system

Abstract

The system according to this embodiment aims to monitor the health of crops in real time and determine the optimal amount of water and fertilizer. [Solution] The system according to the embodiment comprises a collection unit, an analysis unit, a determination unit, and a proposal unit. The collection unit collects data from sensors. The analysis unit analyzes the data collected by the collection unit in real time. The determination unit determines the required amount of water and fertilizer based on the analysis results obtained by the analysis unit. The proposal unit makes automation suggestions based on the results determined by the determination unit.

Description

Technical Field

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

Background Art

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

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the prior art, there is a problem that it is difficult to monitor the health state of crops in real time in agriculture and determine the optimal amounts of water and fertilizer.

[0005] The system according to the embodiment aims to monitor the health state of crops in real time and determine the optimal amounts of water and fertilizer.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a data collection unit, an analysis unit, a determination unit, and a proposal unit. The data collection unit collects data from sensors. The analysis unit analyzes the data collected by the data collection unit in real time. The determination unit determines the required amount of water and fertilizer based on the analysis results obtained by the analysis unit. The proposal unit makes automation suggestions based on the results determined by the determination unit. [Effects of the Invention]

[0007] The system according to this embodiment can monitor the health of crops in real time and determine the optimal amount of water and fertilizer. [Brief explanation of the drawing]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example of form 1) The smart farming support system according to an embodiment of the present invention is a system that uses data from sensors to monitor the health of crops and uses AI to determine the amount of water and fertilizer needed. The smart farming support system is a mechanism that uses data from sensors to monitor the health of crops and uses AI to determine the amount of water and fertilizer needed. This allows farmers to understand the condition of their farmland in real time and perform optimal agricultural management. For example, the smart farming support system uses sensors to collect data such as soil moisture, temperature, and light intensity. This data is analyzed in real time by AI. Based on the collected data, the AI ​​monitors the health of crops and determines the amount of water and fertilizer needed. For example, if the soil moisture is low, the AI ​​suggests supplying water, and if there is a shortage of fertilizer, it suggests an appropriate amount of fertilizer. Next, the AI ​​predicts crop growth and assesses risks. This allows farmers to predict crop growth and assess the risk of pest and disease outbreaks. For example, if the temperature is high and the humidity is low, the risk of pest and disease outbreaks increases, so the AI ​​suggests preventive measures. Furthermore, the AI ​​makes automation suggestions. This allows farmers to receive suggestions for optimizing water and fertilizer. For example, AI can improve agricultural efficiency and reduce environmental impact by suggesting optimal irrigation schedules and optimizing fertilizer use. This leads to increased crop productivity, reduced water and fertilizer use, and lower pest and disease rates. Specifically, crop productivity increases by an average of 20%, water and fertilizer use decreases by 30%, and pest and disease rates decrease by 25%. Furthermore, by leveraging integrated AI and IoT technologies, cloud-based data management, and a user-friendly application interface, this system becomes extremely useful for farmers seeking efficient agricultural management, those who want to minimize their environmental impact, and those who want to improve crop productivity and profitability. In this way, smart agriculture support systems enable farmers to perform optimal agricultural management.

[0029] The smart farming support system according to this embodiment comprises a data collection unit, an analysis unit, a determination unit, and a proposal unit. The data collection unit collects data from sensors. The data collection unit collects data such as soil moisture, temperature, and light intensity. The data collection unit can collect data using soil moisture sensors, temperature sensors, light intensity sensors, etc. The data collection unit measures soil moisture using a soil moisture sensor. The data collection unit can also measure temperature using a temperature sensor. The data collection unit can also measure light intensity using a light intensity sensor. The analysis unit analyzes the data collected by the data collection unit in real time. The analysis unit monitors the health of crops based on the collected data, for example. The analysis unit can analyze the data using AI and evaluate the health of crops. The analysis unit can analyze the data using AI and evaluate the health of crops. The determination unit determines the amount of water and fertilizer needed based on the analysis results obtained by the analysis unit. The judgment unit, for example, uses AI to determine the required amount of water and fertilizer. The judgment unit can analyze data using AI to determine the required amount of water and fertilizer. The judgment unit can also analyze data using AI to determine the required amount of water and fertilizer. The judgment unit can also analyze data using AI to determine the required amount of water and fertilizer. The proposal unit makes automation suggestions based on the results determined by the judgment unit. The proposal unit, for example, uses AI to propose an optimal irrigation schedule. The proposal unit can analyze data using AI to propose an optimal irrigation schedule. The proposal unit can also analyze data using AI to propose an optimal irrigation schedule. The proposal unit can also analyze data using AI to propose an optimal irrigation schedule. The proposal unit can also analyze data using AI to propose an optimal irrigation schedule. The proposal unit can also use AI to propose an optimization of fertilizer usage. The proposal unit can analyze data using AI to propose an optimization of fertilizer usage.The proposal unit can also use AI to analyze data and propose ways to optimize fertilizer usage. This enables farmers to perform optimal agricultural management using the smart farming support system according to the embodiment.

[0030] The data collection unit collects data from sensors. For example, it collects data such as soil moisture, temperature, and light intensity. Specifically, it can collect data using soil moisture sensors, temperature sensors, and light intensity sensors. Soil moisture sensors measure the amount of moisture in the soil, allowing for real-time monitoring of dry or wet conditions. This enables management to ensure crop roots grow in an appropriate moisture environment. Temperature sensors measure the temperature of farmland, providing data to maintain a temperature range suitable for crop growth. Light intensity sensors measure the amount of light crops receive, providing information to ensure sufficient light for photosynthesis. These sensors are installed in farmland and transmit data to the data collection unit using wireless communication technology. The data collection unit centrally manages this data and stores it in a real-time updated database. Furthermore, the data collection unit can flexibly respond to specific crops and environmental conditions by adjusting the data collection frequency and accuracy. For example, if drought is progressing, increasing the data collection frequency of the soil moisture sensor allows for a rapid response. This enables the data collection unit to collect data efficiently and effectively, improving the overall system performance.

[0031] The analysis department analyzes the data collected by the collection department in real time. For example, the analysis department monitors the health of crops based on the collected data. Specifically, it can use AI to analyze the data and evaluate the health of crops. The AI ​​uses machine learning algorithms to learn from past data and patterns and compare them with current data to detect crop abnormalities. For example, it can analyze soil moisture data to detect signs of dryness or excessive moisture early. It can also analyze temperature data to evaluate whether crops are growing within the appropriate temperature range. By analyzing light intensity data, it can check whether crops are receiving enough light and suggest adjusting the light intensity as needed. Furthermore, the analysis department can use anomaly detection algorithms to detect unusual patterns and abnormal data and issue early warnings. This allows the analysis department to quickly and accurately analyze the collected data and understand the health of crops in real time. In addition, the analysis department can utilize past data and statistical information to conduct long-term risk assessments and trend analyses. This allows farmers to constantly monitor the health of their crops and implement appropriate management.

[0032] The determination unit determines the amount of water and fertilizer needed based on the analysis results obtained by the analysis unit. Specifically, it uses AI to determine the amount of water and fertilizer needed. The AI ​​analyzes the collected data and calculates the optimal amount of water and fertilizer for crop growth. For example, it evaluates the current soil moisture status based on soil moisture data and determines the required amount of irrigation. It also considers temperature data and light intensity data to determine the optimal type and amount of fertilizer according to the crop's growth stage. Furthermore, the determination unit can also formulate a long-term fertilizer plan based on past data and crop growth history. This allows the determination unit to provide specific instructions to maintain the health of the crops and provide an optimal growing environment. The determination unit can analyze data using AI and determine the amount of water and fertilizer needed. This allows farmers to manage water and fertilizer efficiently and effectively and optimize crop growth.

[0033] The proposal unit makes automated suggestions based on the results determined by the judgment unit. Specifically, it uses AI to propose an optimal irrigation schedule. The AI ​​analyzes collected data and calculates the optimal timing and amount of irrigation for crop growth. For example, based on soil moisture data, it will suggest immediate irrigation if drought is progressing. It can also consider temperature and light intensity data to propose an optimal irrigation schedule according to the crop's growth stage. Furthermore, the proposal unit can also suggest optimizing fertilizer use. The AI ​​analyzes collected data and calculates the optimal type and amount of fertilizer for crop growth. For example, it evaluates the soil's nutrient status and suggests the necessary type and amount of fertilizer. It can also develop long-term fertilization plans based on past data and crop growth history. This allows the proposal unit to provide farmers with concrete suggestions for optimal agricultural management. Furthermore, by linking these suggestions to an automated system, the proposal unit can automate irrigation and fertilization. This enables farmers to manage their agriculture efficiently and effectively and optimize crop growth.

[0034] The data collection unit can collect data such as soil moisture, temperature, and light intensity. For example, the data collection unit measures soil moisture using a soil moisture sensor. The data collection unit can also measure temperature using a temperature sensor. The data collection unit can also measure light intensity using a light intensity sensor. By collecting data such as soil moisture, temperature, and light intensity, the health of crops can be monitored. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input data from the soil moisture sensor, temperature sensor, and light intensity sensor into a generating AI, and have the generating AI perform data analysis.

[0035] The analysis unit can monitor the health of crops based on the collected data. The analysis unit can, for example, use AI to analyze the collected data and monitor the health of crops. The analysis unit can also use AI to analyze the data and evaluate the health of crops. The analysis unit can also use AI to analyze the data and evaluate the health of crops. This allows the condition of crops to be understood by monitoring their health based on the collected data. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the collected data into a generating AI and have the generating AI perform the monitoring of crop health.

[0036] The determination unit can determine the required amount of water and fertilizer. The determination unit can determine the required amount of water and fertilizer using, for example, AI. The determination unit can also analyze data using AI and determine the required amount of water and fertilizer. The determination unit can also analyze data using AI and determine the required amount of water and fertilizer. By determining the required amount of water and fertilizer, it becomes possible to supply appropriate amounts of water and fertilizer. Some or all of the above-described processes in the determination unit may be performed using, for example, AI, or without AI. For example, the determination unit can input the collected data into a generating AI and have the generating AI perform the determination of the required amount of water and fertilizer.

[0037] The proposal unit can propose an optimal irrigation schedule. For example, the proposal unit can propose an optimal irrigation schedule using AI. The proposal unit can also analyze data using AI and propose an optimal irrigation schedule. By proposing an optimal irrigation schedule, efficient water use becomes possible. Some or all of the above-described processes in the proposal unit may be performed using AI, for example, or without AI. For example, the proposal unit can input collected data into a generating AI and have the generating AI propose an optimal irrigation schedule.

[0038] The proposal unit can make suggestions to optimize fertilizer usage. The proposal unit can, for example, use AI to make suggestions to optimize fertilizer usage. The proposal unit can also use AI to analyze data and make suggestions to optimize fertilizer usage. The proposal unit can also use AI to analyze data and make suggestions to optimize fertilizer usage. By making suggestions to optimize fertilizer usage, efficient use of fertilizer becomes possible. Some or all of the above-described processes in the proposal unit may be performed using AI, for example, or without AI. For example, the proposal unit can input collected data into a generating AI and have the generating AI execute suggestions to optimize fertilizer usage.

[0039] The analysis unit can perform crop growth prediction and risk assessment. The analysis unit can, for example, use AI to perform crop growth prediction and risk assessment. The analysis unit can also use AI to analyze data and perform crop growth prediction and risk assessment. By performing crop growth prediction and risk assessment, it is possible to predict the growth status of crops and assess the risk of pest and disease outbreaks. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input collected data into a generating AI and have the generating AI perform crop growth prediction and risk assessment.

[0040] The proposal unit can propose preventive measures based on the risk of pest and disease outbreaks. The proposal unit can, for example, use AI to propose preventive measures based on the risk of pest and disease outbreaks. The proposal unit can also use AI to analyze data and propose preventive measures based on the risk of pest and disease outbreaks. The proposal unit can also use AI to analyze data and propose preventive measures based on the risk of pest and disease outbreaks. This makes it possible to prevent pest and disease outbreaks by proposing preventive measures based on the risk of pest and disease outbreaks. Some or all of the above processing in the proposal unit may be performed using AI, for example, or without AI. For example, the proposal unit can input collected data into a generating AI and have the generating AI perform the task of proposing preventive measures based on the risk of pest and disease outbreaks.

[0041] The data collection unit can analyze past collected data and select the optimal sensor placement. For example, if the data collection unit finds that data collection is insufficient in a particular area based on past data, it can add sensors to that area. The data collection unit can also replace a sensor if it finds that a particular sensor is malfunctioning based on past data. The data collection unit can also add sensors during a specific time period if data collection is concentrated during that time period based on past data. In this way, the optimal sensor placement can be selected by analyzing past collected data. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input past collected data into a generating AI and have the generating AI select the optimal sensor placement.

[0042] The data collection unit can dynamically change the items to be collected based on the type and growth stage of the crop. For example, when the crop is in the early growth stage, the data collection unit can focus on collecting soil nutrient data. When the crop is in the mid-growth stage, the data collection unit can also focus on collecting light intensity data. When the crop is in the late growth stage, the data collection unit can also focus on collecting temperature data. This allows for the efficient collection of necessary data by dynamically changing the items to be collected according to the type and growth stage of the crop. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input data based on the type and growth stage of the crop into a generating AI, and have the generating AI perform the dynamic changes to the items to be collected.

[0043] The data collection unit can prioritize the collection of highly relevant data, taking into account the geographical location of the farmland during data collection. For example, if the farmland is located at high altitude, the data collection unit can prioritize the collection of temperature data. If the farmland is located at low altitude, the data collection unit can also prioritize the collection of soil moisture data. If the farmland is located near the coast, the data collection unit can also prioritize the collection of salinity data. This enables efficient data collection by prioritizing the collection of highly relevant data, taking into account the geographical location of the farmland. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the geographical location of the farmland into a generating AI and have the generating AI perform the priority collection of highly relevant data.

[0044] The data collection unit can adjust the collection frequency by referring to weather forecast data during data collection. For example, if rain is expected, the collection unit can increase the collection frequency of soil moisture data. If high temperatures are expected, the collection unit can also increase the collection frequency of temperature data. If strong winds are expected, the collection unit can also increase the collection frequency of wind speed data. By adjusting the collection frequency by referring to weather forecast data, efficient data collection becomes possible. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input weather forecast data into a generating AI and have the generating AI adjust the collection frequency.

[0045] The analysis unit can detect outliers during analysis by comparing current data with past data. For example, the analysis unit can detect an abnormally low soil moisture level compared to past data as an outlier. The analysis unit can also detect an abnormally high temperature level compared to past data as an outlier. The analysis unit can also detect an abnormally low light level compared to past data as an outlier. By detecting outliers by comparing current data with past data, abnormal conditions can be detected early. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input past data into a generating AI and have the generating AI perform the detection of outliers.

[0046] The analysis unit can apply different analysis algorithms to each type of crop during analysis. For example, in the case of tomatoes, the analysis unit can apply an algorithm that focuses on analyzing specific nutrient data. In the case of strawberries, the analysis unit can also apply an algorithm that focuses on analyzing specific light intensity data. In the case of lettuce, the analysis unit can also apply an algorithm that focuses on analyzing specific temperature data. By applying different analysis algorithms to each type of crop, appropriate analysis tailored to each crop becomes possible. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input data for each type of crop into a generating AI and have the generating AI apply different analysis algorithms.

[0047] The analysis unit can determine the priority of analysis based on when the collected data was submitted. For example, the analysis unit may prioritize analysis immediately after the data is submitted. The analysis unit can also determine the priority of analysis based on the time of day the data was submitted. The analysis unit can also determine the priority of analysis based on the frequency of data submission. This enables efficient analysis by prioritizing analysis based on the timing of data submission. Some or all of the above processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the timing of data submission into a generating AI and have the generating AI determine the priority of analysis.

[0048] The analysis unit can improve the accuracy of its analysis by referring to relevant research papers during the analysis process. For example, the analysis unit can apply the latest analytical methods by referring to relevant research papers. The analysis unit can also improve the accuracy of its analysis results by referring to relevant research papers. The analysis unit can also optimize its analysis algorithms by referring to relevant research papers. This allows the analysis unit to apply the latest analytical methods and improve the accuracy of its analysis by referring to relevant research papers. Some or all of the above processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input relevant research papers into a generating AI and have the generating AI perform the task of improving the accuracy of the analysis.

[0049] The judgment unit can optimize its judgment algorithm by referring to past judgment results during the judgment process. For example, the judgment unit can optimize its judgment algorithm by referring to past judgment results. The judgment unit can also improve the accuracy of the judgment by referring to past judgment results. The judgment unit can also improve the speed of the judgment by referring to past judgment results. In this way, by referring to past judgment results, the judgment algorithm can be optimized and the accuracy of the judgment can be improved. Some or all of the above processes in the judgment unit may be performed using AI, for example, or without using AI. For example, the judgment unit can input past judgment results into a generating AI and have the generating AI perform the optimization of the judgment algorithm.

[0050] The judgment unit can apply different judgment criteria depending on the growth stage of the crop during the judgment process. For example, the judgment unit applies a specific judgment criterion when the crop is in the early growth stage. The judgment unit can also apply a specific judgment criterion when the crop is in the middle growth stage. The judgment unit can also apply a specific judgment criterion when the crop is in the late growth stage. This allows for appropriate judgment by applying different judgment criteria depending on the growth stage of the crop. Some or all of the above processing in the judgment unit may be performed using AI, for example, or without using AI. For example, the judgment unit can input data based on the growth stage of the crop into a generating AI and cause the generating AI to apply different judgment criteria.

[0051] The determination unit can improve the accuracy of its determination by considering the geographical location information of the farmland during the determination process. For example, the determination unit applies a specific determination criterion if the farmland is located on high ground. The determination unit can also apply a specific determination criterion if the farmland is located on low ground. The determination unit can also apply a specific determination criterion if the farmland is located near the coast. This allows the determination unit to improve the accuracy of its determination by considering the geographical location information of the farmland. Some or all of the above processing in the determination unit may be performed using AI, for example, or without AI. For example, the determination unit can input the geographical location information of the farmland into a generating AI and cause the generating AI to perform the determination accuracy improvement.

[0052] The determination unit can improve the accuracy of its determination by referring to relevant weather data during the determination process. For example, the determination unit can improve the accuracy of its determination by referring to weather data. The determination unit can also improve the speed of its determination by referring to weather data. The determination unit can also improve the reliability of its determination by referring to weather data. In this way, the accuracy of the determination can be improved by referring to relevant weather data. Some or all of the above processing in the determination unit may be performed using AI, for example, or without using AI. For example, the determination unit can input weather data into a generating AI and have the generating AI perform the task of improving the accuracy of the determination.

[0053] The proposal unit can optimize its proposal algorithm by referring to past proposal history when making a proposal. For example, the proposal unit can optimize its proposal algorithm by referring to past proposal history. The proposal unit can also improve the accuracy of proposals by referring to past proposal history. The proposal unit can also improve the speed of proposals by referring to past proposal history. In this way, by referring to past proposal history, the proposal algorithm can be optimized and the accuracy of proposals can be improved. Some or all of the above processing in the proposal unit may be performed using AI, for example, or without using AI. For example, the proposal unit can input past proposal history into a generation AI and have the generation AI perform the optimization of the proposal algorithm.

[0054] The proposal unit can provide different suggestions depending on the type and growth stage of the crop. For example, in the case of tomatoes, the proposal unit can provide suggestions based on specific nutrient data. In the case of strawberries, the proposal unit can also provide suggestions based on specific light intensity data. In the case of lettuce, the proposal unit can also provide suggestions based on specific temperature data. This allows for appropriate suggestions by providing different suggestions depending on the type and growth stage of the crop. Some or all of the above processing in the proposal unit may be performed using AI, for example, or without AI. For example, the proposal unit can input data based on the type and growth stage of the crop into a generating AI and have the generating AI provide different suggestions.

[0055] The proposal unit can make optimal proposals by considering the geographical location information of the farmland when making a proposal. For example, if the farmland is located at high altitude, the proposal unit can provide specific proposals. The proposal unit can also provide specific proposals if the farmland is located at low altitude. The proposal unit can also provide specific proposals if the farmland is located near the coast. This allows the proposal unit to make optimal proposals by considering the geographical location information of the farmland. Some or all of the above processing in the proposal unit may be performed using AI, for example, or without AI. For example, the proposal unit can input the geographical location information of the farmland into a generating AI and have the generating AI provide the optimal proposal.

[0056] The proposal unit can optimize the proposal content by referring to relevant market data when making a proposal. For example, the proposal unit can provide the optimal proposal content by referring to market data. The proposal unit can also improve the accuracy of the proposal content by referring to market data. The proposal unit can also improve the reliability of the proposal content by referring to market data. In this way, the accuracy and reliability of the proposal content can be improved by referring to relevant market data. Some or all of the above processing in the proposal unit may be performed using AI, for example, or without using AI. For example, the proposal unit can input relevant market data into a generating AI and have the generating AI perform the optimization of the proposal content.

[0057] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0058] The data collection unit can monitor not only the health of crops but also the microbial activity of the soil. For example, it can detect the activity of specific microorganisms in the soil using sensors and collect the data. If the data collection unit detects a decrease in microbial activity, it can determine that the soil health is deteriorating and propose appropriate countermeasures. Furthermore, the data collection unit can suggest the use of soil conditioners based on the type and activity level of the microorganisms. This allows for comprehensive management of soil health.

[0059] The analysis unit can monitor not only the health of crops but also changes in the surrounding environment. For example, it can use sensors to detect pesticide use in nearby farmland and collect data. Based on the collected data, the analysis unit can evaluate the impact on crops and propose necessary countermeasures. Furthermore, the analysis unit can monitor the health of crops in real time in response to changes in the surrounding environment and implement appropriate management. This allows for a more accurate understanding of the health of the crops.

[0060] The assessment unit can make judgments considering not only the health of the crops but also the workload of the farmers. For example, it can collect farmers' work schedules and, if the workload is high, suggest distributing the work. The assessment unit can determine the priority of tasks and propose an efficient work schedule to reduce the farmers' workload. It can also suggest concentrating tasks during periods of low workload. This reduces the farmers' workload and enables efficient agricultural management.

[0061] The proposal department can make suggestions considering not only the health of the crops but also the health of the farmers. For example, it can collect health data on farmers and, if their health is deteriorating, propose reducing their workload. The proposal department can determine the priority of tasks according to the farmers' health and make suggestions to maintain their health. It can also propose concentrating work during periods when farmers are in good health. This makes it possible to achieve efficient agricultural management while maintaining the health of farmers.

[0062] The data collection unit can collect not only information on the health of crops but also geological data of farmland. For example, it can use sensors to detect soil composition and structure and collect that data. Based on the geological data, the data collection unit can identify areas where soil improvement is needed and propose appropriate measures. Furthermore, based on the geological data, the data collection unit can also propose appropriate cultivation methods for crops. This allows for comprehensive management of crop health by utilizing geological data of farmland.

[0063] The following briefly describes the processing flow for example form 1.

[0064] Step 1: The collection unit collects data from sensors. The collection unit collects data such as soil moisture, temperature, and light intensity. The collection unit can collect data using soil moisture sensors, temperature sensors, light intensity sensors, etc. For example, the collection unit measures soil moisture using a soil moisture sensor. The collection unit can also measure temperature using a temperature sensor. The collection unit can also measure light intensity using a light intensity sensor. Step 2: The analysis unit analyzes the data collected by the collection unit in real time. For example, the analysis unit monitors the health of crops based on the collected data. The analysis unit can use AI to analyze the data and evaluate the health of crops. Step 3: The determination unit determines the required amount of water and fertilizer based on the analysis results obtained by the analysis unit. The determination unit can determine the required amount of water and fertilizer, for example, by using AI. The determination unit can analyze data using AI and determine the required amount of water and fertilizer. Step 4: The proposal unit makes automated suggestions based on the results determined by the judgment unit. For example, the proposal unit uses AI to propose an optimal irrigation schedule. The proposal unit can analyze data using AI and propose an optimal irrigation schedule. The proposal unit can also use AI to propose ways to optimize fertilizer usage.

[0065] (Example of form 2) The smart farming support system according to an embodiment of the present invention is a system that uses data from sensors to monitor the health of crops and uses AI to determine the amount of water and fertilizer needed. The smart farming support system is a mechanism that uses data from sensors to monitor the health of crops and uses AI to determine the amount of water and fertilizer needed. This allows farmers to understand the condition of their farmland in real time and perform optimal agricultural management. For example, the smart farming support system uses sensors to collect data such as soil moisture, temperature, and light intensity. This data is analyzed in real time by AI. Based on the collected data, the AI ​​monitors the health of crops and determines the amount of water and fertilizer needed. For example, if the soil moisture is low, the AI ​​suggests supplying water, and if there is a shortage of fertilizer, it suggests an appropriate amount of fertilizer. Next, the AI ​​predicts crop growth and assesses risks. This allows farmers to predict crop growth and assess the risk of pest and disease outbreaks. For example, if the temperature is high and the humidity is low, the risk of pest and disease outbreaks increases, so the AI ​​suggests preventive measures. Furthermore, the AI ​​makes automation suggestions. This allows farmers to receive suggestions for optimizing water and fertilizer. For example, AI can improve agricultural efficiency and reduce environmental impact by suggesting optimal irrigation schedules and optimizing fertilizer use. This leads to increased crop productivity, reduced water and fertilizer use, and lower pest and disease rates. Specifically, crop productivity increases by an average of 20%, water and fertilizer use decreases by 30%, and pest and disease rates decrease by 25%. Furthermore, by leveraging integrated AI and IoT technologies, cloud-based data management, and a user-friendly application interface, this system becomes extremely useful for farmers seeking efficient agricultural management, those who want to minimize their environmental impact, and those who want to improve crop productivity and profitability. In this way, smart agriculture support systems enable farmers to perform optimal agricultural management.

[0066] The smart farming support system according to this embodiment comprises a data collection unit, an analysis unit, a determination unit, and a proposal unit. The data collection unit collects data from sensors. The data collection unit collects data such as soil moisture, temperature, and light intensity. The data collection unit can collect data using soil moisture sensors, temperature sensors, light intensity sensors, etc. The data collection unit measures soil moisture using a soil moisture sensor. The data collection unit can also measure temperature using a temperature sensor. The data collection unit can also measure light intensity using a light intensity sensor. The analysis unit analyzes the data collected by the data collection unit in real time. The analysis unit monitors the health of crops based on the collected data, for example. The analysis unit can analyze the data using AI and evaluate the health of crops. The analysis unit can analyze the data using AI and evaluate the health of crops. The determination unit determines the amount of water and fertilizer needed based on the analysis results obtained by the analysis unit. The judgment unit, for example, uses AI to determine the required amount of water and fertilizer. The judgment unit can analyze data using AI to determine the required amount of water and fertilizer. The judgment unit can also analyze data using AI to determine the required amount of water and fertilizer. The judgment unit can also analyze data using AI to determine the required amount of water and fertilizer. The proposal unit makes automation suggestions based on the results determined by the judgment unit. The proposal unit, for example, uses AI to propose an optimal irrigation schedule. The proposal unit can analyze data using AI to propose an optimal irrigation schedule. The proposal unit can also analyze data using AI to propose an optimal irrigation schedule. The proposal unit can also analyze data using AI to propose an optimal irrigation schedule. The proposal unit can also analyze data using AI to propose an optimal irrigation schedule. The proposal unit can also use AI to propose an optimization of fertilizer usage. The proposal unit can analyze data using AI to propose an optimization of fertilizer usage.The proposal unit can also use AI to analyze data and propose ways to optimize fertilizer usage. This enables farmers to perform optimal agricultural management using the smart farming support system according to the embodiment.

[0067] The data collection unit collects data from sensors. For example, it collects data such as soil moisture, temperature, and light intensity. Specifically, it can collect data using soil moisture sensors, temperature sensors, and light intensity sensors. Soil moisture sensors measure the amount of moisture in the soil, allowing for real-time monitoring of dry or wet conditions. This enables management to ensure crop roots grow in an appropriate moisture environment. Temperature sensors measure the temperature of farmland, providing data to maintain a temperature range suitable for crop growth. Light intensity sensors measure the amount of light crops receive, providing information to ensure sufficient light for photosynthesis. These sensors are installed in farmland and transmit data to the data collection unit using wireless communication technology. The data collection unit centrally manages this data and stores it in a real-time updated database. Furthermore, the data collection unit can flexibly respond to specific crops and environmental conditions by adjusting the data collection frequency and accuracy. For example, if drought is progressing, increasing the data collection frequency of the soil moisture sensor allows for a rapid response. This enables the data collection unit to collect data efficiently and effectively, improving the overall system performance.

[0068] The analysis department analyzes the data collected by the collection department in real time. For example, the analysis department monitors the health of crops based on the collected data. Specifically, it can use AI to analyze the data and evaluate the health of crops. The AI ​​uses machine learning algorithms to learn from past data and patterns and compare them with current data to detect crop abnormalities. For example, it can analyze soil moisture data to detect signs of dryness or excessive moisture early. It can also analyze temperature data to evaluate whether crops are growing within the appropriate temperature range. By analyzing light intensity data, it can check whether crops are receiving enough light and suggest adjusting the light intensity as needed. Furthermore, the analysis department can use anomaly detection algorithms to detect unusual patterns and abnormal data and issue early warnings. This allows the analysis department to quickly and accurately analyze the collected data and understand the health of crops in real time. In addition, the analysis department can utilize past data and statistical information to conduct long-term risk assessments and trend analyses. This allows farmers to constantly monitor the health of their crops and implement appropriate management.

[0069] The determination unit determines the amount of water and fertilizer needed based on the analysis results obtained by the analysis unit. Specifically, it uses AI to determine the amount of water and fertilizer needed. The AI ​​analyzes the collected data and calculates the optimal amount of water and fertilizer for crop growth. For example, it evaluates the current soil moisture status based on soil moisture data and determines the required amount of irrigation. It also considers temperature data and light intensity data to determine the optimal type and amount of fertilizer according to the crop's growth stage. Furthermore, the determination unit can also formulate a long-term fertilizer plan based on past data and crop growth history. This allows the determination unit to provide specific instructions to maintain the health of the crops and provide an optimal growing environment. The determination unit can analyze data using AI and determine the amount of water and fertilizer needed. This allows farmers to manage water and fertilizer efficiently and effectively and optimize crop growth.

[0070] The proposal unit makes automated suggestions based on the results determined by the judgment unit. Specifically, it uses AI to propose an optimal irrigation schedule. The AI ​​analyzes collected data and calculates the optimal timing and amount of irrigation for crop growth. For example, based on soil moisture data, it will suggest immediate irrigation if drought is progressing. It can also consider temperature and light intensity data to propose an optimal irrigation schedule according to the crop's growth stage. Furthermore, the proposal unit can also suggest optimizing fertilizer use. The AI ​​analyzes collected data and calculates the optimal type and amount of fertilizer for crop growth. For example, it evaluates the soil's nutrient status and suggests the necessary type and amount of fertilizer. It can also develop long-term fertilization plans based on past data and crop growth history. This allows the proposal unit to provide farmers with concrete suggestions for optimal agricultural management. Furthermore, by linking these suggestions to an automated system, the proposal unit can automate irrigation and fertilization. This enables farmers to manage their agriculture efficiently and effectively and optimize crop growth.

[0071] The data collection unit can collect data such as soil moisture, temperature, and light intensity. For example, the data collection unit measures soil moisture using a soil moisture sensor. The data collection unit can also measure temperature using a temperature sensor. The data collection unit can also measure light intensity using a light intensity sensor. By collecting data such as soil moisture, temperature, and light intensity, the health of crops can be monitored. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input data from the soil moisture sensor, temperature sensor, and light intensity sensor into a generating AI, and have the generating AI perform data analysis.

[0072] The analysis unit can monitor the health of crops based on the collected data. The analysis unit can, for example, use AI to analyze the collected data and monitor the health of crops. The analysis unit can also use AI to analyze the data and evaluate the health of crops. The analysis unit can also use AI to analyze the data and evaluate the health of crops. This allows the condition of crops to be understood by monitoring their health based on the collected data. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the collected data into a generating AI and have the generating AI perform the monitoring of crop health.

[0073] The determination unit can determine the required amount of water and fertilizer. The determination unit can determine the required amount of water and fertilizer using, for example, AI. The determination unit can also analyze data using AI and determine the required amount of water and fertilizer. The determination unit can also analyze data using AI and determine the required amount of water and fertilizer. By determining the required amount of water and fertilizer, it becomes possible to supply appropriate amounts of water and fertilizer. Some or all of the above-described processes in the determination unit may be performed using, for example, AI, or without AI. For example, the determination unit can input the collected data into a generating AI and have the generating AI perform the determination of the required amount of water and fertilizer.

[0074] The proposal unit can propose an optimal irrigation schedule. For example, the proposal unit can propose an optimal irrigation schedule using AI. The proposal unit can also analyze data using AI and propose an optimal irrigation schedule. By proposing an optimal irrigation schedule, efficient water use becomes possible. Some or all of the above-described processes in the proposal unit may be performed using AI, for example, or without AI. For example, the proposal unit can input collected data into a generating AI and have the generating AI propose an optimal irrigation schedule.

[0075] The proposal unit can make suggestions to optimize fertilizer usage. The proposal unit can, for example, use AI to make suggestions to optimize fertilizer usage. The proposal unit can also use AI to analyze data and make suggestions to optimize fertilizer usage. The proposal unit can also use AI to analyze data and make suggestions to optimize fertilizer usage. By making suggestions to optimize fertilizer usage, efficient use of fertilizer becomes possible. Some or all of the above-described processes in the proposal unit may be performed using AI, for example, or without AI. For example, the proposal unit can input collected data into a generating AI and have the generating AI execute suggestions to optimize fertilizer usage.

[0076] The analysis unit can perform crop growth prediction and risk assessment. The analysis unit can, for example, use AI to perform crop growth prediction and risk assessment. The analysis unit can also use AI to analyze data and perform crop growth prediction and risk assessment. By performing crop growth prediction and risk assessment, it is possible to predict the growth status of crops and assess the risk of pest and disease outbreaks. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input collected data into a generating AI and have the generating AI perform crop growth prediction and risk assessment.

[0077] The proposal unit can propose preventive measures based on the risk of pest and disease outbreaks. The proposal unit can, for example, use AI to propose preventive measures based on the risk of pest and disease outbreaks. The proposal unit can also use AI to analyze data and propose preventive measures based on the risk of pest and disease outbreaks. The proposal unit can also use AI to analyze data and propose preventive measures based on the risk of pest and disease outbreaks. This makes it possible to prevent pest and disease outbreaks by proposing preventive measures based on the risk of pest and disease outbreaks. Some or all of the above processing in the proposal unit may be performed using AI, for example, or without AI. For example, the proposal unit can input collected data into a generating AI and have the generating AI perform the task of proposing preventive measures based on the risk of pest and disease outbreaks.

[0078] The data collection unit can estimate the user's emotions and adjust the timing of data collection based on the estimated emotions. For example, if the user is stressed, the data collection unit can reduce the frequency of data collection to alleviate the user's burden. If the user is relaxed, the data collection unit can also increase the frequency of data collection to collect more detailed data. If the user is in a hurry, the data collection unit can optimize the timing of data collection to quickly collect the necessary data. This reduces the user's burden by adjusting the timing of data collection according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the data collection unit may be performed using AI, for example, or not using AI. For example, the data collection unit can input the user's emotion data into a generative AI and have the generative AI adjust the timing of data collection.

[0079] The data collection unit can analyze past collected data and select the optimal sensor placement. For example, if the data collection unit finds that data collection is insufficient in a particular area based on past data, it can add sensors to that area. The data collection unit can also replace a sensor if it finds that a particular sensor is malfunctioning based on past data. The data collection unit can also add sensors during a specific time period if data collection is concentrated during that time period based on past data. In this way, the optimal sensor placement can be selected by analyzing past collected data. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input past collected data into a generating AI and have the generating AI select the optimal sensor placement.

[0080] The data collection unit can dynamically change the items to be collected based on the type and growth stage of the crop. For example, when the crop is in the early growth stage, the data collection unit can focus on collecting soil nutrient data. When the crop is in the mid-growth stage, the data collection unit can also focus on collecting light intensity data. When the crop is in the late growth stage, the data collection unit can also focus on collecting temperature data. This allows for the efficient collection of necessary data by dynamically changing the items to be collected according to the type and growth stage of the crop. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input data based on the type and growth stage of the crop into a generating AI, and have the generating AI perform the dynamic changes to the items to be collected.

[0081] The data collection unit can estimate the user's emotions and determine the priority of data to collect based on the estimated emotions. For example, if the user is stressed, the data collection unit may prioritize collecting only important data. If the user is relaxed, the data collection unit may also prioritize collecting detailed data. If the user is in a hurry, the data collection unit may also prioritize collecting data that can be collected quickly. This allows for the priority collection of important data by determining the priority of data to collect according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the data collection unit may be performed using AI or not using AI. For example, the data collection unit can input user emotion data into a generative AI and have the generative AI determine the priority of data to collect.

[0082] The data collection unit can prioritize the collection of highly relevant data, taking into account the geographical location of the farmland during data collection. For example, if the farmland is located at high altitude, the data collection unit can prioritize the collection of temperature data. If the farmland is located at low altitude, the data collection unit can also prioritize the collection of soil moisture data. If the farmland is located near the coast, the data collection unit can also prioritize the collection of salinity data. This enables efficient data collection by prioritizing the collection of highly relevant data, taking into account the geographical location of the farmland. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the geographical location of the farmland into a generating AI and have the generating AI perform the priority collection of highly relevant data.

[0083] The data collection unit can adjust the collection frequency by referring to weather forecast data during data collection. For example, if rain is expected, the collection unit can increase the collection frequency of soil moisture data. If high temperatures are expected, the collection unit can also increase the collection frequency of temperature data. If strong winds are expected, the collection unit can also increase the collection frequency of wind speed data. By adjusting the collection frequency by referring to weather forecast data, efficient data collection becomes possible. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input weather forecast data into a generating AI and have the generating AI adjust the collection frequency.

[0084] The analysis unit can estimate the user's emotions and adjust the display method of the analysis results based on the estimated emotions. For example, if the user is nervous, the analysis unit can provide a simple and highly visible display method. If the user is relaxed, the analysis unit can also provide a display method that includes detailed information. If the user is in a hurry, the analysis unit can also provide a display method that gets straight to the point. By adjusting the display method of the analysis results according to the user's emotions, it becomes possible to provide a display that is easy for the user to understand. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the analysis unit may be performed using AI, for example, or not using AI. For example, the analysis unit can input the user's emotion data into a generative AI and have the generative AI adjust the display method of the analysis results.

[0085] The analysis unit can detect outliers during analysis by comparing current data with past data. For example, the analysis unit can detect an abnormally low soil moisture level compared to past data as an outlier. The analysis unit can also detect an abnormally high temperature level compared to past data as an outlier. The analysis unit can also detect an abnormally low light level compared to past data as an outlier. By detecting outliers by comparing current data with past data, abnormal conditions can be detected early. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input past data into a generating AI and have the generating AI perform the detection of outliers.

[0086] The analysis unit can apply different analysis algorithms to each type of crop during analysis. For example, in the case of tomatoes, the analysis unit can apply an algorithm that focuses on analyzing specific nutrient data. In the case of strawberries, the analysis unit can also apply an algorithm that focuses on analyzing specific light intensity data. In the case of lettuce, the analysis unit can also apply an algorithm that focuses on analyzing specific temperature data. By applying different analysis algorithms to each type of crop, appropriate analysis tailored to each crop becomes possible. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input data for each type of crop into a generating AI and have the generating AI apply different analysis algorithms.

[0087] The analysis unit can estimate the user's emotions and adjust the level of detail in the analysis results based on the estimated emotions. For example, if the user is tense, the analysis unit can provide a simple and highly visible display method. If the user is relaxed, the analysis unit can also provide a display method that includes detailed information. If the user is in a hurry, the analysis unit can also provide a display method that gets straight to the point. In this way, by adjusting the level of detail in the analysis results according to the user's emotions, the appropriate amount of information can be provided to the user. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the analysis unit may be performed using AI, for example, or not using AI. For example, the analysis unit can input user emotion data into a generative AI and have the generative AI adjust the level of detail in the analysis results.

[0088] The analysis unit can determine the priority of analysis based on when the collected data was submitted. For example, the analysis unit may prioritize analysis immediately after the data is submitted. The analysis unit can also determine the priority of analysis based on the time of day the data was submitted. The analysis unit can also determine the priority of analysis based on the frequency of data submission. This enables efficient analysis by prioritizing analysis based on the timing of data submission. Some or all of the above processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the timing of data submission into a generating AI and have the generating AI determine the priority of analysis.

[0089] The analysis unit can improve the accuracy of its analysis by referring to relevant research papers during the analysis process. For example, the analysis unit can apply the latest analytical methods by referring to relevant research papers. The analysis unit can also improve the accuracy of its analysis results by referring to relevant research papers. The analysis unit can also optimize its analysis algorithms by referring to relevant research papers. This allows the analysis unit to apply the latest analytical methods and improve the accuracy of its analysis by referring to relevant research papers. Some or all of the above processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input relevant research papers into a generating AI and have the generating AI perform the task of improving the accuracy of the analysis.

[0090] The judgment unit can estimate the user's emotions and adjust the notification method of the judgment result based on the estimated user emotions. For example, if the user is nervous, the judgment unit can provide a simple and highly visible notification method. If the user is relaxed, the judgment unit can also provide a notification method that includes detailed information. If the user is in a hurry, the judgment unit can also provide a notification method that gets straight to the point. By adjusting the notification method of the judgment result according to the user's emotions, it becomes possible to provide notifications that are easy for the user to understand. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the judgment unit may be performed using AI, for example, or not using AI. For example, the judgment unit can input the user's emotion data into the generative AI and have the generative AI adjust the notification method of the judgment result.

[0091] The judgment unit can optimize its judgment algorithm by referring to past judgment results during the judgment process. For example, the judgment unit can optimize its judgment algorithm by referring to past judgment results. The judgment unit can also improve the accuracy of the judgment by referring to past judgment results. The judgment unit can also improve the speed of the judgment by referring to past judgment results. In this way, by referring to past judgment results, the judgment algorithm can be optimized and the accuracy of the judgment can be improved. Some or all of the above processes in the judgment unit may be performed using AI, for example, or without using AI. For example, the judgment unit can input past judgment results into a generating AI and have the generating AI perform the optimization of the judgment algorithm.

[0092] The judgment unit can apply different judgment criteria depending on the growth stage of the crop during the judgment process. For example, the judgment unit applies a specific judgment criterion when the crop is in the early growth stage. The judgment unit can also apply a specific judgment criterion when the crop is in the middle growth stage. The judgment unit can also apply a specific judgment criterion when the crop is in the late growth stage. This allows for appropriate judgment by applying different judgment criteria depending on the growth stage of the crop. Some or all of the above processing in the judgment unit may be performed using AI, for example, or without using AI. For example, the judgment unit can input data based on the growth stage of the crop into a generating AI and cause the generating AI to apply different judgment criteria.

[0093] The judgment unit can estimate the user's emotions and determine the priority of judgment results based on the estimated user emotions. For example, if the user is nervous, the judgment unit may prioritize notifying important judgment results. If the user is relaxed, the judgment unit may also prioritize notifying detailed judgment results. If the user is in a hurry, the judgment unit may also prioritize notifying judgment results that can be notified quickly. In this way, by determining the priority of judgment results according to the user's emotions, important judgment results can be notified preferentially. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the judgment unit may be performed using AI, for example, or not using AI. For example, the judgment unit can input user emotion data into a generative AI and have the generative AI perform the determination of the priority of judgment results.

[0094] The determination unit can improve the accuracy of its determination by considering the geographical location information of the farmland during the determination process. For example, the determination unit applies a specific determination criterion if the farmland is located on high ground. The determination unit can also apply a specific determination criterion if the farmland is located on low ground. The determination unit can also apply a specific determination criterion if the farmland is located near the coast. This allows the determination unit to improve the accuracy of its determination by considering the geographical location information of the farmland. Some or all of the above processing in the determination unit may be performed using AI, for example, or without AI. For example, the determination unit can input the geographical location information of the farmland into a generating AI and cause the generating AI to perform the determination accuracy improvement.

[0095] The determination unit can improve the accuracy of its determination by referring to relevant weather data during the determination process. For example, the determination unit can improve the accuracy of its determination by referring to weather data. The determination unit can also improve the speed of its determination by referring to weather data. The determination unit can also improve the reliability of its determination by referring to weather data. In this way, the accuracy of the determination can be improved by referring to relevant weather data. Some or all of the above processing in the determination unit may be performed using AI, for example, or without using AI. For example, the determination unit can input weather data into a generating AI and have the generating AI perform the task of improving the accuracy of the determination.

[0096] The suggestion unit can estimate the user's emotions and adjust the way the suggestion is presented based on the estimated emotions. For example, if the user is nervous, the suggestion unit can provide simple and highly visible suggestions. If the user is relaxed, the suggestion unit can also provide suggestions that include detailed information. If the user is in a hurry, the suggestion unit can also provide suggestions that get straight to the point. By adjusting the way the suggestion is presented according to the user's emotions, it becomes possible to make suggestions that are easy for the user to understand. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the suggestion unit may be performed using AI, or not using AI. For example, the suggestion unit can input user emotion data into a generative AI and have the generative AI adjust the way the suggestion is presented.

[0097] The proposal unit can optimize its proposal algorithm by referring to past proposal history when making a proposal. For example, the proposal unit can optimize its proposal algorithm by referring to past proposal history. The proposal unit can also improve the accuracy of proposals by referring to past proposal history. The proposal unit can also improve the speed of proposals by referring to past proposal history. In this way, by referring to past proposal history, the proposal algorithm can be optimized and the accuracy of proposals can be improved. Some or all of the above processing in the proposal unit may be performed using AI, for example, or without using AI. For example, the proposal unit can input past proposal history into a generation AI and have the generation AI perform the optimization of the proposal algorithm.

[0098] The proposal unit can provide different suggestions depending on the type and growth stage of the crop. For example, in the case of tomatoes, the proposal unit can provide suggestions based on specific nutrient data. In the case of strawberries, the proposal unit can also provide suggestions based on specific light intensity data. In the case of lettuce, the proposal unit can also provide suggestions based on specific temperature data. This allows for appropriate suggestions by providing different suggestions depending on the type and growth stage of the crop. Some or all of the above processing in the proposal unit may be performed using AI, for example, or without AI. For example, the proposal unit can input data based on the type and growth stage of the crop into a generating AI and have the generating AI provide different suggestions.

[0099] The suggestion unit can estimate the user's emotions and prioritize suggestions based on those emotions. For example, if the user is stressed, the suggestion unit will prioritize important suggestions. If the user is relaxed, the suggestion unit may also prioritize detailed suggestions. If the user is in a hurry, the suggestion unit may also prioritize suggestions that can be delivered quickly. This allows for prioritizing important suggestions based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the suggestion unit may be performed using AI or not. For example, the suggestion unit can input user emotion data into a generative AI and have the generative AI determine the priority of suggestions.

[0100] The proposal unit can make optimal proposals by considering the geographical location information of the farmland when making a proposal. For example, if the farmland is located at high altitude, the proposal unit can provide specific proposals. The proposal unit can also provide specific proposals if the farmland is located at low altitude. The proposal unit can also provide specific proposals if the farmland is located near the coast. This allows the proposal unit to make optimal proposals by considering the geographical location information of the farmland. Some or all of the above processing in the proposal unit may be performed using AI, for example, or without AI. For example, the proposal unit can input the geographical location information of the farmland into a generating AI and have the generating AI provide the optimal proposal.

[0101] The proposal unit can optimize the proposal content by referring to relevant market data when making a proposal. For example, the proposal unit can provide the optimal proposal content by referring to market data. The proposal unit can also improve the accuracy of the proposal content by referring to market data. The proposal unit can also improve the reliability of the proposal content by referring to market data. In this way, the accuracy and reliability of the proposal content can be improved by referring to relevant market data. Some or all of the above processing in the proposal unit may be performed using AI, for example, or without using AI. For example, the proposal unit can input relevant market data into a generating AI and have the generating AI perform the optimization of the proposal content.

[0102] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0103] The data collection unit can monitor not only the health of crops but also the microbial activity of the soil. For example, it can detect the activity of specific microorganisms in the soil using sensors and collect the data. If the data collection unit detects a decrease in microbial activity, it can determine that the soil health is deteriorating and propose appropriate countermeasures. Furthermore, the data collection unit can suggest the use of soil conditioners based on the type and activity level of the microorganisms. This allows for comprehensive management of soil health.

[0104] The analysis unit can monitor not only the health of crops but also changes in the surrounding environment. For example, it can use sensors to detect pesticide use in nearby farmland and collect data. Based on the collected data, the analysis unit can evaluate the impact on crops and propose necessary countermeasures. Furthermore, the analysis unit can monitor the health of crops in real time in response to changes in the surrounding environment and implement appropriate management. This allows for a more accurate understanding of the health of the crops.

[0105] The assessment unit can make judgments considering not only the health of the crops but also the workload of the farmers. For example, it can collect farmers' work schedules and, if the workload is high, suggest distributing the work. The assessment unit can determine the priority of tasks and propose an efficient work schedule to reduce the farmers' workload. It can also suggest concentrating tasks during periods of low workload. This reduces the farmers' workload and enables efficient agricultural management.

[0106] The proposal department can make suggestions considering not only the health of the crops but also the health of the farmers. For example, it can collect health data on farmers and, if their health is deteriorating, propose reducing their workload. The proposal department can determine the priority of tasks according to the farmers' health and make suggestions to maintain their health. It can also propose concentrating work during periods when farmers are in good health. This makes it possible to achieve efficient agricultural management while maintaining the health of farmers.

[0107] The data collection unit can collect not only information on the health of crops but also geological data of farmland. For example, it can use sensors to detect soil composition and structure and collect that data. Based on the geological data, the data collection unit can identify areas where soil improvement is needed and propose appropriate measures. Furthermore, based on the geological data, the data collection unit can also propose appropriate cultivation methods for crops. This allows for comprehensive management of crop health by utilizing geological data of farmland.

[0108] The analytics unit can estimate the user's emotions and adjust the timing of notification of analysis results based on those emotions. For example, if the user is stressed, the notification can be delayed to reduce the user's burden. If the user is relaxed, the analytics unit can also send an immediate notification to encourage a quick response. Furthermore, if the user is in a hurry, important analysis results can be prioritized and notified. In this way, by adjusting the notification timing according to the user's emotions, the user's burden can be reduced and efficient responses can be encouraged.

[0109] The judgment unit can estimate the user's emotions and adjust the level of detail in the judgment result based on the estimated emotions. For example, if the user is nervous, it provides a simple and easy-to-read judgment result. If the user is relaxed, the judgment unit can also provide a judgment result that includes detailed information. Furthermore, if the user is in a hurry, it can provide a judgment result that gets straight to the point. In this way, by adjusting the level of detail in the judgment result according to the user's emotions, it is possible to provide the user with an appropriate amount of information.

[0110] The suggestion function can estimate the user's emotions and adjust the timing of suggestion notifications based on those emotions. For example, if the user is stressed, the notification can be delayed to reduce the user's burden. If the user is relaxed, the suggestion function can also send an immediate notification to encourage a quick response. Furthermore, if the user is in a hurry, important suggestions can be prioritized. By adjusting the notification timing according to the user's emotions, the system can reduce the user's burden and encourage efficient responses.

[0111] The data collection unit can estimate the user's emotions and adjust the type of data collected based on those emotions. For example, if the user is stressed, it will collect only essential data. If the user is relaxed, the unit can collect detailed data. If the user is in a hurry, it can prioritize collecting data that can be retrieved quickly. This allows for efficient collection of important data by adjusting the type of data collected according to the user's emotions.

[0112] The analytics unit can estimate the user's emotions and adjust the notification method of the analysis results based on the estimated emotions. For example, if the user is stressed, it can provide a simple and highly visible notification method. If the user is relaxed, the analytics unit can also provide a notification method that includes detailed information. If the user is in a hurry, it can provide a notification method that gets straight to the point. In this way, by adjusting the notification method according to the user's emotions, it becomes possible to provide notifications that are easy for the user to see.

[0113] The following briefly describes the processing flow for example form 2.

[0114] Step 1: The collection unit collects data from sensors. The collection unit collects data such as soil moisture, temperature, and light intensity. The collection unit can collect data using soil moisture sensors, temperature sensors, light intensity sensors, etc. For example, the collection unit measures soil moisture using a soil moisture sensor. The collection unit can also measure temperature using a temperature sensor. The collection unit can also measure light intensity using a light intensity sensor. Step 2: The analysis unit analyzes the data collected by the collection unit in real time. For example, the analysis unit monitors the health of crops based on the collected data. The analysis unit can use AI to analyze the data and evaluate the health of crops. Step 3: The determination unit determines the required amount of water and fertilizer based on the analysis results obtained by the analysis unit. The determination unit can determine the required amount of water and fertilizer, for example, by using AI. The determination unit can analyze data using AI and determine the required amount of water and fertilizer. Step 4: The proposal unit makes automated suggestions based on the results determined by the judgment unit. For example, the proposal unit uses AI to propose an optimal irrigation schedule. The proposal unit can analyze data using AI and propose an optimal irrigation schedule. The proposal unit can also use AI to propose ways to optimize fertilizer usage.

[0115] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0116] Data generation model 58 is a form of so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AI include text generation AI, image generation AI, and multimodal generation AI. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats from audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each of the above parts is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example.Furthermore, processing performed by AI, including generative AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by AI, including generative AI.

[0117] Furthermore, the processing performed by the data processing system 10 described above is carried out by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but it may also be carried out by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0118] Each of the multiple elements described above, including the collection unit, analysis unit, determination unit, and proposal unit, is implemented, for example, by at least one of the smart device 14 and the data processing unit 12. For example, the collection unit collects data such as soil moisture, temperature, and light intensity using the sensors of the smart device 14. The analysis unit analyzes the collected data in real time by the identification processing unit 290 of the data processing unit 12 to monitor the health of the crops. The determination unit determines the amount of water and fertilizer needed by the identification processing unit 290 of the data processing unit 12. The proposal unit proposes the optimal irrigation schedule and fertilizer usage by the identification processing unit 290 of the data processing unit 12. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0119] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0120] As shown in Figure 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0121] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0122] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

[0123] The microphone 238 receives voice commands and other instructions from the user by receiving voice signals. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0124] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0125] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0126] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing by the processor 28. The storage 32 stores the specific processing program 56.

[0127] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0128] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0129] In the smart glasses 214, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0130] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0131] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0132] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0133] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart glasses 214 or an external device, and the smart glasses 214 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0134] Each of the multiple elements described above, including the collection unit, analysis unit, determination unit, and proposal unit, is implemented, for example, by at least one of the smart glasses 214 and the data processing unit 12. For example, the collection unit collects data such as soil moisture, temperature, and light intensity using the sensors of the smart glasses 214. The analysis unit analyzes the collected data in real time by the identification processing unit 290 of the data processing unit 12 to monitor the health of the crops. The determination unit determines the amount of water and fertilizer needed by the identification processing unit 290 of the data processing unit 12. The proposal unit proposes the optimal irrigation schedule and fertilizer usage by the identification processing unit 290 of the data processing unit 12. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0135] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0136] As shown in Figure 5, the data processing system 310 includes a data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0137] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0138] The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52.

[0139] The microphone 238 receives voice commands and other instructions from the user by receiving voice signals. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0140] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0141] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0142] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0143] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0144] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0145] In the headset terminal 314, specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads the specific program 60 from the storage 50 and executes the read specific program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific program 60 executed on the RAM 48. The headset terminal 314 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0146] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0147] The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0148] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0149] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset terminal 314, but may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset terminal 314. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the headset terminal 314 or an external device, and the headset terminal 314 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0150] Each of the multiple elements described above, including the collection unit, analysis unit, determination unit, and proposal unit, is implemented, for example, by at least one of the headset terminal 314 and the data processing unit 12. For example, the collection unit collects data such as soil moisture, temperature, and light intensity using the sensors of the headset terminal 314. The analysis unit analyzes the collected data in real time by the specific processing unit 290 of the data processing unit 12 to monitor the health of the crops. The determination unit determines the amount of water and fertilizer needed by the specific processing unit 290 of the data processing unit 12. The proposal unit proposes the optimal irrigation schedule and fertilizer usage by the specific processing unit 290 of the data processing unit 12. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0151] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0152] As shown in Figure 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0153] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0154] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52.

[0155] The microphone 238 receives voice commands and other instructions from the user by receiving voice signals. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0156] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS image sensor or CCD image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0157] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0158] The controlled object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the robot 414's emotions can be expressed by controlling these motors. The robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0159] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0160] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0161] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0162] In robot 414, specific processing is performed by processor 46. A specific program 60 is stored in storage 50. Processor 46 reads the specific program 60 from storage 50 and executes it on RAM 48. The specific processing is achieved by processor 46 acting as a control unit 46A according to the specific program 60 executed on RAM 48. Robot 414 also has data generation model 58 and emotion identification model 59, similar to those of the robot, and can perform processing similar to that of the specific processing unit 290 using these models.

[0163] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0164] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0165] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0166] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the robot 414 or an external device, and the robot 414 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0167] Each of the multiple elements described above, including the collection unit, analysis unit, determination unit, and proposal unit, is implemented, for example, by at least one of the robot 414 and the data processing unit 12. For example, the collection unit collects data such as soil moisture, temperature, and light intensity using the sensors of the robot 414. The analysis unit analyzes the collected data in real time by the identification processing unit 290 of the data processing unit 12 to monitor the health of the crops. The determination unit determines the amount of water and fertilizer needed by the identification processing unit 290 of the data processing unit 12. The proposal unit proposes the optimal irrigation schedule and fertilizer usage by the identification processing unit 290 of the data processing unit 12. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0168] Furthermore, the emotion identification model 59, acting as an emotion engine, may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to a specific mapping, which is an emotion map (see Figure 9). Similarly, the emotion identification model 59 may also determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0169] Figure 9 shows the emotion map 400, in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[0170] These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression.

[0171] The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become.

[0172] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, and motorcycles, emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated based, for example, on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[0173] The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it is when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is the emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more."

[0174] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​representing each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple training data sets, which are combinations of user input and emotion values ​​representing each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions located close together have similar values, as shown in the emotion map 900 in Figure 10. Figure 10 shows an example where multiple emotions such as "reassured," "calm," and "confident" have similar emotion values.

[0175] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing method for the specific process may be used, which includes computer 22 and multiple other computers.

[0176] In the above embodiment, an example was given in which the specific processing program 56 is stored in the storage 32, but the technology of this disclosure is not limited thereto. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-temporary storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-temporary storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes specific processing according to the specific processing program 56.

[0177] Alternatively, the specific processing program 56 may be stored in a storage device such as a server connected to the data processing device 12 via the network 54, and the specific processing program 56 may be downloaded and installed on the computer 22 in response to a request from the data processing device 12.

[0178] Furthermore, it is not necessary to store the entirety of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

[0179] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

[0180] The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Alternatively, the hardware resource that performs a specific process may consist of a single processor.

[0181] Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources.

[0182] Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose.

[0183] Furthermore, although the above-described examples were divided into four embodiments, some or all of these embodiments may be combined. Also, the smart device 14, smart glasses 214, headset terminal 314, and robot 414 are just examples, and they may be combined, or other devices may be used. Also, although the above-described examples were divided into two embodiments, Embodiment 1 and Embodiment 2, these may be combined.

[0184] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and other things that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[0185] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[0186] (Note 1) A data collection unit that collects data from sensors, An analysis unit analyzes the data collected by the aforementioned collection unit in real time, A determination unit that determines the required amount of water and fertilizer based on the analysis results obtained by the aforementioned analysis unit, The system includes a proposal unit that makes automation suggestions based on the results determined by the determination unit. A system characterized by the following features. (Note 2) The aforementioned collection unit is Collect data such as soil moisture, temperature, and light intensity. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned analysis unit is The health of crops is monitored based on the collected data. The system described in Appendix 1, characterized by the features described herein. (Note 4) The determination unit, Determine the amount of water and fertilizer needed. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned proposal section is, We propose the optimal irrigation schedule. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned proposal section is, We propose optimizing fertilizer usage. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned analysis unit is To predict crop growth and assess risks. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned proposal section is, We propose preventive measures based on the risk of pest and disease outbreaks. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned collection unit is We estimate the user's emotions and adjust the timing of data collection based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned collection unit is Analyze past collected data to select the optimal sensor placement. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned collection unit is During data collection, the data collection items are dynamically changed based on the type of crop and its growth stage. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned collection unit is It estimates the user's emotions and prioritizes the data to collect based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned collection unit is When collecting data, prioritize the collection of highly relevant data, taking into account the geographical location information of the farmland. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned collection unit is When collecting data, the collection frequency is adjusted by referring to weather forecast data. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned analysis unit is It estimates the user's emotions and adjusts how the analysis results are displayed based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned analysis unit is During analysis, detect outliers by comparing them with past data. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned analysis unit is During analysis, different analytical algorithms are applied for each type of crop. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned analysis unit is It estimates the user's emotions and adjusts the level of detail in the analysis results based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned analysis unit is During analysis, prioritize the analysis based on when the collected data was submitted. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned analysis unit is During analysis, we refer to relevant research papers to improve the accuracy of the analysis. The system described in Appendix 1, characterized by the features described herein. (Note 21) The determination unit, The system estimates the user's emotions and adjusts the notification method of the judgment result based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 22) The determination unit, When making a judgment, the judgment algorithm is optimized by referring to past judgment results. The system described in Appendix 1, characterized by the features described herein. (Note 23) The determination unit, When making a determination, different criteria are applied depending on the stage of crop growth. The system described in Appendix 1, characterized by the features described herein. (Note 24) The determination unit, The system estimates the user's emotions and prioritizes the judgment results based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 25) The determination unit, When making a determination, the accuracy of the determination is improved by considering the geographical location information of the farmland. The system described in Appendix 1, characterized by the features described herein. (Note 26) The determination unit, When making a determination, we refer to relevant weather data to improve the accuracy of the determination. The system described in Appendix 1, characterized by the features described herein. (Note 27) The aforementioned proposal section is, It estimates the user's emotions and adjusts the way the suggestions are presented based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 28) The aforementioned proposal section is, When making a proposal, the proposal algorithm is optimized by referring to past proposal history. The system described in Appendix 1, characterized by the features described herein. (Note 29) The aforementioned proposal section is, When making a proposal, we will provide different proposals depending on the type of crop and its growth stage. The system described in Appendix 1, characterized by the features described herein. (Note 30) The aforementioned proposal section is, It estimates the user's emotions and prioritizes suggestions based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 31) The aforementioned proposal section is, When making a proposal, we will consider the geographical location information of the farmland to provide the most suitable proposal. The system described in Appendix 1, characterized by the features described herein. (Note 32) The aforementioned proposal section is, When making a proposal, we optimize the proposal by referring to relevant market data. The system described in Appendix 1, characterized by the features described herein. [Explanation of Symbols]

[0187] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots

Claims

1. A data collection unit that collects data from sensors, An analysis unit analyzes the data collected by the aforementioned collection unit in real time, A determination unit that determines the required amount of water and fertilizer based on the analysis results obtained by the aforementioned analysis unit, The system includes a proposal unit that makes automation suggestions based on the results determined by the determination unit. A system characterized by the following features.

2. The aforementioned collection unit is Collect data such as soil moisture, temperature, and light intensity. The system according to feature 1.

3. The aforementioned analysis unit is The health of crops is monitored based on the collected data. The system according to feature 1.

4. The determination unit, Determine the amount of water and fertilizer needed. The system according to feature 1.

5. The aforementioned proposal section is, We propose the optimal irrigation schedule. The system according to feature 1.

6. The aforementioned proposal section is, We propose optimizing fertilizer usage. The system according to feature 1.

7. The aforementioned analysis unit is To predict crop growth and assess risks. The system according to feature 1.

8. The aforementioned proposal section is, We propose preventive measures based on the risk of pest and disease outbreaks. The system according to feature 1.

9. The aforementioned collection unit is We estimate the user's emotions and adjust the timing of data collection based on those estimated emotions. The system according to feature 1.

10. The aforementioned collection unit is Analyze past collected data to select the optimal sensor placement. The system according to feature 1.

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