System

Abstract

To provide a system that monitors a state of a farmland or a crop in real time to realize efficient farm work.SOLUTION: A system according to an embodiment comprises a monitoring part, an analysis part, a proposal part, and an automation part. The monitoring part monitors a state of a farmland or a crop. The analysis part analyzes data monitored by the monitoring part to perform prediction analysis. The proposal part proposes how to use an appropriate fertilizer or agricultural chemical on the basis of an analysis result obtained by the analysis part. The automation part automates farm work on the basis of a content proposed by the proposal part.SELECTED DRAWING: Figure 1

Description

[Technical field]

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

[0002] Patent document 1 discloses a persona chatbot control method performed by at least one processor, the method including the steps of receiving a user utterance, adding the user utterance to a prompt including a description and related instruction sentence regarding 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] JP 2022-180282 A Summary of the Invention [Problem to be solved by the invention]

[0004] With conventional technology, it was difficult to monitor the condition of farmland and crops in real time and carry out efficient agricultural work.

[0005] The system according to the embodiment aims to monitor the condition of farmland and crops in real time and realize efficient farm work. [Means for solving the problem]

[0006] The system according to the embodiment includes a monitoring unit, an analysis unit, a proposal unit, and an automation unit. The monitoring unit monitors the condition of farmland or crops. The analysis unit analyzes the data monitored by the monitoring unit and performs predictive analysis. The proposal unit proposes appropriate fertilizer or pesticide usage based on the analysis results obtained by the analysis unit. The automation unit automates farm work based on the content proposed by the proposal unit. Effect of the Invention

[0007] The system according to the embodiment can monitor the condition of farmland and crops in real time, enabling efficient farm work. [Brief description 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. [Diagram 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. FIG. [Diagram 3] FIG. 11 is a conceptual diagram showing an example of the configuration of a data processing system according to a second embodiment. [Figure 4] FIG. 11 is a conceptual diagram showing an example of main functions of a data processing device and smart glasses according to a second embodiment. [Diagram 5] FIG. 13 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. 13 is a conceptual diagram showing an example of the configuration of a data processing system according to a fourth embodiment. [Figure 8] FIG. 13 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 PREFERRED EMBODIMENTS

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

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

[0011] In the following embodiments, a signed processor (hereinafter simply referred to as a "processor") may be one arithmetic device or a combination of multiple arithmetic devices. Furthermore, a processor may be one 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 signed RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a working memory by the processor.

[0013] In the following embodiments, the coded storage is one or more non-volatile storage devices that store various programs and 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 code is an interface including a communication processor and an antenna. The communication I / F controls communication between multiple computers. An example of a communication standard applied to the communication I / F is a wireless communication standard including 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark).

[0015] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." In other words, "A and / or B" means that it may be only A, only B, or a combination of A and B. In addition, in this specification, the same idea as "A and / or B" is also applied when three or more things are expressed by connecting them with "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, the RAM 30, and the 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 (e.g., 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., voice and / or text). Display 40A displays visible information such as text and images according to instructions from processor 46. Speaker 40B outputs voice according to 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 Complementary Metal-Oxide-Semiconductor (CMOS) image sensor or a Charge Coupled Device (CCD) image sensor.

[0022] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for transmitting and receiving various types of information between the processor 46 and the processor 28 via the network 54.

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

[0024] As shown in Fig. 2, 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. The specific process program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific process program 56 from the storage 32, and executes the read specific process 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 process program 56 executed on the RAM 30.

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

[0026] In the smart device 14, the specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The specific processing program 60 is used together with the specific processing program 56 by the data processing system 10. The processor 46 reads the specific processing program 60 from the storage 50 and executes the 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. Also, the data processing device 12 may be a server device, or may be a terminal device (e.g., a mobile phone, a robot, a home appliance, etc.) owned by a user. Next, an example of processing by the data processing system 10 according to the first embodiment will be described.

[0028] (Example 1) The smart agriculture system according to the embodiment of the present invention is a system that realizes efficient agricultural work through automation while managing the condition of farmland and crops in real time, and performs agricultural production that is highly profitable and environmentally friendly. In this smart agriculture system, IoT sensors monitor the health of farmland and crops, and automate agricultural work as necessary. It is possible to propose optimal fertilizer and pesticide usage through predictive analysis by AI. For example, IoT sensors monitor soil humidity, temperature, nutrient levels, crop growth status, etc. If the humidity of the soil decreases, the sensor detects it and sends a signal to the irrigation system to automatically supply water. Next, AI analyzes the collected data and performs predictive analysis. This makes it possible to propose optimal fertilizer and pesticide usage. For example, based on crop growth data, AI calculates the optimal type and amount of fertilizer and notifies farmers. It can also predict the risk of pests and diseases and propose the appropriate timing for using pesticides. In addition, AI takes into account weather data and market price trends to propose the optimal timing for harvesting. This makes it possible to maximize the harvest and improve profits. For example, based on weather data, AI predicts the optimal day for harvesting and notifies farmers. This system makes farm work more efficient and reduces the labor force. In addition, the optimal use of resources reduces the burden on the environment and enables sustainable agriculture. For example, by using only the necessary amounts of fertilizer and pesticides, soil and water pollution can be prevented. In this way, a smart agriculture system that combines AI and IoT can manage the condition of farmland and crops in real time and achieve efficient farm work, enabling agricultural production that is both highly profitable and environmentally friendly. In this way, a smart agriculture system can manage the condition of farmland and crops in real time and achieve efficient farm work.

[0029] The smart agriculture system according to the embodiment includes a monitoring unit, an analysis unit, a proposal unit, and an automation unit. The monitoring unit monitors the state of farmland and crops. The state of farmland and crops includes, for example, soil humidity, temperature, nutrient levels, crop growth status, and the like, but is not limited to such examples. For example, the monitoring unit measures soil humidity with a sensor and collects data. The monitoring unit can also measure the temperature of the soil and the environment using a temperature sensor. Furthermore, the monitoring unit can measure the level of nutrients in the soil using a nutrient sensor. For example, the monitoring unit uses a soil moisture sensor to measure soil humidity. The temperature sensor measures the temperature of the soil and the environment in real time and collects data. The nutrient sensor measures the levels of nutrients such as nitrogen, phosphorus, and potassium in the soil and collects data. The analysis unit analyzes the collected data and performs predictive analysis. For example, the analysis unit analyzes the data using AI and proposes the optimal use of fertilizers and pesticides. For example, the analysis unit calculates the optimal type and amount of fertilizer using AI based on crop growth data. The analysis unit can also predict the risk of pests and disease occurrence and propose the appropriate timing for using pesticides. For example, the analysis unit uses AI to analyze crop growth data and calculate the optimal type and amount of fertilizer. The AI ​​proposes the optimal type and amount of fertilizer based on past data and environmental data. In order to predict the risk of pests and disease occurrence, the AI ​​analyzes crop growth data and environmental data and predicts the risk of pests and disease occurrence. The proposal unit proposes the optimal way to use fertilizers and pesticides based on the analysis results obtained by the analysis unit. The proposal unit proposes the optimal type and amount of fertilizer, for example, using AI. In addition, the proposal unit can predict the risk of pests and disease occurrence and propose the appropriate timing for using pesticides. For example, the proposal unit uses AI to propose the optimal type and amount of fertilizer. The AI ​​proposes the optimal type and amount of fertilizer based on past data and environmental data. In order to predict the risk of pests and disease occurrence, the AI ​​analyzes crop growth data and environmental data and predicts the risk of pests and disease occurrence. The automation unit automates farm work based on the content proposed by the proposal unit. The automation unit can, for example, send signals to an irrigation system to automatically supply water, or it can control an automatic fertilizer or pesticide supply system.For example, the automation unit automatically supplies water by sending a signal to an irrigation system. The automation unit uses AI to control the irrigation system at optimal timing. In order to control an automatic fertilizer and pesticide supply system, the automation unit uses AI to supply fertilizer and pesticide at optimal timing. In this way, the smart agriculture system according to the embodiment can manage the condition of farmland and crops in real time and achieve efficient farm work.

[0030] The monitoring unit monitors the condition of the farmland and the crops. The condition of the farmland and the crops includes, for example, soil humidity, temperature, nutrient levels, crop growth status, and the like, but is not limited to such examples. The monitoring unit, for example, measures the humidity of the soil with a sensor and collects data. Specifically, the soil moisture sensor measures the moisture content in the soil in real time and transmits the data to a central database. This makes it possible to grasp the moisture status of the entire farmland in a centralized manner. The monitoring unit can also measure the temperature of the soil and the environment using a temperature sensor. The temperature sensor measures the temperature of the ground surface and underground, and provides data for maintaining a temperature environment suitable for crop growth. Furthermore, the monitoring unit can also measure the level of nutrients in the soil using a nutrient sensor. The nutrient sensor measures the concentration of major nutrients such as nitrogen, phosphorus, and potassium in the soil, and monitors the nutritional status of the crops in real time. This makes it possible to supply the necessary nutrients at the appropriate time. The monitoring unit can centrally manage the data obtained from these sensors and cooperate with other systems and departments as necessary. For example, the collected data is stored in a cloud server and made accessible to the analysis unit and proposal unit. In addition, by adjusting the frequency and accuracy of data collection, it is possible to respond flexibly to specific situations and conditions. This allows the monitoring unit to collect data efficiently and effectively, improving the performance of the entire system.

[0031] The analysis unit analyzes the collected data and performs predictive analysis. For example, the analysis unit uses AI to analyze the data and propose optimal fertilizer and pesticide usage. Specifically, the AI ​​uses machine learning algorithms to analyze past data and environmental data to identify optimal conditions for crop growth. For example, based on crop growth data, the AI ​​calculates the optimal type and amount of fertilizer. The AI ​​considers the soil nutrient level and crop growth stage and proposes the optimal type and amount of fertilizer. The analysis unit can also predict the risk of pest and disease occurrence and propose appropriate timing for pesticide use. The AI ​​analyzes crop growth data and environmental data to predict the risk of pest and disease occurrence. For example, based on temperature and humidity fluctuations and past pest and disease occurrence data, it predicts the period when a specific pest is likely to occur and proposes appropriate timing for pesticide use. Furthermore, the analysis unit can use anomaly detection algorithms to detect unusual patterns and abnormal data and issue early warnings. As a result, the analysis unit can not only grasp the situation in real time, but also handle long-term risk management and anomaly detection, improving the reliability and safety of the entire system.

[0032] The suggestion unit proposes the optimal use of fertilizers and pesticides based on the analysis results obtained by the analysis unit. The suggestion unit, for example, uses AI to propose the optimal type and amount of fertilizer. Specifically, the AI ​​calculates the optimal type and amount of fertilizer for crop growth based on past data and environmental data. For example, the AI ​​proposes the optimal type and amount of fertilizer taking into account the nutrient level of the soil and the growth stage of the crop. The suggestion unit can also predict the risk of pests and diseases and propose the appropriate timing for using pesticides. The AI ​​analyzes crop growth data and environmental data to predict the risk of pests and diseases. For example, based on fluctuations in temperature and humidity and past pest and diseases occurrence data, it predicts the time when a specific pest is likely to occur and proposes the appropriate timing for using pesticides. Furthermore, the suggestion unit can collect user feedback and continuously improve the accuracy and effectiveness of the proposed content. For example, the next proposed content is reviewed based on the results of the use of the proposed fertilizers and pesticides. The suggestion unit can also reliably transmit information using multiple communication methods. For example, important information can be reliably delivered by using not only smartphone notifications but also voice calls, SMS, emails, etc. This enables the suggestion unit to quickly and reliably provide optimal agricultural work suggestions to the user, thereby improving the efficiency and effectiveness of agricultural work.

[0033] The automation unit automates agricultural work based on the content proposed by the proposal unit. For example, the automation unit automatically supplies water by sending a signal to an irrigation system. Specifically, the automation unit uses AI to control the irrigation system at the optimal timing. Based on soil moisture data, the required amount of water is calculated and a signal is sent to the irrigation system to automatically supply water. The automation unit can also control an automatic fertilizer and pesticide supply system. The AI ​​sends a signal to the automatic supply system to supply fertilizer and pesticide at the optimal timing. For example, fertilizer and pesticide are supplied at the optimal timing, taking into account the nutrient level of the soil and the growth stage of the crop. Furthermore, the automation unit can also control an automatic driving system for agricultural machinery. For example, agricultural machinery such as tractors and combines are automatically driven to efficiently perform cultivation and harvesting work. AI calculates the optimal driving route based on the topographical data of the farmland and the arrangement data of the crops, and sends a signal to the agricultural machinery to realize automatic driving. This allows the automation unit to improve the efficiency and accuracy of agricultural work. Furthermore, the automation unit can monitor the progress of agricultural work based on data updated in real time and make adjustments as necessary. For example, the automation section can adjust the timing of irrigation and fertilizer supply according to changes in the weather and the growth status of crops, thereby enabling the farmer to always perform optimal farm work and maximize the productivity of the farmland.

[0034] The monitoring unit can monitor data on soil humidity, temperature, nutrient levels, and crop growth status. The monitoring unit, for example, measures soil humidity with a sensor and collects data. For example, the monitoring unit can measure soil humidity using a soil moisture sensor. The monitoring unit can also measure soil and environmental temperatures using a temperature sensor. For example, the monitoring unit can measure soil and environmental temperatures in real time using a temperature sensor. Furthermore, the monitoring unit can also measure nutrient levels in the soil using a nutrient sensor. For example, the monitoring unit can measure nutrient levels such as nitrogen, phosphorus, and potassium in the soil using a nutrient sensor. This allows the health of farmland and crops to be grasped in detail. 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 data acquired by the sensor to the generation AI and have the generation AI perform analysis of the data.

[0035] The analysis unit can calculate the optimal type and amount of fertilizer based on the collected data. The analysis unit, for example, uses AI to analyze the collected data and calculate the optimal type and amount of fertilizer. For example, the analysis unit uses AI to calculate the optimal type and amount of fertilizer based on crop growth data. The AI ​​proposes the optimal type and amount of fertilizer based on past data and environmental data. For example, the analysis unit uses AI to analyze crop growth data and calculate the optimal type and amount of fertilizer. The AI ​​proposes the optimal type and amount of fertilizer based on past data and environmental data. This enables the use of optimal fertilizer. A part or all of the above-mentioned processing in the analysis unit may be performed, for example, using AI, or may be performed without using AI. For example, the analysis unit can input the collected data to the generation AI and cause the generation AI to calculate the optimal type and amount of fertilizer.

[0036] The analysis unit can predict the risk of pests and propose appropriate timing for using pesticides. The analysis unit, for example, uses AI to predict the risk of pests and propose appropriate timing for using pesticides. For example, the analysis unit uses AI to predict the risk of pests and disease based on crop growth data and environmental data. The AI ​​predicts the risk of pests and disease based on past data and environmental data, and proposes appropriate timing for using pesticides. For example, the analysis unit uses AI to analyze crop growth data and environmental data, and predicts the risk of pests and disease. The AI ​​predicts the risk of pests and disease based on past data and environmental data, and proposes appropriate timing for using pesticides. This makes it possible to predict the occurrence of pests and disease early and take appropriate measures. Part or all of the above-mentioned processing in the analysis unit may be performed, for example, using AI, or may be performed without using AI. For example, the analysis unit can input collected data to the generation AI and cause the generation AI to execute a prediction of the risk of pests and disease.

[0037] The analysis unit can propose the appropriate timing for harvesting, taking into account weather data or market price trends. The analysis unit, for example, uses AI to propose the appropriate timing for harvesting, taking into account weather data and market price trends. For example, the analysis unit uses AI to predict the optimal day for harvesting based on weather data. The AI ​​proposes the optimal timing for harvesting based on past data and environmental data. For example, the analysis unit uses AI to analyze weather data and market price trends and propose the appropriate timing for harvesting. The AI ​​proposes the optimal timing for harvesting based on past data and environmental data. This makes it possible to maximize the harvest volume and improve profits. Some or all of the above-mentioned processing in the analysis unit may be performed, for example, using AI, or may be performed without using AI. For example, the analysis unit can input weather data and market price trends to the generation AI and cause the generation AI to execute a proposal for the appropriate timing for harvesting.

[0038] The automation unit can automatically supply water by sending a signal to the irrigation system. The automation unit can automatically supply water by, for example, sending a signal to the irrigation system. For example, the automation unit uses AI to control the irrigation system at optimal timing. The automation unit uses AI to analyze soil moisture data and send a signal to the irrigation system at optimal timing. This enables efficient irrigation by automatically supplying water. Some or all of the above-mentioned processing in the automation unit may be performed, for example, using AI, or may be performed without using AI. For example, the automation unit can input soil moisture data to the generation AI and cause the generation AI to control the irrigation system.

[0039] The monitoring unit monitors the microbial activity of the soil and can grasp the health state of the crop in more detail. The monitoring unit, for example, monitors the activity of specific microorganisms in the soil and evaluates the nutritional state of the crop. For example, the monitoring unit analyzes the activity pattern of the microorganisms and predicts the health state of the soil. The monitoring unit can also suggest the type and amount of fertilizer and improver required based on the activity data of the microorganisms. For example, the monitoring unit monitors the activity of specific microorganisms in the soil and evaluates the nutritional state of the crop. The monitoring unit analyzes the activity pattern of the microorganisms and predicts the health state of the soil. The monitoring unit suggests the type and amount of fertilizer and improver required based on the activity data of the microorganisms. In this way, the health state of the crop can be grasped in detail by monitoring the microbial activity of the soil. Part 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 the microbial activity data of the soil to the generation AI and cause the generation AI to execute the analysis of the data.

[0040] The monitoring unit monitors the color and shape of the leaves of the crop, and can detect nutritional deficiency and pests at an early stage. The monitoring unit, for example, monitors the color of the leaves of the crop and detects signs of nutritional deficiency. For example, the monitoring unit monitors the shape of the leaves of the crop and detects early symptoms of pests. The monitoring unit can also propose necessary measures based on the monitoring data. For example, the monitoring unit monitors the color of the leaves of the crop and detects signs of nutritional deficiency. The monitoring unit monitors the shape of the leaves of the crop and detects early symptoms of pests. The monitoring unit proposes necessary measures based on the monitoring data. As a result, early detection of nutritional deficiency and pests is possible by monitoring the color and shape of the leaves of the crop. 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 data on the color and shape of the leaves of the crop to the generation AI, and cause the generation AI to analyze the data.

[0041] The monitoring unit can set different monitoring parameters taking into account the geographical characteristics of the farmland. The monitoring unit adjusts the monitoring parameters of temperature and humidity, for example, according to the altitude of the farmland. For example, the monitoring unit adjusts the monitoring parameters of temperature and humidity according to the altitude of the farmland. The monitoring unit can also set the monitoring parameters of nutrients according to the soil type of the farmland. For example, the monitoring unit sets the monitoring parameters of nutrients according to the soil type of the farmland. Furthermore, the monitoring unit can also adjust the monitoring frequency and the data acquisition method according to the climatic conditions of the farmland. For example, the monitoring unit adjusts the monitoring frequency and the data acquisition method according to the climatic conditions of the farmland. This enables monitoring according to the geographical characteristics of the farmland. A part 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 geographical characteristic data of the farmland to the generation AI and cause the generation AI to execute the setting of the monitoring parameters.

[0042] The monitoring unit can jointly solve problems by storing the monitoring data in the cloud and sharing it with other farmers. The monitoring unit, for example, stores the monitoring data in the cloud and shares it with other farmers. For example, the monitoring unit stores the monitoring data in the cloud and shares it with other farmers. The monitoring unit can also jointly consider solutions to problems based on the shared data. For example, the monitoring unit jointly consider solutions to problems based on the shared data. Furthermore, the monitoring unit can also utilize the shared data to improve agricultural efficiency in the entire region. For example, the monitoring unit utilizes the shared data to improve agricultural efficiency in the entire region. This makes it possible to jointly solve problems by sharing the monitoring data. 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 the monitoring data to the generation AI and have the generation AI execute analysis of the data.

[0043] The analysis unit can apply different analysis algorithms for each growth stage of the crop. For example, the analysis unit applies an analysis algorithm that emphasizes the growth rate in the early growth stage of the crop. For example, the analysis unit applies an analysis algorithm that emphasizes the growth rate in the early growth stage of the crop. The analysis unit can also apply an analysis algorithm that emphasizes the absorption efficiency of nutrients in the middle growth stage of the crop. For example, the analysis unit applies an analysis algorithm that emphasizes the absorption efficiency of nutrients in the middle growth stage of the crop. Furthermore, the analysis unit can also apply an analysis algorithm that predicts the optimum harvest time in the pre-harvest stage of the crop. For example, the analysis unit applies an analysis algorithm that predicts the optimum harvest time in the pre-harvest stage of the crop. This enables analysis according to the growth stage of the crop. A part 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 growth data of the crop to the generation AI and cause the generation AI to execute an analysis for each growth stage.

[0044] The analysis unit detects abnormal values ​​by comparing with past data during analysis, and can take measures early. The analysis unit, for example, detects abnormal temperature changes by comparing with past data. For example, the analysis unit detects abnormal temperature changes by comparing with past data. The analysis unit can also detect abnormal humidity changes by comparing with past data. For example, the analysis unit detects abnormal humidity changes by comparing with past data. Furthermore, the analysis unit can also detect abnormal nutrient levels by comparing with past data. For example, the analysis unit detects abnormal nutrient levels by comparing with past data. This makes it possible to detect abnormal values ​​early and take measures by comparing with past data. Part or all of the above-mentioned processing in the analysis unit may be performed, for example, using AI, or may be performed without using AI. For example, the analysis unit can input past data to the generation AI and cause the generation AI to detect abnormal values.

[0045] The analysis unit can compare the analysis results with data on other farmland to improve agricultural efficiency throughout the region. The analysis unit, for example, compares the analysis results with data on other farmland to propose an optimal type and amount of fertilizer. For example, the analysis unit compares the analysis results with data on other farmland to propose an optimal type and amount of fertilizer. The analysis unit can also compare the analysis results with data on other farmland to propose an optimal irrigation method. For example, the analysis unit compares the analysis results with data on other farmland to propose an optimal irrigation method. The analysis unit can also compare the analysis results with data on other farmland to propose an optimal pest control measure. For example, the analysis unit compares the analysis results with data on other farmland to propose an optimal pest control measure. This makes it possible to improve agricultural efficiency throughout the region by comparing the analysis results with data on other farmland. 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 data on other farmland to the generation AI and cause the generation AI to perform a comparison of the data.

[0046] The analysis unit can visualize the analysis results so that the user can intuitively understand them. For example, the analysis unit displays the analysis results in a graph or chart to make it easier to understand visually. For example, the analysis unit displays the analysis results in a graph or chart to make it easier to understand visually. The analysis unit can also plot the analysis results on a map to make it easier to understand the state of the farmland intuitively. For example, the analysis unit can plot the analysis results on a map to make it easier to understand the state of the farmland intuitively. Furthermore, the analysis unit can display the analysis results in animation to visually show changes in the data. For example, the analysis unit displays the analysis results in animation to visually show changes in the data. In this way, the analysis results can be visualized so that the user can intuitively understand them. A part or all of the above-mentioned processing in the analysis unit may be performed, for example, using AI, or may be performed without using AI. For example, the analysis unit can input the analysis results to the generation AI and cause the generation AI to execute visualization.

[0047] The proposal unit can apply different proposal algorithms depending on the type of crop and the cultivation method at the time of proposal. The proposal unit, for example, proposes the optimal type and amount of fertilizer depending on the type of crop. For example, the proposal unit proposes the optimal type and amount of fertilizer depending on the type of crop. The proposal unit can also propose the optimal irrigation method depending on the cultivation method. For example, the proposal unit proposes the optimal irrigation method depending on the cultivation method. Furthermore, the proposal unit can also propose the optimal pest control depending on the growth stage of the crop. For example, the proposal unit proposes the optimal pest control depending on the growth stage of the crop. This makes it possible to make proposals depending on the type of crop and the cultivation method. Part 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 data on the type of crop and the cultivation method to the generation AI and cause the generation AI to apply the proposal algorithm.

[0048] The suggestion unit can add an option for minimizing the impact on the environment to the proposal content. The suggestion unit, for example, suggests the use of environmentally friendly fertilizers and pesticides. For example, the suggestion unit suggests the use of environmentally friendly fertilizers and pesticides. The suggestion unit can also suggest an irrigation method that takes into account the conservation of water resources. For example, the suggestion unit suggests an irrigation method that takes into account the conservation of water resources. Furthermore, the suggestion unit can also suggest agricultural practices for maintaining soil health. For example, the suggestion unit suggests agricultural practices for maintaining soil health. This enables a proposal that minimizes the impact on the environment. A part or all of the above-mentioned processing in the suggestion unit may be performed, for example, using AI, or may be performed without using AI. For example, the suggestion unit can input an option for minimizing the impact on the environment to the generation AI and cause the generation AI to execute the generation of the proposal content.

[0049] The proposal unit can share the proposal content with other farmers and jointly explore the optimal agricultural method. The proposal unit, for example, saves the proposal content in a cloud and shares it with other farmers. For example, the proposal unit saves the proposal content in a cloud and shares it with other farmers. The proposal unit can also jointly consider the optimal agricultural method based on the shared proposal content. For example, the proposal unit jointly considers the optimal agricultural method based on the shared proposal content. Furthermore, the proposal unit can also utilize the shared data to improve agricultural efficiency in the entire region. For example, the proposal unit utilizes the shared data to improve agricultural efficiency in the entire region. As a result, by sharing the proposal content, it is possible to jointly explore the optimal agricultural method. A part 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 the proposal content to the generation AI and cause the generation AI to share the data.

[0050] The suggestion unit can notify the proposed content through a smartphone app so that the user can respond immediately. The suggestion unit can, for example, notify the proposed content through a smartphone app so that the user can immediately check it. For example, the suggestion unit can notify the proposed content through a smartphone app so that the user can immediately check it. The suggestion unit can also enable an action based on the proposed content to be quickly executed through the smartphone app. For example, the suggestion unit can enable an action based on the proposed content to be quickly executed through the smartphone app. Furthermore, the suggestion unit can also collect feedback on the proposed content through the smartphone app and use it to improve the system. For example, the suggestion unit can collect feedback on the proposed content through the smartphone app and use it to improve the system. As a result, the proposed content is notified through the smartphone app so that the user can respond immediately. A part or all of the above-mentioned processing in the suggestion unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the suggestion unit can input the proposed content to a generation AI and cause the generation AI to execute a notification through the smartphone app.

[0051] During automation, the automation unit can apply different automation processes according to the growth stage of the crop. For example, in the early growth stage of the crop, the automation unit automatically adjusts the irrigation system to supply appropriate moisture. For example, in the early growth stage of the crop, the automation unit automatically adjusts the irrigation system to supply appropriate moisture. In addition, the automation unit can also apply an automatic fertilizer supply system to provide necessary nutrients in the middle growth stage of the crop. For example, the automation unit applies an automatic fertilizer supply system to provide necessary nutrients in the middle growth stage of the crop. Furthermore, the automation unit can also apply an automated process for pest control in the pre-harvest stage of the crop to maintain the health of the crop. For example, the automation unit applies an automated process for pest control in the pre-harvest stage of the crop to maintain the health of the crop. This enables automation according to the growth stage of the crop. Part or all of the above-mentioned processing in the automation unit may be performed, for example, using AI, or may be performed without using AI. For example, the automation unit can input crop growth data to the generation AI and cause the generation AI to execute an automation process for each growth stage.

[0052] The automation unit can add a function for optimizing energy efficiency to the automation system. For example, the automation unit introduces an algorithm for optimizing energy efficiency into the automation system to reduce power consumption. For example, the automation unit introduces an algorithm for optimizing energy efficiency into the automation system to reduce power consumption. The automation unit can also incorporate solar power generation into the automation system to utilize renewable energy. For example, the automation unit incorporates solar power generation into the automation system to utilize renewable energy. Furthermore, the automation unit can also add an energy consumption monitoring function to realize efficient operation. For example, the automation unit adds an energy consumption monitoring function to realize efficient operation. As a result, power consumption can be reduced by optimizing energy efficiency. A part or all of the above-mentioned processing in the automation unit may be performed, for example, using AI, or may be performed without using AI. For example, the automation unit can input energy consumption data to the generation AI and cause the generation AI to execute optimization of energy efficiency.

[0053] The automation unit can link the automation system with other farmlands to realize efficient agriculture throughout the region. The automation unit, for example, connects the automation system to the cloud and shares data with other farmlands. For example, the automation unit connects the automation system to the cloud and shares data with other farmlands. The automation unit can also execute optimal agricultural practices throughout the region based on the shared data. For example, the automation unit executes optimal agricultural practices throughout the region based on the shared data. Furthermore, the automation unit can link the automation systems to realize efficient irrigation and fertilizer supply throughout the region. For example, the automation unit links the automation systems to realize efficient irrigation and fertilizer supply throughout the region. As a result, efficient agriculture is possible throughout the region by linking the automation systems. Some or all of the above-mentioned processing in the automation unit may be performed, for example, using AI, or may be performed without using AI. For example, the automation unit can input data of other farmlands into the generation AI and cause the generation AI to share the data.

[0054] The automation unit can add an anomaly detection function to the automation system and immediately notify when a problem occurs. The automation unit, for example, introduces an anomaly detection algorithm into the automation system and transmits an alert when an anomaly occurs. For example, the automation unit introduces an anomaly detection algorithm into the automation system and transmits an alert when an anomaly occurs. The automation unit can also utilize the anomaly detection function to detect a failure in the irrigation system early. For example, the automation unit utilizes the anomaly detection function to detect a failure in the irrigation system early. The automation unit can also utilize the anomaly detection function to detect an anomaly in the fertilizer supply system early. For example, the automation unit utilizes the anomaly detection function to detect an anomaly in the fertilizer supply system early. By adding the anomaly detection function, it is possible to immediately respond when a problem occurs. A part or all of the above-mentioned processing in the automation unit may be performed using, for example, AI, or may be performed without using AI. For example, the automation unit can input anomaly detection data to the generation AI and cause the generation AI to execute the detection of anomalies.

[0055] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0056] The smart agriculture system can further set different monitoring parameters taking into account the geographical characteristics of the farmland. For example, the monitoring unit can adjust the monitoring parameters of temperature and humidity according to the altitude of the farmland. The monitoring unit can also set the monitoring parameters of nutrients according to the soil type of the farmland. Furthermore, the monitoring unit can adjust the monitoring frequency and data acquisition method according to the climatic conditions of the farmland. This enables monitoring according to the geographical characteristics of the farmland.

[0057] The monitoring unit monitors microbial activity in the soil, enabling a more detailed understanding of the health of the crop. For example, the monitoring unit monitors the activity of specific microorganisms in the soil to evaluate the nutritional state of the crop. The monitoring unit analyzes the microbial activity patterns and predicts the health of the soil. Furthermore, the monitoring unit can suggest the type and amount of fertilizer or conditioner required based on the microbial activity data. In this way, by monitoring microbial activity in the soil, the health of the crop can be understood in detail.

[0058] The analysis unit can apply different analysis algorithms for each growth stage of the crop. For example, in the early growth stage of the crop, an analysis algorithm that emphasizes the growth rate can be applied. In addition, in the middle growth stage of the crop, an analysis algorithm that emphasizes the efficiency of nutrient absorption can be applied. Furthermore, in the pre-harvest stage of the crop, an analysis algorithm that predicts the optimum harvest time can be applied. This makes it possible to perform analysis according to the growth stage of the crop.

[0059] During analysis, the analysis unit can detect abnormal values ​​by comparing with past data and take measures early. For example, abnormal temperature changes can be detected by comparing with past data. Also, abnormal humidity changes can be detected by comparing with past data. Furthermore, abnormal nutrient levels can be detected by comparing with past data. Thus, by comparing with past data, abnormal values ​​can be detected early and measures can be taken.

[0060] When making a proposal, the proposal unit can apply different proposal algorithms depending on the type of crop and the cultivation method. For example, the proposal unit can propose the optimal type and amount of fertilizer depending on the type of crop. The proposal unit can also propose the optimal irrigation method depending on the cultivation method. Furthermore, the proposal unit can also propose the optimal pest control measures depending on the growth stage of the crop. This makes it possible to make proposals according to the type of crop and the cultivation method.

[0061] The proposal department can add options to the proposal to minimize the impact on the environment. For example, it can suggest the use of environmentally friendly fertilizers and pesticides. It can also suggest irrigation methods that take into account the conservation of water resources. It can also suggest agricultural practices to maintain soil health. This makes it possible to make proposals that minimize the impact on the environment.

[0062] The automation department can add a function to optimize energy efficiency to the automation system. For example, an algorithm that optimizes energy efficiency can be introduced into the automation system to reduce power consumption. In addition, solar power generation can be incorporated into the automation system to utilize renewable energy. Furthermore, an energy consumption monitoring function can be added to achieve efficient operation. This makes it possible to reduce power consumption by optimizing energy efficiency.

[0063] The process flow of the first embodiment will be briefly described below.

[0064] Step 1: The monitoring unit monitors the condition of the farmland and crops. Specifically, it uses sensors to measure soil moisture, temperature, nutrient levels, crop growth status, and other data, and collects data. For example, soil moisture sensors, temperature sensors, and nutrient sensors are used to collect this data in real time. Step 2: The analysis unit analyzes the collected data and performs predictive analysis. Specifically, it uses AI to analyze the data and proposes optimal fertilizer and pesticide usage. For example, based on crop growth data, AI calculates the optimal type and amount of fertilizer and predicts the risk of pest outbreaks. Step 3: The proposal section proposes optimal methods of using fertilizers and pesticides based on the analysis results obtained by the analysis section. Specifically, it uses AI to propose optimal types and amounts of fertilizer, predicts the risk of pest outbreaks, and suggests the appropriate timing for using pesticides. Step 4: The automation unit automates agricultural work based on the content proposed by the proposal unit. Specifically, it automatically supplies water by sending signals to the irrigation system and controls the automatic fertilizer and pesticide supply system. AI is used to control these systems at the optimal timing.

[0065] (Example 2) The smart agriculture system according to the embodiment of the present invention is a system that realizes efficient agricultural work through automation while managing the condition of farmland and crops in real time, and performs agricultural production that is highly profitable and environmentally friendly. In this smart agriculture system, IoT sensors monitor the health of farmland and crops, and automate agricultural work as necessary. It is possible to propose optimal fertilizer and pesticide usage through predictive analysis by AI. For example, IoT sensors monitor soil humidity, temperature, nutrient levels, crop growth status, etc. If the humidity of the soil decreases, the sensor detects it and sends a signal to the irrigation system to automatically supply water. Next, AI analyzes the collected data and performs predictive analysis. This makes it possible to propose optimal fertilizer and pesticide usage. For example, based on crop growth data, AI calculates the optimal type and amount of fertilizer and notifies farmers. It can also predict the risk of pests and diseases and propose the appropriate timing for using pesticides. In addition, AI takes into account weather data and market price trends to propose the optimal timing for harvesting. This makes it possible to maximize the harvest and improve profits. For example, based on weather data, AI predicts the optimal day for harvesting and notifies farmers. This system makes farm work more efficient and reduces the labor force. In addition, the optimal use of resources reduces the burden on the environment and enables sustainable agriculture. For example, by using only the necessary amounts of fertilizer and pesticides, soil and water pollution can be prevented. In this way, a smart agriculture system that combines AI and IoT can manage the condition of farmland and crops in real time and achieve efficient farm work, enabling agricultural production that is both highly profitable and environmentally friendly. In this way, a smart agriculture system can manage the condition of farmland and crops in real time and achieve efficient farm work.

[0066] The smart agriculture system according to the embodiment includes a monitoring unit, an analysis unit, a proposal unit, and an automation unit. The monitoring unit monitors the state of farmland and crops. The state of farmland and crops includes, for example, soil humidity, temperature, nutrient levels, crop growth status, and the like, but is not limited to such examples. For example, the monitoring unit measures soil humidity with a sensor and collects data. The monitoring unit can also measure the temperature of the soil and the environment using a temperature sensor. Furthermore, the monitoring unit can measure the level of nutrients in the soil using a nutrient sensor. For example, the monitoring unit uses a soil moisture sensor to measure soil humidity. The temperature sensor measures the temperature of the soil and the environment in real time and collects data. The nutrient sensor measures the levels of nutrients such as nitrogen, phosphorus, and potassium in the soil and collects data. The analysis unit analyzes the collected data and performs predictive analysis. For example, the analysis unit analyzes the data using AI and proposes the optimal use of fertilizers and pesticides. For example, the analysis unit calculates the optimal type and amount of fertilizer using AI based on crop growth data. The analysis unit can also predict the risk of pests and disease occurrence and propose the appropriate timing for using pesticides. For example, the analysis unit uses AI to analyze crop growth data and calculate the optimal type and amount of fertilizer. The AI ​​proposes the optimal type and amount of fertilizer based on past data and environmental data. In order to predict the risk of pests and disease occurrence, the AI ​​analyzes crop growth data and environmental data and predicts the risk of pests and disease occurrence. The proposal unit proposes the optimal way to use fertilizers and pesticides based on the analysis results obtained by the analysis unit. The proposal unit proposes the optimal type and amount of fertilizer, for example, using AI. In addition, the proposal unit can predict the risk of pests and disease occurrence and propose the appropriate timing for using pesticides. For example, the proposal unit uses AI to propose the optimal type and amount of fertilizer. The AI ​​proposes the optimal type and amount of fertilizer based on past data and environmental data. In order to predict the risk of pests and disease occurrence, the AI ​​analyzes crop growth data and environmental data and predicts the risk of pests and disease occurrence. The automation unit automates farm work based on the content proposed by the proposal unit. The automation unit can, for example, send signals to an irrigation system to automatically supply water, or it can control an automatic fertilizer or pesticide supply system.For example, the automation unit automatically supplies water by sending a signal to an irrigation system. The automation unit uses AI to control the irrigation system at optimal timing. In order to control an automatic fertilizer and pesticide supply system, the automation unit uses AI to supply fertilizer and pesticide at optimal timing. In this way, the smart agriculture system according to the embodiment can manage the condition of farmland and crops in real time and achieve efficient farm work.

[0067] The monitoring unit monitors the condition of the farmland and the crops. The condition of the farmland and the crops includes, for example, soil humidity, temperature, nutrient levels, crop growth status, and the like, but is not limited to such examples. The monitoring unit, for example, measures the humidity of the soil with a sensor and collects data. Specifically, the soil moisture sensor measures the moisture content in the soil in real time and transmits the data to a central database. This makes it possible to grasp the moisture status of the entire farmland in a centralized manner. The monitoring unit can also measure the temperature of the soil and the environment using a temperature sensor. The temperature sensor measures the temperature of the ground surface and underground, and provides data for maintaining a temperature environment suitable for crop growth. Furthermore, the monitoring unit can also measure the level of nutrients in the soil using a nutrient sensor. The nutrient sensor measures the concentration of major nutrients such as nitrogen, phosphorus, and potassium in the soil, and monitors the nutritional status of the crops in real time. This makes it possible to supply the necessary nutrients at the appropriate time. The monitoring unit can centrally manage the data obtained from these sensors and cooperate with other systems and departments as necessary. For example, the collected data is stored in a cloud server and made accessible to the analysis unit and proposal unit. In addition, by adjusting the frequency and accuracy of data collection, it is possible to respond flexibly to specific situations and conditions. This allows the monitoring unit to collect data efficiently and effectively, improving the performance of the entire system.

[0068] The analysis unit analyzes the collected data and performs predictive analysis. For example, the analysis unit uses AI to analyze the data and propose optimal fertilizer and pesticide usage. Specifically, the AI ​​uses machine learning algorithms to analyze past data and environmental data to identify optimal conditions for crop growth. For example, based on crop growth data, the AI ​​calculates the optimal type and amount of fertilizer. The AI ​​considers the soil nutrient level and crop growth stage and proposes the optimal type and amount of fertilizer. The analysis unit can also predict the risk of pest and disease occurrence and propose appropriate timing for pesticide use. The AI ​​analyzes crop growth data and environmental data to predict the risk of pest and disease occurrence. For example, based on temperature and humidity fluctuations and past pest and disease occurrence data, it predicts the period when a specific pest is likely to occur and proposes appropriate timing for pesticide use. Furthermore, the analysis unit can use anomaly detection algorithms to detect unusual patterns and abnormal data and issue early warnings. As a result, the analysis unit can not only grasp the situation in real time, but also handle long-term risk management and anomaly detection, improving the reliability and safety of the entire system.

[0069] The suggestion unit proposes the optimal use of fertilizers and pesticides based on the analysis results obtained by the analysis unit. The suggestion unit, for example, uses AI to propose the optimal type and amount of fertilizer. Specifically, the AI ​​calculates the optimal type and amount of fertilizer for crop growth based on past data and environmental data. For example, the AI ​​proposes the optimal type and amount of fertilizer taking into account the nutrient level of the soil and the growth stage of the crop. The suggestion unit can also predict the risk of pests and diseases and propose the appropriate timing for using pesticides. The AI ​​analyzes crop growth data and environmental data to predict the risk of pests and diseases. For example, based on fluctuations in temperature and humidity and past pest and diseases occurrence data, it predicts the time when a specific pest is likely to occur and proposes the appropriate timing for using pesticides. Furthermore, the suggestion unit can collect user feedback and continuously improve the accuracy and effectiveness of the proposed content. For example, the next proposed content is reviewed based on the results of the use of the proposed fertilizers and pesticides. The suggestion unit can also reliably transmit information using multiple communication methods. For example, important information can be reliably delivered by using not only smartphone notifications but also voice calls, SMS, emails, etc. This enables the suggestion unit to quickly and reliably provide optimal agricultural work suggestions to the user, thereby improving the efficiency and effectiveness of agricultural work.

[0070] The automation unit automates agricultural work based on the content proposed by the proposal unit. For example, the automation unit automatically supplies water by sending a signal to an irrigation system. Specifically, the automation unit uses AI to control the irrigation system at the optimal timing. Based on soil moisture data, the required amount of water is calculated and a signal is sent to the irrigation system to automatically supply water. The automation unit can also control an automatic fertilizer and pesticide supply system. The AI ​​sends a signal to the automatic supply system to supply fertilizer and pesticide at the optimal timing. For example, fertilizer and pesticide are supplied at the optimal timing, taking into account the nutrient level of the soil and the growth stage of the crop. Furthermore, the automation unit can also control an automatic driving system for agricultural machinery. For example, agricultural machinery such as tractors and combines are automatically driven to efficiently perform cultivation and harvesting work. AI calculates the optimal driving route based on the topographical data of the farmland and the arrangement data of the crops, and sends a signal to the agricultural machinery to realize automatic driving. This allows the automation unit to improve the efficiency and accuracy of agricultural work. Furthermore, the automation unit can monitor the progress of agricultural work based on data updated in real time and make adjustments as necessary. For example, the automation section can adjust the timing of irrigation and fertilizer supply according to changes in the weather and the growth status of crops, thereby enabling the farmer to always perform optimal farm work and maximize the productivity of the farmland.

[0071] The monitoring unit can monitor data on soil humidity, temperature, nutrient levels, and crop growth status. The monitoring unit, for example, measures soil humidity with a sensor and collects data. For example, the monitoring unit can measure soil humidity using a soil moisture sensor. The monitoring unit can also measure soil and environmental temperatures using a temperature sensor. For example, the monitoring unit can measure soil and environmental temperatures in real time using a temperature sensor. Furthermore, the monitoring unit can also measure nutrient levels in the soil using a nutrient sensor. For example, the monitoring unit can measure nutrient levels such as nitrogen, phosphorus, and potassium in the soil using a nutrient sensor. This allows the health of farmland and crops to be grasped in detail. 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 data acquired by the sensor to the generation AI and have the generation AI perform analysis of the data.

[0072] The analysis unit can calculate the optimal type and amount of fertilizer based on the collected data. The analysis unit, for example, uses AI to analyze the collected data and calculate the optimal type and amount of fertilizer. For example, the analysis unit uses AI to calculate the optimal type and amount of fertilizer based on crop growth data. The AI ​​proposes the optimal type and amount of fertilizer based on past data and environmental data. For example, the analysis unit uses AI to analyze crop growth data and calculate the optimal type and amount of fertilizer. The AI ​​proposes the optimal type and amount of fertilizer based on past data and environmental data. This enables the use of optimal fertilizer. A part or all of the above-mentioned processing in the analysis unit may be performed, for example, using AI, or may be performed without using AI. For example, the analysis unit can input the collected data to the generation AI and cause the generation AI to calculate the optimal type and amount of fertilizer.

[0073] The analysis unit can predict the risk of pests and propose appropriate timing for using pesticides. The analysis unit, for example, uses AI to predict the risk of pests and propose appropriate timing for using pesticides. For example, the analysis unit uses AI to predict the risk of pests and disease based on crop growth data and environmental data. The AI ​​predicts the risk of pests and disease based on past data and environmental data, and proposes appropriate timing for using pesticides. For example, the analysis unit uses AI to analyze crop growth data and environmental data, and predicts the risk of pests and disease. The AI ​​predicts the risk of pests and disease based on past data and environmental data, and proposes appropriate timing for using pesticides. This makes it possible to predict the occurrence of pests and disease early and take appropriate measures. Part or all of the above-mentioned processing in the analysis unit may be performed, for example, using AI, or may be performed without using AI. For example, the analysis unit can input collected data to the generation AI and cause the generation AI to execute a prediction of the risk of pests and disease.

[0074] The analysis unit can propose the appropriate timing for harvesting, taking into account weather data or market price trends. The analysis unit, for example, uses AI to propose the appropriate timing for harvesting, taking into account weather data and market price trends. For example, the analysis unit uses AI to predict the optimal day for harvesting based on weather data. The AI ​​proposes the optimal timing for harvesting based on past data and environmental data. For example, the analysis unit uses AI to analyze weather data and market price trends and propose the appropriate timing for harvesting. The AI ​​proposes the optimal timing for harvesting based on past data and environmental data. This makes it possible to maximize the harvest volume and improve profits. Some or all of the above-mentioned processing in the analysis unit may be performed, for example, using AI, or may be performed without using AI. For example, the analysis unit can input weather data and market price trends to the generation AI and cause the generation AI to execute a proposal for the appropriate timing for harvesting.

[0075] The automation unit can automatically supply water by sending a signal to the irrigation system. The automation unit can automatically supply water by, for example, sending a signal to the irrigation system. For example, the automation unit uses AI to control the irrigation system at optimal timing. The automation unit uses AI to analyze soil moisture data and send a signal to the irrigation system at optimal timing. This enables efficient irrigation by automatically supplying water. Some or all of the above-mentioned processing in the automation unit may be performed, for example, using AI, or may be performed without using AI. For example, the automation unit can input soil moisture data to the generation AI and cause the generation AI to control the irrigation system.

[0076] The monitoring unit can estimate the user's emotion and adjust the acquisition frequency of the monitoring data based on the estimated user's emotion. The monitoring unit, for example, estimates the user's emotion and adjusts the acquisition frequency of the monitoring data based on the estimated user's emotion. For example, when the user is feeling stressed, the monitoring unit sets the acquisition frequency of the monitoring data low and reduces notifications. In addition, when the user is relaxed, the monitoring unit can also set the acquisition frequency of the monitoring data high and provide detailed information. Furthermore, when the user is in a hurry, the monitoring unit can preferentially acquire only important data and quickly notify the user. In this way, the burden on the user can be reduced by adjusting the acquisition frequency of the monitoring data according to the user's emotion. The estimation of the emotion is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned 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 can input the user's emotion data to the generation AI and cause the generation AI to execute emotion estimation.

[0077] The monitoring unit monitors the microbial activity of the soil and can grasp the health state of the crop in more detail. The monitoring unit, for example, monitors the activity of specific microorganisms in the soil and evaluates the nutritional state of the crop. For example, the monitoring unit analyzes the activity pattern of the microorganisms and predicts the health state of the soil. The monitoring unit can also suggest the type and amount of fertilizer and improver required based on the activity data of the microorganisms. For example, the monitoring unit monitors the activity of specific microorganisms in the soil and evaluates the nutritional state of the crop. The monitoring unit analyzes the activity pattern of the microorganisms and predicts the health state of the soil. The monitoring unit suggests the type and amount of fertilizer and improver required based on the activity data of the microorganisms. In this way, the health state of the crop can be grasped in detail by monitoring the microbial activity of the soil. Part 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 the microbial activity data of the soil to the generation AI and cause the generation AI to execute the analysis of the data.

[0078] The monitoring unit monitors the color and shape of the leaves of the crop, and can detect nutritional deficiency and pests at an early stage. The monitoring unit, for example, monitors the color of the leaves of the crop and detects signs of nutritional deficiency. For example, the monitoring unit monitors the shape of the leaves of the crop and detects early symptoms of pests. The monitoring unit can also propose necessary measures based on the monitoring data. For example, the monitoring unit monitors the color of the leaves of the crop and detects signs of nutritional deficiency. The monitoring unit monitors the shape of the leaves of the crop and detects early symptoms of pests. The monitoring unit proposes necessary measures based on the monitoring data. As a result, early detection of nutritional deficiency and pests is possible by monitoring the color and shape of the leaves of the crop. 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 data on the color and shape of the leaves of the crop to the generation AI, and cause the generation AI to analyze the data.

[0079] The monitoring unit can estimate the user's emotion and determine the priority of the monitoring data based on the estimated user's emotion. The monitoring unit, for example, estimates the user's emotion and determines the priority of the monitoring data based on the estimated user's emotion. For example, when the user is feeling stressed, the monitoring unit preferentially displays only important data. In addition, when the user is relaxed, the monitoring unit can also preferentially display detailed data. Furthermore, when the user is in a hurry, the monitoring unit can also preferentially display data that requires a quick response. In this way, by determining the priority of the monitoring data according to the user's emotion, important data can be preferentially displayed. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned 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 can input the user's emotion data to the generation AI and cause the generation AI to execute emotion estimation.

[0080] The monitoring unit can set different monitoring parameters taking into account the geographical characteristics of the farmland. The monitoring unit adjusts the monitoring parameters of temperature and humidity, for example, according to the altitude of the farmland. For example, the monitoring unit adjusts the monitoring parameters of temperature and humidity according to the altitude of the farmland. The monitoring unit can also set the monitoring parameters of nutrients according to the soil type of the farmland. For example, the monitoring unit sets the monitoring parameters of nutrients according to the soil type of the farmland. Furthermore, the monitoring unit can also adjust the monitoring frequency and the data acquisition method according to the climatic conditions of the farmland. For example, the monitoring unit adjusts the monitoring frequency and the data acquisition method according to the climatic conditions of the farmland. This enables monitoring according to the geographical characteristics of the farmland. A part 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 geographical characteristic data of the farmland to the generation AI and cause the generation AI to execute the setting of the monitoring parameters.

[0081] The monitoring unit can jointly solve problems by storing the monitoring data in the cloud and sharing it with other farmers. The monitoring unit, for example, stores the monitoring data in the cloud and shares it with other farmers. For example, the monitoring unit stores the monitoring data in the cloud and shares it with other farmers. The monitoring unit can also jointly consider solutions to problems based on the shared data. For example, the monitoring unit jointly consider solutions to problems based on the shared data. Furthermore, the monitoring unit can also utilize the shared data to improve agricultural efficiency in the entire region. For example, the monitoring unit utilizes the shared data to improve agricultural efficiency in the entire region. This makes it possible to jointly solve problems by sharing the monitoring data. 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 the monitoring data to the generation AI and have the generation AI execute analysis of the data.

[0082] The analysis unit can estimate the user's emotion and adjust the display method of the analysis result based on the estimated user's emotion. The analysis unit, for example, estimates the user's emotion and adjusts the display method of the analysis result based on the estimated user's emotion. For example, when the user is nervous, the analysis unit provides a simple and highly visible display method. Furthermore, when the user is relaxed, the analysis unit can also provide a display method including detailed information. Furthermore, when the user is in a hurry, the analysis unit can also provide a display method that focuses on the main points. This allows the display method of the analysis result to be adjusted according to the user's emotion, making it possible to display the analysis result in a way that is easy for the user to see. The estimation of the emotion is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned 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 can input the user's emotion data to the generation AI and cause the generation AI to execute the emotion estimation.

[0083] The analysis unit can apply different analysis algorithms for each growth stage of the crop. For example, the analysis unit applies an analysis algorithm that emphasizes the growth rate in the early growth stage of the crop. For example, the analysis unit applies an analysis algorithm that emphasizes the growth rate in the early growth stage of the crop. The analysis unit can also apply an analysis algorithm that emphasizes the absorption efficiency of nutrients in the middle growth stage of the crop. For example, the analysis unit applies an analysis algorithm that emphasizes the absorption efficiency of nutrients in the middle growth stage of the crop. Furthermore, the analysis unit can also apply an analysis algorithm that predicts the optimum harvest time in the pre-harvest stage of the crop. For example, the analysis unit applies an analysis algorithm that predicts the optimum harvest time in the pre-harvest stage of the crop. This enables analysis according to the growth stage of the crop. A part 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 growth data of the crop to the generation AI and cause the generation AI to execute an analysis for each growth stage.

[0084] The analysis unit detects abnormal values ​​by comparing with past data during analysis, and can take measures early. The analysis unit, for example, detects abnormal temperature changes by comparing with past data. For example, the analysis unit detects abnormal temperature changes by comparing with past data. The analysis unit can also detect abnormal humidity changes by comparing with past data. For example, the analysis unit detects abnormal humidity changes by comparing with past data. Furthermore, the analysis unit can also detect abnormal nutrient levels by comparing with past data. For example, the analysis unit detects abnormal nutrient levels by comparing with past data. This makes it possible to detect abnormal values ​​early and take measures by comparing with past data. Part or all of the above-mentioned processing in the analysis unit may be performed, for example, using AI, or may be performed without using AI. For example, the analysis unit can input past data to the generation AI and cause the generation AI to detect abnormal values.

[0085] The analysis unit can estimate the user's emotion and determine the priority of the analysis results based on the estimated user's emotion. The analysis unit, for example, estimates the user's emotion and determines the priority of the analysis results based on the estimated user's emotion. For example, when the user is stressed, the analysis unit preferentially displays only important analysis results. In addition, when the user is relaxed, the analysis unit can also preferentially display detailed analysis results. Furthermore, when the user is in a hurry, the analysis unit can also preferentially display analysis results that require a quick response. In this way, by determining the priority of the analysis results according to the user's emotion, important analysis results can be preferentially displayed. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned 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 can input the user's emotion data to the generation AI and cause the generation AI to execute emotion estimation.

[0086] The analysis unit can compare the analysis results with data on other farmland to improve agricultural efficiency throughout the region. The analysis unit, for example, compares the analysis results with data on other farmland to propose an optimal type and amount of fertilizer. For example, the analysis unit compares the analysis results with data on other farmland to propose an optimal type and amount of fertilizer. The analysis unit can also compare the analysis results with data on other farmland to propose an optimal irrigation method. For example, the analysis unit compares the analysis results with data on other farmland to propose an optimal irrigation method. The analysis unit can also compare the analysis results with data on other farmland to propose an optimal pest control measure. For example, the analysis unit compares the analysis results with data on other farmland to propose an optimal pest control measure. This makes it possible to improve agricultural efficiency throughout the region by comparing the analysis results with data on other farmland. 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 data on other farmland to the generation AI and cause the generation AI to perform a comparison of the data.

[0087] The analysis unit can visualize the analysis results so that the user can intuitively understand them. For example, the analysis unit displays the analysis results in a graph or chart to make it easier to understand visually. For example, the analysis unit displays the analysis results in a graph or chart to make it easier to understand visually. The analysis unit can also plot the analysis results on a map to make it easier to understand the state of the farmland intuitively. For example, the analysis unit can plot the analysis results on a map to make it easier to understand the state of the farmland intuitively. Furthermore, the analysis unit can display the analysis results in animation to visually show changes in the data. For example, the analysis unit displays the analysis results in animation to visually show changes in the data. In this way, the analysis results can be visualized so that the user can intuitively understand them. A part or all of the above-mentioned processing in the analysis unit may be performed, for example, using AI, or may be performed without using AI. For example, the analysis unit can input the analysis results to the generation AI and cause the generation AI to execute visualization.

[0088] The suggestion unit can estimate the user's emotion and adjust the expression method of the proposed content based on the estimated user's emotion. The suggestion unit, for example, estimates the user's emotion and adjusts the expression method of the proposed content based on the estimated user's emotion. For example, when the user is nervous, the suggestion unit provides a simple and highly visible proposed content. In addition, when the user is relaxed, the suggestion unit can also provide a proposed content including detailed information. Furthermore, when the user is in a hurry, the suggestion unit can also provide a proposed content that covers the main points. This allows a suggestion that is easy for the user to understand to be made by adjusting the expression method of the proposed content according to the user's emotion. The estimation of the emotion is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the suggestion unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the suggestion unit can input the user's emotion data to the generation AI and cause the generation AI to execute emotion estimation.

[0089] The proposal unit can apply different proposal algorithms depending on the type of crop and the cultivation method at the time of proposal. The proposal unit, for example, proposes the optimal type and amount of fertilizer depending on the type of crop. For example, the proposal unit proposes the optimal type and amount of fertilizer depending on the type of crop. The proposal unit can also propose the optimal irrigation method depending on the cultivation method. For example, the proposal unit proposes the optimal irrigation method depending on the cultivation method. Furthermore, the proposal unit can also propose the optimal pest control depending on the growth stage of the crop. For example, the proposal unit proposes the optimal pest control depending on the growth stage of the crop. This makes it possible to make proposals depending on the type of crop and the cultivation method. Part 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 data on the type of crop and the cultivation method to the generation AI and cause the generation AI to apply the proposal algorithm.

[0090] The suggestion unit can add an option for minimizing the impact on the environment to the proposal content. The suggestion unit, for example, suggests the use of environmentally friendly fertilizers and pesticides. For example, the suggestion unit suggests the use of environmentally friendly fertilizers and pesticides. The suggestion unit can also suggest an irrigation method that takes into account the conservation of water resources. For example, the suggestion unit suggests an irrigation method that takes into account the conservation of water resources. Furthermore, the suggestion unit can also suggest agricultural practices for maintaining soil health. For example, the suggestion unit suggests agricultural practices for maintaining soil health. This enables a proposal that minimizes the impact on the environment. A part or all of the above-mentioned processing in the suggestion unit may be performed, for example, using AI, or may be performed without using AI. For example, the suggestion unit can input an option for minimizing the impact on the environment to the generation AI and cause the generation AI to execute the generation of the proposal content.

[0091] The suggestion unit can estimate the user's emotion and determine the priority of the proposed contents based on the estimated user's emotion. The suggestion unit, for example, estimates the user's emotion and determines the priority of the proposed contents based on the estimated user's emotion. For example, when the user is stressed, the suggestion unit preferentially displays only important proposed contents. In addition, when the user is relaxed, the suggestion unit can also preferentially display detailed proposed contents. Furthermore, when the user is in a hurry, the suggestion unit can also preferentially display proposed contents that require a quick response. In this way, by determining the priority of the proposed contents according to the user's emotion, important proposed contents can be preferentially displayed. The estimation of the emotion is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the suggestion unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the suggestion unit can input the user's emotion data to the generation AI and cause the generation AI to execute emotion estimation.

[0092] The proposal unit can share the proposal content with other farmers and jointly explore the optimal agricultural method. The proposal unit, for example, saves the proposal content in a cloud and shares it with other farmers. For example, the proposal unit saves the proposal content in a cloud and shares it with other farmers. The proposal unit can also jointly consider the optimal agricultural method based on the shared proposal content. For example, the proposal unit jointly considers the optimal agricultural method based on the shared proposal content. Furthermore, the proposal unit can also utilize the shared data to improve agricultural efficiency in the entire region. For example, the proposal unit utilizes the shared data to improve agricultural efficiency in the entire region. As a result, by sharing the proposal content, it is possible to jointly explore the optimal agricultural method. A part 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 the proposal content to the generation AI and cause the generation AI to share the data.

[0093] The suggestion unit can notify the proposed content through a smartphone app so that the user can respond immediately. The suggestion unit can, for example, notify the proposed content through a smartphone app so that the user can immediately check it. For example, the suggestion unit can notify the proposed content through a smartphone app so that the user can immediately check it. The suggestion unit can also enable an action based on the proposed content to be quickly executed through the smartphone app. For example, the suggestion unit can enable an action based on the proposed content to be quickly executed through the smartphone app. Furthermore, the suggestion unit can also collect feedback on the proposed content through the smartphone app and use it to improve the system. For example, the suggestion unit can collect feedback on the proposed content through the smartphone app and use it to improve the system. As a result, the proposed content is notified through the smartphone app so that the user can respond immediately. A part or all of the above-mentioned processing in the suggestion unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the suggestion unit can input the proposed content to a generation AI and cause the generation AI to execute a notification through the smartphone app.

[0094] The automation unit can estimate the user's emotion and adjust the timing of automation based on the estimated user's emotion. The automation unit, for example, estimates the user's emotion and adjusts the timing of automation based on the estimated user's emotion. For example, the automation unit delays the timing of automation when the user is stressed. Also, the automation unit can advance the timing of automation when the user is relaxed. Furthermore, the automation unit can quickly execute automation when the user is in a hurry. This can reduce the burden on the user by adjusting the timing of automation according to the user's emotion. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the automation unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the automation unit can input the user's emotion data to the generation AI and cause the generation AI to execute emotion estimation.

[0095] During automation, the automation unit can apply different automation processes according to the growth stage of the crop. For example, in the early growth stage of the crop, the automation unit automatically adjusts the irrigation system to supply appropriate moisture. For example, in the early growth stage of the crop, the automation unit automatically adjusts the irrigation system to supply appropriate moisture. In addition, the automation unit can also apply an automatic fertilizer supply system to provide necessary nutrients in the middle growth stage of the crop. For example, the automation unit applies an automatic fertilizer supply system to provide necessary nutrients in the middle growth stage of the crop. Furthermore, the automation unit can also apply an automated process for pest control in the pre-harvest stage of the crop to maintain the health of the crop. For example, the automation unit applies an automated process for pest control in the pre-harvest stage of the crop to maintain the health of the crop. This enables automation according to the growth stage of the crop. Part or all of the above-mentioned processing in the automation unit may be performed, for example, using AI, or may be performed without using AI. For example, the automation unit can input crop growth data to the generation AI and cause the generation AI to execute an automation process for each growth stage.

[0096] The automation unit can add a function for optimizing energy efficiency to the automation system. For example, the automation unit introduces an algorithm for optimizing energy efficiency into the automation system to reduce power consumption. For example, the automation unit introduces an algorithm for optimizing energy efficiency into the automation system to reduce power consumption. The automation unit can also incorporate solar power generation into the automation system to utilize renewable energy. For example, the automation unit incorporates solar power generation into the automation system to utilize renewable energy. Furthermore, the automation unit can also add an energy consumption monitoring function to realize efficient operation. For example, the automation unit adds an energy consumption monitoring function to realize efficient operation. As a result, power consumption can be reduced by optimizing energy efficiency. A part or all of the above-mentioned processing in the automation unit may be performed, for example, using AI, or may be performed without using AI. For example, the automation unit can input energy consumption data to the generation AI and cause the generation AI to execute optimization of energy efficiency.

[0097] The automation unit can estimate the user's emotion and determine the priority of automation based on the estimated user's emotion. The automation unit, for example, estimates the user's emotion and determines the priority of automation based on the estimated user's emotion. For example, when the user is stressed, the automation unit prioritizes execution of only important automation processes. In addition, when the user is relaxed, the automation unit can also prioritize execution of detailed automation processes. Furthermore, when the user is in a hurry, the automation unit can also prioritize execution of automation processes that require a quick response. In this way, important automation processes can be prioritized by determining the priority of automation according to the user's emotion. The estimation of emotion is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the automation unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the automation unit can input the user's emotion data to the generation AI and cause the generation AI to execute emotion estimation.

[0098] The automation unit can link the automation system with other farmlands to realize efficient agriculture throughout the region. The automation unit, for example, connects the automation system to the cloud and shares data with other farmlands. For example, the automation unit connects the automation system to the cloud and shares data with other farmlands. The automation unit can also execute optimal agricultural practices throughout the region based on the shared data. For example, the automation unit executes optimal agricultural practices throughout the region based on the shared data. Furthermore, the automation unit can link the automation systems to realize efficient irrigation and fertilizer supply throughout the region. For example, the automation unit links the automation systems to realize efficient irrigation and fertilizer supply throughout the region. As a result, efficient agriculture is possible throughout the region by linking the automation systems. Some or all of the above-mentioned processing in the automation unit may be performed, for example, using AI, or may be performed without using AI. For example, the automation unit can input data of other farmlands into the generation AI and cause the generation AI to share the data.

[0099] The automation unit can add an anomaly detection function to the automation system and immediately notify when a problem occurs. The automation unit, for example, introduces an anomaly detection algorithm into the automation system and transmits an alert when an anomaly occurs. For example, the automation unit introduces an anomaly detection algorithm into the automation system and transmits an alert when an anomaly occurs. The automation unit can also utilize the anomaly detection function to detect a failure in the irrigation system early. For example, the automation unit utilizes the anomaly detection function to detect a failure in the irrigation system early. The automation unit can also utilize the anomaly detection function to detect an anomaly in the fertilizer supply system early. For example, the automation unit utilizes the anomaly detection function to detect an anomaly in the fertilizer supply system early. By adding the anomaly detection function, it is possible to immediately respond when a problem occurs. A part or all of the above-mentioned processing in the automation unit may be performed using, for example, AI, or may be performed without using AI. For example, the automation unit can input anomaly detection data to the generation AI and cause the generation AI to execute the detection of anomalies.

[0100] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0101] The smart agriculture system can further set different monitoring parameters taking into account the geographical characteristics of the farmland. For example, the monitoring unit can adjust the monitoring parameters of temperature and humidity according to the altitude of the farmland. The monitoring unit can also set the monitoring parameters of nutrients according to the soil type of the farmland. Furthermore, the monitoring unit can adjust the monitoring frequency and data acquisition method according to the climatic conditions of the farmland. This enables monitoring according to the geographical characteristics of the farmland.

[0102] The monitoring unit monitors microbial activity in the soil, enabling a more detailed understanding of the health of the crop. For example, the monitoring unit monitors the activity of specific microorganisms in the soil to evaluate the nutritional state of the crop. The monitoring unit analyzes the microbial activity patterns and predicts the health of the soil. Furthermore, the monitoring unit can suggest the type and amount of fertilizer or conditioner required based on the microbial activity data. In this way, by monitoring microbial activity in the soil, the health of the crop can be understood in detail.

[0103] 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. For example, if the user is nervous, a simple, highly visible display method can be provided. If the user is relaxed, a display method including detailed information can be provided. Furthermore, if the user is in a hurry, a display method that focuses on the main points can be provided. In this way, by adjusting the display method of the analysis results according to the user's emotions, it is possible to provide a display that is easy for the user to see.

[0104] The analysis unit can apply different analysis algorithms for each growth stage of the crop. For example, in the early growth stage of the crop, an analysis algorithm that emphasizes the growth rate can be applied. In addition, in the middle growth stage of the crop, an analysis algorithm that emphasizes the efficiency of nutrient absorption can be applied. Furthermore, in the pre-harvest stage of the crop, an analysis algorithm that predicts the optimum harvest time can be applied. This makes it possible to perform analysis according to the growth stage of the crop.

[0105] During analysis, the analysis unit can detect abnormal values ​​by comparing with past data and take measures early. For example, abnormal temperature changes can be detected by comparing with past data. Also, abnormal humidity changes can be detected by comparing with past data. Furthermore, abnormal nutrient levels can be detected by comparing with past data. Thus, by comparing with past data, abnormal values ​​can be detected early and measures can be taken.

[0106] The suggestion unit can estimate the user's emotions and adjust the way in which the suggested content is expressed based on the estimated user's emotions. For example, if the user is nervous, the suggestion unit can provide simple, highly visible suggested content. If the user is relaxed, the suggestion unit can provide detailed information. If the user is in a hurry, the suggestion unit can provide the main points. In this way, by adjusting the way in which the suggested content is expressed according to the user's emotions, it is possible to provide suggestions that are easy for the user to understand.

[0107] When making a proposal, the proposal unit can apply different proposal algorithms depending on the type of crop and the cultivation method. For example, the proposal unit can propose the optimal type and amount of fertilizer depending on the type of crop. The proposal unit can also propose the optimal irrigation method depending on the cultivation method. Furthermore, the proposal unit can also propose the optimal pest control measures depending on the growth stage of the crop. This makes it possible to make proposals according to the type of crop and the cultivation method.

[0108] The proposal department can add options to the proposal to minimize the impact on the environment. For example, it can suggest the use of environmentally friendly fertilizers and pesticides. It can also suggest irrigation methods that take into account the conservation of water resources. It can also suggest agricultural practices to maintain soil health. This makes it possible to make proposals that minimize the impact on the environment.

[0109] The automation unit can estimate the user's emotions and adjust the timing of automation based on the estimated user's emotions. For example, if the user is feeling stressed, the automation timing can be delayed. Also, if the user is relaxed, the automation timing can be advanced. Furthermore, if the user is in a hurry, the automation can be executed quickly. In this way, the burden on the user can be reduced by adjusting the automation timing according to the user's emotions.

[0110] The automation department can add a function to optimize energy efficiency to the automation system. For example, an algorithm that optimizes energy efficiency can be introduced into the automation system to reduce power consumption. In addition, solar power generation can be incorporated into the automation system to utilize renewable energy. Furthermore, an energy consumption monitoring function can be added to achieve efficient operation. This makes it possible to reduce power consumption by optimizing energy efficiency.

[0111] The process flow of the second embodiment will be briefly described below.

[0112] Step 1: The monitoring unit monitors the condition of the farmland and crops. Specifically, it uses sensors to measure soil moisture, temperature, nutrient levels, crop growth status, and other data, and collects data. For example, soil moisture sensors, temperature sensors, and nutrient sensors are used to collect this data in real time. Step 2: The analysis unit analyzes the collected data and performs predictive analysis. Specifically, it uses AI to analyze the data and proposes optimal fertilizer and pesticide usage. For example, based on crop growth data, AI calculates the optimal type and amount of fertilizer and predicts the risk of pest outbreaks. Step 3: The proposal section proposes optimal methods of using fertilizers and pesticides based on the analysis results obtained by the analysis section. Specifically, it uses AI to propose optimal types and amounts of fertilizer, predicts the risk of pest outbreaks, and suggests the appropriate timing for using pesticides. Step 4: The automation unit automates agricultural work based on the content proposed by the proposal unit. Specifically, it automatically supplies water by sending signals to the irrigation system and controls the automatic fertilizer and pesticide supply system. AI is used to control these systems at the optimal timing.

[0113] 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 a voice indicating a user input for the result of the specific processing. The control unit 46A transmits the voice 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 voice data.

[0114] 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<URL: https: / / openai.com / blog / chatgpt> ) and other generation AIs. 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 a voice, text data indicating a text, and image data indicating an image (e.g., still image data or video data) are input. The data generation model 58 infers the input inference data according to the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, and image data. The data generation model 58 includes, for example, a text generation AI, an image generation AI, and a multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing while 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, and in this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. In the data processing device 12 and the like, the data generation model 58 includes a plurality of types, and the data generation model 58 includes an AI other than the generation AI. The AI ​​other than the generation AI is, for example, a linear regression, a logistic regression, a decision tree, a random forest, a support vector machine (SVM), a k-means clustering, a convolutional neural network (CNN), a recurrent neural network (RNN), a generative adversarial network (GAN), or a naive Bayes, and can perform various processes, but is not limited to such examples. The AI ​​may also be an AI agent. In addition, when the processes of each of the above-mentioned parts are performed by an AI, the processes are performed in part or in whole by the AI, but are not limited to such examples.In addition, processing performed by AI including the generation AI may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by AI including the generation AI.

[0115] Furthermore, the above-described processing by the data processing system 10 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 required for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information required for processing from the data processing device 12 or an external device.

[0116] Each of the multiple elements including the above-mentioned monitoring unit, analysis unit, proposal unit, and automation unit is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the monitoring unit uses the sensor of the smart device 14 to measure the soil humidity, temperature, and nutrient level, and collects data. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12, analyzes the collected data with AI, and proposes the optimal use of fertilizers and pesticides. The proposal unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and notifies the farmer based on the analysis result. The automation unit is realized, for example, by the control unit 46A of the smart device 14, and controls the irrigation system and the automatic supply system of fertilizers and pesticides. The correspondence between each unit and the device and control unit is not limited to the above-mentioned example, and various changes are possible.

[0117] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0118] 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.

[0119] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. 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.

[0120] 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.

[0121] 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 the voice according to instructions from the processor 46.

[0122] Camera 42 is a small digital camera equipped with an optical system including a lens, an aperture, and a 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 (e.g., an imaging range defined by an angle of view equivalent to the width of the field of vision of an average healthy person).

[0123] 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 performed in a secure state.

[0124] Fig. 4 shows an example of 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.

[0125] 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.

[0126] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the emotion of the user using the emotion identification model 59 and perform identification processing using the user's emotion. In the emotion estimation function (emotion identification function) using the emotion identification model 59, various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, are performed, but are not limited to such examples. In addition, the estimation and prediction of the emotion also include, for example, analysis of the emotion.

[0127] In the smart glasses 214, the specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the smart glasses 214 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 also perform processing similar to that of the specific processing unit 290 using these models.

[0128] 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 obtains a processing result (such as a prediction result) using the data generation model 58 by communicating with the server device having the data generation model 58. In addition, the data processing device 12 may be a server device, or may be a terminal device (such as a mobile phone, a robot, or a home appliance) owned by a user.

[0129] 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 a voice indicating a user input for the result of the specific processing. The control unit 46A transmits the 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.

[0130] 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. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating a voice, text data indicating a text, and image data indicating an image (e.g., still image data or video data) is input. The data generation model 58 infers the input inference data according to the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, and image data. The data generation model 58 includes, for example, a text generation AI, an image generation AI, and a multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing while 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, and in this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. In the data processing device 12 and the like, the data generation model 58 includes a plurality of types, and the data generation model 58 includes an AI other than the generation AI. The AI ​​other than the generation AI is, for example, a linear regression, a logistic regression, a decision tree, a random forest, a support vector machine (SVM), a k-means clustering, a convolutional neural network (CNN), a recurrent neural network (RNN), a generative adversarial network (GAN), or a naive Bayes, and can perform various processes, but is not limited to such examples. The AI ​​may be an AI agent. In addition, when the processes of each part described above are performed by an AI, the processes are performed in part or in whole by the AI, but are not limited to such examples. In addition, the processes performed by the AI ​​including the generation AI may be replaced with rule-based processes, and the rule-based processes may be replaced with processes performed by the AI ​​including the generation AI.

[0131] 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 be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart glasses 214 or an external device, and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device.

[0132] Each of the multiple elements including the above-mentioned monitoring unit, analysis unit, proposal unit, and automation unit is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the monitoring unit uses the sensor of the smart glasses 214 to measure the humidity, temperature, and nutrient level of the soil and collect data. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12, analyzes the collected data with AI, and proposes the optimal use of fertilizers and pesticides. The proposal unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and notifies the farmer based on the analysis result. The automation unit is realized, for example, by the control unit 46A of the smart glasses 214, and controls the irrigation system and the automatic supply system of fertilizers and pesticides. The correspondence between each unit and the device and the control unit is not limited to the above-mentioned example, and various changes are possible.

[0133] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0134] 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.

[0135] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. 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.

[0136] 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.

[0137] 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 the voice according to instructions from the processor 46.

[0138] Camera 42 is a small digital camera equipped with an optical system including a lens, an aperture, and a 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 (e.g., an imaging range defined by an angle of view equivalent to the width of the field of vision of an average healthy person).

[0139] 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 performed in a secure state.

[0140] 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.

[0141] 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.

[0142] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the emotion of the user using the emotion identification model 59 and perform identification processing using the user's emotion. In the emotion estimation function (emotion identification function) using the emotion identification model 59, various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, are performed, but are not limited to such examples. In addition, the estimation and prediction of the emotion also include, for example, analysis of the emotion.

[0143] In the headset type terminal 314, the specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads the specific program 60 from the storage 50, and executes the read specific program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific 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.

[0144] 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 obtains a processing result (such as a prediction result) using the data generation model 58 by communicating with the server device having the data generation model 58. In addition, the data processing device 12 may be a server device, or may be a terminal device (such as a mobile phone, a robot, or a home appliance) owned by a user.

[0145] 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 voice indicating a user input for 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.

[0146] 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. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating a voice, text data indicating a text, and image data indicating an image (e.g., still image data or video data) is input. The data generation model 58 infers the input inference data according to the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, and image data. The data generation model 58 includes, for example, a text generation AI, an image generation AI, and a multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing while 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, and in this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. In the data processing device 12 and the like, the data generation model 58 includes a plurality of types, and the data generation model 58 includes an AI other than the generation AI. The AI ​​other than the generation AI is, for example, a linear regression, a logistic regression, a decision tree, a random forest, a support vector machine (SVM), a k-means clustering, a convolutional neural network (CNN), a recurrent neural network (RNN), a generative adversarial network (GAN), or a naive Bayes, and can perform various processes, but is not limited to such examples. The AI ​​may be an AI agent. In addition, when the processes of each part described above are performed by an AI, the processes are performed in part or in whole by the AI, but are not limited to such examples. In addition, the processes performed by the AI ​​including the generation AI may be replaced with rule-based processes, and the rule-based processes may be replaced with processes performed by the AI ​​including the generation AI.

[0147] 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 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. In addition, 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, and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device.

[0148] Each of the multiple elements including the above-mentioned monitoring unit, analysis unit, proposal unit, and automation unit is realized, for example, by at least one of the headset type terminal 314 and the data processing device 12. For example, the monitoring unit uses a sensor of the headset type terminal 314 to measure the soil humidity, temperature, and nutrient level, and collects data. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12, analyzes the collected data with AI, and proposes the optimal use of fertilizers and pesticides. The proposal unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and notifies farmers based on the analysis results. The automation unit is realized, for example, by the control unit 46A of the headset type terminal 314, and controls an irrigation system and an automatic fertilizer / pesticide supply system. The correspondence between each unit and the device or control unit is not limited to the above-mentioned example, and various changes are possible.

[0149] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0150] 7, a 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.

[0151] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. 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.

[0152] 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.

[0153] 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 the voice according to instructions from the processor 46.

[0154] Camera 42 is a small digital camera equipped with an optical system including a lens, an aperture, and a shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (e.g., an imaging range defined by an angle of view equivalent to the width of the field of vision of a typical healthy person).

[0155] 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 performed in a secure state.

[0156] The control object 443 includes a display device, LEDs in the eyes, and motors for driving the arms, hands, and legs. The posture and behavior of the robot 414 are controlled by controlling the motors of the arms, hands, and legs. 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.

[0157] 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.

[0158] 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.

[0159] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the emotion of the user using the emotion identification model 59 and perform identification processing using the user's emotion. In the emotion estimation function (emotion identification function) using the emotion identification model 59, various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, are performed, but are not limited to such examples. In addition, the estimation and prediction of the emotion also include, for example, analysis of the emotion.

[0160] In the robot 414, the specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads out the specific program 60 from the storage 50, and executes the specific 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 program 60 executed on the RAM 48. The robot 414 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.

[0161] 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 obtains a processing result (such as a prediction result) using the data generation model 58 by communicating with the server device having the data generation model 58. In addition, the data processing device 12 may be a server device, or may be a terminal device (such as a mobile phone, a robot, or a home appliance) owned by a user.

[0162] 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 a voice indicating a user input for 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.

[0163] 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. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating a voice, text data indicating a text, and image data indicating an image (e.g., still image data or video data) is input. The data generation model 58 infers the input inference data according to the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, and image data. The data generation model 58 includes, for example, a text generation AI, an image generation AI, and a multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing while 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, and in this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. In the data processing device 12 and the like, the data generation model 58 includes a plurality of types, and the data generation model 58 includes an AI other than the generation AI. The AI ​​other than the generation AI is, for example, a linear regression, a logistic regression, a decision tree, a random forest, a support vector machine (SVM), a k-means clustering, a convolutional neural network (CNN), a recurrent neural network (RNN), a generative adversarial network (GAN), or a naive Bayes, and can perform various processes, but is not limited to such examples. The AI ​​may be an AI agent. In addition, when the processes of each part described above are performed by an AI, the processes are performed in part or in whole by the AI, but are not limited to such examples. In addition, the processes performed by the AI ​​including the generation AI may be replaced with rule-based processes, and the rule-based processes may be replaced with processes performed by the AI ​​including the generation AI.

[0164] 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 be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information 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.

[0165] Each of the multiple elements including the above-mentioned monitoring unit, analysis unit, proposal unit, and automation unit is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the monitoring unit uses a sensor of the robot 414 to measure the humidity, temperature, and nutrient level of the soil and collect data. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12, analyzes the collected data with AI, and proposes the optimal use of fertilizers and pesticides. The proposal unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and notifies the farmer based on the analysis result. The automation unit is realized, for example, by the control unit 46A of the robot 414, and controls the irrigation system and the automatic supply system of fertilizers and pesticides. The correspondence between each unit and the device and control unit is not limited to the above-mentioned example, and various changes are possible.

[0166] The emotion identification model 59 as an emotion engine may determine the emotion of the user according to a specific mapping. Specifically, the emotion identification model 59 may determine the emotion of the user according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the emotion of the robot, and the identification processing unit 290 may perform identification processing using the emotion of the robot.

[0167] FIG. 9 is a diagram showing 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. The closer to the center of the concentric circles, the more primitive emotions are arranged. The more outside the concentric circles, the more emotions that represent states and actions that arise from a state of mind are arranged. Emotions are a concept that includes emotions and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions that occur in the brain are arranged. On the right side of the concentric circles, emotions that are generally induced by situational judgment are arranged. On the upper and lower sides of the concentric circles, emotions that are generally generated from reactions that occur in the brain and are induced by situational judgment are arranged. In addition, on the upper side of the concentric circles, emotions of "pleasure" are arranged, and on the lower side, emotions of "discomfort" are arranged. In this way, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0168] These emotions are distributed in the 3 o'clock direction of emotion map 400 and usually 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.

[0169] The inside of emotion map 400 represents what is going on inside one's mind, and the outside of emotion map 400 represents behavior, so the further out on emotion map 400 you go, the more visible the emotions become (the more they are expressed in behavior).

[0170] Here, human emotions are based on various balances such as posture and blood sugar level, and when these balances are far from the ideal, it indicates an unpleasant state, and when they are close to the ideal, it indicates a pleasant state. Emotions can also be created for robots, cars, motorcycles, etc., based on various balances such as posture and battery level, so that when these balances are far from the ideal, it indicates an unpleasant state, and when they are close to the ideal, it indicates a pleasant state. The emotion map may be generated, for example, based on the emotion map of Dr. Mitsuyoshi (Research on speech emotion recognition and emotion brain physiological signal analysis system, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). On the left half of the emotion map, emotions belonging to an area called "reaction" where sensation is dominant are lined up. On the right half of the emotion map, emotions belonging to an area called "situation" where situation recognition is dominant are lined up.

[0171] The emotion map defines two emotions that promote learning. The first is the negative emotion around the middle of "repentance" or "remorse" on the situation side. In other words, this is when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is the positive emotion around "desire" on the response side. In other words, this is when the robot experiences positive feelings such as "I want more" or "I want to know more."

[0172] The emotion identification model 59 inputs the user input to a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the emotion of the user. This neural network is pre-trained based on multiple learning data that are combinations of the 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, "relief," "calm," and "encouraging," have similar emotion values.

[0173] 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.

[0174] 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 Universal Serial Bus (USB) 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.

[0175] In addition, 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 upon request from the data processing device 12.

[0176] 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.

[0177] As the hardware resource for executing the specific process, various processors as shown below can be used. An example of the processor is a CPU, which is a general-purpose processor that functions as a hardware resource for executing the specific process by executing software, i.e., a program. Another example of the processor is a dedicated electric circuit, which is a processor having a circuit configuration designed exclusively for executing a specific process, such as a Field-Programmable Gate Array (FPGA), a Programmable Logic Device (PLD), or an Application Specific Integrated Circuit (ASIC). Each processor has a built-in or connected memory, and each processor executes the specific process by using the memory.

[0178] The hardware resource that executes the specific process may be one of these various processors, or may be 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.

[0179] As an example of a configuration using one 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 configuration using a processor that realizes the functions of the 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.

[0180] Furthermore, more specifically, the hardware structure of these various processors can be an electric circuit that combines circuit elements such as semiconductor elements. The specific processes described above are merely examples. It goes without saying that unnecessary steps may be deleted, new steps may be added, or the order of processes may be changed without departing from the spirit of the invention.

[0181] In the above example, the first to fourth embodiments are described separately, but some or all of these embodiments may be combined. The smart device 14, the smart glasses 214, the headset terminal 314, and the robot 414 are only examples, and they may be combined with each other, or may be other devices. In the above example, the first and second embodiments are described separately, but they may be combined.

[0182] The above description and illustrations are detailed descriptions 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, function, action, and effect is an example of the configuration, function, action, and effect 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 replaced with respect to the description and illustrations shown above, within the scope of the gist of the technology of the present disclosure. In addition, in order to avoid confusion and to facilitate understanding of the parts related to the technology of the present disclosure, the description and illustrations shown above omit explanations of technical common sense that do not require particular explanation in enabling the implementation of the technology of the present disclosure.

[0183] All publications, patent applications, and standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, and standard was specifically and individually indicated to be incorporated by reference.

[0184] (Appendix 1) A monitoring unit for monitoring the condition of farmland or crops; an analysis unit that analyzes the data monitored by the monitoring unit and performs predictive analysis; a suggestion unit that suggests appropriate use of fertilizers or pesticides based on the analysis results obtained by the analysis unit; An automation unit that automates agricultural work based on the content proposed by the suggestion unit. A system characterized by: (Appendix 2) The system of claim 1, wherein the monitoring unit monitors data on soil moisture, temperature, nutrient levels, and crop growth status. (Appendix 3) The analysis unit is Calculate the optimal fertilizer type and amount based on the collected data 2. The system of claim 1, (Appendix 4) The analysis unit is Predicting the risk of pests and diseases and proposing the appropriate timing for using pesticides 2. The system of claim 1, (Appendix 5) The system of claim 1, wherein the analysis unit takes into account weather data or market price trends to suggest the appropriate timing for harvesting. (Appendix 6) The automation unit includes: Send a signal to your irrigation system to automatically water it 2. The system of claim 1, (Appendix 7) The monitoring unit is A user's emotion is estimated, and the frequency of acquiring monitoring data is adjusted based on the estimated user's emotion. 2. The system of claim 1, (Appendix 8) The monitoring unit is Monitor soil microbial activity to gain greater insight into crop health 2. The system of claim 1, (Appendix 9) The monitoring unit is Monitors the color and shape of crop leaves to detect nutritional deficiencies and pests and diseases at an early stage 2. The system of claim 1, (Appendix 10) The monitoring unit is Inferring user emotions and prioritizing monitoring data based on the estimated user emotions 2. The system of claim 1, (Appendix 11) The monitoring unit is Set different monitoring parameters taking into account the geographical characteristics of the farmland 2. The system of claim 1, (Appendix 12) The monitoring unit is Monitoring data is stored in the cloud and shared with other farmers to solve problems collaboratively. 2. The system of claim 1, (Appendix 13) The analysis unit is Infer user emotions and adjust the way analysis results are displayed based on the inferred user emotions 2. The system of claim 1, (Appendix 14) The analysis unit is Applying different analysis algorithms to each stage of crop growth 2. The system of claim 1, (Appendix 15) The analysis unit is During analysis, compare with past data to detect outliers and take action early. 2. The system of claim 1, (Appendix 16) The analysis unit is Infer user emotions and prioritize analysis results based on the estimated user emotions. 2. The system of claim 1, (Appendix 17) The analysis unit is The analysis results will be compared with data from other farms to improve agricultural efficiency across the region. 2. The system of claim 1, (Appendix 18) The analysis unit is Visualize the analysis results to help users understand them intuitively 2. The system of claim 1, (Appendix 19) The suggestion unit, Infer the user's emotions and adjust the way the suggestions are presented based on the estimated user emotions 2. The system of claim 1, (Appendix 20) The suggestion unit, When making suggestions, different suggestion algorithms are applied depending on the type of crop and the cultivation method. 2. The system of claim 1, (Appendix 21) The suggestion unit, Include options in your proposals to minimize environmental impact 2. The system of claim 1, (Appendix 22) The suggestion unit, Estimate user emotions and prioritize suggestions based on the estimated user emotions 2. The system of claim 1, (Appendix 23) The suggestion unit, Share the proposals with other farmers and work together to find the best agricultural practices. 2. The system of claim 1, (Appendix 24) The suggestion unit, The proposed content is notified via a smartphone app, allowing users to respond immediately. 2. The system of claim 1, (Appendix 25) The automation unit includes: Infer user emotions and adjust automation timing based on the inferred user emotions 2. The system of claim 1, (Appendix 26) The automation unit includes: When automating, apply different automation processes depending on the crop growth stage 2. The system of claim 1, (Appendix 27) The automation unit includes: Adding functionality to your automation systems to optimize energy efficiency 2. The system of claim 1, (Appendix 28) The automation unit includes: Infer user sentiment and prioritize automation based on the inferred sentiment 2. The system of claim 1, (Appendix 29) The automation unit includes: By linking the automated system with other farmland, we can achieve more efficient agriculture across the entire region. 2. The system of claim 1, (Appendix 30) The automation unit includes: Add anomaly detection capabilities to your automation systems to instantly notify you when a problem occurs. 2. The system of claim 1, [Explanation of symbols]

[0185] 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 monitoring unit for monitoring the condition of farmland or crops; an analysis unit that analyzes the data monitored by the monitoring unit and performs predictive analysis; a suggestion unit that suggests appropriate use of fertilizers or pesticides based on the analysis results obtained by the analysis unit; An automation unit that automates agricultural work based on the content proposed by the suggestion unit. A system characterized by:

2. The monitoring unit is Monitor soil moisture, temperature, nutrient levels and crop growth data 2. The system of claim 1 .

3. The analysis unit is Calculate the optimal fertilizer type and amount based on the collected data 2. The system of claim 1 .

4. The analysis unit is Predicting the risk of pests and diseases and proposing the appropriate timing for using pesticides 2. The system of claim 1 .

5. The system of claim 1, wherein the analysis unit takes into account weather data or market price trends to suggest an appropriate time for harvesting.

6. The automation unit includes: Send a signal to your irrigation system to automatically water it 2. The system of claim 1 .

7. The monitoring unit is A user's emotion is estimated, and the frequency of acquiring monitoring data is adjusted based on the estimated user's emotion.

2. The system of claim 1 .

8. The monitoring unit is Monitor soil microbial activity to gain greater insight into crop health 2. The system of claim 1 .

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