The measurement unit can also integrate the measurement data and analyze the overall condition of the soil. The measurement unit can also compare the data from each depth to understand the changes in soil at different depths. This allows for simultaneous measurement of soil conditions at different depths and detailed analysis. Some or all of the above-described processes in the measurement unit may be performed using, for example, a generating AI, or not using a generating AI. For example, the measurement unit can input soil data from different depths into a generating AI and have the generating AI perform data integration and analysis.

[0042] The measurement unit can measure the concentration of specific nutrients in soil in real time and provide immediate feedback. For example, the measurement unit can measure the concentration of specific nutrients such as nitrogen, phosphorus, and potassium in real time when measuring soil. The measurement unit can also display the measurement results immediately and provide feedback to the user. The measurement unit can also upload the measurement data to the cloud and share it with other farmers. This allows for real-time measurement of the concentration of specific nutrients and immediate feedback. Some or all of the above processing in the measurement unit may be performed using, for example, a generating AI, or not using a generating AI. For example, the measurement unit can input concentration data of specific nutrients into a generating AI and have the generating AI perform real-time measurement and feedback.

[0043] The measurement unit can acquire ambient environmental data simultaneously with soil measurements and perform a comprehensive analysis. For example, the measurement unit can simultaneously acquire environmental data such as temperature, humidity, and wind speed when measuring soil. The measurement unit can also integrate the measurement data and environmental data to perform a comprehensive analysis. The measurement unit can also take environmental data into consideration to more accurately understand the soil condition. This enables the simultaneous acquisition of ambient environmental data and a comprehensive analysis. Some or all of the above-described processes in the measurement unit may be performed using, for example, a generation AI, or without a generation AI. For example, the measurement unit can input environmental data into a generation AI and have the generation AI perform a comprehensive analysis.

[0044] The measurement unit can automatically upload measurement data to the cloud and share it with other farmers when measuring soil. For example, the measurement unit can upload soil measurement data to the cloud in real time. The measurement unit can also share data on the cloud and exchange information with other farmers. Based on the shared data, the measurement unit can also understand the soil conditions of the entire region. This allows the measurement data to be automatically uploaded to the cloud and shared with other farmers. Some or all of the above processes in the measurement unit may be performed using, for example, a generative AI, or not using a generative AI. For example, the measurement unit can input measurement data into a generative AI and have the generative AI perform the uploading and sharing to the cloud.

[0045] The data collection unit can predict extreme weather events by comparing current weather forecast data with past weather data when collecting weather forecast data. For example, the data collection unit can compare past weather data with current weather forecast data to detect signs of extreme weather events. The data collection unit can also notify the user if extreme weather events are predicted. The data collection unit can also upload the predicted extreme weather data to the cloud and share it with other farmers. This allows for prediction of extreme weather events by comparing it with past weather data. Some or all of the above processing in the data collection unit may be performed using, for example, a generative AI, or without a generative AI. For example, the data collection unit can input past weather data and current weather forecast data into a generative AI and have the generative AI perform extreme weather predictions.

[0046] The data collection unit can collect regional microclimate data when collecting weather forecast data, enabling more accurate predictions. For example, the data collection unit can collect regional microclimate data and integrate it with weather forecast data. Based on the microclimate data, the data collection unit can also provide more accurate weather forecasts. The data collection unit can also upload the microclimate data to the cloud and share it with other farmers. This allows for the collection of regional microclimate data and more accurate predictions. Some or all of the above processing in the data collection unit may be performed using, for example, a generative AI, or not. For example, the data collection unit can input regional microclimate data into a generative AI and have the generative AI perform improvements to the accuracy of the predictions.

[0047] The data collection unit can collect other agricultural data simultaneously when collecting weather forecast data. For example, the data collection unit can collect crop market price data at the same time as weather forecast data. The data collection unit can also integrate the collected data and perform a comprehensive analysis. The data collection unit can also optimize the harvest time based on the market price data. This allows other agricultural data to be collected simultaneously when collecting weather forecast data. Some or all of the above processing in the data collection unit may be performed using, for example, a generative AI, or without a generative AI. For example, the data collection unit can input other agricultural data into a generative AI and have the generative AI perform data collection and analysis.

[0048] The data collection unit can automatically upload weather forecast data to the cloud and share it with other farmers when collecting it. For example, the data collection unit can upload weather forecast data to the cloud in real time. The data collection unit can also share data on the cloud and exchange information with other farmers. Based on the shared data, the data collection unit can also grasp the weather forecast for the entire region. This allows the data to be automatically uploaded to the cloud and shared with other farmers when collecting weather forecast data. Some or all of the above processing in the data collection unit may be performed using, for example, a generative AI, or not using a generative AI. For example, the data collection unit can input weather forecast data into a generative AI and have the generative AI perform the uploading and sharing to the cloud.

[0049] The prediction unit can improve prediction accuracy by referring to past harvest data during the prediction process. For example, the prediction unit can improve prediction accuracy by referring to past harvest data. The prediction unit can also suggest the optimal harvest time based on the harvest data. The prediction unit can also upload prediction data to the cloud and share it with other farmers. This allows for improved prediction accuracy by referring to past harvest data. Some or all of the above processing in the prediction unit may be performed using, for example, a generative AI, or without a generative AI. For example, the prediction unit can input past harvest data into a generative AI and have the generative AI perform the task of improving prediction accuracy.

[0050] The forecasting unit can adjust the harvest timing by considering other agricultural data during the forecasting process. For example, the forecasting unit adjusts the harvest timing based on market price data for crops. The forecasting unit can also propose the optimal harvest timing by considering market price data. The forecasting unit can also upload the harvest timing data to the cloud and share it with other farmers. This allows for adjustment of the harvest timing by considering other agricultural data. Some or all of the above processing in the forecasting unit may be performed using, for example, a generative AI, or not using a generative AI. For example, the forecasting unit can input other agricultural data into a generative AI and have the generative AI perform the harvest timing adjustment.

[0051] The forecasting unit can automatically upload forecast results to the cloud and share them with other farmers. For example, the forecasting unit can upload forecast results to the cloud in real time. The forecasting unit can also share data on the cloud and exchange information with other farmers. Based on the shared data, the forecasting unit can also grasp the harvest timing for the entire region. This allows the forecasting unit to automatically upload forecast results to the cloud and share them with other farmers. Some or all of the above processes in the forecasting unit may be performed using, for example, a generative AI, or not using a generative AI. For example, the forecasting unit can input forecast results into a generative AI and have the generative AI perform the uploading and sharing to the cloud.

[0052] The monitoring unit can monitor the health of crops in real time and immediately notify if an abnormality is detected. For example, the monitoring unit can monitor the health of crops in real time using sensors and notify the user if an abnormality is detected. When an abnormality is detected, the monitoring unit can also provide detailed information about the abnormality at the same time as the notification. Depending on the type of abnormality, the monitoring unit can also suggest appropriate countermeasures. This allows for real-time monitoring of crop health and immediate notification if an abnormality is detected. Some or all of the above processing in the monitoring unit may be performed using, for example, a generative AI, or without a generative AI. For example, the monitoring unit can input crop health data into a generative AI and have the generative AI perform abnormality detection and notification.

[0053] The monitoring unit can predict the risk of specific pest and disease outbreaks and propose preventive measures during monitoring. For example, the monitoring unit predicts the risk of specific pest and disease outbreaks based on crop monitoring data. If the risk of outbreak is high, the monitoring unit can also propose preventive measures. The monitoring unit can also provide detailed instructions on how to implement the preventive measures. This allows the monitoring unit to predict the risk of specific pest and disease outbreaks and propose preventive measures. Some or all of the above processing in the monitoring unit may be performed using, for example, a generative AI, or without a generative AI. For example, the monitoring unit can input pest and disease outbreak risk data into a generative AI and have the generative AI propose preventive measures.

[0054] The monitoring unit can acquire ambient environmental data simultaneously with crop monitoring and perform a comprehensive analysis. For example, the monitoring unit can simultaneously acquire environmental data such as temperature, humidity, and wind speed when monitoring crops. The monitoring unit can also integrate monitoring data and environmental data to perform a comprehensive analysis. By considering the environmental data, the monitoring unit can more accurately grasp the health status of the crops. This allows for the simultaneous acquisition of ambient environmental data and a comprehensive analysis when monitoring crops. Some or all of the above-described processes in the monitoring unit may be performed using, for example, a generative AI, or without a generative AI. For example, the monitoring unit can input environmental data into a generative AI and have the generative AI perform a comprehensive analysis.

[0055] The monitoring unit can automatically upload monitoring data to the cloud and share it with other farmers. For example, the monitoring unit can upload monitoring data to the cloud in real time. The monitoring unit can also share data on the cloud and exchange information with other farmers. Based on the shared data, the monitoring unit can also understand the health status of crops throughout the region. This allows monitoring data to be automatically uploaded to the cloud and shared with other farmers. Some or all of the above processes in the monitoring unit may be performed using, for example, a generative AI, or not using a generative AI. For example, the monitoring unit can input monitoring data into a generative AI and have the generative AI perform the uploading and sharing to the cloud.

[0056] The proposal unit can propose treatment methods according to the type of abnormality and provide specific treatment procedures. For example, the proposal unit proposes the optimal treatment method according to the type of abnormality. The proposal unit can also provide a detailed explanation of the specific procedures of the proposed treatment method. The proposal unit can also upload the treatment procedures to the cloud and share them with other farmers. This allows the proposal unit to propose treatment methods according to the type of abnormality and provide specific treatment procedures. Some or all of the above processing in the proposal unit may be performed using, for example, a generative AI, or not using a generative AI. For example, the proposal unit can input abnormality type data into a generative AI and have the generative AI perform the task of proposing treatment methods and providing procedures.

[0057] The proposal unit can improve the accuracy of its proposals by referring to past treatment data when making a proposal. For example, the proposal unit can improve the accuracy of its proposals by referring to past treatment data. The proposal unit can also propose the optimal treatment method based on the treatment data. The proposal unit can also upload the proposal data to the cloud and share it with other farmers. This allows it to improve the accuracy of its proposals by referring to past treatment data. Some or all of the above processing in the proposal unit may be performed using, for example, a generative AI, or not using a generative AI. For example, the proposal unit can input past treatment data into a generative AI and have the generative AI perform the task of improving the accuracy of its proposals.

[0058] The proposal unit can propose treatment methods by considering other agricultural data when making a proposal. For example, the proposal unit can propose treatment methods based on market price data for crops. The proposal unit can also propose the optimal treatment method by considering market price data. The proposal unit can also upload treatment method data to the cloud and share it with other farmers. This allows it to propose treatment methods by considering other agricultural data. Some or all of the above processing in the proposal unit may be performed using, for example, a generative AI, or not using a generative AI. For example, the proposal unit can input other agricultural data into a generative AI and have the generative AI execute the treatment method proposal.

[0059] The proposal unit can automatically upload the proposal results to the cloud and share them with other farmers. For example, the proposal unit can upload the proposal results to the cloud in real time. The proposal unit can also share data on the cloud and exchange information with other farmers. Based on the shared data, the proposal unit can also understand treatment methods for the entire region. This allows the proposal results to be automatically uploaded to the cloud and shared with other farmers. Some or all of the above processes in the proposal unit may be performed using, for example, a generative AI, or not using a generative AI. For example, the proposal unit can input the proposal results into a generative AI and have the generative AI perform the uploading and sharing to the cloud.

[0060] The planning department can create appropriate water and fertilizer supply plans according to the type and growth stage of the crops. For example, the planning department can create an optimal water and fertilizer supply plan depending on the type of crop. The planning department can also adjust the supply plan based on the growth stage. The planning department can also upload the supply plan to the cloud and share it with other farmers. This allows for the creation of appropriate supply plans according to the type and growth stage of the crops. Some or all of the above processes in the planning department may be performed using, for example, a generative AI, or not using a generative AI. For example, the planning department can input crop type and growth stage data into a generative AI and have the generative AI create a supply plan.

[0061] The planning department can improve the accuracy of its plans by referring to past supply data during the planning stage. For example, the planning department can improve the accuracy of its plans by referring to past supply data. The planning department can also create an optimal supply plan based on the supply data. The planning department can also upload planning data to the cloud and share it with other farmers. This allows for improvement of planning accuracy by referring to past supply data. Some or all of the above processes in the planning department may be performed using, for example, a generative AI, or not using a generative AI. For example, the planning department can input past supply data into a generative AI and have the generative AI perform the task of improving the accuracy of the plans.

[0062] The planning department can create a supply plan by taking other agricultural data into consideration during the planning stage. For example, the planning department can create a supply plan based on market price data for crops. The planning department can also create an optimal supply plan by taking market price data into consideration. The planning department can also upload the supply plan data to the cloud and share it with other farmers. This allows for the creation of a supply plan that takes other agricultural data into consideration. Some or all of the above processes in the planning department may be performed using, for example, a generative AI, or not using a generative AI. For example, the planning department can input other agricultural data into a generative AI and have the generative AI create a supply plan.

[0063] The planning department can automatically upload planning results to the cloud and share them with other farmers. For example, the planning department can upload planning results to the cloud in real time. The planning department can also share data on the cloud and exchange information with other farmers. Based on the shared data, the planning department can also grasp the supply plan for the entire region. This allows the planning results to be automatically uploaded to the cloud and shared with other farmers. Some or all of the above processes in the planning department may be performed using, for example, a generative AI, or not using a generative AI. For example, the planning department can input planning results into a generative AI and have the generative AI perform the uploading and sharing to the cloud.

[0064] The sharing unit can determine the sharing priority based on the importance of the data when sharing. For example, the sharing unit can evaluate the importance of the data and prioritize the sharing of important data. The sharing unit can also postpone the sharing of less important data and share important data quickly. The sharing unit can also upload the priority of the shared data to the cloud and share it with other farmers. This allows for the determination of sharing priority based on the importance of the data. Some or all of the above processing in the sharing unit may be performed using, for example, generative AI, or not using generative AI. For example, the sharing unit can input the importance of the data into the generative AI and have the generative AI execute the sharing priority.

[0065] The sharing section can standardize the data format during sharing, making it easily understandable to other farmers. For example, the sharing section can standardize the format of shared data, making it easily understandable to other farmers. The sharing section can also facilitate information sharing by standardizing the data format. The sharing section can upload the standardized data format to the cloud and share it with other farmers. This standardizes the data format, making it easily understandable to other farmers. Some or all of the above processing in the sharing section may be performed using, for example, a generative AI, or without a generative AI. For example, the sharing section can input the data format standardization into a generative AI and have the generative AI perform the data format standardization.

[0066] The sharing unit can share other agricultural data simultaneously when sharing data. For example, the sharing unit can share market price data of crops at the same time as the shared data. The sharing unit can also integrate the collected data and perform comprehensive analysis. The sharing unit can also optimize the harvest time based on the market price data. This allows other agricultural data to be shared simultaneously. Some or all of the above processing in the sharing unit may be performed using, for example, a generative AI, or not using a generative AI. For example, the sharing unit can input other agricultural data into a generative AI and have the generative AI perform data sharing and analysis.

[0067] The sharing unit can determine the sharing priority based on the importance of the data when sharing. For example, the sharing unit can evaluate the importance of the data and prioritize the sharing of important data. The sharing unit can also postpone the sharing of less important data and share important data quickly. The sharing unit can also upload the priority of the shared data to the cloud and share it with other farmers. This allows for the determination of sharing priority based on the importance of the data. Some or all of the above processing in the sharing unit may be performed using, for example, generative AI, or not using generative AI. For example, the sharing unit can input the importance of the data into the generative AI and have the generative AI execute the sharing priority.

[0068] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0069] AI assistants for improving agricultural productivity can also be equipped with the ability to automatically control irrigation systems based on crop growth forecasts. For example, the forecasting unit analyzes crop growth stages and weather forecast data to determine the optimal irrigation timing. The irrigation control unit can then automatically activate the irrigation system based on instructions from the forecasting unit. The irrigation control unit can also monitor soil moisture data in real time and adjust the amount of irrigation as needed. This ensures optimal water supply for crop growth. Furthermore, if extreme weather is predicted, the irrigation control unit can automatically modify the irrigation plan to protect the crops.

[0070] AI assistants for improving agricultural productivity can also be equipped with the ability to automatically control harvesting robots based on crop growth predictions. For example, the prediction unit analyzes crop growth stages and weather forecast data to determine the optimal harvesting timing. The harvesting control unit can then automatically operate the harvesting robots based on instructions from the prediction unit. The harvesting control unit can also monitor the condition of the crops in real time and fine-tune the harvesting timing. This ensures efficient harvesting while maintaining maximum crop quality. Furthermore, if extreme weather is predicted, the harvesting control unit can automatically modify the harvesting plan to protect the crops.

[0071] AI assistants for improving agricultural productivity can also be equipped with the ability to automatically control fertilizer application systems based on crop growth predictions. For example, the prediction unit analyzes crop growth stages and soil nutrient data to determine the optimal timing for fertilizer application. The fertilizer application control unit can then automatically activate the fertilizer application system based on instructions from the prediction unit. The fertilizer application control unit can also monitor soil nutrient data in real time and adjust the type and amount of fertilizer as needed. This ensures optimal nutrient supply for crop growth. Furthermore, if extreme weather is predicted, the fertilizer application control unit can automatically modify the fertilizer application plan to protect the crops.

[0072] AI assistants for improving agricultural productivity can also be equipped with the ability to automatically control temperature management systems based on crop growth forecasts. For example, the forecasting unit analyzes crop growth stages and weather forecast data to determine the optimal timing for temperature management. The temperature management control unit can then automatically activate the temperature management system based on instructions from the forecasting unit. The temperature management control unit can also monitor the condition of the crops in real time and adjust the temperature as needed. This ensures an optimal temperature environment for crop growth. Furthermore, if extreme weather is predicted, the temperature management control unit can automatically modify the temperature management plan to protect the crops.

[0073] AI assistants for improving agricultural productivity can also be equipped with the ability to automatically control the light irradiation system based on crop growth predictions. For example, the prediction unit analyzes crop growth stages and weather forecast data to determine the optimal timing for light irradiation. The light irradiation control unit can then automatically activate the light irradiation system based on instructions from the prediction unit. The light irradiation control unit can also monitor the condition of the crops in real time and adjust the light intensity and irradiation time as needed. This ensures an optimal light environment for crop growth. Furthermore, if extreme weather is predicted, the light irradiation control unit can automatically change the light irradiation plan to protect the crops.

[0074] The following briefly describes the processing flow for example form 1.

[0075] Step 1: The measuring unit measures the soil condition. Soil condition includes humidity, pH, and nutrient concentration. For example, the measuring unit measures soil humidity in real time using a sensor and collects the data. It can also periodically measure soil pH and nutrient concentration and collect the data. Step 2: The data collection unit collects weather forecast data. This data includes temperature, precipitation, wind speed, etc. The data collection unit can, for example, collect temperature data in real time and analyze it. It can also collect precipitation and wind speed data periodically and analyze that data. Step 3: The prediction unit analyzes the data obtained by the measurement and collection units to predict the harvest time. The prediction unit can predict the harvest time based on the crop's growth stage, weather conditions, and historical data. For example, it can analyze the crop's growth stage and weather conditions to predict the optimal harvest time. Step 4: The monitoring unit monitors the condition of the crops. This includes leaf color, growth rate, and the presence or absence of pests and diseases. For example, the monitoring unit can monitor leaf color in real time and collect data. It can also periodically monitor growth rate and the presence or absence of pests and diseases and collect data. Step 5: The proposal unit analyzes the data obtained by the monitoring unit and proposes methods for early detection and treatment of pests and diseases. The proposal unit can propose treatment methods and preventive measures according to the type of pest or disease, as well as countermeasures according to the occurrence situation. For example, it can identify the type of pest or disease and propose the optimal treatment method. Step 6: The planning department analyzes soil moisture and nutrient data and develops a water or fertilizer supply plan. The planning department can develop an optimal water or fertilizer supply plan based on soil moisture, nutrient concentration, and crop growth stage. For example, it can analyze soil moisture and develop an optimal water supply plan.

[0076] (Example of form 2) An AI assistant for improving agricultural productivity according to an embodiment of the present invention is a system that predicts harvest time based on soil conditions and weather forecasts, enables early detection and treatment suggestions for pests and diseases, and creates an optimal water and fertilizer supply plan. The AI ​​assistant for improving agricultural productivity has a measurement unit that measures soil conditions, thereby acquiring data on soil moisture and nutrients. Next, there is a collection unit that collects weather forecast data, thereby acquiring weather forecast data. There is a prediction unit that analyzes this data to predict the harvest time. Furthermore, there is a monitoring unit that monitors the condition of crops, thereby monitoring the health of the crops. There is a proposal unit that analyzes the data obtained by the monitoring unit and proposes early detection and treatment methods for pests and diseases. Finally, there is a planning unit that analyzes soil moisture and nutrient data and creates an appropriate water or fertilizer supply plan. For example, if soil moisture is low, the planning unit will create an appropriate water supply plan. It is also possible to adjust the harvest time based on the growth stage of the crop. Furthermore, it can propose treatment methods according to the type of abnormality. For example, if a specific pest or disease occurs, it will propose a treatment method according to its type. It is also possible to propose a long-term agricultural plan based on the data collected by the AI. For example, it can analyze past data and predict future yields. Furthermore, the collected data can be stored in the cloud and shared with other farmers. This allows farmers to share information and achieve more efficient agriculture. In addition, it is possible to estimate the user's emotions and adjust the frequency of soil measurements based on those emotions. For example, if the user is stressed, the measurement frequency can be reduced to alleviate the user's burden. It is also possible to measure soil conditions at different depths simultaneously. This allows for a more detailed understanding of soil conditions. As a result, the AI ​​assistant for improving agricultural productivity can predict harvest times based on soil conditions and weather forecasts, provide early detection and treatment suggestions for pests and diseases, and create optimal water and fertilizer supply plans.

[0077] The AI ​​assistant for improving agricultural productivity according to this embodiment comprises a measurement unit, a data collection unit, a prediction unit, a monitoring unit, a proposal unit, and a planning unit. The measurement unit measures the condition of the soil. The condition of the soil includes, but is not limited to, humidity, pH, and nutrient concentration. For example, the measurement unit measures the humidity of the soil using a sensor. The measurement unit can also measure the pH of the soil. The measurement unit can also measure the nutrient concentration of the soil. For example, the measurement unit measures the humidity of the soil in real time and collects the data. The measurement unit can also periodically measure the pH of the soil and collect the data. The measurement unit can also periodically measure the nutrient concentration of the soil and collect the data. The data collection unit collects weather forecast data. The weather forecast data includes, but is not limited to, temperature, precipitation, and wind speed. For example, the data collection unit collects temperature data. The data collection unit can also collect precipitation data. The data collection unit can also collect wind speed data. For example, the data collection unit collects temperature data in real time and analyzes the data. The data collection unit can also collect precipitation data periodically and analyze the data. The data collection unit can also collect wind speed data periodically and analyze the data. The forecasting unit analyzes the data obtained by the measurement and data collection units and predicts the harvest time. The forecasting unit predicts the harvest time based on, for example, the growth stage of the crop. The forecasting unit can also predict the harvest time based on weather conditions. The forecasting unit can also predict the harvest time based on past data. For example, the forecasting unit analyzes the growth stage of the crop and predicts the optimal harvest time. The forecasting unit can also analyze weather conditions and predict the optimal harvest time. The forecasting unit can also analyze past data and predict the optimal harvest time. The monitoring unit monitors the condition of the crop. The condition of the crop includes, but is not limited to, leaf color, growth rate, and the presence or absence of pests and diseases. The monitoring unit monitors leaf color, for example. The monitoring unit can also monitor the growth rate. The monitoring unit can also monitor the presence or absence of pests and diseases. For example, the monitoring unit can monitor leaf color in real time and collect data. The monitoring unit can also periodically monitor growth rate and collect data.The monitoring unit can also periodically monitor for the presence of pests and diseases and collect data. The proposal unit analyzes the data obtained by the monitoring unit and proposes methods for early detection and treatment of pests and diseases. For example, the proposal unit proposes treatment methods according to the type of pest or disease. The proposal unit can also propose preventive measures according to the risk of pest and disease outbreaks. The proposal unit can also propose countermeasures according to the current state of pest and disease outbreaks. For example, the proposal unit can identify the type of pest or disease and propose the optimal treatment method. The proposal unit can also analyze the risk of pest and disease outbreaks and propose preventive measures. The proposal unit can also analyze the current state of pest and disease outbreaks and propose countermeasures. The planning unit analyzes data on soil moisture and nutrients and develops plans for water or fertilizer supply. For example, the planning unit develops a water supply plan based on soil moisture. The planning unit can also develop a fertilizer supply plan based on the concentration of nutrients in the soil. The planning unit can also develop water and fertilizer supply plans based on the growth stage of the crop. For example, the planning unit analyzes soil moisture and develops an optimal water supply plan. The planning unit can also analyze the concentration of nutrients in the soil and create an optimal fertilizer supply plan. The planning unit can also analyze the growth stage of crops and create an optimal water and fertilizer supply plan. As a result, the AI ​​assistant for improving agricultural productivity according to this embodiment can predict harvest times based on soil conditions and weather forecasts, provide early detection and treatment suggestions for pests and diseases, and create an optimal water and fertilizer supply plan.

[0078] The measurement unit measures soil conditions. Soil conditions include, but are not limited to, humidity, pH, and nutrient concentrations. For example, the measurement unit measures soil humidity using a sensor. Specifically, a soil humidity sensor detects the amount of moisture in the soil in real time and transmits the data to a central database. This allows farmers to immediately understand whether the soil is dry or wet and to plan appropriate irrigation. The measurement unit can also measure soil pH. A soil pH sensor periodically measures the acidity or alkalinity of the soil and collects the data. This allows farmers to maintain the pH balance of the soil and provide an optimal environment for crop growth. Furthermore, the measurement unit can also measure the concentration of nutrients in the soil. For example, it uses sensors to measure the concentrations of major nutrients such as nitrogen, phosphorus, and potassium in the soil and collects the data. This allows farmers to understand the nutritional status of crops and add fertilizer as needed to maximize crop health and yield. The measurement unit collects this data in real time and transmits it to a central database, enabling farmers to quickly and accurately understand soil conditions and take appropriate measures.

[0079] The data collection unit collects weather forecast data. This data includes, but is not limited to, temperature, precipitation, and wind speed. For example, the unit collects temperature data. Specifically, it obtains temperature data in real time from a weather data service and stores this data in a central database. This allows farmers to plan appropriate agricultural activities in response to temperature fluctuations. The data collection unit can also collect precipitation data. Precipitation data is obtained from rainfall sensors and weather data services and is updated in real time. This allows farmers to adjust irrigation plans based on rainfall forecasts and ensure the efficient use of water resources. Furthermore, the data collection unit can also collect wind speed data. Wind speed data is obtained from wind speed sensors and weather data services and is updated in real time. This allows farmers to plan appropriate agricultural activities in response to wind speed fluctuations, helping to protect crops and prevent the spread of pests and diseases. The data collection unit centrally manages this weather forecast data and can collaborate with other systems and departments as needed. For example, the collected data is stored on a cloud server, making it accessible to the forecasting and planning units. This allows the data collection unit to efficiently and effectively collect weather forecast data, improving the overall system performance.

[0080] The prediction unit analyzes data obtained by the measurement and collection units to predict the harvest time. Specifically, it predicts the harvest time based on the growth stage of the crop. For example, to analyze the growth stage of the crop, it uses image recognition technology to evaluate the condition of the leaves and stems of the crop and understand the progress of growth. The prediction unit can also predict the harvest time based on weather conditions. For example, it analyzes weather data such as temperature, precipitation, and sunshine hours to identify the optimal weather conditions for crop growth. Furthermore, the prediction unit can also predict the harvest time based on historical data. For example, it analyzes past harvest data and weather data to identify the optimal harvest time for a particular crop. The prediction unit integrates this data to predict the optimal harvest time with high accuracy. In addition, the prediction unit uses AI to simulate multiple scenarios and identify the most likely harvest time. This allows farmers to optimize the timing of harvesting and maximize crop quality and yield. The prediction unit can continuously revise its prediction results based on real-time updated data to respond to the latest conditions. As a result, the prediction unit can always provide highly accurate harvest time predictions based on the latest information, supporting farmers' decision-making.

[0081] The monitoring unit monitors the condition of crops. This includes, but is not limited to, leaf color, growth rate, and the presence or absence of pests and diseases. For example, the unit monitors leaf color. Specifically, it uses image recognition technology to analyze the leaf color of crops and assess their health. Changes in leaf color often indicate early signs of nutrient deficiencies or pests and diseases, allowing for early detection and countermeasures. The monitoring unit can also monitor growth rate. Growth rate is evaluated by analyzing regularly captured image data and data from sensors. This allows for early countermeasures if crop growth is slow or abnormal growth patterns are observed. Furthermore, the monitoring unit can also monitor for the presence or absence of pests and diseases. Pest and disease monitoring uses image recognition technology and sensors to analyze the surface of crops and their surrounding environment to detect the presence of pests and diseases. This enables early detection and rapid countermeasures against pests and diseases. The monitoring unit collects this data in real time and transmits it to a central database, enabling farmers to quickly and accurately understand the condition of their crops and take appropriate measures.

[0082] The proposal department analyzes data obtained by the monitoring department and proposes methods for early detection and treatment of pests and diseases. Specifically, it proposes treatment methods tailored to the type of pest or disease. For example, it uses image recognition technology to identify the type of pest or disease and proposes the optimal treatment method for that type. The proposal department can also propose preventive measures based on the risk of pest and disease outbreaks. For example, it analyzes past data and weather conditions to identify the times and conditions under which the risk of a particular pest or disease outbreak increases and proposes preventive measures. Furthermore, the proposal department can propose countermeasures based on the current state of pest and disease outbreaks. For example, if a pest or disease outbreak is confirmed, it evaluates its scope and impact and proposes rapid and effective countermeasures. The proposal department integrates this data and uses AI to propose optimal treatment methods and preventive measures with high accuracy. This allows farmers to detect pests and diseases early and take rapid countermeasures, maximizing crop health and yield. Based on data updated in real time, the proposal department continuously modifies its proposals to respond to the latest situations. This allows the proposal department to always provide highly accurate proposals based on the latest information, supporting farmers' decision-making.

[0083] The planning department analyzes soil moisture and nutrient data to create water and fertilizer supply plans. Specifically, it creates water supply plans based on soil moisture. For example, it analyzes data from soil moisture sensors to create an irrigation schedule that maintains the optimal moisture level for crop growth. The planning department can also create fertilizer supply plans based on soil nutrient concentrations. For example, it analyzes the concentrations of major nutrients such as nitrogen, phosphorus, and potassium in the soil to create a fertilizer schedule that supplies the nutrients necessary for crop growth at the appropriate time. Furthermore, the planning department can create water and fertilizer supply plans based on the crop's growth stage. For example, it analyzes the crop's growth stage and plans the optimal amount of water and fertilizer to supply according to each stage of growth. The planning department can integrate this data and use AI to create highly accurate optimal supply plans. This allows farmers to optimize water and fertilizer supply and maximize crop health and yield. The planning department can continuously revise supply plans based on real-time updated data to respond to the latest conditions. This allows the planning department to provide highly accurate supply plans based on the latest information at all times, supporting the decision-making of agricultural workers.

[0084] The prediction unit can adjust the harvest time based on the growth stage of the crop. For example, the prediction unit can analyze the growth stage of the crop and adjust the optimal harvest time. The prediction unit can also adjust the harvest time based on the growth stage of the crop. The prediction unit can also analyze the growth stage of the crop and adjust the harvest time. This makes it possible to adjust the harvest time according to the growth stage of the crop. Some or all of the above processing in the prediction unit may be performed using, for example, a generating AI, or without using a generating AI. For example, the prediction unit can input crop growth stage data into a generating AI and have the generating AI perform the adjustment of the harvest time.

[0085] The suggestion unit can propose treatment methods according to the type of abnormality. For example, the suggestion unit can propose treatment methods according to the type of pest or disease. The suggestion unit can also propose treatment methods according to the type of abnormality. The suggestion unit can analyze the type of abnormality and propose treatment methods. This allows for the proposal of appropriate treatment methods according to the type of abnormality. Some or all of the above-described processes in the suggestion unit may be performed using, for example, a generative AI, or without using a generative AI. For example, the suggestion unit can input abnormality type data into a generative AI and have the generative AI execute the suggestion of treatment methods.

[0086] The planning unit can create water and fertilizer supply plans according to the type and growth stage of the crops. For example, the planning unit can create water and fertilizer supply plans according to the type of crop. The planning unit can also create water and fertilizer supply plans based on the growth stage of the crops. The planning unit can also analyze the type and growth stage of the crops and create supply plans. This makes it possible to create appropriate supply plans according to the type and growth stage of the crops. Some or all of the above processes in the planning unit may be performed using, for example, a generative AI, or not using a generative AI. For example, the planning unit can input crop type and growth stage data into a generative AI and have the generative AI create a supply plan.

[0087] The proposal unit can propose long-term agricultural plans based on data collected by the AI. For example, the proposal unit can analyze past data and predict future yields. The proposal unit can also propose long-term agricultural plans. The proposal unit can analyze collected data and propose long-term agricultural plans. This allows it to propose long-term agricultural plans. Some or all of the above processing in the proposal unit may be performed using, for example, a generative AI, or without a generative AI. For example, the proposal unit can input collected data into a generative AI and have the generative AI execute a proposal for a long-term agricultural plan.

[0088] The sharing unit can store the collected data in the cloud and share it with other farmers. The sharing unit can, for example, upload the collected data to the cloud. The sharing unit can also share the data stored in the cloud with other farmers. The sharing unit can store the collected data in the cloud and share it with other farmers. This allows the data to be stored in the cloud and shared with other farmers. Some or all of the above processing in the sharing unit may be performed using, for example, a generative AI, or not using a generative AI. For example, the sharing unit can input the collected data into a generative AI and have the generative AI perform the storage and sharing to the cloud.

[0089] The measurement unit can estimate the user's emotions and adjust the soil measurement frequency based on the estimated emotions. For example, if the user is stressed, the measurement unit can reduce the measurement frequency to alleviate the user's burden. If the user is relaxed, the measurement unit can also increase the measurement frequency to collect more detailed data. If the user is in a hurry, the measurement unit can minimize the measurement frequency to quickly acquire data. This allows the soil measurement frequency to be adjusted according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the measurement unit may be performed using a generative AI, or not. For example, the measurement unit can input user emotion data into a generative AI and have the generative AI adjust the measurement frequency.

[0090] The measurement unit can simultaneously measure soil conditions at different depths and integrate and analyze the data from each depth. For example, the measurement unit can simultaneously acquire soil samples at different depths and measure the moisture and nutrients at each depth. The measurement unit can also integrate the measurement data and analyze the overall condition of the soil. The measurement unit can also compare the data from each depth to understand the changes in soil at different depths. This allows for simultaneous measurement of soil conditions at different depths and detailed analysis. Some or all of the above-described processes in the measurement unit may be performed using, for example, a generating AI, or not using a generating AI. For example, the measurement unit can input soil data from different depths into a generating AI and have the generating AI perform data integration and analysis.

[0091] The measurement unit can measure the concentration of specific nutrients in soil in real time and provide immediate feedback. For example, the measurement unit can measure the concentration of specific nutrients such as nitrogen, phosphorus, and potassium in real time when measuring soil. The measurement unit can also display the measurement results immediately and provide feedback to the user. The measurement unit can also upload the measurement data to the cloud and share it with other farmers. This allows for real-time measurement of the concentration of specific nutrients and immediate feedback. Some or all of the above processing in the measurement unit may be performed using, for example, a generating AI, or not using a generating AI. For example, the measurement unit can input concentration data of specific nutrients into a generating AI and have the generating AI perform real-time measurement and feedback.

[0092] The measurement unit can estimate the user's emotions and adjust the display method of the measurement results based on the estimated user emotions. For example, if the user is stressed, the measurement unit can provide a simple display method to reduce visual burden. If the user is relaxed, the measurement unit can also display detailed data and provide information. If the user is in a hurry, the measurement unit can provide a concise display method to quickly convey information. This allows the display method of the measurement results to be adjusted according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the measurement unit may be performed using a generative AI, or not using a generative AI. For example, the measurement unit can input user emotion data into a generative AI and have the generative AI adjust the display method.

[0093] The measurement unit can acquire ambient environmental data simultaneously with soil measurements and perform a comprehensive analysis. For example, the measurement unit can simultaneously acquire environmental data such as temperature, humidity, and wind speed when measuring soil. The measurement unit can also integrate the measurement data and environmental data to perform a comprehensive analysis. The measurement unit can also take environmental data into consideration to more accurately understand the soil condition. This enables the simultaneous acquisition of ambient environmental data and a comprehensive analysis. Some or all of the above-described processes in the measurement unit may be performed using, for example, a generation AI, or without a generation AI. For example, the measurement unit can input environmental data into a generation AI and have the generation AI perform a comprehensive analysis.

[0094] The measurement unit can automatically upload measurement data to the cloud and share it with other farmers when measuring soil. For example, the measurement unit can upload soil measurement data to the cloud in real time. The measurement unit can also share data on the cloud and exchange information with other farmers. Based on the shared data, the measurement unit can also understand the soil conditions of the entire region. This allows the measurement data to be automatically uploaded to the cloud and shared with other farmers. Some or all of the above processes in the measurement unit may be performed using, for example, a generative AI, or not using a generative AI. For example, the measurement unit can input measurement data into a generative AI and have the generative AI perform the uploading and sharing to the cloud.

[0095] The data collection unit can estimate the user's emotions and adjust the frequency of weather forecast data collection based on the estimated emotions. For example, if the user is stressed, the data collection unit can reduce the collection frequency to alleviate the user's burden. If the user is relaxed, the data collection unit can increase the collection frequency to collect more detailed data. If the user is in a hurry, the data collection unit can minimize the collection frequency to quickly acquire data. This allows the data collection frequency of weather forecast data to be adjusted according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processing in the data collection unit may be performed using, for example, a generative AI, or not using a generative AI. For example, the data collection unit can input user emotion data into a generative AI and have the generative AI adjust the collection frequency.

[0096] The data collection unit can predict extreme weather events by comparing current weather forecast data with past weather data when collecting weather forecast data. For example, the data collection unit can compare past weather data with current weather forecast data to detect signs of extreme weather events. The data collection unit can also notify the user if extreme weather events are predicted. The data collection unit can also upload the predicted extreme weather data to the cloud and share it with other farmers. This allows for prediction of extreme weather events by comparing it with past weather data. Some or all of the above processing in the data collection unit may be performed using, for example, a generative AI, or without a generative AI. For example, the data collection unit can input past weather data and current weather forecast data into a generative AI and have the generative AI perform extreme weather predictions.

[0097] The data collection unit can collect regional microclimate data when collecting weather forecast data, enabling more accurate predictions. For example, the data collection unit can collect regional microclimate data and integrate it with weather forecast data. Based on the microclimate data, the data collection unit can also provide more accurate weather forecasts. The data collection unit can also upload the microclimate data to the cloud and share it with other farmers. This allows for the collection of regional microclimate data and more accurate predictions. Some or all of the above processing in the data collection unit may be performed using, for example, a generative AI, or not. For example, the data collection unit can input regional microclimate data into a generative AI and have the generative AI perform improvements to the accuracy of the predictions.

[0098] The data collection unit can estimate the user's emotions and adjust how the collected data is displayed based on the estimated emotions. For example, if the user is stressed, the data collection unit can provide a simple display method to reduce visual burden. If the user is relaxed, the data collection unit can also display detailed data to provide information. If the user is in a hurry, the data collection unit can provide a concise display method to quickly convey information. This allows the display method of collected data to be adjusted according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the data collection unit may be performed using a generative AI, or not using a generative AI. For example, the data collection unit can input user emotion data into a generative AI and have the generative AI adjust the display method.

[0099] The data collection unit can collect other agricultural data simultaneously when collecting weather forecast data. For example, the data collection unit can collect crop market price data at the same time as weather forecast data. The data collection unit can also integrate the collected data and perform a comprehensive analysis. The data collection unit can also optimize the harvest time based on the market price data. This allows other agricultural data to be collected simultaneously when collecting weather forecast data. Some or all of the above processing in the data collection unit may be performed using, for example, a generative AI, or without a generative AI. For example, the data collection unit can input other agricultural data into a generative AI and have the generative AI perform data collection and analysis.

[0100] The data collection unit can automatically upload weather forecast data to the cloud and share it with other farmers when collecting it. For example, the data collection unit can upload weather forecast data to the cloud in real time. The data collection unit can also share data on the cloud and exchange information with other farmers. Based on the shared data, the data collection unit can also grasp the weather forecast for the entire region. This allows the data to be automatically uploaded to the cloud and shared with other farmers when collecting weather forecast data. Some or all of the above processing in the data collection unit may be performed using, for example, a generative AI, or not using a generative AI. For example, the data collection unit can input weather forecast data into a generative AI and have the generative AI perform the uploading and sharing to the cloud.

[0101] The prediction unit can estimate the user's emotions and adjust the harvest time prediction method based on the estimated user emotions. For example, if the user is stressed, the prediction unit can provide a simple prediction method to reduce visual burden. If the user is relaxed, the prediction unit can also provide a detailed prediction method to provide information. If the user is in a hurry, the prediction unit can also provide a concise prediction method to quickly convey information. This allows the harvest time prediction method to be adjusted according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the prediction unit may be performed using, for example, a generative AI, or not using a generative AI. For example, the prediction unit can input user emotion data into a generative AI and have the generative AI perform the adjustment of the prediction method.

[0102] The prediction unit can improve prediction accuracy by referring to past harvest data during the prediction process. For example, the prediction unit can improve prediction accuracy by referring to past harvest data. The prediction unit can also suggest the optimal harvest time based on the harvest data. The prediction unit can also upload prediction data to the cloud and share it with other farmers. This allows for improved prediction accuracy by referring to past harvest data. Some or all of the above processing in the prediction unit may be performed using, for example, a generative AI, or without a generative AI. For example, the prediction unit can input past harvest data into a generative AI and have the generative AI perform the task of improving prediction accuracy.

[0103] The prediction unit can estimate the user's emotions and adjust the display method of the prediction results based on the estimated user emotions. For example, if the user is stressed, the prediction unit can provide a simple display method to reduce visual burden. If the user is relaxed, the prediction unit can also display detailed data and provide information. If the user is in a hurry, the prediction unit can provide a concise display method to quickly convey information. This allows the display method of the prediction results to be adjusted according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the prediction unit may be performed using a generative AI, or not using a generative AI. For example, the prediction unit can input user emotion data into a generative AI and have the generative AI adjust the display method.

[0104] The forecasting unit can adjust the harvest timing by considering other agricultural data during the forecasting process. For example, the forecasting unit adjusts the harvest timing based on market price data for crops. The forecasting unit can also propose the optimal harvest timing by considering market price data. The forecasting unit can also upload the harvest timing data to the cloud and share it with other farmers. This allows for adjustment of the harvest timing by considering other agricultural data. Some or all of the above processing in the forecasting unit may be performed using, for example, a generative AI, or not using a generative AI. For example, the forecasting unit can input other agricultural data into a generative AI and have the generative AI perform the harvest timing adjustment.

[0105] The forecasting unit can automatically upload forecast results to the cloud and share them with other farmers. For example, the forecasting unit can upload forecast results to the cloud in real time. The forecasting unit can also share data on the cloud and exchange information with other farmers. Based on the shared data, the forecasting unit can also grasp the harvest timing for the entire region. This allows the forecasting unit to automatically upload forecast results to the cloud and share them with other farmers. Some or all of the above processes in the forecasting unit may be performed using, for example, a generative AI, or not using a generative AI. For example, the forecasting unit can input forecast results into a generative AI and have the generative AI perform the uploading and sharing to the cloud.

[0106] The monitoring unit can estimate the user's emotions and adjust the frequency of crop monitoring based on the estimated emotions. For example, if the user is stressed, the monitoring unit can reduce the monitoring frequency to alleviate the user's burden. If the user is relaxed, the monitoring unit can also increase the monitoring frequency to collect more detailed data. If the user is in a hurry, the monitoring unit can minimize the monitoring frequency to quickly acquire data. This allows the monitoring frequency of crops to be adjusted according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the monitoring unit may be performed using or without a generative AI. For example, the monitoring unit can input user emotion data into a generative AI and have the generative AI adjust the monitoring frequency.

[0107] The monitoring unit can monitor the health of crops in real time and immediately notify if an abnormality is detected. For example, the monitoring unit can monitor the health of crops in real time using sensors and notify the user if an abnormality is detected. When an abnormality is detected, the monitoring unit can also provide detailed information about the abnormality at the same time as the notification. Depending on the type of abnormality, the monitoring unit can also suggest appropriate countermeasures. This allows for real-time monitoring of crop health and immediate notification if an abnormality is detected. Some or all of the above processing in the monitoring unit may be performed using, for example, a generative AI, or without a generative AI. For example, the monitoring unit can input crop health data into a generative AI and have the generative AI perform abnormality detection and notification.

[0108] The monitoring unit can predict the risk of specific pest and disease outbreaks and propose preventive measures during monitoring. For example, the monitoring unit predicts the risk of specific pest and disease outbreaks based on crop monitoring data. If the risk of outbreak is high, the monitoring unit can also propose preventive measures. The monitoring unit can also provide detailed instructions on how to implement the preventive measures. This allows the monitoring unit to predict the risk of specific pest and disease outbreaks and propose preventive measures. Some or all of the above processing in the monitoring unit may be performed using, for example, a generative AI, or without a generative AI. For example, the monitoring unit can input pest and disease outbreak risk data into a generative AI and have the generative AI propose preventive measures.

[0109] The monitoring unit can estimate the user's emotions and adjust the display method of the monitoring results based on the estimated user emotions. For example, if the user is stressed, the monitoring unit can provide a simple display method to reduce visual burden. If the user is relaxed, the monitoring unit can also display detailed data and provide information. If the user is in a hurry, the monitoring unit can provide a concise display method to quickly convey information. This allows the display method of monitoring results to be adjusted according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the monitoring unit may be performed using a generative AI, or not using a generative AI. For example, the monitoring unit can input user emotion data into a generative AI and have the generative AI adjust the display method.

[0110] The monitoring unit can acquire ambient environmental data simultaneously with crop monitoring and perform a comprehensive analysis. For example, the monitoring unit can simultaneously acquire environmental data such as temperature, humidity, and wind speed when monitoring crops. The monitoring unit can also integrate monitoring data and environmental data to perform a comprehensive analysis. By considering the environmental data, the monitoring unit can more accurately grasp the health status of the crops. This allows for the simultaneous acquisition of ambient environmental data and a comprehensive analysis when monitoring crops. Some or all of the above-described processes in the monitoring unit may be performed using, for example, a generative AI, or without a generative AI. For example, the monitoring unit can input environmental data into a generative AI and have the generative AI perform a comprehensive analysis.

[0111] The monitoring unit can automatically upload monitoring data to the cloud and share it with other farmers. For example, the monitoring unit can upload monitoring data to the cloud in real time. The monitoring unit can also share data on the cloud and exchange information with other farmers. Based on the shared data, the monitoring unit can also understand the health status of crops throughout the region. This allows monitoring data to be automatically uploaded to the cloud and shared with other farmers. Some or all of the above processes in the monitoring unit may be performed using, for example, a generative AI, or not using a generative AI. For example, the monitoring unit can input monitoring data into a generative AI and have the generative AI perform the uploading and sharing to the cloud.

[0112] The suggestion unit can estimate the user's emotions and adjust the suggested treatment methods based on the estimated emotions. For example, if the user is stressed, the suggestion unit may suggest a simple treatment method to reduce visual burden. If the user is relaxed, the suggestion unit may also suggest a detailed treatment method and provide information. If the user is in a hurry, the suggestion unit may also suggest a concise treatment method to quickly convey information. This allows the suggested treatment methods to be adjusted according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the suggestion unit may be performed using a generative AI, or not. For example, the suggestion unit can input the user's emotion data into a generative AI and have the generative AI execute the suggested treatment methods.

[0113] The proposal unit can propose treatment methods according to the type of abnormality and provide specific treatment procedures. For example, the proposal unit proposes the optimal treatment method according to the type of abnormality. The proposal unit can also provide a detailed explanation of the specific procedures of the proposed treatment method. The proposal unit can also upload the treatment procedures to the cloud and share them with other farmers. This allows the proposal unit to propose treatment methods according to the type of abnormality and provide specific treatment procedures. Some or all of the above processing in the proposal unit may be performed using, for example, a generative AI, or not using a generative AI. For example, the proposal unit can input abnormality type data into a generative AI and have the generative AI perform the task of proposing treatment methods and providing procedures.

[0114] The proposal unit can improve the accuracy of its proposals by referring to past treatment data when making a proposal. For example, the proposal unit can improve the accuracy of its proposals by referring to past treatment data. The proposal unit can also propose the optimal treatment method based on the treatment data. The proposal unit can also upload the proposal data to the cloud and share it with other farmers. This allows it to improve the accuracy of its proposals by referring to past treatment data. Some or all of the above processing in the proposal unit may be performed using, for example, a generative AI, or not using a generative AI. For example, the proposal unit can input past treatment data into a generative AI and have the generative AI perform the task of improving the accuracy of its proposals.

[0115] The suggestion unit can estimate the user's emotions and adjust the display method of the suggestion results based on the estimated user emotions. For example, if the user is stressed, the suggestion unit can provide a simple display method to reduce visual burden. If the user is relaxed, the suggestion unit can also display detailed data and provide information. If the user is in a hurry, the suggestion unit can provide a concise display method to quickly convey information. This allows the display method of the suggestion results to be adjusted according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the suggestion unit may be performed using a generative AI, or not using a generative AI. For example, the suggestion unit can input user emotion data into a generative AI and have the generative AI adjust the display method.

[0116] The proposal unit can propose treatment methods by considering other agricultural data when making a proposal. For example, the proposal unit can propose treatment methods based on market price data for crops. The proposal unit can also propose the optimal treatment method by considering market price data. The proposal unit can also upload treatment method data to the cloud and share it with other farmers. This allows it to propose treatment methods by considering other agricultural data. Some or all of the above processing in the proposal unit may be performed using, for example, a generative AI, or not using a generative AI. For example, the proposal unit can input other agricultural data into a generative AI and have the generative AI execute the treatment method proposal.

[0117] The proposal unit can automatically upload the proposal results to the cloud and share them with other farmers. For example, the proposal unit can upload the proposal results to the cloud in real time. The proposal unit can also share data on the cloud and exchange information with other farmers. Based on the shared data, the proposal unit can also understand treatment methods for the entire region. This allows the proposal results to be automatically uploaded to the cloud and shared with other farmers. Some or all of the above processes in the proposal unit may be performed using, for example, a generative AI, or not using a generative AI. For example, the proposal unit can input the proposal results into a generative AI and have the generative AI perform the uploading and sharing to the cloud.

[0118] The planning unit can estimate the user's emotions and adjust the supply plan based on those emotions. For example, if the user is stressed, the planning unit can provide a simple supply plan to reduce visual burden. If the user is relaxed, the planning unit can also provide a detailed supply plan and information. If the user is in a hurry, the planning unit can provide a concise supply plan and quickly convey information. This allows the supply plan to be adjusted according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the planning unit may be performed using or without generative AI. For example, the planning unit can input user emotion data into a generative AI and have the generative AI adjust the supply plan.

[0119] The planning department can create appropriate water and fertilizer supply plans according to the type and growth stage of the crops. For example, the planning department can create an optimal water and fertilizer supply plan depending on the type of crop. The planning department can also adjust the supply plan based on the growth stage. The planning department can also upload the supply plan to the cloud and share it with other farmers. This allows for the creation of appropriate supply plans according to the type and growth stage of the crops. Some or all of the above processes in the planning department may be performed using, for example, a generative AI, or not using a generative AI. For example, the planning department can input crop type and growth stage data into a generative AI and have the generative AI create a supply plan.

[0120] The planning department can improve the accuracy of its plans by referring to past supply data during the planning stage. For example, the planning department can improve the accuracy of its plans by referring to past supply data. The planning department can also create an optimal supply plan based on the supply data. The planning department can also upload planning data to the cloud and share it with other farmers. This allows for improvement of planning accuracy by referring to past supply data. Some or all of the above processes in the planning department may be performed using, for example, a generative AI, or not using a generative AI. For example, the planning department can input past supply data into a generative AI and have the generative AI perform the task of improving the accuracy of the plans.

[0121] The planning unit can estimate the user's emotions and adjust how the planning results are displayed based on the estimated emotions. For example, if the user is stressed, the planning unit can provide a simple display method to reduce visual burden. If the user is relaxed, the planning unit can also display detailed data and provide information. If the user is in a hurry, the planning unit can provide a concise display method to quickly convey information. This allows the display method of the planning results to be adjusted according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the planning unit may be performed using or without a generative AI. For example, the planning unit can input user emotion data into a generative AI and have the generative AI adjust the display method.

[0122] The planning department can create a supply plan by taking other agricultural data into consideration during the planning stage. For example, the planning department can create a supply plan based on market price data for crops. The planning department can also create an optimal supply plan by taking market price data into consideration. The planning department can also upload the supply plan data to the cloud and share it with other farmers. This allows for the creation of a supply plan that takes other agricultural data into consideration. Some or all of the above processes in the planning department may be performed using, for example, a generative AI, or not using a generative AI. For example, the planning department can input other agricultural data into a generative AI and have the generative AI create a supply plan.

[0123] The planning department can automatically upload planning results to the cloud and share them with other farmers. For example, the planning department can upload planning results to the cloud in real time. The planning department can also share data on the cloud and exchange information with other farmers. Based on the shared data, the planning department can also grasp the supply plan for the entire region. This allows the planning results to be automatically uploaded to the cloud and shared with other farmers. Some or all of the above processes in the planning department may be performed using, for example, a generative AI, or not using a generative AI. For example, the planning department can input planning results into a generative AI and have the generative AI perform the uploading and sharing to the cloud.

[0124] The sharing unit can estimate the user's emotions and select shared data based on the estimated emotions. For example, if the user is stressed, the sharing unit can share only essential data to reduce visual burden. If the user is relaxed, the sharing unit can also share detailed data to provide information. If the user is in a hurry, the sharing unit can share concise data to quickly convey information. This allows for the selection of shared data according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the sharing unit may be performed using or without a generative AI. For example, the sharing unit can input user emotion data into a generative AI and have the generative AI perform the selection of shared data.

[0125] The sharing unit can determine the sharing priority based on the importance of the data when sharing. For example, the sharing unit can evaluate the importance of the data and prioritize the sharing of important data. The sharing unit can also postpone the sharing of less important data and share important data quickly. The sharing unit can also upload the priority of the shared data to the cloud and share it with other farmers. This allows for the determination of sharing priority based on the importance of the data. Some or all of the above processing in the sharing unit may be performed using, for example, generative AI, or not using generative AI. For example, the sharing unit can input the importance of the data into the generative AI and have the generative AI execute the sharing priority.

[0126] The sharing section can standardize the data format during sharing, making it easily understandable to other farmers. For example, the sharing section can standardize the format of shared data, making it easily understandable to other farmers. The sharing section can also facilitate information sharing by standardizing the data format. The sharing section can upload the standardized data format to the cloud and share it with other farmers. This standardizes the data format, making it easily understandable to other farmers. Some or all of the above processing in the sharing section may be performed using, for example, a generative AI, or without a generative AI. For example, the sharing section can input the data format standardization into a generative AI and have the generative AI perform the data format standardization.

[0127] The shared section can estimate the user's emotions and adjust how shared data is displayed based on the estimated emotions. For example, if the user is stressed, the shared section can provide a simple display to reduce visual burden. If the user is relaxed, the shared section can also display detailed data and provide information. If the user is in a hurry, the shared section can provide a concise display to quickly convey information. This allows the display of shared data to be adjusted according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the shared section may be performed using a generative AI, or not. For example, the shared section can input user emotion data into a generative AI and have the generative AI adjust the display method.

[0128] The sharing unit can share other agricultural data simultaneously when sharing data. For example, the sharing unit can share market price data of crops at the same time as the shared data. The sharing unit can also integrate the collected data and perform comprehensive analysis. The sharing unit can also optimize the harvest time based on the market price data. This allows other agricultural data to be shared simultaneously. Some or all of the above processing in the sharing unit may be performed using, for example, a generative AI, or not using a generative AI. For example, the sharing unit can input other agricultural data into a generative AI and have the generative AI perform data sharing and analysis.

[0129] The sharing unit can determine the sharing priority based on the importance of the data when sharing. For example, the sharing unit can evaluate the importance of the data and prioritize the sharing of important data. The sharing unit can also postpone the sharing of less important data and share important data quickly. The sharing unit can also upload the priority of the shared data to the cloud and share it with other farmers. This allows for the determination of sharing priority based on the importance of the data. Some or all of the above processing in the sharing unit may be performed using, for example, generative AI, or not using generative AI. For example, the sharing unit can input the importance of the data into the generative AI and have the generative AI execute the sharing priority.

[0130] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0131] AI assistants for improving agricultural productivity can also be equipped with the ability to automatically control irrigation systems based on crop growth forecasts. For example, the forecasting unit analyzes crop growth stages and weather forecast data to determine the optimal irrigation timing. The irrigation control unit can then automatically activate the irrigation system based on instructions from the forecasting unit. The irrigation control unit can also monitor soil moisture data in real time and adjust the amount of irrigation as needed. This ensures optimal water supply for crop growth. Furthermore, if extreme weather is predicted, the irrigation control unit can automatically modify the irrigation plan to protect the crops.

[0132] AI assistants for improving agricultural productivity can also be equipped with the ability to automatically control harvesting robots based on crop growth predictions. For example, the prediction unit analyzes crop growth stages and weather forecast data to determine the optimal harvesting timing. The harvesting control unit can then automatically operate the harvesting robots based on instructions from the prediction unit. The harvesting control unit can also monitor the condition of the crops in real time and fine-tune the harvesting timing. This ensures efficient harvesting while maintaining maximum crop quality. Furthermore, if extreme weather is predicted, the harvesting control unit can automatically modify the harvesting plan to protect the crops.

[0133] AI assistants for improving agricultural productivity can also be equipped with the ability to automatically control fertilizer application systems based on crop growth predictions. For example, the prediction unit analyzes crop growth stages and soil nutrient data to determine the optimal timing for fertilizer application. The fertilizer application control unit can then automatically activate the fertilizer application system based on instructions from the prediction unit. The fertilizer application control unit can also monitor soil nutrient data in real time and adjust the type and amount of fertilizer as needed. This ensures optimal nutrient supply for crop growth. Furthermore, if extreme weather is predicted, the fertilizer application control unit can automatically modify the fertilizer application plan to protect the crops.

[0134] AI assistants for improving agricultural productivity can also be equipped with the ability to automatically control temperature management systems based on crop growth forecasts. For example, the forecasting unit analyzes crop growth stages and weather forecast data to determine the optimal timing for temperature management. The temperature management control unit can then automatically activate the temperature management system based on instructions from the forecasting unit. The temperature management control unit can also monitor the condition of the crops in real time and adjust the temperature as needed. This ensures an optimal temperature environment for crop growth. Furthermore, if extreme weather is predicted, the temperature management control unit can automatically modify the temperature management plan to protect the crops.

[0135] AI assistants for improving agricultural productivity can also be equipped with the ability to automatically control the light irradiation system based on crop growth predictions. For example, the prediction unit analyzes crop growth stages and weather forecast data to determine the optimal timing for light irradiation. The light irradiation control unit can then automatically activate the light irradiation system based on instructions from the prediction unit. The light irradiation control unit can also monitor the condition of the crops in real time and adjust the light intensity and irradiation time as needed. This ensures an optimal light environment for crop growth. Furthermore, if extreme weather is predicted, the light irradiation control unit can automatically change the light irradiation plan to protect the crops.

[0136] An AI assistant for improving agricultural productivity can further estimate the user's emotions and adjust crop growth predictions based on those emotions. For example, if the user is stressed, the prediction unit can provide a simple prediction method to reduce visual burden. If the user is relaxed, the prediction unit can also provide a detailed prediction method to provide information. If the user is in a hurry, the prediction unit can provide a concise prediction method to quickly convey information. This allows crop growth predictions to be adjusted according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the prediction unit may be performed using or without generative AI. For example, the prediction unit can input user emotion data into the generative AI and have the generative AI adjust the prediction method.

[0137] An AI assistant for improving agricultural productivity can further estimate the user's emotions and adjust crop monitoring methods based on those emotions. For example, if the user is stressed, the monitoring unit can provide a simple monitoring method to reduce visual burden. If the user is relaxed, the monitoring unit can also provide a detailed monitoring method and information. If the user is in a hurry, the monitoring unit can provide a concise monitoring method to quickly convey information. This allows crop monitoring methods to be adjusted according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the monitoring unit may be performed using or without generative AI. For example, the monitoring unit can input user emotion data into a generative AI and have the generative AI adjust the monitoring method.

[0138] An AI assistant for improving agricultural productivity can further estimate the user's emotions and adjust its suggestion methods based on those emotions. For example, if the user is stressed, the suggestion unit can provide simple suggestions to reduce visual burden. If the user is relaxed, the suggestion unit can also provide detailed suggestions and information. If the user is in a hurry, the suggestion unit can provide concise suggestions to quickly convey information. This allows the suggestion method to be adjusted according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the suggestion unit may be performed using or without a generative AI. For example, the suggestion unit can input user emotion data into a generative AI and have the generative AI adjust the suggestion method.

[0139] An AI assistant for improving agricultural productivity can further estimate the user's emotions and adjust the planning method based on those emotions. For example, if the user is stressed, the planning unit can provide a simple planning method and reduce visual burden. If the user is relaxed, the planning unit can also provide a detailed planning method and information. If the user is in a hurry, the planning unit can provide a concise planning method and quickly convey information. This allows the planning method to be adjusted according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the planning unit may be performed using or without generative AI. For example, the planning unit can input user emotion data into a generative AI and have the generative AI perform the adjustment of the planning method.

[0140] An AI assistant for improving agricultural productivity can also estimate the user's emotions and adjust the sharing method based on those emotions. For example, if the user is stressed, the sharing function can provide a simple sharing method to reduce visual burden. If the user is relaxed, the sharing function can also provide a detailed sharing method to deliver information. If the user is in a hurry, the sharing function can provide a concise sharing method to quickly convey information. This allows the sharing method to be adjusted according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the sharing function may be performed using a generative AI, or not. For example, the sharing function can input user emotion data into a generative AI and have the generative AI adjust the sharing method.

[0141] The following briefly describes the processing flow for example form 2.

[0142] Step 1: The measuring unit measures the soil condition. Soil condition includes humidity, pH, and nutrient concentration. For example, the measuring unit measures soil humidity in real time using a sensor and collects the data. It can also periodically measure soil pH and nutrient concentration and collect the data. Step 2: The data collection unit collects weather forecast data. This data includes temperature, precipitation, wind speed, etc. The data collection unit can, for example, collect temperature data in real time and analyze it. It can also collect precipitation and wind speed data periodically and analyze that data. Step 3: The prediction unit analyzes the data obtained by the measurement and collection units to predict the harvest time. The prediction unit can predict the harvest time based on the crop's growth stage, weather conditions, and historical data. For example, it can analyze the crop's growth stage and weather conditions to predict the optimal harvest time. Step 4: The monitoring unit monitors the condition of the crops. This includes leaf color, growth rate, and the presence or absence of pests and diseases. For example, the monitoring unit can monitor leaf color in real time and collect data. It can also periodically monitor growth rate and the presence or absence of pests and diseases and collect data. Step 5: The proposal unit analyzes the data obtained by the monitoring unit and proposes methods for early detection and treatment of pests and diseases. The proposal unit can propose treatment methods and preventive measures according to the type of pest or disease, as well as countermeasures according to the occurrence situation. For example, it can identify the type of pest or disease and propose the optimal treatment method. Step 6: The planning department analyzes soil moisture and nutrient data and develops a water or fertilizer supply plan. The planning department can develop an optimal water or fertilizer supply plan based on soil moisture, nutrient concentration, and crop growth stage. For example, it can analyze soil moisture and develop an optimal water supply plan.

[0143] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0144] Data generation model 58 is a form of so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AI include text generation AI, image generation AI, and multimodal generation AI. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats from audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each of the above parts is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example.Furthermore, processing performed by AI, including generative AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by AI, including generative AI.

[0145] Furthermore, the processing performed by the data processing system 10 described above is carried out by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but it may also be carried out by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0146] For example, the measurement unit can acquire data on soil moisture and nutrients using the sensors of the smart device 14. The collection unit can collect weather forecast data via the communication I / F 44 of the smart device 14. The prediction unit can predict the harvest time using the specific processing unit 290 of the data processing device 12. The monitoring unit can monitor the condition of crops using the camera 42 of the smart device 14. The proposal unit can propose methods for early detection and treatment of pests and diseases using the specific processing unit 290 of the data processing device 12. The planning unit can create a water and fertilizer supply plan using the specific processing unit 290 of the data processing device 12. The sharing unit can save data to the cloud via the data processing device 12 and share it with other farmers. The correspondence between each unit and the devices and control units is not limited to the examples described above and can be modified in various ways.

[0147] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0148] As shown in Figure 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0149] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0150] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

[0151] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0152] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0153] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0154] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing by the processor 28. The storage 32 stores the specific processing program 56.

[0155] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0156] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0157] In the smart glasses 214, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0158] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0159] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0160] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0161] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart glasses 214 or an external device, and the smart glasses 214 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0162] For example, the measurement unit can acquire data on soil moisture and nutrients using the sensors of the smart glasses 214. The collection unit can collect weather forecast data via the communication I / F 44 of the smart glasses 214. The prediction unit can predict the harvest time using the specific processing unit 290 of the data processing device 12. The monitoring unit can monitor the condition of crops using the camera 42 of the smart glasses 214. The proposal unit can propose methods for early detection and treatment of pests and diseases using the specific processing unit 290 of the data processing device 12. The planning unit can create a water and fertilizer supply plan using the specific processing unit 290 of the data processing device 12. The sharing unit can save data to the cloud via the data processing device 12 and share it with other farmers. The correspondence between each unit and the devices and control units is not limited to the examples described above and can be modified in various ways.

[0163] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0164] As shown in Figure 5, the data processing system 310 includes a data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0165] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0166] The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52.

[0167] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0168] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0169] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0170] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0171] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0172] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0173] In the headset terminal 314, specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads the specific program 60 from the storage 50 and executes the read specific program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific program 60 executed on the RAM 48. The headset terminal 314 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0174] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0175] The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0176] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0177] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset terminal 314, but may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset terminal 314. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the headset terminal 314 or an external device, and the headset terminal 314 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0178] For example, the measurement unit can acquire data on soil moisture and nutrients using the sensors of the headset terminal 314. The collection unit can collect weather forecast data via the communication I / F 44 of the headset terminal 314. The prediction unit can predict the harvest time using the specific processing unit 290 of the data processing device 12. The monitoring unit can monitor the condition of crops using the camera 42 of the headset terminal 314. The proposal unit can propose methods for early detection and treatment of pests and diseases using the specific processing unit 290 of the data processing device 12. The planning unit can create a water and fertilizer supply plan using the specific processing unit 290 of the data processing device 12. The sharing unit can save data to the cloud via the data processing device 12 and share it with other farmers. The correspondence between each unit and the devices and control units is not limited to the examples described above, and various modifications are possible.

[0179] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0180] As shown in Figure 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0181] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0182] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52.

[0183] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0184] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS image sensor or CCD image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0185] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0186] The controlled object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the robot 414's emotions can be expressed by controlling these motors. The robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0187] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0188] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0189] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0190] In robot 414, specific processing is performed by processor 46. A specific program 60 is stored in storage 50. Processor 46 reads the specific program 60 from storage 50 and executes it on RAM 48. The specific processing is achieved by processor 46 acting as a control unit 46A according to the specific program 60 executed on RAM 48. Robot 414 also has data generation model 58 and emotion identification model 59, similar to those of the robot, and can perform processing similar to that of the specific processing unit 290 using these models.

[0191] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0192] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0193] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0194] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the robot 414 or an external device, and the robot 414 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0195] For example, the measurement unit can acquire data on soil moisture and nutrients using the sensors of the robot 414. The collection unit can collect weather forecast data via the communication I / F 44 of the robot 414. The prediction unit can predict the harvest time using the specific processing unit 290 of the data processing unit 12. The monitoring unit can monitor the condition of crops using the camera 42 of the robot 414. The proposal unit can propose methods for early detection and treatment of pests and diseases using the specific processing unit 290 of the data processing unit 12. The planning unit can create a water and fertilizer supply plan using the specific processing unit 290 of the data processing unit 12. The sharing unit can save data to the cloud via the data processing unit 12 and share it with other farmers. The correspondence between each unit and the devices and control units is not limited to the examples described above and can be modified in various ways.

[0196] Furthermore, the emotion identification model 59, acting as an emotion engine, may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to a specific mapping, which is an emotion map (see Figure 9). Similarly, the emotion identification model 59 may also determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0197] Figure 9 shows the emotion map 400, in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[0198] These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression.

[0199] The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become.

[0200] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, and motorcycles, emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated based, for example, on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[0201] The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it is when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is the emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more."

[0202] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​representing each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple training data sets, which are combinations of user input and emotion values ​​representing each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions located close together have similar values, as shown in the emotion map 900 in Figure 10. Figure 10 shows an example where multiple emotions such as "reassured," "calm," and "confident" have similar emotion values.

[0203] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing method for the specific process may be used, which includes computer 22 and multiple other computers.

[0204] In the above embodiment, an example was given in which the specific processing program 56 is stored in the storage 32, but the technology of this disclosure is not limited thereto. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-temporary storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-temporary storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes specific processing according to the specific processing program 56.

[0205] 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.

[0206] Furthermore, it is not necessary to store the entirety of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

[0207] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

[0208] The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Alternatively, the hardware resource that performs a specific process may consist of a single processor.

[0209] Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources.

[0210] Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose.

[0211] Furthermore, although the above-described examples were divided into four embodiments, some or all of these embodiments may be combined. Also, the smart device 14, smart glasses 214, headset terminal 314, and robot 414 are just examples, and they may be combined, or other devices may be used. Also, although the above-described examples were divided into two embodiments, Embodiment 1 and Embodiment 2, these may be combined.

[0212] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and other things that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[0213] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[0214] (Note 1) A measuring unit for measuring soil conditions, The collection unit collects weather forecast data, A prediction unit analyzes the data obtained from the measurement unit and the collection unit to predict the harvest time, A monitoring unit that monitors the condition of crops, The monitoring unit analyzes the data obtained and proposes methods for early detection and treatment of pests and diseases, and It includes a planning unit that analyzes soil moisture and nutrient data and creates a plan for supplying water or fertilizer. A system characterized by the following features. (Note 2) The prediction unit, Adjust the harvest time based on the growth stage of the crop. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned proposal section is, We propose treatment methods tailored to the type of abnormality. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned planning department, Develop a plan for supplying water and fertilizer according to the type of crop and its growth stage. The system described in Appendix 1, characterized by the features described herein. (Note 5) It has a proposal department that proposes long-term agricultural plans based on data collected by AI. The system described in Appendix 1, characterized by the features described herein. (Note 6) It includes a shared section that stores collected data in the cloud and allows sharing with other farmers. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned measuring unit is The system estimates the user's emotions and adjusts the soil measurement frequency based on those emotions. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned measuring unit is The soil conditions are measured simultaneously at different depths, and the data from each depth is integrated and analyzed. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned measuring unit is During soil measurement, the concentration of specific nutrients is measured in real time, and immediate feedback is provided. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned measuring unit is It estimates the user's emotions and adjusts how the measurement results are displayed based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned measuring unit is When measuring soil, surrounding environmental data is also acquired simultaneously for comprehensive analysis. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned measuring unit is When measuring soil, the measurement data is automatically uploaded to the cloud and shared with other farmers. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned collection unit is The system estimates the user's emotions and adjusts the frequency of weather forecast data collection based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned collection unit is When collecting weather forecast data, extreme weather events are predicted by comparing it with past weather data. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned collection unit is When collecting weather forecast data, we collect microclimate data for each region to enable more accurate predictions. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned collection unit is It estimates the user's emotions and adjusts how collected data is displayed based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned collection unit is When collecting weather forecast data, other agriculture-related data will also be collected at the same time. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned collection unit is When collecting weather forecast data, the data is automatically uploaded to the cloud and shared with other farmers. The system described in Appendix 1, characterized by the features described herein. (Note 19) The prediction unit, We estimate the user's emotions and adjust the harvest time prediction method based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 20) The prediction unit, When making predictions, historical harvest data is referenced to improve prediction accuracy. The system described in Appendix 1, characterized by the features described herein. (Note 21) The prediction unit, It estimates the user's emotions and adjusts how the prediction results are displayed based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 22) The prediction unit, When making predictions, adjust the harvest timing by taking other agricultural data into consideration. The system described in Appendix 1, characterized by the features described herein. (Note 23) The prediction unit, The forecast results are automatically uploaded to the cloud and shared with other farmers. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned monitoring unit, The system estimates the user's emotions and adjusts the frequency of crop monitoring based on those emotions. The system described in Appendix 1, characterized by the features described herein. (Note 25) The aforementioned monitoring unit, It monitors the health of crops in real time and immediately notifies if any abnormalities are detected. The system described in Appendix 1, characterized by the features described herein. (Note 26) The aforementioned monitoring unit, During monitoring, predict the risk of specific pest and disease outbreaks and propose preventive measures. The system described in Appendix 1, characterized by the features described herein. (Note 27) The aforementioned monitoring unit, It estimates the user's emotions and adjusts how monitoring results are displayed based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 28) The aforementioned monitoring unit, When monitoring crops, surrounding environmental data is also acquired simultaneously and comprehensive analysis is performed. The system described in Appendix 1, characterized by the features described herein. (Note 29) The aforementioned monitoring unit, Automatically upload monitoring data to the cloud and share it with other farmers. The system described in Appendix 1, characterized by the features described herein. (Note 30) The aforementioned proposal section is, It estimates the user's emotions and adjusts treatment suggestions based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 31) The aforementioned proposal section is, We propose treatment methods tailored to the type of abnormality and provide specific treatment procedures. The system described in Appendix 1, characterized by the features described herein. (Note 32) The aforementioned proposal section is, When making a proposal, we refer to past treatment data to improve the accuracy of the proposal. The system described in Appendix 1, characterized by the features described herein. (Note 33) The aforementioned proposal section is, It estimates the user's emotions and adjusts how the suggested results are displayed based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 34) The aforementioned proposal section is, When proposing a treatment method, consider other agricultural data. The system described in Appendix 1, characterized by the features described herein. (Note 35) The aforementioned proposal section is, The proposal results are automatically uploaded to the cloud and shared with other farmers. The system described in Appendix 1, characterized by the features described herein. (Note 36) The aforementioned planning department, The system estimates user sentiment and adjusts supply plans based on the estimated user sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 37) The aforementioned planning department, Develop an appropriate water and fertilizer supply plan based on the type of crop and its growth stage. The system described in Appendix 1, characterized by the features described herein. (Note 38) The aforementioned planning department, During the planning stage, we improve the accuracy of the plan by referring to past supply data. The system described in Appendix 1, characterized by the features described herein. (Note 39) The aforementioned planning department, It estimates the user's emotions and adjusts how the plan results are displayed based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 40) The aforementioned planning department, When planning, supply plans should be developed taking into account other agricultural data. The system described in Appendix 1, characterized by the features described herein. (Note 41) The aforementioned planning department, The planning results are automatically uploaded to the cloud and shared with other farmers. The system described in Appendix 1, characterized by the features described herein. (Note 42) The aforementioned shared portion is, The system estimates user emotions and selects shared data based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 43) The aforementioned shared portion is, When sharing data, prioritize sharing based on its importance. The system described in Appendix 1, characterized by the features described herein. (Note 44) The aforementioned shared portion is, When sharing data, standardize the format to make it easy for other farmers to understand. The system described in Appendix 1, characterized by the features described herein. (Note 45) The aforementioned shared portion is, It estimates the user's emotions and adjusts how shared data is displayed based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 46) The aforementioned shared portion is, When sharing, other agriculture-related data will also be shared simultaneously. The system described in Appendix 1, characterized by the features described herein. (Note 47) The aforementioned shared portion is, When sharing data, prioritize sharing based on its importance. The system described in Appendix 1, characterized by the features described herein. [Explanation of symbols]

[0215] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots

Claims

1. A measuring unit for measuring soil conditions, The collection unit collects weather forecast data, A prediction unit analyzes the data obtained from the measurement unit and the collection unit to predict the harvest time, A monitoring unit that monitors the condition of crops, The monitoring unit analyzes the data obtained and proposes methods for early detection and treatment of pests and diseases, and It includes a planning unit that analyzes soil moisture and nutrient data and creates a plan for supplying water or fertilizer. A system characterized by the following features.

2. The prediction unit, Adjust the harvest time based on the growth stage of the crop. The system according to feature 1.

3. The aforementioned proposal section is, We propose treatment methods tailored to the type of abnormality. The system according to feature 1.

4. The aforementioned planning department, Develop a plan for supplying water and fertilizer according to the type of crop and its growth stage. The system according to feature 1.

5. It includes a shared section that stores collected data in the cloud and allows sharing with other farmers. The system according to feature 1.

6. The aforementioned measuring unit is The system estimates the user's emotions and adjusts the soil measurement frequency based on those emotions. The system according to feature 1.

7. The aforementioned measuring unit is The soil conditions are measured simultaneously at different depths, and the data from each depth is integrated and analyzed. The system according to feature 1.

8. The aforementioned measuring unit is During soil measurement, the concentration of specific nutrients is measured in real time, and immediate feedback is provided. The system according to feature 1.

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