system

Abstract

The system according to the embodiment aims to reduce the burden on farmers and increase production. [Solution] A system according to an embodiment includes a collection unit, an analysis unit, a proposal unit, a work unit, and a monitoring unit. The collection unit collects weather data or soil data. The analysis unit analyzes the data collected by the collection unit. The proposal unit proposes a cultivation method based on the analysis results obtained by the analysis unit. The work unit automatically performs cultivation work based on the cultivation method proposed by the proposal unit. The monitoring unit monitors the results of the cultivation work performed by the work unit.

Description

[Technical Field]

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

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

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

[0004] With conventional technology, extreme weather and an aging population could increase the burden on agricultural workers and lead to reduced crop yields.

[0005] The system according to the embodiment aims to reduce the burden on farmers and increase production. [Means for solving the problem]

[0006] The system according to the embodiment includes a collection unit, an analysis unit, a proposal unit, a work unit, and a monitoring unit. The collection unit collects weather data or soil data. The analysis unit analyzes the data collected by the collection unit. The proposal unit proposes a cultivation method based on the analysis results obtained by the analysis unit. The work unit automatically performs cultivation work based on the cultivation method proposed by the proposal unit. The monitoring unit monitors the results of the cultivation work performed by the work unit. [Effects of the Invention]

[0007] The system according to the embodiment can reduce the burden on farmers and increase production. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) An agricultural support system according to an embodiment of the present invention uses AI to improve the efficiency of agricultural quality control and cultivation work, reduce the burden on farmers, and increase production. The agricultural support system collects and analyzes weather and soil data, proposes optimal cultivation methods, and automatically performs and monitors cultivation work. For example, the agricultural support system collects weather and soil data, such as temperature, humidity, precipitation, and the nutritional status of the soil. Next, the agricultural support system analyzes the collected data and proposes optimal cultivation methods. For example, when the temperature is high, the system proposes the appropriate timing and amount of watering. The system also proposes the type and amount of fertilizer required depending on the nutritional status of the soil. Next, the agricultural support system automatically performs cultivation work. For example, an automatic irrigation system waters at the proposed timing. The system also uses an automatic fertilization system to apply the proposed fertilizer in the appropriate amount. Next, the agricultural support system monitors the growth status of crops. For example, a drone is used to photograph the growth status of the crops, and AI analyzes the images. This allows for early detection of abnormalities and the implementation of countermeasures. For example, if it detects the occurrence of pests and diseases, it will suggest appropriate control methods. This allows the agricultural support system to minimize the impact of abnormal weather and an aging population, achieving efficient agriculture. This allows the agricultural support system to reduce the burden on farmers and increase production. For example, by following the cultivation methods suggested by AI, harvest yields will increase and quality will also improve. Furthermore, by having AI automatically perform cultivation tasks, it is possible to shorten the working hours of farmers and reduce physical fatigue.

[0029] An agricultural support system according to an embodiment includes a collection unit, an analysis unit, a proposal unit, a working unit, and a monitoring unit. The collection unit collects weather data or soil data. The weather data includes, for example, temperature, humidity, precipitation, and wind speed. The soil data includes, for example, soil nutritional status, pH value, and moisture content. The collection unit acquires data in real time using, for example, a sensor. The analysis unit analyzes the data collected by the collection unit. The analysis is performed using, for example, statistical analysis of data or a machine learning algorithm. The proposal unit proposes a cultivation method based on the analysis results obtained by the analysis unit. The cultivation method includes, for example, watering timing, type and amount of fertilizer, planting interval, and the like. For example, when the temperature is high, the proposal unit proposes appropriate watering timing and amount. The proposal unit also proposes the type and amount of fertilizer required depending on the nutritional status of the soil. The working unit automatically performs cultivation work based on the cultivation method proposed by the proposal unit. For example, the working unit waters plants at the proposed timing using an automatic irrigation system. In addition, an automatic fertilization system is used to apply the suggested fertilizer in the appropriate amount. The monitoring unit monitors the results of the cultivation work performed by the work unit. The monitoring unit, for example, uses a drone to photograph the growth status of the crops, which is then analyzed by AI. This allows for early detection of any abnormalities and the implementation of countermeasures. For example, if an outbreak of pests or diseases is detected, an appropriate control method is suggested. As a result, the agricultural support system according to the embodiment can reduce the burden on farmers and increase production.

[0030] The collection unit can collect data on temperature, humidity, precipitation, and soil nutritional status. The collection unit, for example, collects temperature data. The temperature data includes ground temperature and weather station data. The collection unit, for example, collects humidity data. The humidity data includes relative humidity and absolute humidity. The collection unit, for example, collects precipitation data. The precipitation data includes rain gauge data and weather station data. The collection unit, for example, collects soil nutritional status data. The soil nutritional status data includes nitrogen, phosphorus, and potassium content. By collecting data such as temperature, humidity, precipitation, and soil nutritional status, the optimal environment for crop growth can be identified. Some or all of the above-described processing in the collection unit may be performed using, or without, AI. For example, the collection unit may input data acquired by a sensor into a generation AI and have the generation AI analyze the data.

[0031] The analysis unit can analyze the collected data and propose an optimal cultivation method. The analysis unit, for example, performs statistical analysis on the collected data. Statistical analysis includes calculating the mean, median, standard deviation, etc. of the data. The analysis unit, for example, analyzes the collected data using a machine learning algorithm. Machine learning algorithms include regression analysis, clustering, deep learning, etc. The analysis unit, for example, proposes an optimal cultivation method based on the collected data. The cultivation method includes watering timing, type and amount of fertilizer, planting interval, etc. In this way, by analyzing the collected data and proposing an optimal cultivation method, the quality of the crop can be improved. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the collected data into a generation AI and have the generation AI propose an optimal cultivation method.

[0032] The suggestion unit can suggest the timing or amount of watering when the temperature is high. For example, the suggestion unit can suggest the timing of watering when the temperature is high. The suggestion unit can suggest what time to water when the temperature is above a certain degree. For example, the suggestion unit can suggest the amount of watering when the temperature is high. The suggestion unit can suggest the amount of watering per square meter or the amount of water depending on the type of crop. This can improve the quality of crops by suggesting the appropriate timing and amount of watering when the temperature is high. Some or all of the above-mentioned processing by the suggestion unit may be performed using, or without, AI, for example. For example, the suggestion unit can input temperature data into the generation AI and cause the generation AI to suggest the timing and amount of watering.

[0033] The suggestion unit can suggest the type and amount of fertilizer needed depending on the nutritional state of the soil. For example, the suggestion unit can suggest the type of fertilizer needed depending on the nutritional state of the soil. The suggestion unit can suggest organic fertilizer, chemical fertilizer, fertilizer containing specific nutrients, etc. The suggestion unit can suggest the amount of fertilizer needed depending on the nutritional state of the soil, for example. The suggestion unit can suggest the amount of fertilizer needed per square meter or the amount of fertilizer needed depending on the type of crop. This allows the quality of crops to be improved by suggesting the type and amount of fertilizer needed depending on the nutritional state of the soil. Some or all of the above-mentioned processing in the suggestion unit may be performed using, or without, AI, for example. For example, the suggestion unit can input soil data into the generation AI and cause the generation AI to suggest the type and amount of fertilizer.

[0034] The working unit can water crops at the timing suggested by an automatic irrigation system. The working unit, for example, waters crops at the timing suggested by the automatic irrigation system. Automatic irrigation systems include drip irrigation and sprinkler irrigation. The working unit waters crops using, for example, a drip irrigation system. A drip irrigation system supplies water directly to the roots of crops, reducing water waste. The working unit waters crops using, for example, a sprinkler irrigation system. A sprinkler irrigation system supplies water evenly over a wide area and is suitable for large-scale farmland. In this way, watering crops at the timing suggested by the automatic irrigation system reduces the burden on farmers and realizes efficient cultivation work. Some or all of the above-described processing in the working unit may be performed using, for example, AI, or may be performed without AI. For example, the working unit can input the suggested watering timing to a generation AI and have the generation AI control the automatic irrigation system.

[0035] The working unit can apply the suggested fertilizer in an appropriate amount using an automatic fertilization system. The working unit, for example, applies the suggested fertilizer in an appropriate amount using an automatic fertilization system. Automatic fertilization systems include automatic liquid fertilizer supply systems and automatic solid fertilizer spraying systems. The working unit applies fertilizer using, for example, an automatic liquid fertilizer supply system. The automatic liquid fertilizer supply system is a system that dissolves fertilizer in water and supplies it, and can supply the fertilizer evenly. The working unit applies fertilizer using, for example, an automatic solid fertilizer spraying system. The automatic solid fertilizer spraying system is a system that sprays fertilizer evenly and can supply fertilizer efficiently. In this way, applying the suggested fertilizer in an appropriate amount using the automatic fertilization system reduces the burden on farmers and realizes efficient cultivation work. Some or all of the above-mentioned processing in the working unit may be performed using, for example, AI, or may be performed without using AI. For example, the working unit can input the type and amount of fertilizer suggested to the generation AI and cause the generation AI to control the automatic fertilization system.

[0036] The monitoring unit uses a drone to photograph the growth state of crops, and if an abnormality occurs, it can be detected early and measures can be taken. The monitoring unit, for example, uses a drone to photograph the growth state of crops. Drones include fixed-wing drones and multi-rotor drones. The monitoring unit, for example, uses a fixed-wing drone to photograph a wide area of ​​crops. Fixed-wing drones are capable of long flight times and can efficiently monitor a wide area of ​​crops. The monitoring unit, for example, uses a multi-rotor drone to photograph the detailed growth state of crops. Multi-rotor drones are capable of stable flight and can acquire detailed images. The monitoring unit, for example, uses AI to analyze the photographed images and detect abnormalities. Abnormalities include the occurrence of pests and diseases and slow growth. In this way, by photographing the growth state of crops using a drone, and if an abnormality occurs, it can be detected early and measures can be taken, thereby improving the quality of the crops. Some or all of the above-mentioned processing in the monitoring unit may be performed, for example, using AI or without AI. For example, the monitoring unit can input image data acquired by a drone into the generation AI and have the generation AI detect abnormalities.

[0037] The monitoring unit can detect the occurrence of pests and diseases and propose appropriate control methods. The monitoring unit detects the occurrence of pests and diseases, for example. Pests and diseases include aphids and powdery mildew. The monitoring unit detects the occurrence of pests and diseases, for example, using AI. The AI ​​can detect the occurrence of pests and diseases using image analysis technology. For example, when the monitoring unit detects the occurrence of pests and diseases, it proposes an appropriate control method. Control methods include the use of pesticides and physical control methods. The monitoring unit proposes the use of pesticides, for example. The use of pesticides includes selecting pesticides that are effective against specific pests and diseases. The monitoring unit proposes physical control methods, for example. Physical control methods include capturing and removing pests and diseases. This makes it possible to detect the occurrence of pests and diseases and propose appropriate control methods, thereby improving the quality of crops. Some or all of the above-mentioned processing in the monitoring unit may be performed, for example, using AI, or may be performed without using AI. For example, the monitoring unit can input image data for detecting the occurrence of pests and diseases into the generation AI and have the generation AI propose control methods.

[0038] When collecting weather data or soil data, the collection unit can select the optimal collection method by referring to past data history. The collection unit, for example, selects the most effective data collection timing based on past weather data. Past weather data includes data from the past year and data under specific conditions. The collection unit, for example, refers to past soil data to determine the optimal sensor placement. Past soil data includes data corresponding to the growth stage of a specific crop. The collection unit, for example, analyzes the accuracy of past collected data and proposes the optimal collection method. In this way, the accuracy of data collection can be improved by selecting the optimal collection method by referring to past data history. Some or all of the above-mentioned processing in the collection unit may be performed, for example, using AI, or may be performed without using AI. For example, the collection unit can input past data history to the generation AI and have the generation AI select the optimal collection method.

[0039] When collecting data, the collection unit can change the type of data to be collected depending on the growth stage of a specific crop. For example, when the crop is in the germination stage, the collection unit focuses on collecting soil moisture content and temperature. For example, when the crop is in the growing stage, the collection unit focuses on collecting soil nutritional status and sunlight amount. For example, when the crop is in the harvesting stage, the collection unit focuses on collecting weather data and the occurrence of pests and diseases. In this way, by changing the type of data to be collected depending on the growth stage of a specific crop, more appropriate data can be collected. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input the type of data collection depending on the growth stage of the crop to the generation AI and cause the generation AI to adjust the data collection.

[0040] The collection unit can dynamically change the placement location of the sensor during data collection to acquire optimal data. The collection unit automatically changes the placement location of the sensor according to, for example, weather conditions. The collection unit optimizes the placement location of the sensor according to, for example, the growth status of the crop. The collection unit dynamically adjusts the placement location of the sensor according to, for example, the condition of the soil. This allows the accuracy of data collection to be improved by dynamically changing the placement location of the sensor to acquire optimal data. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input the placement locations of the sensors according to the weather conditions and the growth status of the crop to the generation AI, and have the generation AI adjust the placement locations.

[0041] When collecting data, the collection unit can select the type of data to collect by taking geographical characteristics into consideration. For example, in mountainous areas, the collection unit focuses on collecting temperature and precipitation. For example, in plains, the collection unit focuses on collecting soil nutritional status and solar radiation. For example, in coastal areas, the collection unit focuses on collecting salinity concentration and wind speed. In this way, by selecting the type of data to collect by taking geographical characteristics into consideration, more appropriate data can be collected. Some or all of the above-mentioned processing in the collection unit may be performed using, or without, AI, for example. For example, the collection unit can input the type of data collection according to geographical characteristics to the generation AI and have the generation AI adjust the data collection.

[0042] When collecting data, the collection unit can customize the collection method by referring to data from other farmers. For example, the collection unit refers to the sensor placement methods used by other farmers. For example, the collection unit refers to the types of data collected by other farmers. For example, the collection unit analyzes the accuracy of the data collected by other farmers and proposes the optimal collection method. In this way, the accuracy of data collection can be improved by customizing the collection method by referring to the data of other farmers. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input data from other farmers into the generation AI and have the generation AI customize the collection method.

[0043] The collection unit can improve data accuracy by combining different sensor technologies when collecting data. For example, the collection unit collects data by combining a weather sensor and a soil sensor. For example, the collection unit collects data by combining a drone and a ground sensor. For example, the collection unit improves data accuracy by combining sensors from different manufacturers. This allows for more accurate data to be collected by improving data accuracy by combining different sensor technologies. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input different sensor technologies into the generation AI and have the generation AI adjust the data collection.

[0044] During analysis, the analysis unit can optimize the analysis algorithm by referring to past analysis results. The analysis unit, for example, selects an optimal analysis algorithm based on past analysis results. Past analysis results include analysis results from the past year and analysis results under specific conditions. The analysis unit, for example, analyzes past analysis results to improve the accuracy of the algorithm. The analysis unit, for example, refers to past analysis results to customize the analysis method. In this way, the analysis accuracy can be improved by optimizing the analysis algorithm by referring to past analysis results. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input past analysis results into a generation AI and have the generation AI optimize the analysis algorithm.

[0045] During analysis, the analysis unit can change the analysis method according to the growth stage of a specific crop. For example, when the crop is in the germination period, the analysis unit focuses on analyzing the soil moisture content and temperature. For example, when the crop is in the growth period, the analysis unit focuses on analyzing the soil nutritional state and the amount of sunlight. For example, when the crop is in the harvest period, the analysis unit focuses on analyzing weather data and the occurrence of pests and diseases. This allows for more appropriate analysis by changing the analysis method according to the growth stage of a specific crop. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input an analysis method according to the growth stage of the crop to the generation AI and have the generation AI adjust the analysis method.

[0046] During analysis, the analysis unit can integrate information from different data sources to improve the accuracy of the analysis. For example, the analysis unit integrates meteorological data and soil data for analysis. For example, the analysis unit integrates image data from a drone and data from a ground sensor for analysis. For example, the analysis unit integrates data from sensors from different manufacturers to improve the accuracy of the analysis. In this way, by integrating information from different data sources to improve the accuracy of the analysis, more accurate analysis can be performed. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input information from different data sources into the generation AI and have the generation AI integrate and analyze the data.

[0047] The analysis unit can select an analysis method taking geographical characteristics into consideration during analysis. For example, in mountainous areas, the analysis unit focuses on analyzing temperature and precipitation. For example, in plains areas, the analysis unit focuses on analyzing soil nutritional status and solar radiation. For example, in coastal areas, the analysis unit focuses on analyzing salinity concentration and wind speed. In this way, by selecting an analysis method taking geographical characteristics into consideration, more appropriate analysis can be performed. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input an analysis method according to geographical characteristics to the generation AI and have the generation AI select the analysis method.

[0048] During analysis, the analysis unit can customize the analysis method by referring to the data of other farmers. For example, the analysis unit refers to the analysis methods used by other farmers. For example, the analysis unit refers to the types of data analyzed by other farmers. For example, the analysis unit analyzes the analysis results of other farmers and proposes the optimal analysis method. In this way, by customizing the analysis method by referring to the data of other farmers, the accuracy of the analysis can be improved. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the data of other farmers into the generation AI and have the generation AI customize the analysis method.

[0049] The analysis unit can improve the accuracy of the analysis by combining different analysis algorithms during analysis. The analysis unit, for example, combines a machine learning algorithm and a statistical analysis algorithm to perform the analysis. The analysis unit, for example, combines analysis algorithms from different manufacturers to perform the analysis. The analysis unit, for example, integrates information from different data sources and combines multiple analysis algorithms to improve the accuracy of the analysis. This allows for more accurate analysis by combining different analysis algorithms to improve the accuracy of the analysis. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input different analysis algorithms into the generation AI and have the generation AI adjust the analysis.

[0050] When making a proposal, the proposal unit can change the proposal content according to the growth stage of the crop. For example, when the crop is in the germination period, the proposal unit makes a proposal regarding the soil moisture content and temperature. For example, when the crop is in the growth period, the proposal unit makes a proposal regarding the soil nutritional state and the amount of sunlight. For example, when the crop is in the harvest period, the proposal unit makes a proposal regarding weather data and the occurrence of pests and diseases. In this way, by changing the proposal content according to the growth stage of the crop, more appropriate proposals can be made. Some or all of the above-mentioned processing in the proposal unit may be performed using, for example, AI, or may be performed without using AI. For example, the proposal unit can input proposal content according to the growth stage of the crop to the generation AI and cause the generation AI to adjust the proposal content.

[0051] When making a proposal, the proposal unit can optimize the proposal algorithm by referring to past proposal results. The proposal unit, for example, selects an optimal proposal algorithm based on past proposal results. Past proposal results include proposal results from the past year and proposal results under specific conditions. The proposal unit, for example, analyzes past proposal results to improve the accuracy of the algorithm. The proposal unit, for example, refers to past proposal results to customize the proposal method. In this way, the proposal accuracy can be improved by optimizing the proposal algorithm by referring to past proposal results. Some or all of the above-mentioned processing in the proposal unit may be performed, for example, using AI or without AI. For example, the proposal unit can input past proposal results to a generation AI and cause the generation AI to optimize the proposal algorithm.

[0052] When making a proposal, the proposal unit can integrate information from different data sources to improve the accuracy of the proposal. For example, the proposal unit integrates weather data and soil data to make a proposal. For example, the proposal unit integrates image data from a drone and data from a ground sensor to make a proposal. For example, the proposal unit integrates data from sensors from different manufacturers to improve the accuracy of the proposal. This allows for more accurate proposals by integrating information from different data sources to improve the accuracy of the proposal. Some or all of the above-mentioned processing in the proposal unit may be performed using, or without, AI, for example. For example, the proposal unit can input information from different data sources into a generation AI and cause the generation AI to integrate the data and make a proposal.

[0053] When making a proposal, the proposal unit can select the proposal content taking geographical characteristics into consideration. For example, in mountainous areas, the proposal unit makes proposals regarding temperature and precipitation. For example, in plain areas, the proposal unit makes proposals regarding soil nutritional status and sunlight amount. For example, in coastal areas, the proposal unit makes proposals regarding salinity concentration and wind speed. In this way, by selecting the proposal content taking geographical characteristics into consideration, more appropriate proposals can be made. Some or all of the above-mentioned processing in the proposal unit may be performed using AI, for example, or may be performed without using AI. For example, the proposal unit can input proposal content according to geographical characteristics to the generation AI and cause the generation AI to select the proposal content.

[0054] When making a proposal, the proposal unit can customize the proposal method by referring to data of other farmers. The proposal unit, for example, refers to proposal methods used by other farmers. The proposal unit, for example, refers to the types of data proposed by other farmers. The proposal unit, for example, analyzes the proposal results of other farmers and proposes the optimal proposal method. In this way, by customizing the proposal method by referring to the data of other farmers, the accuracy of the proposal can be improved. Some or all of the above-mentioned processing in the proposal unit may be performed, for example, using AI, or may be performed without using AI. For example, the proposal unit can input data of other farmers into the generation AI and have the generation AI customize the proposal method.

[0055] The suggestion unit can improve the accuracy of the suggestion by combining different suggestion algorithms when making a suggestion. The suggestion unit, for example, combines a machine learning algorithm and a statistical analysis algorithm to make a suggestion. The suggestion unit, for example, combines suggestion algorithms from different manufacturers to make a suggestion. The suggestion unit, for example, integrates information from different data sources and combines multiple suggestion algorithms to improve the accuracy of the suggestion. This allows for more accurate suggestions to be made by combining different suggestion algorithms to improve the accuracy of the suggestion. Some or all of the above-mentioned processing in the suggestion unit may be performed using, or without, AI, for example. For example, the suggestion unit can input different suggestion algorithms to the generation AI and cause the generation AI to adjust the suggestion.

[0056] The work unit can change the work content during work according to the growth stage of a specific crop. For example, when the crop is in the germination period, the work unit focuses on managing the soil moisture content and temperature. For example, when the crop is in the growth period, the work unit focuses on managing the soil's nutritional state and the amount of sunlight. For example, when the crop is in the harvest period, the work unit focuses on managing weather data and the occurrence of pests and diseases. This allows for more appropriate work by changing the work content according to the growth stage of a specific crop. Some or all of the above-mentioned processing in the work unit may be performed using, for example, AI, or may be performed without using AI. For example, the work unit can input work content according to the crop's growth stage into the generation AI and have the generation AI adjust the work content.

[0057] During work, the work unit can optimize the work algorithm by referring to past work results. The work unit, for example, selects an optimal work algorithm based on past work results. Past work results include work results from the past year and work results under specific conditions. The work unit, for example, analyzes past work results and improves the accuracy of the algorithm. The work unit, for example, refers to past work results and customizes the work method. In this way, the accuracy of the work can be improved by optimizing the work algorithm by referring to past work results. Some or all of the above-mentioned processing in the work unit may be performed, for example, using AI, or may be performed without using AI. For example, the work unit can input past work results into a generation AI and have the generation AI optimize the work algorithm.

[0058] The work unit can improve work efficiency by combining different work methods during work. For example, the work unit combines an automatic irrigation system and an automatic fertilization system to perform work. For example, the work unit combines a drone and a ground robot to perform work. For example, the work unit improves work efficiency by combining work equipment from different manufacturers. In this way, work can be performed more efficiently by combining different work methods to improve work efficiency. Some or all of the above-mentioned processing in the work unit may be performed using, for example, AI, or may be performed without using AI. For example, the work unit can input different work methods into a generation AI and have the generation AI adjust the work.

[0059] The work unit can select work content taking geographical characteristics into consideration when working. For example, in mountainous areas, the work unit performs work according to temperature and precipitation. For example, in plains areas, the work unit performs work according to the nutrient state of the soil and the amount of sunlight. For example, in coastal areas, the work unit performs work according to salinity concentration and wind speed. In this way, by selecting work content taking geographical characteristics into consideration, more appropriate work can be performed. Some or all of the above-mentioned processing in the work unit may be performed using, for example, AI, or may be performed without using AI. For example, the work unit can input work content according to geographical characteristics into a generation AI and have the generation AI select the work content.

[0060] The work unit can customize the work method by referring to data of other farmers when working. For example, the work unit refers to the work techniques used by other farmers. For example, the work unit refers to the work content performed by other farmers. For example, the work unit analyzes the work results of other farmers and proposes the optimal work method. In this way, the accuracy of the work can be improved by customizing the work method by referring to the data of other farmers. Some or all of the above-mentioned processing in the work unit may be performed using, for example, AI, or may be performed without using AI. For example, the work unit can input data of other farmers into the generation AI and have the generation AI customize the work method.

[0061] The work unit can improve work efficiency by combining different work equipment during work. For example, the work unit combines an automatic irrigation system and an automatic fertilization system to perform work. For example, the work unit combines a drone and a ground robot to perform work. For example, the work unit improves work efficiency by combining work equipment from different manufacturers. In this way, by combining different work equipment to improve work efficiency, more efficient work can be performed. Some or all of the above-mentioned processing in the work unit may be performed using, for example, AI, or may be performed without using AI. For example, the work unit can input different work equipment into a generation AI and have the generation AI adjust the work.

[0062] During monitoring, the monitoring unit can change the monitoring method according to the growth stage of a specific crop. For example, when the crop is in the germination stage, the monitoring unit focuses on monitoring the soil moisture content and temperature. For example, when the crop is in the growth stage, the monitoring unit focuses on monitoring the soil's nutritional state and the amount of sunlight. For example, when the crop is in the harvest stage, the monitoring unit focuses on monitoring weather data and the occurrence of pests and diseases. This allows for more appropriate monitoring by changing the monitoring method according to the growth stage of a specific crop. Some or all of the above-mentioned processing in the monitoring unit may be performed using, for example, AI, or may be performed without using AI. For example, the monitoring unit can input a monitoring method according to the crop's growth stage into the generation AI and have the generation AI adjust the monitoring method.

[0063] During monitoring, the monitoring unit can optimize the monitoring algorithm by referring to past monitoring results. The monitoring unit, for example, selects an optimal monitoring algorithm based on past monitoring results. Past monitoring results include monitoring results from the past year and monitoring results under specific conditions. The monitoring unit, for example, analyzes past monitoring results to improve the accuracy of the algorithm. The monitoring unit, for example, refers to past monitoring results to customize the monitoring method. In this way, the accuracy of monitoring can be improved by optimizing the monitoring algorithm by referring to past monitoring results. Some or all of the above-mentioned processing in the monitoring unit may be performed, for example, using AI or without AI. For example, the monitoring unit can input past monitoring results into a generation AI and cause the generation AI to optimize the monitoring algorithm.

[0064] During monitoring, the monitoring unit can integrate information from different data sources to improve the accuracy of the monitoring. For example, the monitoring unit integrates meteorological data and soil data for monitoring. For example, the monitoring unit integrates image data from a drone and data from a ground sensor for monitoring. For example, the monitoring unit integrates data from sensors from different manufacturers to improve the accuracy of the monitoring. In this way, by integrating information from different data sources to improve the accuracy of the monitoring, more accurate monitoring can be performed. Some or all of the above-described processing in the monitoring unit may be performed using, for example, AI, or may be performed without using AI. For example, the monitoring unit can input information from different data sources into a generation AI and have the generation AI perform data integration and monitoring.

[0065] During monitoring, the monitoring unit can select a monitoring method taking geographical characteristics into consideration. For example, in mountainous areas, the monitoring unit focuses on monitoring temperature and precipitation. For example, in plains areas, the monitoring unit focuses on monitoring soil nutritional status and sunlight amount. For example, in coastal areas, the monitoring unit focuses on monitoring salinity concentration and wind speed. In this way, more appropriate monitoring can be performed by selecting a monitoring method taking geographical characteristics into consideration. Some or all of the above-described processing in the monitoring unit may be performed using, for example, AI, or may be performed without using AI. For example, the monitoring unit can input a monitoring method according to geographical characteristics into the generation AI and have the generation AI select a monitoring method.

[0066] During monitoring, the monitoring unit can customize the monitoring method by referring to the data of other farmers. For example, the monitoring unit refers to the monitoring methods used by other farmers. For example, the monitoring unit refers to the types of data monitored by other farmers. For example, the monitoring unit analyzes the monitoring results of other farmers and proposes the optimal monitoring method. In this way, the accuracy of monitoring can be improved by customizing the monitoring method by referring to the data of other farmers. Some or all of the above-mentioned processing in the monitoring unit may be performed using, for example, AI, or may be performed without using AI. For example, the monitoring unit can input the data of other farmers into the generation AI and have the generation AI customize the monitoring method.

[0067] The monitoring unit can improve the accuracy of monitoring by combining different monitoring devices during monitoring. The monitoring unit, for example, performs monitoring by combining a weather sensor and a soil sensor. The monitoring unit, for example, performs monitoring by combining a drone and a ground sensor. The monitoring unit, for example, improves the accuracy of monitoring by combining monitoring devices from different manufacturers. In this way, more accurate monitoring can be performed by combining different monitoring devices. Some or all of the above-mentioned processing in the monitoring unit may be performed using, for example, AI, or may be performed without using AI. For example, the monitoring unit can input different monitoring devices into the generation AI and have the generation AI adjust the monitoring.

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

[0069] The agricultural support system can further include a prediction unit. The prediction unit can predict future weather conditions and crop growth conditions based on the collected data. For example, the prediction unit can combine past weather data with current weather data to predict future temperature and precipitation. The prediction unit can also analyze crop growth patterns and predict harvest times and yields. Furthermore, the prediction unit can predict the risk of pest and disease outbreaks and propose prevention measures in advance. This allows farmers to understand future conditions and take appropriate measures.

[0070] The collection unit can also collect environmental data. Environmental data includes, for example, the concentration of carbon dioxide and air pollutants in the air. Based on this data, the collection unit can understand environmental factors that affect crop growth. For example, a high carbon dioxide concentration may improve the photosynthetic efficiency of crops, so the collection unit can provide this information to the analysis unit and suggest optimal cultivation methods. Furthermore, a high concentration of air pollutants may affect the health of crops, so the collection unit can provide this information to the monitoring unit and use it to detect abnormalities early.

[0071] The analysis unit can also be equipped with an anomaly detection function. This function can analyze collected data in real time and detect abnormal patterns. For example, the analysis unit can detect sudden changes in temperature or humidity and warn of the occurrence of abnormal weather. In addition, if there is a sudden change in the nutritional status of the soil, the analysis unit can identify the cause and propose appropriate countermeasures. Furthermore, if abnormalities are detected in crop growth patterns, the analysis unit can predict the risk of pest outbreaks and take early prevention measures. This allows farmers to detect abnormalities early and respond quickly.

[0072] The suggestion unit can further customize the content of the suggestions based on the user's past behavior history. The suggestion unit can analyze the cultivation methods and results of the user's past use and make optimal suggestions. For example, if the user has used a specific fertilizer in the past and obtained good results, the suggestion unit can suggest that fertilizer again. Also, the suggestion unit can suggest the optimal watering method for the current weather conditions based on the timing and amount of watering the user has done in the past. Furthermore, the suggestion unit can learn the user's past behavior patterns and make suggestions tailored to the user's preferences. This allows the user to practice more effective cultivation methods.

[0073] The work unit can further be equipped with an automatic harvesting system. The automatic harvesting system can determine the harvest time for crops based on suggestions from the analysis unit and carry out the harvesting work automatically. For example, the automatic harvesting system can use a camera to detect the color and size of fruit and determine the timing of harvesting. The automatic harvesting system can also properly classify harvested crops and process them to maintain their quality. Furthermore, the automatic harvesting system can monitor the progress of the harvesting work in real time and adjust the work plan as necessary. This reduces the burden on farmers and enables more efficient harvesting work.

[0074] The processing flow of the first embodiment will be briefly explained below.

[0075] Step 1: The collection unit collects weather or soil data. Weather data includes temperature, humidity, precipitation, wind speed, etc., while soil data includes soil nutritional status, pH value, moisture content, etc. The collection unit acquires data in real time using sensors. Step 2: The analysis unit analyzes the data collected by the collection unit. The analysis is performed using statistical analysis of the data and machine learning algorithms. Step 3: The proposal unit proposes cultivation methods based on the analysis results obtained by the analysis unit. Cultivation methods include watering timing, type and amount of fertilizer, planting intervals, etc. For example, when temperatures are high, the system proposes the appropriate watering timing and amount, and the type and amount of fertilizer needed depending on the nutritional status of the soil. Step 4: The working unit automatically performs cultivation work based on the cultivation method proposed by the proposing unit. The working unit uses the automatic irrigation system to water at the proposed timing and the automatic fertilization system to apply the proposed fertilizer in the appropriate amount. Step 5: The monitoring unit monitors the results of the cultivation work carried out by the work unit. The monitoring unit uses a drone to take pictures of the crop's growth status, which are then analyzed by AI. This allows for early detection of any abnormalities and the implementation of countermeasures. For example, if the occurrence of pests or diseases is detected, appropriate control methods will be proposed.

[0076] (Example 2) An agricultural support system according to an embodiment of the present invention uses AI to improve the efficiency of agricultural quality control and cultivation work, reduce the burden on farmers, and increase production. The agricultural support system collects and analyzes weather and soil data, proposes optimal cultivation methods, and automatically performs and monitors cultivation work. For example, the agricultural support system collects weather and soil data, such as temperature, humidity, precipitation, and the nutritional status of the soil. Next, the agricultural support system analyzes the collected data and proposes optimal cultivation methods. For example, when the temperature is high, the system proposes the appropriate timing and amount of watering. The system also proposes the type and amount of fertilizer required depending on the nutritional status of the soil. Next, the agricultural support system automatically performs cultivation work. For example, an automatic irrigation system waters at the proposed timing. The system also uses an automatic fertilization system to apply the proposed fertilizer in the appropriate amount. Next, the agricultural support system monitors the growth status of crops. For example, a drone is used to photograph the growth status of the crops, and AI analyzes the images. This allows for early detection of abnormalities and the implementation of countermeasures. For example, if it detects the occurrence of pests and diseases, it will suggest appropriate control methods. This allows the agricultural support system to minimize the impact of abnormal weather and an aging population, achieving efficient agriculture. This allows the agricultural support system to reduce the burden on farmers and increase production. For example, by following the cultivation methods suggested by AI, harvest yields will increase and quality will also improve. Furthermore, by having AI automatically perform cultivation tasks, it is possible to shorten the working hours of farmers and reduce physical fatigue.

[0077] An agricultural support system according to an embodiment includes a collection unit, an analysis unit, a proposal unit, a working unit, and a monitoring unit. The collection unit collects weather data or soil data. The weather data includes, for example, temperature, humidity, precipitation, and wind speed. The soil data includes, for example, soil nutritional status, pH value, and moisture content. The collection unit acquires data in real time using, for example, a sensor. The analysis unit analyzes the data collected by the collection unit. The analysis is performed using, for example, statistical analysis of data or a machine learning algorithm. The proposal unit proposes a cultivation method based on the analysis results obtained by the analysis unit. The cultivation method includes, for example, watering timing, type and amount of fertilizer, planting interval, and the like. For example, when the temperature is high, the proposal unit proposes appropriate watering timing and amount. The proposal unit also proposes the type and amount of fertilizer required depending on the nutritional status of the soil. The working unit automatically performs cultivation work based on the cultivation method proposed by the proposal unit. For example, the working unit waters plants at the proposed timing using an automatic irrigation system. In addition, an automatic fertilization system is used to apply the suggested fertilizer in the appropriate amount. The monitoring unit monitors the results of the cultivation work performed by the work unit. The monitoring unit, for example, uses a drone to photograph the growth status of the crops, which is then analyzed by AI. This allows for early detection of any abnormalities and the implementation of countermeasures. For example, if an outbreak of pests or diseases is detected, an appropriate control method is suggested. As a result, the agricultural support system according to the embodiment can reduce the burden on farmers and increase production.

[0078] The collection unit can collect data on temperature, humidity, precipitation, and soil nutritional status. The collection unit, for example, collects temperature data. The temperature data includes ground temperature and weather station data. The collection unit, for example, collects humidity data. The humidity data includes relative humidity and absolute humidity. The collection unit, for example, collects precipitation data. The precipitation data includes rain gauge data and weather station data. The collection unit, for example, collects soil nutritional status data. The soil nutritional status data includes nitrogen, phosphorus, and potassium content. By collecting data such as temperature, humidity, precipitation, and soil nutritional status, the optimal environment for crop growth can be identified. Some or all of the above-described processing in the collection unit may be performed using, or without, AI. For example, the collection unit may input data acquired by a sensor into a generation AI and have the generation AI analyze the data.

[0079] The analysis unit can analyze the collected data and propose an optimal cultivation method. The analysis unit, for example, performs statistical analysis on the collected data. Statistical analysis includes calculating the mean, median, standard deviation, etc. of the data. The analysis unit, for example, analyzes the collected data using a machine learning algorithm. Machine learning algorithms include regression analysis, clustering, deep learning, etc. The analysis unit, for example, proposes an optimal cultivation method based on the collected data. The cultivation method includes watering timing, type and amount of fertilizer, planting interval, etc. In this way, by analyzing the collected data and proposing an optimal cultivation method, the quality of the crop can be improved. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the collected data into a generation AI and have the generation AI propose an optimal cultivation method.

[0080] The suggestion unit can suggest the timing or amount of watering when the temperature is high. For example, the suggestion unit can suggest the timing of watering when the temperature is high. The suggestion unit can suggest what time to water when the temperature is above a certain degree. For example, the suggestion unit can suggest the amount of watering when the temperature is high. The suggestion unit can suggest the amount of watering per square meter or the amount of water depending on the type of crop. This can improve the quality of crops by suggesting the appropriate timing and amount of watering when the temperature is high. Some or all of the above-mentioned processing by the suggestion unit may be performed using, or without, AI, for example. For example, the suggestion unit can input temperature data into the generation AI and cause the generation AI to suggest the timing and amount of watering.

[0081] The suggestion unit can suggest the type and amount of fertilizer needed depending on the nutritional state of the soil. For example, the suggestion unit can suggest the type of fertilizer needed depending on the nutritional state of the soil. The suggestion unit can suggest organic fertilizer, chemical fertilizer, fertilizer containing specific nutrients, etc. The suggestion unit can suggest the amount of fertilizer needed depending on the nutritional state of the soil, for example. The suggestion unit can suggest the amount of fertilizer needed per square meter or the amount of fertilizer needed depending on the type of crop. This allows the quality of crops to be improved by suggesting the type and amount of fertilizer needed depending on the nutritional state of the soil. Some or all of the above-mentioned processing in the suggestion unit may be performed using, or without, AI, for example. For example, the suggestion unit can input soil data into the generation AI and cause the generation AI to suggest the type and amount of fertilizer.

[0082] The working unit can water crops at the timing suggested by an automatic irrigation system. The working unit, for example, waters crops at the timing suggested by the automatic irrigation system. Automatic irrigation systems include drip irrigation and sprinkler irrigation. The working unit waters crops using, for example, a drip irrigation system. A drip irrigation system supplies water directly to the roots of crops, reducing water waste. The working unit waters crops using, for example, a sprinkler irrigation system. A sprinkler irrigation system supplies water evenly over a wide area and is suitable for large-scale farmland. In this way, watering crops at the timing suggested by the automatic irrigation system reduces the burden on farmers and realizes efficient cultivation work. Some or all of the above-described processing in the working unit may be performed using, for example, AI, or may be performed without AI. For example, the working unit can input the suggested watering timing to a generation AI and have the generation AI control the automatic irrigation system.

[0083] The working unit can apply the suggested fertilizer in an appropriate amount using an automatic fertilization system. The working unit, for example, applies the suggested fertilizer in an appropriate amount using an automatic fertilization system. Automatic fertilization systems include automatic liquid fertilizer supply systems and automatic solid fertilizer spraying systems. The working unit applies fertilizer using, for example, an automatic liquid fertilizer supply system. The automatic liquid fertilizer supply system is a system that dissolves fertilizer in water and supplies it, and can supply the fertilizer evenly. The working unit applies fertilizer using, for example, an automatic solid fertilizer spraying system. The automatic solid fertilizer spraying system is a system that sprays fertilizer evenly and can supply fertilizer efficiently. In this way, applying the suggested fertilizer in an appropriate amount using the automatic fertilization system reduces the burden on farmers and realizes efficient cultivation work. Some or all of the above-mentioned processing in the working unit may be performed using, for example, AI, or may be performed without using AI. For example, the working unit can input the type and amount of fertilizer suggested to the generation AI and cause the generation AI to control the automatic fertilization system.

[0084] The monitoring unit uses a drone to photograph the growth state of crops, and if an abnormality occurs, it can be detected early and measures can be taken. The monitoring unit, for example, uses a drone to photograph the growth state of crops. Drones include fixed-wing drones and multi-rotor drones. The monitoring unit, for example, uses a fixed-wing drone to photograph a wide area of ​​crops. Fixed-wing drones are capable of long flight times and can efficiently monitor a wide area of ​​crops. The monitoring unit, for example, uses a multi-rotor drone to photograph the detailed growth state of crops. Multi-rotor drones are capable of stable flight and can acquire detailed images. The monitoring unit, for example, uses AI to analyze the photographed images and detect abnormalities. Abnormalities include the occurrence of pests and diseases and slow growth. In this way, by photographing the growth state of crops using a drone, and if an abnormality occurs, it can be detected early and measures can be taken, thereby improving the quality of the crops. Some or all of the above-mentioned processing in the monitoring unit may be performed, for example, using AI or without AI. For example, the monitoring unit can input image data acquired by a drone into the generation AI and have the generation AI detect abnormalities.

[0085] The monitoring unit can detect the occurrence of pests and diseases and propose appropriate control methods. The monitoring unit detects the occurrence of pests and diseases, for example. Pests and diseases include aphids and powdery mildew. The monitoring unit detects the occurrence of pests and diseases, for example, using AI. The AI ​​can detect the occurrence of pests and diseases using image analysis technology. For example, when the monitoring unit detects the occurrence of pests and diseases, it proposes an appropriate control method. Control methods include the use of pesticides and physical control methods. The monitoring unit proposes the use of pesticides, for example. The use of pesticides includes selecting pesticides that are effective against specific pests and diseases. The monitoring unit proposes physical control methods, for example. Physical control methods include capturing and removing pests and diseases. This makes it possible to detect the occurrence of pests and diseases and propose appropriate control methods, thereby improving the quality of crops. Some or all of the above-mentioned processing in the monitoring unit may be performed, for example, using AI, or may be performed without using AI. For example, the monitoring unit can input image data for detecting the occurrence of pests and diseases into the generation AI and have the generation AI propose control methods.

[0086] The collection unit can estimate the user's emotions and adjust the timing of data collection based on the estimated user's emotions. The collection unit, for example, estimates the user's emotions. Emotion estimation uses technologies such as facial expression recognition and voice analysis. The collection unit, for example, estimates the user's emotions using facial expression recognition technology. Facial expression recognition technology can analyze the user's facial expressions captured by a camera to estimate the emotions. The collection unit, for example, estimates the user's emotions using voice analysis technology. Voice analysis technology can analyze the tone and speed of the user's voice to estimate the emotions. The collection unit, for example, adjusts the timing of data collection based on the estimated user's emotions. If the user is feeling stressed, the collection unit can reduce the frequency of data collection to reduce the user's burden. If the user is relaxed, the collection unit can increase the frequency of data collection to collect detailed data. If the user is in a hurry, the collection unit can optimize the timing of data collection and quickly collect necessary data. This reduces the user's burden by adjusting the timing of data collection according to the user's emotions. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the collection unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the collection unit may input the user's facial expression data into the generation AI and cause the generation AI to estimate emotions.

[0087] When collecting weather data or soil data, the collection unit can select the optimal collection method by referring to past data history. The collection unit, for example, selects the most effective data collection timing based on past weather data. Past weather data includes data from the past year and data under specific conditions. The collection unit, for example, refers to past soil data to determine the optimal sensor placement. Past soil data includes data corresponding to the growth stage of a specific crop. The collection unit, for example, analyzes the accuracy of past collected data and proposes the optimal collection method. In this way, the accuracy of data collection can be improved by selecting the optimal collection method by referring to past data history. Some or all of the above-mentioned processing in the collection unit may be performed, for example, using AI, or may be performed without using AI. For example, the collection unit can input past data history to the generation AI and have the generation AI select the optimal collection method.

[0088] When collecting data, the collection unit can change the type of data to be collected depending on the growth stage of a specific crop. For example, when the crop is in the germination stage, the collection unit focuses on collecting soil moisture content and temperature. For example, when the crop is in the growing stage, the collection unit focuses on collecting soil nutritional status and sunlight amount. For example, when the crop is in the harvesting stage, the collection unit focuses on collecting weather data and the occurrence of pests and diseases. In this way, by changing the type of data to be collected depending on the growth stage of a specific crop, more appropriate data can be collected. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input the type of data collection depending on the growth stage of the crop to the generation AI and cause the generation AI to adjust the data collection.

[0089] The collection unit can dynamically change the placement location of the sensor during data collection to acquire optimal data. The collection unit automatically changes the placement location of the sensor according to, for example, weather conditions. The collection unit optimizes the placement location of the sensor according to, for example, the growth status of the crop. The collection unit dynamically adjusts the placement location of the sensor according to, for example, the condition of the soil. This allows the accuracy of data collection to be improved by dynamically changing the placement location of the sensor to acquire optimal data. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input the placement locations of the sensors according to the weather conditions and the growth status of the crop to the generation AI, and have the generation AI adjust the placement locations.

[0090] The collection unit can estimate the user's emotions and determine the priority of data to be collected based on the estimated user's emotions. The collection unit, for example, estimates the user's emotions. Emotion estimation uses technologies such as facial expression recognition and voice analysis. The collection unit, for example, estimates the user's emotions using facial expression recognition technology. The facial expression recognition technology can analyze the user's facial expressions captured by a camera to estimate the emotions. The collection unit, for example, estimates the user's emotions using voice analysis technology. The voice analysis technology can analyze the tone and speed of the user's voice to estimate the emotions. The collection unit, for example, determines the priority of data to be collected based on the estimated user's emotions. If the user is feeling stressed, the collection unit can prioritize collecting only important data. If the user is relaxed, the collection unit can prioritize collecting detailed data. If the user is in a hurry, the collection unit can prioritize collecting data that can be collected quickly. In this way, the burden on the user can be reduced by determining the priority of data to be collected according to the user's emotions. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the collection unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the collection unit may input the user's facial expression data into the generation AI and cause the generation AI to estimate emotions.

[0091] When collecting data, the collection unit can select the type of data to collect by taking geographical characteristics into consideration. For example, in mountainous areas, the collection unit focuses on collecting temperature and precipitation. For example, in plains, the collection unit focuses on collecting soil nutritional status and solar radiation. For example, in coastal areas, the collection unit focuses on collecting salinity concentration and wind speed. In this way, by selecting the type of data to collect by taking geographical characteristics into consideration, more appropriate data can be collected. Some or all of the above-mentioned processing in the collection unit may be performed using, or without, AI, for example. For example, the collection unit can input the type of data collection according to geographical characteristics to the generation AI and have the generation AI adjust the data collection.

[0092] When collecting data, the collection unit can customize the collection method by referring to data from other farmers. For example, the collection unit refers to the sensor placement methods used by other farmers. For example, the collection unit refers to the types of data collected by other farmers. For example, the collection unit analyzes the accuracy of the data collected by other farmers and proposes the optimal collection method. In this way, the accuracy of data collection can be improved by customizing the collection method by referring to the data of other farmers. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input data from other farmers into the generation AI and have the generation AI customize the collection method.

[0093] The collection unit can improve data accuracy by combining different sensor technologies when collecting data. For example, the collection unit collects data by combining a weather sensor and a soil sensor. For example, the collection unit collects data by combining a drone and a ground sensor. For example, the collection unit improves data accuracy by combining sensors from different manufacturers. This allows for more accurate data to be collected by improving data accuracy by combining different sensor technologies. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input different sensor technologies into the generation AI and have the generation AI adjust the data collection.

[0094] The analysis unit can estimate the user's emotions and adjust the display method of the analysis results based on the estimated user's emotions. The analysis unit, for example, estimates the user's emotions. Technologies such as facial expression recognition and voice analysis are used for emotion estimation. The analysis unit, for example, estimates the user's emotions using facial expression recognition technology. Facial expression recognition technology can analyze the user's facial expressions captured by a camera to estimate the emotions. The analysis unit, for example, estimates the user's emotions using voice analysis technology. Voice analysis technology can analyze the tone and speed of the user's voice to estimate the emotions. The analysis unit, for example, adjusts the display method of the analysis results based on the estimated user's emotions. If the user is nervous, the analysis unit can provide a simple, highly visible display method. If the user is relaxed, the analysis unit can provide a display method including detailed information. If the user is in a hurry, the analysis unit can provide a display method that focuses on the main points. This reduces the burden on the user by adjusting the display method of the analysis results according to the user's emotions. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the analysis unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the analysis unit may input the user's facial expression data into the generation AI and cause the generation AI to estimate emotions.

[0095] During analysis, the analysis unit can optimize the analysis algorithm by referring to past analysis results. The analysis unit, for example, selects an optimal analysis algorithm based on past analysis results. Past analysis results include analysis results from the past year and analysis results under specific conditions. The analysis unit, for example, analyzes past analysis results to improve the accuracy of the algorithm. The analysis unit, for example, refers to past analysis results to customize the analysis method. In this way, the analysis accuracy can be improved by optimizing the analysis algorithm by referring to past analysis results. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input past analysis results into a generation AI and have the generation AI optimize the analysis algorithm.

[0096] During analysis, the analysis unit can change the analysis method according to the growth stage of a specific crop. For example, when the crop is in the germination period, the analysis unit focuses on analyzing the soil moisture content and temperature. For example, when the crop is in the growth period, the analysis unit focuses on analyzing the soil nutritional state and the amount of sunlight. For example, when the crop is in the harvest period, the analysis unit focuses on analyzing weather data and the occurrence of pests and diseases. This allows for more appropriate analysis by changing the analysis method according to the growth stage of a specific crop. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input an analysis method according to the growth stage of the crop to the generation AI and have the generation AI adjust the analysis method.

[0097] During analysis, the analysis unit can integrate information from different data sources to improve the accuracy of the analysis. For example, the analysis unit integrates meteorological data and soil data for analysis. For example, the analysis unit integrates image data from a drone and data from a ground sensor for analysis. For example, the analysis unit integrates data from sensors from different manufacturers to improve the accuracy of the analysis. In this way, by integrating information from different data sources to improve the accuracy of the analysis, more accurate analysis can be performed. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input information from different data sources into the generation AI and have the generation AI integrate and analyze the data.

[0098] The analysis unit can estimate the user's emotions and determine the priority of analysis results based on the estimated user's emotions. The analysis unit, for example, estimates the user's emotions. Technologies such as facial expression recognition and voice analysis are used for emotion estimation. The analysis unit, for example, estimates the user's emotions using facial expression recognition technology. The facial expression recognition technology can analyze the user's facial expressions captured by a camera to estimate the emotions. The analysis unit, for example, uses voice analysis technology to estimate the user's emotions. The voice analysis technology can analyze the tone and speed of the user's voice to estimate the emotions. The analysis unit, for example, determines the priority of analysis results based on the estimated user's emotions. If the user is feeling stressed, the analysis unit can prioritize displaying only important analysis results. If the user is relaxed, the analysis unit can prioritize displaying detailed analysis results. If the user is in a hurry, the analysis unit can prioritize displaying analysis results that can be displayed quickly. In this way, the burden on the user can be reduced by determining the priority of analysis results according to the user's emotions. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the analysis unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the analysis unit may input the user's facial expression data into the generation AI and cause the generation AI to estimate emotions.

[0099] The analysis unit can select an analysis method taking geographical characteristics into consideration during analysis. For example, in mountainous areas, the analysis unit focuses on analyzing temperature and precipitation. For example, in plains areas, the analysis unit focuses on analyzing soil nutritional status and solar radiation. For example, in coastal areas, the analysis unit focuses on analyzing salinity concentration and wind speed. In this way, by selecting an analysis method taking geographical characteristics into consideration, more appropriate analysis can be performed. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input an analysis method according to geographical characteristics to the generation AI and have the generation AI select the analysis method.

[0100] During analysis, the analysis unit can customize the analysis method by referring to the data of other farmers. For example, the analysis unit refers to the analysis methods used by other farmers. For example, the analysis unit refers to the types of data analyzed by other farmers. For example, the analysis unit analyzes the analysis results of other farmers and proposes the optimal analysis method. In this way, by customizing the analysis method by referring to the data of other farmers, the accuracy of the analysis can be improved. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the data of other farmers into the generation AI and have the generation AI customize the analysis method.

[0101] The analysis unit can improve the accuracy of the analysis by combining different analysis algorithms during analysis. The analysis unit, for example, combines a machine learning algorithm and a statistical analysis algorithm to perform the analysis. The analysis unit, for example, combines analysis algorithms from different manufacturers to perform the analysis. The analysis unit, for example, integrates information from different data sources and combines multiple analysis algorithms to improve the accuracy of the analysis. This allows for more accurate analysis by combining different analysis algorithms to improve the accuracy of the analysis. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input different analysis algorithms into the generation AI and have the generation AI adjust the analysis.

[0102] The suggestion unit can estimate the user's emotion and adjust the way the suggestion is expressed based on the estimated user's emotion. The suggestion unit, for example, estimates the user's emotion. Emotion estimation uses technologies such as facial expression recognition and voice analysis. The suggestion unit, for example, estimates the user's emotion using facial expression recognition technology. The facial expression recognition technology can analyze the user's facial expression captured by a camera to estimate the emotion. The suggestion unit, for example, estimates the user's emotion using voice analysis technology. The voice analysis technology can analyze the tone and speed of the user's voice to estimate the emotion. The suggestion unit, for example, adjusts the way the suggestion is expressed based on the estimated user's emotion. If the user is nervous, the suggestion unit can provide a simple and highly visible suggestion method. If the user is relaxed, the suggestion unit can provide a suggestion method including detailed information. If the user is in a hurry, the suggestion unit can provide a suggestion method that focuses on the main points. This allows the way the suggestion is expressed to be adjusted according to the user's emotion, thereby reducing the burden on the user. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the proposal unit may be performed using, or without, an AI. For example, the proposal unit may input the user's facial expression data into the generation AI and cause the generation AI to estimate emotions.

[0103] When making a proposal, the proposal unit can change the proposal content according to the growth stage of the crop. For example, when the crop is in the germination period, the proposal unit makes a proposal regarding the soil moisture content and temperature. For example, when the crop is in the growth period, the proposal unit makes a proposal regarding the soil nutritional state and the amount of sunlight. For example, when the crop is in the harvest period, the proposal unit makes a proposal regarding weather data and the occurrence of pests and diseases. In this way, by changing the proposal content according to the growth stage of the crop, more appropriate proposals can be made. Some or all of the above-mentioned processing in the proposal unit may be performed using, for example, AI, or may be performed without using AI. For example, the proposal unit can input proposal content according to the growth stage of the crop to the generation AI and cause the generation AI to adjust the proposal content.

[0104] When making a proposal, the proposal unit can optimize the proposal algorithm by referring to past proposal results. The proposal unit, for example, selects an optimal proposal algorithm based on past proposal results. Past proposal results include proposal results from the past year and proposal results under specific conditions. The proposal unit, for example, analyzes past proposal results to improve the accuracy of the algorithm. The proposal unit, for example, refers to past proposal results to customize the proposal method. In this way, the proposal accuracy can be improved by optimizing the proposal algorithm by referring to past proposal results. Some or all of the above-mentioned processing in the proposal unit may be performed, for example, using AI or without AI. For example, the proposal unit can input past proposal results to a generation AI and cause the generation AI to optimize the proposal algorithm.

[0105] When making a proposal, the proposal unit can integrate information from different data sources to improve the accuracy of the proposal. For example, the proposal unit integrates weather data and soil data to make a proposal. For example, the proposal unit integrates image data from a drone and data from a ground sensor to make a proposal. For example, the proposal unit integrates data from sensors from different manufacturers to improve the accuracy of the proposal. This allows for more accurate proposals by integrating information from different data sources to improve the accuracy of the proposal. Some or all of the above-mentioned processing in the proposal unit may be performed using, or without, AI, for example. For example, the proposal unit can input information from different data sources into a generation AI and cause the generation AI to integrate the data and make a proposal.

[0106] The suggestion unit can estimate the user's emotions and prioritize suggestions based on the estimated user's emotions. The suggestion unit, for example, estimates the user's emotions. Emotion estimation uses technologies such as facial expression recognition and voice analysis. The suggestion unit, for example, estimates the user's emotions using facial expression recognition technology. The facial expression recognition technology can analyze the user's facial expressions captured by a camera to estimate emotions. The suggestion unit, for example, uses voice analysis technology to estimate the user's emotions. The voice analysis technology can analyze the tone and speed of the user's voice to estimate emotions. The suggestion unit, for example, prioritizes suggestions based on the estimated user's emotions. If the user is feeling stressed, the suggestion unit can prioritize displaying only important suggestions. If the user is relaxed, the suggestion unit can prioritize displaying detailed suggestions. If the user is in a hurry, the suggestion unit can prioritize displaying suggestions that can be displayed quickly. This reduces the burden on the user by prioritizing suggestions according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generative AI. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the suggestion unit may be performed using AI, or may be performed without using AI. For example, the suggestion unit may input user facial expression data to the generation AI and cause the generation AI to estimate emotions.

[0107] When making a proposal, the proposal unit can select the proposal content taking geographical characteristics into consideration. For example, in mountainous areas, the proposal unit makes proposals regarding temperature and precipitation. For example, in plain areas, the proposal unit makes proposals regarding soil nutritional status and sunlight amount. For example, in coastal areas, the proposal unit makes proposals regarding salinity concentration and wind speed. In this way, by selecting the proposal content taking geographical characteristics into consideration, more appropriate proposals can be made. Some or all of the above-mentioned processing in the proposal unit may be performed using AI, for example, or may be performed without using AI. For example, the proposal unit can input proposal content according to geographical characteristics to the generation AI and cause the generation AI to select the proposal content.

[0108] When making a proposal, the proposal unit can customize the proposal method by referring to data of other farmers. The proposal unit, for example, refers to proposal methods used by other farmers. The proposal unit, for example, refers to the types of data proposed by other farmers. The proposal unit, for example, analyzes the proposal results of other farmers and proposes the optimal proposal method. In this way, by customizing the proposal method by referring to the data of other farmers, the accuracy of the proposal can be improved. Some or all of the above-mentioned processing in the proposal unit may be performed, for example, using AI, or may be performed without using AI. For example, the proposal unit can input data of other farmers into the generation AI and have the generation AI customize the proposal method.

[0109] The suggestion unit can improve the accuracy of the suggestion by combining different suggestion algorithms when making a suggestion. The suggestion unit, for example, combines a machine learning algorithm and a statistical analysis algorithm to make a suggestion. The suggestion unit, for example, combines suggestion algorithms from different manufacturers to make a suggestion. The suggestion unit, for example, integrates information from different data sources and combines multiple suggestion algorithms to improve the accuracy of the suggestion. This allows for more accurate suggestions to be made by combining different suggestion algorithms to improve the accuracy of the suggestion. Some or all of the above-mentioned processing in the suggestion unit may be performed using, or without, AI, for example. For example, the suggestion unit can input different suggestion algorithms to the generation AI and cause the generation AI to adjust the suggestion.

[0110] The work unit can estimate the user's emotions and adjust the timing of tasks based on the estimated user's emotions. The work unit, for example, estimates the user's emotions. Technologies such as facial expression recognition and voice analysis are used for emotion estimation. The work unit, for example, estimates the user's emotions using facial expression recognition technology. The facial expression recognition technology can analyze the user's facial expressions captured by a camera to estimate the emotions. The work unit, for example, estimates the user's emotions using voice analysis technology. The voice analysis technology can analyze the tone and speed of the user's voice to estimate the emotions. The work unit, for example, adjusts the timing of tasks based on the estimated user's emotions. If the user is feeling stressed, the work unit can reduce the frequency of tasks to reduce the user's burden. If the user is relaxed, the work unit can increase the frequency of tasks and perform detailed tasks. If the user is in a hurry, the work unit can optimize the timing of tasks and quickly perform necessary tasks. In this way, the burden on the user can be reduced by adjusting the timing of tasks according to the user's emotions. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI may be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the working unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the working unit may input the user's facial expression data into the generation AI and cause the generation AI to estimate emotions.

[0111] The work unit can change the work content during work according to the growth stage of a specific crop. For example, when the crop is in the germination period, the work unit focuses on managing the soil moisture content and temperature. For example, when the crop is in the growth period, the work unit focuses on managing the soil's nutritional state and the amount of sunlight. For example, when the crop is in the harvest period, the work unit focuses on managing weather data and the occurrence of pests and diseases. This allows for more appropriate work by changing the work content according to the growth stage of a specific crop. Some or all of the above-mentioned processing in the work unit may be performed using, for example, AI, or may be performed without using AI. For example, the work unit can input work content according to the crop's growth stage into the generation AI and have the generation AI adjust the work content.

[0112] During work, the work unit can optimize the work algorithm by referring to past work results. The work unit, for example, selects an optimal work algorithm based on past work results. Past work results include work results from the past year and work results under specific conditions. The work unit, for example, analyzes past work results and improves the accuracy of the algorithm. The work unit, for example, refers to past work results and customizes the work method. In this way, the accuracy of the work can be improved by optimizing the work algorithm by referring to past work results. Some or all of the above-mentioned processing in the work unit may be performed, for example, using AI, or may be performed without using AI. For example, the work unit can input past work results into a generation AI and have the generation AI optimize the work algorithm.

[0113] The work unit can improve work efficiency by combining different work methods during work. For example, the work unit combines an automatic irrigation system and an automatic fertilization system to perform work. For example, the work unit combines a drone and a ground robot to perform work. For example, the work unit improves work efficiency by combining work equipment from different manufacturers. In this way, work can be performed more efficiently by combining different work methods to improve work efficiency. Some or all of the above-mentioned processing in the work unit may be performed using, for example, AI, or may be performed without using AI. For example, the work unit can input different work methods into a generation AI and have the generation AI adjust the work.

[0114] The work unit can estimate the user's emotions and determine the priority of tasks based on the estimated user's emotions. The work unit, for example, estimates the user's emotions. Emotion estimation uses technologies such as facial expression recognition and voice analysis. The work unit, for example, estimates the user's emotions using facial expression recognition technology. Facial expression recognition technology can estimate the user's emotions by analyzing the user's facial expressions captured by a camera. The work unit, for example, estimates the user's emotions using voice analysis technology. Voice analysis technology can estimate the user's emotions by analyzing the tone and speed of the user's voice. The work unit, for example, determines the priority of tasks based on the estimated user's emotions. If the user is feeling stressed, the work unit can prioritize only important tasks. If the user is relaxed, the work unit can prioritize detailed tasks. If the user is in a hurry, the work unit can prioritize tasks that can be completed quickly. This reduces the user's burden by determining the priority of tasks according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generative AI. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the working unit may be performed using AI, or may be performed without using AI. For example, the working unit may input the user's facial expression data into the generation AI and cause the generation AI to estimate emotions.

[0115] The work unit can select work content taking geographical characteristics into consideration when working. For example, in mountainous areas, the work unit performs work according to temperature and precipitation. For example, in plains areas, the work unit performs work according to the nutrient state of the soil and the amount of sunlight. For example, in coastal areas, the work unit performs work according to salinity concentration and wind speed. In this way, by selecting work content taking geographical characteristics into consideration, more appropriate work can be performed. Some or all of the above-mentioned processing in the work unit may be performed using, for example, AI, or may be performed without using AI. For example, the work unit can input work content according to geographical characteristics into a generation AI and have the generation AI select the work content.

[0116] The work unit can customize the work method by referring to data of other farmers when working. For example, the work unit refers to the work techniques used by other farmers. For example, the work unit refers to the work content performed by other farmers. For example, the work unit analyzes the work results of other farmers and proposes the optimal work method. In this way, the accuracy of the work can be improved by customizing the work method by referring to the data of other farmers. Some or all of the above-mentioned processing in the work unit may be performed using, for example, AI, or may be performed without using AI. For example, the work unit can input data of other farmers into the generation AI and have the generation AI customize the work method.

[0117] The work unit can improve work efficiency by combining different work equipment during work. For example, the work unit combines an automatic irrigation system and an automatic fertilization system to perform work. For example, the work unit combines a drone and a ground robot to perform work. For example, the work unit improves work efficiency by combining work equipment from different manufacturers. In this way, by combining different work equipment to improve work efficiency, more efficient work can be performed. Some or all of the above-mentioned processing in the work unit may be performed using, for example, AI, or may be performed without using AI. For example, the work unit can input different work equipment into a generation AI and have the generation AI adjust the work.

[0118] The monitoring unit can estimate the user's emotions and adjust the display method of the monitoring results based on the estimated user's emotions. The monitoring unit, for example, estimates the user's emotions. Technologies such as facial expression recognition and voice analysis are used for emotion estimation. The monitoring unit, for example, estimates the user's emotions using facial expression recognition technology. The facial expression recognition technology can analyze the user's facial expressions captured by a camera to estimate the emotions. The monitoring unit, for example, estimates the user's emotions using voice analysis technology. The voice analysis technology can analyze the tone and speed of the user's voice to estimate the emotions. The monitoring unit adjusts the display method of the monitoring results based on the estimated user's emotions, for example. The monitoring unit can provide a simple, highly visible display method when the user is nervous. The monitoring unit can provide a display method including detailed information when the user is relaxed. The monitoring unit can provide a display method that focuses on the main points when the user is in a hurry. This allows the display method of the monitoring results to be adjusted according to the user's emotions, thereby reducing the burden on the user. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the monitoring unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the monitoring unit may input the user's facial expression data into the generation AI and cause the generation AI to estimate emotions.

[0119] During monitoring, the monitoring unit can change the monitoring method according to the growth stage of a specific crop. For example, when the crop is in the germination stage, the monitoring unit focuses on monitoring the soil moisture content and temperature. For example, when the crop is in the growth stage, the monitoring unit focuses on monitoring the soil's nutritional state and the amount of sunlight. For example, when the crop is in the harvest stage, the monitoring unit focuses on monitoring weather data and the occurrence of pests and diseases. This allows for more appropriate monitoring by changing the monitoring method according to the growth stage of a specific crop. Some or all of the above-mentioned processing in the monitoring unit may be performed using, for example, AI, or may be performed without using AI. For example, the monitoring unit can input a monitoring method according to the crop's growth stage into the generation AI and have the generation AI adjust the monitoring method.

[0120] During monitoring, the monitoring unit can optimize the monitoring algorithm by referring to past monitoring results. The monitoring unit, for example, selects an optimal monitoring algorithm based on past monitoring results. Past monitoring results include monitoring results from the past year and monitoring results under specific conditions. The monitoring unit, for example, analyzes past monitoring results to improve the accuracy of the algorithm. The monitoring unit, for example, refers to past monitoring results to customize the monitoring method. In this way, the accuracy of monitoring can be improved by optimizing the monitoring algorithm by referring to past monitoring results. Some or all of the above-mentioned processing in the monitoring unit may be performed, for example, using AI or without AI. For example, the monitoring unit can input past monitoring results into a generation AI and cause the generation AI to optimize the monitoring algorithm.

[0121] During monitoring, the monitoring unit can integrate information from different data sources to improve the accuracy of the monitoring. For example, the monitoring unit integrates meteorological data and soil data for monitoring. For example, the monitoring unit integrates image data from a drone and data from a ground sensor for monitoring. For example, the monitoring unit integrates data from sensors from different manufacturers to improve the accuracy of the monitoring. In this way, by integrating information from different data sources to improve the accuracy of the monitoring, more accurate monitoring can be performed. Some or all of the above-described processing in the monitoring unit may be performed using, for example, AI, or may be performed without using AI. For example, the monitoring unit can input information from different data sources into a generation AI and have the generation AI perform data integration and monitoring.

[0122] The monitoring unit can estimate the user's emotions and determine the priority of monitoring results based on the estimated user's emotions. The monitoring unit, for example, estimates the user's emotions. Technologies such as facial expression recognition and voice analysis are used for emotion estimation. The monitoring unit, for example, estimates the user's emotions using facial expression recognition technology. The facial expression recognition technology can analyze the user's facial expressions captured by a camera to estimate the emotions. The monitoring unit, for example, uses voice analysis technology to estimate the user's emotions. The voice analysis technology can analyze the tone and speed of the user's voice to estimate the emotions. The monitoring unit, for example, determines the priority of monitoring results based on the estimated user's emotions. If the user is feeling stressed, the monitoring unit can prioritize displaying only important monitoring results. If the user is relaxed, the monitoring unit can prioritize displaying detailed monitoring results. If the user is in a hurry, the monitoring unit can prioritize displaying monitoring results that can be displayed quickly. In this way, the burden on the user can be reduced by determining the priority of monitoring results according to the user's emotions. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the monitoring unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the monitoring unit may input the user's facial expression data into the generation AI and cause the generation AI to estimate emotions.

[0123] During monitoring, the monitoring unit can select a monitoring method taking geographical characteristics into consideration. For example, in mountainous areas, the monitoring unit focuses on monitoring temperature and precipitation. For example, in plains areas, the monitoring unit focuses on monitoring soil nutritional status and sunlight amount. For example, in coastal areas, the monitoring unit focuses on monitoring salinity concentration and wind speed. In this way, more appropriate monitoring can be performed by selecting a monitoring method taking geographical characteristics into consideration. Some or all of the above-described processing in the monitoring unit may be performed using, for example, AI, or may be performed without using AI. For example, the monitoring unit can input a monitoring method according to geographical characteristics into the generation AI and have the generation AI select a monitoring method.

[0124] During monitoring, the monitoring unit can customize the monitoring method by referring to the data of other farmers. For example, the monitoring unit refers to the monitoring methods used by other farmers. For example, the monitoring unit refers to the types of data monitored by other farmers. For example, the monitoring unit analyzes the monitoring results of other farmers and proposes the optimal monitoring method. In this way, the accuracy of monitoring can be improved by customizing the monitoring method by referring to the data of other farmers. Some or all of the above-mentioned processing in the monitoring unit may be performed using, for example, AI, or may be performed without using AI. For example, the monitoring unit can input the data of other farmers into the generation AI and have the generation AI customize the monitoring method.

[0125] The monitoring unit can improve the accuracy of monitoring by combining different monitoring devices during monitoring. The monitoring unit, for example, performs monitoring by combining a weather sensor and a soil sensor. The monitoring unit, for example, performs monitoring by combining a drone and a ground sensor. The monitoring unit, for example, improves the accuracy of monitoring by combining monitoring devices from different manufacturers. In this way, more accurate monitoring can be performed by combining different monitoring devices. Some or all of the above-mentioned processing in the monitoring unit may be performed using, for example, AI, or may be performed without using AI. For example, the monitoring unit can input different monitoring devices into the generation AI and have the generation AI adjust the monitoring. === Hard Collateral 1-1 === Each of the multiple elements including the collection unit, analysis unit, proposal unit, operation unit, and monitoring unit described above is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the collection unit collects weather data and soil data using a sensor in the smart device 14, and the data is analyzed by the specific processing unit 290 in the data processing device 12. The proposal unit proposes a cultivation method using the specific processing unit 290 in the data processing device 12, and the operation unit performs automatic irrigation and automatic fertilization using the control unit 46A in the smart device 14. The monitoring unit photographs the growth state of the crops using the camera 42 in the smart device 14, and the data is analyzed by the specific processing unit 290 in the data processing device 12. === Hard Collateral 1-2 === Each of the multiple elements including the collection unit, analysis unit, proposal unit, operation unit, and monitoring unit described above is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the collection unit collects weather data and soil data using a sensor in the smart glasses 214, and the data is analyzed by the specific processing unit 290 in the data processing device 12. The proposal unit proposes a cultivation method using the specific processing unit 290 in the data processing device 12, and the operation unit performs automatic irrigation and automatic fertilization using the control unit 46A of the smart glasses 214. The monitoring unit photographs the growth state of the crops using the camera 42 in the smart glasses 214, and the data is analyzed by the specific processing unit 290 in the data processing device 12. === Hard Collateral 1-3 === Each of the multiple elements including the above-mentioned collection unit, analysis unit, proposal unit, operation unit, and monitoring unit is realized, for example, by at least one of the headset terminal 314 and the data processing device 12. For example, the collection unit collects weather data and soil data using a sensor in the headset terminal 314, and the data is analyzed by the specific processing unit 290 in the data processing device 12. The proposal unit proposes a cultivation method using the specific processing unit 290 in the data processing device 12, and the operation unit performs automatic irrigation and automatic fertilization using the control unit 46A in the headset terminal 314. The monitoring unit photographs the growth state of the crops using the camera 42 in the headset terminal 314, and the data is analyzed by the specific processing unit 290 in the data processing device 12. === Hard Collateral 1-4 === Each of the multiple elements including the collection unit, analysis unit, proposal unit, working unit, and monitoring unit described above is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the collection unit collects weather data and soil data using sensors in the robot 414, and the data is analyzed by the specific processing unit 290 in the data processing device 12. The proposal unit proposes a cultivation method using the specific processing unit 290 in the data processing device 12, and the working unit performs automatic irrigation and automatic fertilization using the control unit 46A of the robot 414. The monitoring unit photographs the growth state of the crops using the camera 42 in the robot 414, and the data is analyzed by the specific processing unit 290 in the data processing device 12.

[0126] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0127] The agricultural support system can further include a prediction unit. The prediction unit can predict future weather conditions and crop growth conditions based on the collected data. For example, the prediction unit can combine past weather data with current weather data to predict future temperature and precipitation. The prediction unit can also analyze crop growth patterns and predict harvest times and yields. Furthermore, the prediction unit can predict the risk of pest and disease outbreaks and propose prevention measures in advance. This allows farmers to understand future conditions and take appropriate measures.

[0128] The collection unit can also collect environmental data. Environmental data includes, for example, the concentration of carbon dioxide and air pollutants in the air. Based on this data, the collection unit can understand environmental factors that affect crop growth. For example, a high carbon dioxide concentration may improve the photosynthetic efficiency of crops, so the collection unit can provide this information to the analysis unit and suggest optimal cultivation methods. Furthermore, a high concentration of air pollutants may affect the health of crops, so the collection unit can provide this information to the monitoring unit and use it to detect abnormalities early.

[0129] The analysis unit can also be equipped with an anomaly detection function. This function can analyze collected data in real time and detect abnormal patterns. For example, the analysis unit can detect sudden changes in temperature or humidity and warn of the occurrence of abnormal weather. In addition, if there is a sudden change in the nutritional status of the soil, the analysis unit can identify the cause and propose appropriate countermeasures. Furthermore, if abnormalities are detected in crop growth patterns, the analysis unit can predict the risk of pest outbreaks and take early prevention measures. This allows farmers to detect abnormalities early and respond quickly.

[0130] The suggestion unit can further customize the content of the suggestions based on the user's past behavior history. The suggestion unit can analyze the cultivation methods and results of the user's past use and make optimal suggestions. For example, if the user has used a specific fertilizer in the past and obtained good results, the suggestion unit can suggest that fertilizer again. Also, the suggestion unit can suggest the optimal watering method for the current weather conditions based on the timing and amount of watering the user has done in the past. Furthermore, the suggestion unit can learn the user's past behavior patterns and make suggestions tailored to the user's preferences. This allows the user to practice more effective cultivation methods.

[0131] The work unit can further be equipped with an automatic harvesting system. The automatic harvesting system can determine the harvest time for crops based on suggestions from the analysis unit and carry out the harvesting work automatically. For example, the automatic harvesting system can use a camera to detect the color and size of fruit and determine the timing of harvesting. The automatic harvesting system can also properly classify harvested crops and process them to maintain their quality. Furthermore, the automatic harvesting system can monitor the progress of the harvesting work in real time and adjust the work plan as necessary. This reduces the burden on farmers and enables more efficient harvesting work.

[0132] The collection unit can estimate the user's emotions and adjust the timing of data collection based on the estimated user's emotions. The collection unit, for example, estimates the user's emotions. Emotion estimation uses technologies such as facial expression recognition and voice analysis. The collection unit, for example, estimates the user's emotions using facial expression recognition technology. Facial expression recognition technology can analyze the user's facial expressions captured by a camera to estimate the emotions. The collection unit, for example, estimates the user's emotions using voice analysis technology. Voice analysis technology can analyze the tone and speed of the user's voice to estimate the emotions. The collection unit, for example, adjusts the timing of data collection based on the estimated user's emotions. If the user is feeling stressed, the collection unit can reduce the frequency of data collection to reduce the user's burden. If the user is relaxed, the collection unit can increase the frequency of data collection to collect detailed data. If the user is in a hurry, the collection unit can optimize the timing of data collection and quickly collect necessary data. This reduces the user's burden by adjusting the timing of data collection according to the user's emotions.

[0133] The analysis unit can estimate the user's emotions and adjust the display method of the analysis results based on the estimated user's emotions. The analysis unit, for example, estimates the user's emotions. Technologies such as facial expression recognition and voice analysis are used for emotion estimation. The analysis unit, for example, estimates the user's emotions using facial expression recognition technology. Facial expression recognition technology can analyze the user's facial expressions captured by a camera to estimate the emotions. The analysis unit, for example, estimates the user's emotions using voice analysis technology. Voice analysis technology can analyze the tone and speed of the user's voice to estimate the emotions. The analysis unit, for example, adjusts the display method of the analysis results based on the estimated user's emotions. If the user is nervous, the analysis unit can provide a simple, highly visible display method. If the user is relaxed, the analysis unit can provide a display method including detailed information. If the user is in a hurry, the analysis unit can provide a display method that focuses on the main points. This reduces the burden on the user by adjusting the display method of the analysis results according to the user's emotions.

[0134] The suggestion unit can estimate the user's emotion and adjust the way the suggestion is expressed based on the estimated user's emotion. The suggestion unit, for example, estimates the user's emotion. Emotion estimation uses technologies such as facial expression recognition and voice analysis. The suggestion unit, for example, estimates the user's emotion using facial expression recognition technology. The facial expression recognition technology can analyze the user's facial expression captured by a camera to estimate the emotion. The suggestion unit, for example, estimates the user's emotion using voice analysis technology. The voice analysis technology can analyze the tone and speed of the user's voice to estimate the emotion. The suggestion unit, for example, adjusts the way the suggestion is expressed based on the estimated user's emotion. If the user is nervous, the suggestion unit can provide a simple and highly visible suggestion method. If the user is relaxed, the suggestion unit can provide a suggestion method including detailed information. If the user is in a hurry, the suggestion unit can provide a suggestion method that focuses on the main points. This allows the way the suggestion is expressed to be adjusted according to the user's emotion, thereby reducing the burden on the user.

[0135] The work unit can estimate the user's emotions and adjust the timing of tasks based on the estimated user's emotions. The work unit, for example, estimates the user's emotions. Technologies such as facial expression recognition and voice analysis are used for emotion estimation. The work unit, for example, estimates the user's emotions using facial expression recognition technology. The facial expression recognition technology can analyze the user's facial expressions captured by a camera to estimate the emotions. The work unit, for example, estimates the user's emotions using voice analysis technology. The voice analysis technology can analyze the tone and speed of the user's voice to estimate the emotions. The work unit, for example, adjusts the timing of tasks based on the estimated user's emotions. If the user is feeling stressed, the work unit can reduce the frequency of tasks to reduce the user's burden. If the user is relaxed, the work unit can increase the frequency of tasks and perform detailed tasks. If the user is in a hurry, the work unit can optimize the timing of tasks and quickly perform necessary tasks. In this way, the burden on the user can be reduced by adjusting the timing of tasks according to the user's emotions.

[0136] The monitoring unit can estimate the user's emotions and adjust the display method of the monitoring results based on the estimated user's emotions. The monitoring unit, for example, estimates the user's emotions. Technologies such as facial expression recognition and voice analysis are used for emotion estimation. The monitoring unit, for example, estimates the user's emotions using facial expression recognition technology. The facial expression recognition technology can analyze the user's facial expressions captured by a camera to estimate the emotions. The monitoring unit, for example, estimates the user's emotions using voice analysis technology. The voice analysis technology can analyze the tone and speed of the user's voice to estimate the emotions. The monitoring unit adjusts the display method of the monitoring results based on the estimated user's emotions, for example. The monitoring unit can provide a simple, highly visible display method when the user is nervous. The monitoring unit can provide a display method including detailed information when the user is relaxed. The monitoring unit can provide a display method that focuses on the main points when the user is in a hurry. This allows the display method of the monitoring results to be adjusted according to the user's emotions, thereby reducing the burden on the user.

[0137] The processing flow of the second embodiment will be briefly explained below.

[0138] Step 1: The collection unit collects weather or soil data. Weather data includes temperature, humidity, precipitation, wind speed, etc., while soil data includes soil nutritional status, pH value, moisture content, etc. The collection unit acquires data in real time using sensors. Step 2: The analysis unit analyzes the data collected by the collection unit. The analysis is performed using statistical analysis of the data and machine learning algorithms. Step 3: The proposal unit proposes cultivation methods based on the analysis results obtained by the analysis unit. Cultivation methods include watering timing, type and amount of fertilizer, planting intervals, etc. For example, when temperatures are high, the system proposes the appropriate watering timing and amount, and the type and amount of fertilizer needed depending on the nutritional status of the soil. Step 4: The working unit automatically performs cultivation work based on the cultivation method proposed by the proposing unit. The working unit uses the automatic irrigation system to water at the proposed timing and the automatic fertilization system to apply the proposed fertilizer in the appropriate amount. Step 5: The monitoring unit monitors the results of the cultivation work carried out by the work unit. The monitoring unit uses a drone to take pictures of the crop's growth status, which are then analyzed by AI. This allows for early detection of any abnormalities and the implementation of countermeasures. For example, if the occurrence of pests or diseases is detected, appropriate control methods will be proposed.

[0139] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0140] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0141] Furthermore, the processing by the data processing system 10 described above is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0142] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0143] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0144] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0145] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0146] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0147] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0148] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0149] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0150] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0151] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0152] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0153] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0154] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0155] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0156] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0157] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart glasses 214 or an external device, etc., and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0158] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0159] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0160] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0161] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0162] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[0163] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0164] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0165] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0166] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0167] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0168] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0169] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the identification processing unit 290 using these models.

[0170] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0171] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0172] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0173] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset type terminal 314, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset type terminal 314. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the headset type terminal 314 or an external device, etc., and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0174] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0175] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0176] 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.

[0177] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0178] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[0179] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0180] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0181] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0182] The control object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[0183] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0184] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0185] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0186] In the robot 414, the processor 46 performs the identification process. The storage 50 stores the identification program 60. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as the control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 also has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform the same process as the identification processing unit 290 using these models.

[0187] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0188] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0189] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0190] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the robot 414 or an external device, etc., and the robot 414 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0191] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0192] The emotion identification model 59 as an emotion engine may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0193] FIG. 9 illustrates an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and behaviors arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion encompasses both emotions and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0194] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[0195] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0196] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the area called "reaction," where sensation is dominant. The right half of the emotion map lists emotions belonging to the area called "situation," where situational awareness is dominant.

[0197] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[0198] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[0199] In the above embodiment, an example was given in which a specific process is performed by one computer 22, but the technology disclosed herein is not limited to this, and distributed processing of the specific process may be performed by multiple computers including computer 22.

[0200] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[0201] 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.

[0202] It is not necessary to store all of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[0203] The hardware resource for executing a specific process can be any of the following types of processors: A processor, for example, is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A processor also includes a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[0204] The hardware resource that executes the specific process may be configured with one of these various processors, or may be configured with a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific process may be a single processor.

[0205] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[0206] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[0207] In the above example, the first to fourth embodiments have been described separately, but some or all of these embodiments may be combined. The smart device 14, smart glasses 214, headset terminal 314, and robot 414 are merely examples, and they may be combined, or other devices may be used. In the above example, the first and second embodiments have been described separately, but they may be combined.

[0208] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0209] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference.

[0210] [Explanation of symbols]

[0211] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. a collection unit that collects meteorological data or soil data; an analysis unit that analyzes the data collected by the collection unit; a proposal unit that proposes a cultivation method based on the analysis results obtained by the analysis unit; a working unit that automatically performs cultivation work based on the cultivation method proposed by the suggestion unit; A monitoring unit that monitors the results of the cultivation work performed by the work unit. A system characterized by:

2. The collecting unit Collect data on temperature, humidity, precipitation, and soil nutrient status 2. The system of claim 1.

3. The analysis unit Analyzing the collected data and proposing optimal cultivation methods 2. The system of claim 1.

4. The proposal unit Suggestions for watering timing or amount when temperatures are high 2. The system of claim 1.

5. The proposal unit Suggest the type and amount of fertilizer needed based on the nutritional status of the soil 2. The system of claim 1.

6. The working unit includes: Water at suggested times with an automated irrigation system 2. The system of claim 1.

7. The working unit includes: Apply the suggested fertilizer in the correct amount using an automated fertilization system 2. The system of claim 1.

8. The monitoring unit Drones are used to photograph the state of crop growth, allowing for early detection of any abnormalities and the implementation of countermeasures.

2. The system of claim 1.

Images