system

Abstract

We provide the system. [Solution] A means for acquiring soil information, analyzing it, and calculating and presenting the optimal amounts of fertilizer and irrigation to be used, A means of acquiring weather information and proposing the optimal timing for cultivation and harvesting based on it, A means of analyzing images, evaluating the health of plants, and suggesting control measures if abnormalities are detected, A means of analyzing economic data and proposing the optimal sales strategy, A means of providing users with optimal cultivation conditions and management methods for urban agriculture, A system that includes this.

Description

Technical Field

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

Background Art

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

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In conventional agriculture, it has been difficult to appropriately monitor soil conditions and weather conditions, and to determine the supply of fertilizers and water, the timing of planting and harvesting based on them. Also, it has been difficult to quickly evaluate the health state of crops, to early detect the occurrence of pests and diseases, and to take appropriate control measures. Furthermore, formulating a sales strategy according to market fluctuations is also complicated, and there is a need for a new system to integrally solve these problems.

Means for Solving the Problems

[0005] This invention provides a system that includes means for acquiring and analyzing soil data to provide optimal amounts of fertilizer and water, means for suggesting optimal planting and harvesting timing using weather information, means for evaluating crop health by analyzing images collected by drones, and means for suggesting optimal sales strategies by analyzing market data. This enables the optimization and sustainability of agricultural production and provides data-driven decision-making support to farmers.

[0006] "Soil data" refers to data that indicates the condition of agricultural land, such as soil moisture content, nutrient levels, temperature, and pH value.

[0007] "Weather information" refers to data on weather conditions related to agricultural work, such as temperature, precipitation, humidity, and wind speed.

[0008] "Fertilizer usage" refers to the appropriate amount of fertilizer needed for crop growth, based on the soil's nutrient status.

[0009] "Optimal water usage" refers to the appropriate amount of water necessary to prevent soil moisture deficiency or excess, which is essential for healthy crop growth.

[0010] "Planting timing" refers to the schedule used to determine the appropriate time to sow crop seeds.

[0011] "Harvest timing" refers to the plan for determining when crops should be harvested in their best condition.

[0012] "Crop health" refers to the condition of a crop to assess whether it is affected by diseases or pests, or by nutrient deficiencies or excesses.

[0013] "Pest control measures" refer to specific methods and means used to prevent diseases, pests, and other abnormalities that appear in crops.

[0014] "Market data" refers to information regarding fluctuations in the prices and demand for agricultural products that are bought and sold.

[0015] A "sales strategy" is a plan for maximizing the sales of agricultural products considering market trends.

[0016] A "system" is a device or method that performs a series of processes by combining the above various measurement and analysis means.

Brief Description of Drawings

[0017] [Figure 1] It is a conceptual diagram showing an example of the configuration of a data processing system according to the first embodiment. [Figure 2] It is a conceptual diagram showing an example of the main functions of a data processing device and a smart device according to the first embodiment. [Figure 3] It is a conceptual diagram showing an example of the configuration of a data processing system according to the second embodiment. [Figure 4] It 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] It is a conceptual diagram showing an example of the configuration of a data processing system according to the third embodiment. [Figure 6] It 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] It is a conceptual diagram showing an example of the configuration of a data processing system according to the fourth embodiment. [Figure 8] It 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] It shows an emotion map to which a plurality of emotions are mapped. [Figure 10] It shows an emotion map to which a plurality of emotions are mapped. [Figure 11] It is a sequence diagram showing the processing flow of the data processing system in Example 1. [Figure 12] It is a sequence diagram showing the processing flow of the data processing system in Application Example 1. [Figure 13]It is a sequence diagram showing the processing flow of the data processing system in Example 2 when the emotion engine is combined. [Figure 14] It is a sequence diagram showing the processing flow of the data processing system in Application Example 2 when the emotion engine is combined.

Mode for Carrying Out the Invention

[0018] Hereinafter, an example of an embodiment of the system according to the technology of the present disclosure will be described with reference to the accompanying drawings.

[0019] First, the terms used in the following description will be explained.

[0020] In the following embodiments, a numbered processor (hereinafter simply referred to as "processor") may be a single arithmetic unit or a combination of multiple arithmetic units. Also, the processor may be a single type of arithmetic unit or a combination of multiple types of arithmetic units. Examples of arithmetic units include a CPU (Central Processing Unit), a GPU (Graphics Processing Unit), a GPGPU (General-Purpose computing on Graphics Processing Units), an APU (Accelerated Processing Unit), and the like.

[0021] In the following embodiments, a numbered RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a work memory by the processor.

[0022] In the following embodiments, a numbered storage is one or more non-volatile storage devices that store various programs and various parameters, etc. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), or magnetic tapes, etc.

[0023] In the following embodiments, the signed communication interface (I / F) is an interface that includes a communication processor and an antenna, etc. The communication interface manages communication between multiple computers. Examples of communication standards applicable to the communication interface include wireless communication standards such as 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark).

[0024] 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 A alone, or B alone, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" applies when expressing three or more things linked by "and / or."

[0025] [First Embodiment]

[0026] Figure 1 shows an example of the configuration of the data processing system 10 according to the first embodiment.

[0027] As shown in Figure 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.

[0028] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. 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 (Wide Area Network) and / or a LAN (Local Area Network).

[0029] The smart device 14 comprises a computer 36, a reception 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 reception device 38, output device 40, and camera 42 are also connected to the bus 52.

[0030] The reception device 38 is equipped with a touch panel 38A and a microphone 38B, etc., and receives user input. The touch panel 38A receives user input by detecting contact with an object (e.g., a pen or finger). The microphone 38B receives user input 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 device 12. In the data processing device 12, the specific processing unit 290 acquires the data indicating the user input.

[0031] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user 20 by outputting the data in a form perceptible to the user 20 (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.

[0032] 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.

[0033] Figure 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0034] 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.

[0035] The 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.

[0036] In the smart device 14, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The reception output program 60 is used in conjunction with a specific processing program 56 by the data processing system 10. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0037] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the smart device 14 as the "terminal".

[0038] This invention provides a system for optimizing and achieving sustainability in agricultural production. This system consists of a server, terminals, and users, and provides comprehensive support to agricultural workers.

[0039] The server first receives soil data transmitted from soil sensors. Based on this data, it analyzes the soil's moisture, nutrients, and temperature, and calculates the appropriate amount of fertilizer and water to be used. The calculation results are then provided to the user via a terminal.

[0040] Furthermore, the server obtains real-time weather information from meteorological agencies. Based on this, it analyzes the optimal timing for planting and harvesting and notifies the user. For example, it may suggest whether to harvest earlier based on future rainfall forecasts.

[0041] In addition, the server receives and analyzes images of crops taken by drones. This analysis assesses the health of the crops and detects signs of pests and diseases. If an abnormality is detected early, the server suggests control measures to the user via the terminal. For example, it may recommend the use of specific pesticides on crops showing signs of pests or diseases.

[0042] The server also collects domestic and international market data and analyzes price fluctuations and changes in demand. This allows it to present users with market-specific sales strategies to maximize profits. For example, it might suggest opportunities to increase profits by exporting specific crops at specific times.

[0043] In summary, this system enables agricultural workers to make data-driven decisions and, as a result, improve agricultural production in an efficient and sustainable manner.

[0044] The following describes the processing flow.

[0045] Step 1:

[0046] The server receives soil data from soil sensors. This data includes soil moisture content, temperature, and nutrient levels, which are used for subsequent analysis.

[0047] Step 2:

[0048] The server analyzes the received soil data and calculates the optimal amount of fertilizer and water to use for each crop. The analysis utilizes machine learning models and statistical methods, and compares the current data with historical data.

[0049] Step 3:

[0050] The server generates calculation results and sends the optimal amount of fertilizer and water to the terminal. The terminal visualizes this information for the user, providing guidance for planning actual fertilization and watering.

[0051] Step 4:

[0052] The server retrieves real-time weather data from meteorological agencies. This data, including short-term and long-term weather forecasts, is used for scheduling agricultural work.

[0053] Step 5:

[0054] The server analyzes the acquired weather data and calculates the optimal timing for planting and harvesting. Based on the analysis results and predicted weather conditions, it notifies the user and suggests necessary actions.

[0055] Step 6:

[0056] The server receives images of crops taken by drones and analyzes them. Computer vision technology is used for image analysis to assess the health of the crops based on their leaf color and shape.

[0057] Step 7:

[0058] The server evaluates the health of the analyzed crops and, if any abnormalities in pests or diseases are detected, sends the details to the terminal. The terminal then suggests control measures and pesticide usage methods to the user, urging them to take prompt action.

[0059] Step 8:

[0060] The server collects market price and demand data from the market database. The collected data serves as foundational information for developing crop sales strategies.

[0061] Step 9:

[0062] The server analyzes collected market data to formulate the optimal sales timing and pricing strategy. The terminal communicates this to the user, supporting management decisions to maximize profits.

[0063] (Example 1)

[0064] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server," and the smart device 14 will be referred to as the "terminal."

[0065] Modern agriculture demands maximizing production efficiency while flexibly responding to environmental changes and market demands. However, traditional methods involve managing soil and weather conditions, crop health, and market trends individually, making it difficult to optimize these factors comprehensively. Solving this problem is essential to achieving sustainable agriculture and improving profitability.

[0066] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0067] This invention includes a server that acquires and analyzes soil information to calculate and present the optimal amount of agricultural materials and liquids to be used, a server that acquires information on natural phenomena and proposes the optimal timing for cultivation and harvesting based on this information, and a server that analyzes video to evaluate the health of plants and presents preventive measures if abnormalities are detected. This enables agricultural workers to make data-driven decisions, making efficient and environmentally friendly agricultural production possible.

[0068] "Soil information" refers to data about the physical and chemical properties of soil, including moisture, nutrients, and temperature.

[0069] "Natural phenomena information" refers to real-time and forecast data such as weather, temperature, precipitation, and wind speed, obtained from meteorological agencies and other sources.

[0070] "Agricultural materials and liquids" refer to chemicals and water used in agricultural activities, such as fertilizers and water, that are used to promote plant growth.

[0071] "Video" refers to visual data acquired by drones and other filming devices, which is used to understand the condition of crops and the state of the field.

[0072] "Plant health" refers to an indicator of a crop's growth status, and is judged based on visual information such as the plant's appearance, color, and shape.

[0073] "Distribution information" refers to economic data related to the sale of agricultural products, such as domestic and international market trends, price fluctuations, and demand forecasts.

[0074] "Data-driven processing" is an approach that involves comprehensively analyzing large amounts of collected data and making decisions based on the results, and is useful for optimizing agricultural production.

[0075] This invention provides a system for optimizing agricultural production based on data. This system primarily consists of a server, terminals, and users, each with a specific function.

[0076] The server receives data from soil sensors, drone cameras, weather agencies, and market databases. Soil data is primarily used to monitor soil moisture, nutrients, and temperature. This utilizes sensor technology and data analysis libraries (e.g., Python and R). For image analysis, generative AI models such as TENSORFLOW® are used based on data acquired by drones to detect crop health and abnormalities.

[0077] The server retrieves real-time weather information via API and analyzes weather forecasts and their impact using statistical methods and machine learning algorithms. This allows it to suggest optimal planting and harvesting times to users. Market data is obtained from online market databases, and price and demand trends are analyzed. This is done using database management systems and analytical software.

[0078] The terminal receives analytical data provided by the server and displays it to the user in a visually easy-to-understand format. This enables users to make quick decisions on-site.

[0079] Based on the information provided by the system, users select and implement specific agricultural measures. This includes appropriate input of agricultural materials and adjustment of harvest timing.

[0080] As a concrete example, a server might analyze weather information and, based on rainfall forecasts, notify the user whether or not they should harvest earlier. An example of a prompt message would be, "Please tell me the data needed to determine the next harvest timing and how to analyze it."

[0081] This system supports users in improving agricultural efficiency and achieving sustainable production by comprehensively analyzing various types of data.

[0082] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0083] Step 1:

[0084] The server receives soil information from soil sensors. Specifically, raw data regarding soil moisture, nutrients, and temperature is input. The server processes this data using analysis software to calculate the optimal amount of fertilizer and water to use. The data obtained from this calculation is output, allowing for the development of an efficient plan for the application of agricultural materials.

[0085] Step 2:

[0086] The server acquires weather information. It inputs weather data from meteorological agencies' APIs and calculates the optimal timing for planting and harvesting based on that data. It analyzes weather trends using statistical methods and predicts future weather. This forecast information is output, and the user is offered a suggestion for the optimal work schedule.

[0087] Step 3:

[0088] The server receives video data of crops captured by drones. It takes image data as input and uses a generative AI model for analysis. The server outputs the results of an evaluation of the crops' health and signs of pests and diseases. If an abnormality is detected, the user is notified of appropriate control measures.

[0089] Step 4:

[0090] The server collects market data and analyzes distribution information. It inputs price and demand data from online market databases and analyzes market trends. As a result of the trend analysis, data suggesting the optimal sales strategy is output and notified to the user.

[0091] Step 5:

[0092] The terminal receives analysis results from the server and provides them to the user. Specifically, it visualizes the analysis data and outputs it in a visually easy-to-understand format. Based on this output, the user adjusts their agricultural activities.

[0093] Step 6:

[0094] Users develop specific agricultural activities based on the information provided. They apply materials according to calculated fertilizer and water usage amounts and schedule appropriate work based on weather forecasts. This enables data-driven decision-making and promotes efficient and sustainable agriculture.

[0095] (Application Example 1)

[0096] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart device 14 will be referred to as the "terminal."

[0097] With the advancement of urbanization in modern society, urban agriculture is attracting attention, but there is a lack of appropriate information and technology for developing efficient cultivation management and profitable market strategies. As a result, it is difficult for individual users to obtain the optimal agricultural experience in their home gardens or community farms.

[0098] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0099] This invention includes a server that includes means for acquiring and analyzing soil information to calculate and present the optimal amounts of fertilizer and irrigation to be used; means for acquiring weather information and suggesting the optimal timing for cultivation and harvesting based on it; means for analyzing images to evaluate the health of plants and suggesting control measures if abnormalities are detected; means for analyzing economic data and suggesting the optimal sales strategy; and means for providing users with the optimal cultivation conditions and management methods for urban agriculture. This enables comprehensive support for practicing efficient and sustainable agriculture even in urban areas.

[0100] "Soil information" refers to data about the conditions below the soil surface, which are fundamental to plant growth. Specifically, it includes moisture content, nutrient levels, temperature, and other factors.

[0101] "Weather information" refers to data about atmospheric conditions, including elements such as temperature, precipitation, humidity, and wind speed, and has an impact on agricultural activities.

[0102] "Fertilization" refers to the act of providing the soil with nutrients necessary to promote plant growth, and mainly involves using fertilizers containing nitrogen, phosphorus, and potassium.

[0103] "Irrigation" is the act of supplying plants with the water they need, and is especially common in dry climates and soils lacking moisture.

[0104] "Image analysis" is a computer processing technique that uses digital images, and its purpose is to detect the health status and abnormalities of plants.

[0105] "Control measures" refer to actions taken to prevent or mitigate damage to plants from diseases, pests, and extreme weather conditions, and include, for example, the use of specific pesticides and the introduction of appropriate management techniques.

[0106] "Economic data" refers to information related to supply and demand in the market, including data on price fluctuations and competing products.

[0107] "Urban agriculture" refers to agricultural activities carried out in urban areas, including crop cultivation in home gardens and community farms.

[0108] "Cultivation conditions" refer to the environmental factors necessary for plant growth, including light, water, temperature, and soil nutrients.

[0109] A "sales strategy" is a plan or policy for efficiently selling a product in the market, and includes initiatives aimed at maximizing profits.

[0110] The system used to realize this application is a complex information processing system designed to support urban agriculture. The server communicates with sensors to collect soil information and analyzes that data. AWS Lambda is used for the analysis, evaluating soil moisture, nutrients, and temperature in real time, and calculating the optimal amount of fertilizer and irrigation. The OpenWeather API is also used to obtain weather information, suggesting optimal planting and harvesting times based on local weather conditions.

[0111] The terminal is the user's smartphone, which sends images of plants taken with its camera to the Google® Cloud Vision API to evaluate the plant's health. Based on these results, if an abnormality is detected, the system can instruct the user on appropriate control measures. For economic data analysis, the Python libraries Pandas and BeautifulSoup are used to analyze trends and provide the user with better sales strategies.

[0112] Furthermore, the system provides users with appropriate cultivation conditions and management methods for urban agriculture, supporting them in practicing efficient and sustainable agricultural activities. Such a system makes agricultural activities in urban environments easy and effective. An example of a prompt message would be: "This app provides optimal growing and sales strategies based on soil data, weather information, and market fluctuations to improve the efficiency of urban home gardens. Specifically, please tell me which crops to grow and how to grow them at what time of year."

[0113] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0114] Step 1:

[0115] The server acquires soil information from soil sensors and analyzes it in real time using AWS Lambda. This analysis evaluates soil moisture content, nutrients, and temperature, calculates the optimal amount of fertilizer and irrigation to be used, and sends this information to the terminal. The input is soil sensor data, and the output is the optimal amount of fertilizer and irrigation to be used.

[0116] Step 2:

[0117] The server retrieves weather data via the OpenWeather API. Based on this data, it calculates the optimal timing for planting and harvesting and notifies the user of the results. The input is weather data, and the output is the recommended planting and harvesting times.

[0118] Step 3:

[0119] The user sends an image of a plant taken with their smartphone camera to the device, which then sends this image to the Google Cloud Vision API. The server analyzes the image to assess the plant's health and, if it detects signs of pests or diseases, instructs the user on appropriate control measures. The input is an image of the plant, and the output is the plant's health status and recommended control measures.

[0120] Step 4:

[0121] The server collects economic data using Python's Pandas and BeautifulSoup libraries and analyzes trends. The results of this analysis are then presented to the user as an optimal sales strategy. The input is market trends and economic data, and the output is the optimal sales strategy.

[0122] Step 5:

[0123] Users receive reminders and information on cultivation methods related to various farming tasks through their devices. This supports efficient and sustainable urban farming practices. Inputs are information on cultivation and management conditions, and outputs are specific reminders and cultivation methods corresponding to those conditions.

[0124] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0125] This invention is an integrated agricultural support system based on soil data, weather information, and market trends, aimed at improving the production efficiency and reducing the mental stress of agricultural workers. Furthermore, this system incorporates an emotion engine that monitors and adjusts the user's emotional state.

[0126] The server receives soil data from soil sensors, analyzes it, and calculates the optimal amount of fertilizer and water to use. The calculated information is provided to the user via a terminal. The terminal presents the information in a visually easy-to-understand format to support specific agricultural activities.

[0127] Regarding weather information acquisition, the server retrieves the latest data from meteorological agencies and suggests planting and harvesting timings. This allows the system to present users with agricultural plans that are suitable for changing weather conditions.

[0128] Drones are used to acquire images of crops, and a server uses this image data to assess the health of the crops. If signs of pests or diseases are found, the server suggests control measures to the user via a terminal, supporting a rapid response to protect the crops.

[0129] Market data analysis involves servers monitoring domestic and international market trends and formulating sales strategies tailored to fluctuating market conditions. Users can access these strategies through their terminals to develop profitable sales plans.

[0130] The newly added emotion engine uses the device's sensors to detect emotions from the user's voice, facial expressions, and other data, and adjusts the information and notifications provided according to the user's emotional state. For example, if the user is feeling stressed, it helps the user understand by presenting complex information in a concise manner. Furthermore, the emotion engine continuously learns from the user's feedback, enabling more accurate emotion analysis.

[0131] Thus, the present invention is designed to enable agricultural workers to perform their agricultural activities efficiently and comfortably by combining data analysis and emotion recognition technology.

[0132] The following describes the processing flow.

[0133] Step 1:

[0134] The server collects soil data received from soil sensors. This includes information such as soil moisture content, pH level, and nutrient content. The server processes this data in real time and stores it in a database.

[0135] Step 2:

[0136] The server analyzes the collected soil data and calculates the optimal amount of fertilizer and water for crop growth. This calculation uses machine learning algorithms and compares the current data with historical data.

[0137] Step 3:

[0138] The server sends the results of optimized fertilizer and water usage to the terminal. The terminal visualizes this information for the user and presents an automatically adjusted replenishment plan. The user then adjusts their daily farming activities based on this information.

[0139] Step 4:

[0140] The server periodically retrieves weather data from external meteorological agencies. This data includes factors that affect agriculture, such as temperature, precipitation, and wind speed.

[0141] Step 5:

[0142] The server analyzes the acquired weather data and calculates the optimal timing for planting and harvesting. The analysis results are notified to the user via the terminal, and necessary actions are suggested.

[0143] Step 6:

[0144] The server receives images of crops transmitted from the drone. The received images are analyzed using computer vision technology to assess the health of the crops based on the condition of their leaves and stems.

[0145] Step 7:

[0146] Based on the image analysis results, the server calculates countermeasures if abnormalities are found in the crops and sends them to the terminal. The terminal then issues a warning to the user and suggests appropriate control measures.

[0147] Step 8:

[0148] The server collects domestic and international market data and forecasts agricultural product price trends and demand. Based on the analysis results, it formulates the optimal sales strategy.

[0149] Step 9:

[0150] The terminal notifies the user of sales strategy proposals from the server, assisting with specific sales timing and pricing. The user then uses this information to adjust their market activities.

[0151] Step 10:

[0152] The emotion engine recognizes emotions through the user's voice and facial expressions and adjusts how information is presented on the device. If the user is stressed, the emotion engine simplifies the information and presents it to the user in a more easily understandable way.

[0153] Step 11:

[0154] Users provide feedback on the information and suggestions offered through their devices. The emotion engine learns from this feedback and improves the accuracy of future emotion recognition.

[0155] (Example 2)

[0156] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the smart device 14 as the "terminal".

[0157] The aim is to solve the challenges faced by agricultural workers in improving production efficiency and reducing mental stress. Specifically, it aims to reduce the burden on workers by acquiring and analyzing various types of information necessary in the agricultural field, thereby formulating optimal agricultural plans and sales strategies. In addition, it aims to alleviate mental burden and support smooth production activities by providing information tailored to the emotional state of workers.

[0158] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0159] In this invention, the server includes means for acquiring and analyzing soil information to calculate and present the optimal amount of fertilizer and water to be used; means for acquiring weather information and proposing the optimal timing for cultivation and harvesting based on it; and means for analyzing images to evaluate the health of plants and presenting control measures if abnormalities are detected. This enables users to accurately acquire the information necessary to carry out production activities efficiently. Furthermore, profitability can be improved through means for analyzing market information and proposing the optimal sales strategy. In addition, by providing means for detecting the user's emotional state and adjusting the information provided based on it, it is possible to reduce mental stress.

[0160] "Soil information" refers to data that shows soil characteristics related to crop growth, such as pH, humidity, and nutrient content of farmland.

[0161] "Weather information" refers to data on meteorological conditions that affect agricultural activities, such as temperature, precipitation, and wind speed.

[0162] "Image information" refers to digital image data captured to visually understand the condition of crops.

[0163] An "unmanned aerial vehicle" refers to a drone that is remotely controlled or flies autonomously and is used for the purpose of acquiring data from the air.

[0164] A "user" refers to an individual or group that uses the system to conduct agricultural activities.

[0165] "Emotional state" refers to the psychological and emotional state of a user, obtained by analyzing their voice, facial expressions, and other data.

[0166] A "server" is a centralized computer system that receives and analyzes data and provides information to users.

[0167] "Plant health" refers to the state of a crop that shows signs of disease or pest infestation, or abnormalities in its growth.

[0168] This invention relates to an agricultural support system that aims to improve agricultural production efficiency and reduce mental stress through data communication and analysis between a server, terminal, and user.

[0169] The server plays a central role in receiving and analyzing various data necessary for agriculture. Soil information is collected from soil sensors installed in the fields and analyzed on the server. A data analysis algorithm using Python is applied to the analysis, which calculates the optimal amount of fertilizer and water to be used. Weather information is obtained through APIs provided by meteorological agencies, and the optimal timing for cultivation and harvesting is calculated on the server.

[0170] The user provides image information obtained by the unmanned aerial vehicle to the server. The server analyzes this image and uses it to assess the health of the plants. If signs of pests or diseases are detected, the server develops appropriate control measures and notifies the user via the terminal.

[0171] The server also collects and analyzes market information and presents users with sales strategies tailored to fluctuating market conditions. The R programming language is used for time-series analysis. Furthermore, the server incorporates a generative AI model that monitors the user's emotional state through the terminal. A TensorFlow-based model is used for emotion analysis, evaluating the user's psychological state from their voice and facial expressions. If stress is detected, the server adjusts the information it provides to reduce the user's burden.

[0172] A terminal is a device that receives data from a server and presents information to the user visually. An intuitive interface is employed here. For example, weather forecasts and soil analysis results could be displayed in graph format.

[0173] For example, if a user prompts, "Analyze the soil pH data and tell me the appropriate type and amount of fertilizer," the server will calculate the optimal fertilizer based on the soil information and notify the terminal. When plant images are provided to the server, a prompt such as, "Identify signs of pests and diseases from the crop images and tell me how to control them," can be used.

[0174] Thus, the present invention utilizes data analysis and emotion recognition technology to enable agricultural workers to perform their agricultural activities efficiently and comfortably.

[0175] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0176] Step 1:

[0177] The server acquires soil information from soil sensors. This data includes pH, humidity, and nutrient content. The server analyzes this information using a data analysis algorithm based on Python to calculate the optimal amount of fertilizer and water to use. The input data is soil characteristics, and the output is a proposal for optimized material usage. This proposal is notified to the user via a terminal.

[0178] Step 2:

[0179] The server obtains weather information from meteorological agencies via APIs. This data includes temperature, precipitation, wind speed, and other factors. Based on this information, the server calculates the optimal timing for planting and harvesting. The input data is weather conditions, and the output is the recommended timing for farm work. Users visually confirm this information through their terminals.

[0180] Step 3:

[0181] The user acquires image data of the farm using an unmanned aerial vehicle. The server receives this image data and analyzes it using OpenCV. The data analysis targets the health status of plants in the images, and if abnormalities are detected, control measures are suggested. The input data is image data, and the output is a health assessment and necessary control measures.

[0182] Step 4:

[0183] The server collects market information and analyzes it using the R programming language. The data includes market supply and demand, and price trends. Based on this, the server develops an optimal sales strategy. The input data is market conditions, and the output is the recommended sales strategy. Users can view this via their terminal.

[0184] Step 5:

[0185] The device records the user's voice and facial expressions via a microphone and camera. The server uses this data to analyze emotions using a generative AI model based on TensorFlow. The input data is the user's voice and facial expression information, and the output is an evaluation of their emotional state. Based on this evaluation, the server adjusts the information delivery method to reduce the burden on the user.

[0186] (Application Example 2)

[0187] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as a "server" and the smart device 14 as a "terminal".

[0188] In recent years, with the development of urban agriculture, farmers need to quickly and accurately utilize various environmental, meteorological, and market information to maintain efficient production activities. However, it is difficult to comprehensively analyze this information and formulate appropriate agricultural plans, and especially in urban areas where sudden climate changes and market fluctuations are common, flexible and adaptive responses are crucial. Furthermore, farmers experience high levels of mental stress, and emotional support is needed.

[0189] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0190] In this invention, the server includes means for acquiring and analyzing soil information to calculate and present the optimal amount of fertilizer and water to be used; means for acquiring weather information and suggesting the optimal timing for sowing and harvesting based on it; means for analyzing images to evaluate the health of crops and suggesting control measures if abnormalities are detected; and means for monitoring the emotional state of the user and adjusting the information provided. This makes it possible to improve production efficiency and reduce mental burden in urban agriculture.

[0191] "Soil information" refers to information about the physical and chemical properties of land, obtained in order to optimize the management of fertilizers and water necessary for agricultural activities.

[0192] "Weather information" refers to data on weather conditions such as temperature, precipitation, humidity, and wind speed, which is necessary when formulating agricultural plans.

[0193] "Image" refers to still images or video data captured and used to visually represent the health of crops.

[0194] "Market information" refers to data on price trends, supply and demand balance, and consumer needs that are necessary for formulating sales strategies for agricultural products.

[0195] "User emotional state" refers to the emotional and psychological state of individuals engaged in agricultural activities, and is a factor used to adjust the provision of information.

[0196] A "flying robot" is a type of unmanned aerial vehicle used to collect and analyze information while moving through the air.

[0197] The system for implementing this invention consists of a server, terminals, various sensors, and a flying robot. Users receive information through the terminals, aiming to improve the efficiency of agricultural activities and reduce mental stress.

[0198] The server analyzes soil information obtained from multiple soil sensors to calculate the optimal amount of fertilizer and water to use. This information is provided in real time to the user's device. Furthermore, the server receives weather information from meteorological agencies and suggests the optimal timing for planting and harvesting for the environment. A cloud-based weather forecasting system is used to aggregate and analyze weather data for weather analysis.

[0199] The flying robot periodically takes images of crops and transmits the data to a server. The server uses image analysis technology to evaluate the health of the crops, and if any abnormalities are detected, it quickly presents control measures to the user. Computer vision technology is used for this image analysis.

[0200] For market information analysis, the server uses market trend data from a large-scale database to generate and propose the latest sales strategies to users. This allows for the development of flexible sales strategies that adapt to market fluctuations.

[0201] Furthermore, the device's built-in emotion recognition sensor and generative AI model analyze the user's voice and facial expressions to evaluate their emotional state. If the user is experiencing stress, the system adjusts how information is presented to make it easier to understand. In this example prompt, it would be used in the form of, "Please tell me how you would like information presented to help alleviate the stress the user is experiencing. This system will provide information tailored to the user."

[0202] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0203] Step 1:

[0204] The server acquires soil information in real time from soil sensors. The main inputs are soil moisture, temperature, and pH value. This data is analyzed to calculate the optimal amount of fertilizer and water to use. An algorithm is used for the analysis, and the calculation results are sent to the terminal. The output is a fertilizer and water usage instruction presented to the user.

[0205] Step 2:

[0206] The server receives weather data provided by meteorological agencies. Inputs include information such as temperature, precipitation, wind speed, and humidity. This data is analyzed comprehensively to predict the optimal timing for planting and harvesting. The output is a suggested message regarding that timing, which is sent to the user via a terminal.

[0207] Step 3:

[0208] The flying robot periodically takes images of the crops. This image data is sent to a server. The server uses image analysis software to evaluate the health of the crops. The input is the image data, and the output is an evaluation of whether the crops are healthy and any control measures if abnormalities are detected. This is also sent to the terminal.

[0209] Step 4:

[0210] The server retrieves and analyzes market information from an online database. Inputs include current prices, demand trends, and inventory data. Based on the analysis results, the server generates an optimal sales strategy and outputs a message to the user proposing a sales plan.

[0211] Step 5:

[0212] The emotion recognition sensor built into the device captures the user's voice and facial expressions. The sensor's measurement data is used as input, and a generative AI model is used to determine emotions. Based on the determination result, the server adjusts how the information is displayed to make it easily understandable. The output is information and suggestions optimized for the user's state.

[0213] 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.

[0214] Data generation model 58 is a 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> ), Gemini (registered trademark) (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. 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. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0215] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the smart device 14.

[0216] [Second Embodiment]

[0217] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0218] 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.

[0219] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. 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 (Wide Area Network) and / or a LAN (Local Area Network).

[0220] 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.

[0221] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, 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.

[0222] 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, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0223] 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.

[0224] 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 using the processor 28. The storage 32 stores the specific processing program 56.

[0225] The specific processing program 56 is an example of a "program" relating 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 in accordance with the specific processing program 56 executed on the RAM 30.

[0226] The 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.

[0227] In the smart glasses 214, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0228] Next, the identification processing performed by the identification processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the smart glasses 214 will be referred to as the "terminal".

[0229] This invention provides a system for optimizing and achieving sustainability in agricultural production. This system consists of a server, terminals, and users, and provides comprehensive support to agricultural workers.

[0230] The server first receives soil data transmitted from soil sensors. Based on this data, it analyzes the soil's moisture, nutrients, and temperature, and calculates the appropriate amount of fertilizer and water to be used. The calculation results are then provided to the user via a terminal.

[0231] Furthermore, the server obtains real-time weather information from meteorological agencies. Based on this, it analyzes the optimal timing for planting and harvesting and notifies the user. For example, it may suggest whether to harvest earlier based on future rainfall forecasts.

[0232] In addition, the server receives and analyzes images of crops taken by drones. This analysis assesses the health of the crops and detects signs of pests and diseases. If an abnormality is detected early, the server suggests control measures to the user via the terminal. For example, it may recommend the use of specific pesticides on crops showing signs of pests or diseases.

[0233] The server also collects domestic and international market data and analyzes price fluctuations and changes in demand. This allows it to present users with market-specific sales strategies to maximize profits. For example, it might suggest opportunities to increase profits by exporting specific crops at specific times.

[0234] In summary, this system enables agricultural workers to make data-driven decisions and, as a result, improve agricultural production in an efficient and sustainable manner.

[0235] The following describes the processing flow.

[0236] Step 1:

[0237] The server receives soil data from soil sensors. This data includes soil moisture content, temperature, and nutrient levels, which are used for subsequent analysis.

[0238] Step 2:

[0239] The server analyzes the received soil data and calculates the optimal amount of fertilizer and water to use for each crop. The analysis utilizes machine learning models and statistical methods, and compares the current data with historical data.

[0240] Step 3:

[0241] The server generates calculation results and sends the optimal amount of fertilizer and water to the terminal. The terminal visualizes this information for the user, providing guidance for planning actual fertilization and watering.

[0242] Step 4:

[0243] The server retrieves real-time weather data from meteorological agencies. This data, including short-term and long-term weather forecasts, is used for scheduling agricultural work.

[0244] Step 5:

[0245] The server analyzes the acquired weather data and calculates the optimal timing for planting and harvesting. Based on the analysis results and predicted weather conditions, it notifies the user and suggests necessary actions.

[0246] Step 6:

[0247] The server receives images of crops taken by drones and analyzes them. Computer vision technology is used for image analysis to assess the health of the crops based on their leaf color and shape.

[0248] Step 7:

[0249] The server evaluates the health of the analyzed crops and, if any abnormalities in pests or diseases are detected, sends the details to the terminal. The terminal then suggests control measures and pesticide usage methods to the user, urging them to take prompt action.

[0250] Step 8:

[0251] The server collects market price and demand data from the market database. The collected data serves as foundational information for developing crop sales strategies.

[0252] Step 9:

[0253] The server analyzes collected market data to formulate the optimal sales timing and pricing strategy. The terminal communicates this to the user, supporting management decisions to maximize profits.

[0254] (Example 1)

[0255] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal."

[0256] Modern agriculture demands maximizing production efficiency while flexibly responding to environmental changes and market demands. However, traditional methods involve managing soil and weather conditions, crop health, and market trends individually, making it difficult to optimize these factors comprehensively. Solving this problem is essential to achieving sustainable agriculture and improving profitability.

[0257] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0258] This invention includes a server that acquires and analyzes soil information to calculate and present the optimal amount of agricultural materials and liquids to be used, a server that acquires information on natural phenomena and proposes the optimal timing for cultivation and harvesting based on this information, and a server that analyzes video to evaluate the health of plants and presents preventive measures if abnormalities are detected. This enables agricultural workers to make data-driven decisions, making efficient and environmentally friendly agricultural production possible.

[0259] "Soil information" refers to data about the physical and chemical properties of soil, including moisture, nutrients, and temperature.

[0260] "Natural phenomena information" refers to real-time and forecast data such as weather, temperature, precipitation, and wind speed, obtained from meteorological agencies and other sources.

[0261] "Agricultural materials and liquids" refer to chemicals and water used in agricultural activities, such as fertilizers and water, that are used to promote plant growth.

[0262] "Video" refers to visual data acquired by drones and other filming devices, which is used to understand the condition of crops and the state of the field.

[0263] "Plant health" refers to an indicator of a crop's growth status, and is judged based on visual information such as the plant's appearance, color, and shape.

[0264] "Distribution information" refers to economic data related to the sale of agricultural products, such as domestic and international market trends, price fluctuations, and demand forecasts.

[0265] "Data-driven processing" is an approach that involves comprehensively analyzing large amounts of collected data and making decisions based on the results, and is useful for optimizing agricultural production.

[0266] This invention provides a system for optimizing agricultural production based on data. This system primarily consists of a server, terminals, and users, each with a specific function.

[0267] The server receives data from soil sensors, drone cameras, weather agencies, and market databases. Soil data is primarily used to monitor soil moisture, nutrients, and temperature. This utilizes sensor technology and data analysis libraries (e.g., Python and R). For image analysis, generative AI models such as TensorFlow are used based on data acquired by drones to detect crop health and abnormalities.

[0268] The server retrieves real-time weather information via API and analyzes weather forecasts and their impact using statistical methods and machine learning algorithms. This allows it to suggest optimal planting and harvesting times to users. Market data is obtained from online market databases, and price and demand trends are analyzed. This is done using database management systems and analytical software.

[0269] The terminal receives analytical data provided by the server and displays it to the user in a visually easy-to-understand format. This enables users to make quick decisions on-site.

[0270] Based on the information provided by the system, users select and implement specific agricultural measures. This includes appropriate input of agricultural materials and adjustment of harvest timing.

[0271] As a concrete example, a server might analyze weather information and, based on rainfall forecasts, notify the user whether or not they should harvest earlier. An example of a prompt message would be, "Please tell me the data needed to determine the next harvest timing and how to analyze it."

[0272] This system supports users in improving agricultural efficiency and achieving sustainable production by comprehensively analyzing various types of data.

[0273] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0274] Step 1:

[0275] The server receives soil information from soil sensors. Specifically, raw data regarding soil moisture, nutrients, and temperature is input. The server processes this data using analysis software to calculate the optimal amount of fertilizer and water to use. The data obtained from this calculation is output, allowing for the development of an efficient plan for the application of agricultural materials.

[0276] Step 2:

[0277] The server acquires meteorological information. It inputs weather data from the API of meteorological institutions and calculates the optimal timing for sowing and harvesting based on it. It analyzes the weather trend using statistical methods and predicts future weather. This predicted information is output, and an optimal work schedule is proposed to the user.

[0278] Step 3:

[0279] The server receives video data of crops taken by a drone. It acquires image data as input and conducts analysis using a generated AI model. The results of evaluating the health status of crops and signs of pests and diseases are output, and if an abnormality is detected, appropriate control measures are notified to the user.

[0280] Step 4:

[0281] The server collects market data and analyzes distribution information. It inputs price and demand data from an online market database and analyzes market trends. As a result of trend analysis, proposed data for an optimal sales strategy is output and notified to the user.

[0282] Step 5:

[0283] The terminal receives the analysis results from the server and provides them to the user. Specifically, it visualizes the analysis data and outputs it in a visually easy-to-understand format. Based on this output, the user adjusts agricultural activities.

[0284] Step 6:

[0285] The user specifically develops agricultural activities based on the provided information. Input materials according to the calculated amount of fertilizer and water used, and schedule appropriate work based on the weather prediction. Thereby, data-driven decision-making is realized, and efficient and sustainable agriculture is promoted.

[0286] (Application Example 1) <00009​ Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal."

[0288] With the advancement of urbanization in modern society, urban agriculture is attracting attention, but there is a lack of appropriate information and technology for developing efficient cultivation management and profitable market strategies. As a result, it is difficult for individual users to obtain the optimal agricultural experience in their home gardens or community farms.

[0289] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0290] This invention includes a server that includes means for acquiring and analyzing soil information to calculate and present the optimal amounts of fertilizer and irrigation to be used; means for acquiring weather information and suggesting the optimal timing for cultivation and harvesting based on it; means for analyzing images to evaluate the health of plants and suggesting control measures if abnormalities are detected; means for analyzing economic data and suggesting the optimal sales strategy; and means for providing users with the optimal cultivation conditions and management methods for urban agriculture. This enables comprehensive support for practicing efficient and sustainable agriculture even in urban areas.

[0291] "Soil information" refers to data about the conditions below the soil surface, which are fundamental to plant growth. Specifically, it includes moisture content, nutrient levels, temperature, and other factors.

[0292] "Weather information" refers to data about atmospheric conditions, including elements such as temperature, precipitation, humidity, and wind speed, and has an impact on agricultural activities.

[0293] "Fertilization" refers to the act of providing the soil with nutrients necessary to promote plant growth, and mainly involves using fertilizers containing nitrogen, phosphorus, and potassium.

[0294] "Irrigation" is the act of supplying plants with the water they need, and is especially common in dry climates and soils lacking moisture.

[0295] "Image analysis" is a computer processing technique that uses digital images, and its purpose is to detect the health status and abnormalities of plants.

[0296] "Control measures" refer to actions taken to prevent or mitigate damage to plants from diseases, pests, and extreme weather conditions, and include, for example, the use of specific pesticides and the introduction of appropriate management techniques.

[0297] "Economic data" refers to information related to supply and demand in the market, including data on price fluctuations and competing products.

[0298] "Urban agriculture" refers to agricultural activities carried out in urban areas, including crop cultivation in home gardens and community farms.

[0299] "Cultivation conditions" refer to the environmental factors necessary for plant growth, including light, water, temperature, and soil nutrients.

[0300] A "sales strategy" is a plan or policy for efficiently selling a product in the market, and includes initiatives aimed at maximizing profits.

[0301] The system used to realize this application is a complex information processing system designed to support urban agriculture. The server communicates with sensors to collect soil information and analyzes that data. AWS Lambda is used for the analysis, evaluating soil moisture, nutrients, and temperature in real time, and calculating the optimal amount of fertilizer and irrigation. The OpenWeather API is also used to obtain weather information, suggesting optimal planting and harvesting times based on local weather conditions.

[0302] The terminal is the user's smartphone, which sends the image of the plant taken by the camera to the Google Cloud Vision API to evaluate the health condition of the plant. Based on this result, if an abnormality is detected, appropriate control measures can be instructed to the user. For the analysis of economic data, the Python libraries Pandas and BeautifulSoup are used to analyze the trends and provide the user with a better sales strategy.

[0303] Furthermore, the system appropriately presents cultivation conditions and management methods for urban agriculture to the users and provides support for practicing efficient and sustainable agricultural activities. With such a system, agricultural activities in the urban environment can be carried out easily and effectively. As a specific example of the prompt text, "This app provides optimal growth and sales strategies based on soil data, weather information, and market fluctuations to increase the efficiency of home gardens in the city. Specifically, please tell me when and how to grow which crops effectively." can be cited.

[0304] The flow of the specific process in Application Example 1 will be described using FIG. 12.

[0305] Step 1:

[0306] The server acquires soil information from the soil sensor and analyzes it in real time using AWS Lambda. In this analysis, the moisture content, nutrients, and temperature of the soil are evaluated, the optimal usage amounts of fertilization and irrigation are calculated, and this is sent to the terminal. The input is the soil sensor data, and the output is the optimal usage amounts of fertilization and irrigation.

[0307] Step 2:

[0308] The server acquires weather data via the OpenWeather API. Based on this data, the optimal timing of cultivation and harvesting is calculated, and the result is notified to the user. The input is the weather data, and the output is the recommended time for sowing and harvesting.

[0309] Step 3:

[0310] The user sends an image of a plant taken with their smartphone camera to the device, which then sends this image to the Google Cloud Vision API. The server analyzes the image to assess the plant's health and, if it detects signs of pests or diseases, instructs the user on appropriate control measures. The input is an image of the plant, and the output is the plant's health status and recommended control measures.

[0311] Step 4:

[0312] The server collects economic data using Python's Pandas and BeautifulSoup libraries and analyzes trends. The results of this analysis are then presented to the user as an optimal sales strategy. The input is market trends and economic data, and the output is the optimal sales strategy.

[0313] Step 5:

[0314] Users receive reminders and information on cultivation methods related to various farming tasks through their devices. This supports efficient and sustainable urban farming practices. Inputs are information on cultivation and management conditions, and outputs are specific reminders and cultivation methods corresponding to those conditions.

[0315] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0316] This invention is an integrated agricultural support system based on soil data, weather information, and market trends, aimed at improving the production efficiency and reducing the mental stress of agricultural workers. Furthermore, this system incorporates an emotion engine that monitors and adjusts the user's emotional state.

[0317] The server receives soil data from soil sensors, analyzes it, and calculates the optimal amount of fertilizer and water to use. The calculated information is provided to the user via a terminal. The terminal presents the information in a visually easy-to-understand format to support specific agricultural activities.

[0318] Regarding weather information acquisition, the server retrieves the latest data from meteorological agencies and suggests planting and harvesting timings. This allows the system to present users with agricultural plans that are suitable for changing weather conditions.

[0319] Drones are used to acquire images of crops, and a server uses this image data to assess the health of the crops. If signs of pests or diseases are found, the server suggests control measures to the user via a terminal, supporting a rapid response to protect the crops.

[0320] Market data analysis involves servers monitoring domestic and international market trends and formulating sales strategies tailored to fluctuating market conditions. Users can access these strategies through their terminals to develop profitable sales plans.

[0321] The newly added emotion engine uses the device's sensors to detect emotions from the user's voice, facial expressions, and other data, and adjusts the information and notifications provided according to the user's emotional state. For example, if the user is feeling stressed, it helps the user understand by presenting complex information in a concise manner. Furthermore, the emotion engine continuously learns from the user's feedback, enabling more accurate emotion analysis.

[0322] Thus, the present invention is designed to enable agricultural workers to perform their agricultural activities efficiently and comfortably by combining data analysis and emotion recognition technology.

[0323] The following describes the processing flow.

[0324] Step 1:

[0325] The server collects soil data received from soil sensors. This includes information such as soil moisture content, pH level, and nutrient content. The server processes this data in real time and stores it in a database.

[0326] Step 2:

[0327] The server analyzes the collected soil data and calculates the optimal amount of fertilizer and water for crop growth. This calculation uses machine learning algorithms and compares the current data with historical data.

[0328] Step 3:

[0329] The server sends the results of optimized fertilizer and water usage to the terminal. The terminal visualizes this information for the user and presents an automatically adjusted replenishment plan. The user then adjusts their daily farming activities based on this information.

[0330] Step 4:

[0331] The server periodically retrieves weather data from external meteorological agencies. This data includes factors that affect agriculture, such as temperature, precipitation, and wind speed.

[0332] Step 5:

[0333] The server analyzes the acquired weather data and calculates the optimal timing for planting and harvesting. The analysis results are notified to the user via the terminal, and necessary actions are suggested.

[0334] Step 6:

[0335] The server receives images of crops transmitted from the drone. The received images are analyzed using computer vision technology to assess the health of the crops based on the condition of their leaves and stems.

[0336] Step 7:

[0337] Based on the image analysis results, the server calculates countermeasures if abnormalities are found in the crops and sends them to the terminal. The terminal then issues a warning to the user and suggests appropriate control measures.

[0338] Step 8:

[0339] The server collects domestic and international market data and forecasts agricultural product price trends and demand. Based on the analysis results, it formulates the optimal sales strategy.

[0340] Step 9:

[0341] The terminal notifies the user of sales strategy proposals from the server, assisting with specific sales timing and pricing. The user then uses this information to adjust their market activities.

[0342] Step 10:

[0343] The emotion engine recognizes emotions through the user's voice and facial expressions and adjusts how information is presented on the device. If the user is stressed, the emotion engine simplifies the information and presents it to the user in a more easily understandable way.

[0344] Step 11:

[0345] Users provide feedback on the information and suggestions offered through their devices. The emotion engine learns from this feedback and improves the accuracy of future emotion recognition.

[0346] (Example 2)

[0347] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the smart glasses 214 will be referred to as the "terminal".

[0348] The aim is to solve the challenges faced by agricultural workers in improving production efficiency and reducing mental stress. Specifically, it aims to reduce the burden on workers by acquiring and analyzing various types of information necessary in the agricultural field, thereby formulating optimal agricultural plans and sales strategies. In addition, it aims to alleviate mental burden and support smooth production activities by providing information tailored to the emotional state of workers.

[0349] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0350] In this invention, the server includes means for acquiring and analyzing soil information to calculate and present the optimal amount of fertilizer and water to be used; means for acquiring weather information and proposing the optimal timing for cultivation and harvesting based on it; and means for analyzing images to evaluate the health of plants and presenting control measures if abnormalities are detected. This enables users to accurately acquire the information necessary to carry out production activities efficiently. Furthermore, profitability can be improved through means for analyzing market information and proposing the optimal sales strategy. In addition, by providing means for detecting the user's emotional state and adjusting the information provided based on it, it is possible to reduce mental stress.

[0351] "Soil information" refers to data that shows soil characteristics related to crop growth, such as pH, humidity, and nutrient content of farmland.

[0352] "Weather information" refers to data on meteorological conditions that affect agricultural activities, such as temperature, precipitation, and wind speed.

[0353] "Image information" refers to digital image data captured to visually understand the condition of crops.

[0354] An "unmanned aerial vehicle" refers to a drone that is remotely controlled or flies autonomously and is used for the purpose of acquiring data from the air.

[0355] A "user" refers to an individual or group that uses the system to conduct agricultural activities.

[0356] "Emotional state" refers to the psychological and emotional state of a user, obtained by analyzing their voice, facial expressions, and other data.

[0357] A "server" is a centralized computer system that receives and analyzes data and provides information to users.

[0358] "Plant health" refers to the state of a crop that shows signs of disease or pest infestation, or abnormalities in its growth.

[0359] This invention relates to an agricultural support system that aims to improve agricultural production efficiency and reduce mental stress through data communication and analysis between a server, terminal, and user.

[0360] The server plays a central role in receiving and analyzing various data necessary for agriculture. Soil information is collected from soil sensors installed in the fields and analyzed on the server. A data analysis algorithm using Python is applied to the analysis, which calculates the optimal amount of fertilizer and water to be used. Weather information is obtained through APIs provided by meteorological agencies, and the optimal timing for cultivation and harvesting is calculated on the server.

[0361] The user provides image information obtained by the unmanned aerial vehicle to the server. The server analyzes this image and uses it to assess the health of the plants. If signs of pests or diseases are detected, the server develops appropriate control measures and notifies the user via the terminal.

[0362] The server also collects and analyzes market information and presents users with sales strategies tailored to fluctuating market conditions. The R programming language is used for time-series analysis. Furthermore, the server incorporates a generative AI model that monitors the user's emotional state through the terminal. A TensorFlow-based model is used for emotion analysis, evaluating the user's psychological state from their voice and facial expressions. If stress is detected, the server adjusts the information it provides to reduce the user's burden.

[0363] A terminal is a device that receives data from a server and presents information to the user visually. An intuitive interface is employed here. For example, weather forecasts and soil analysis results could be displayed in graph format.

[0364] For example, if a user prompts, "Analyze the soil pH data and tell me the appropriate type and amount of fertilizer," the server will calculate the optimal fertilizer based on the soil information and notify the terminal. When plant images are provided to the server, a prompt such as, "Identify signs of pests and diseases from the crop images and tell me how to control them," can be used.

[0365] Thus, the present invention utilizes data analysis and emotion recognition technology to enable agricultural workers to perform their agricultural activities efficiently and comfortably.

[0366] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0367] Step 1:

[0368] The server acquires soil information from soil sensors. This data includes pH, humidity, and nutrient content. The server analyzes this information using a data analysis algorithm based on Python to calculate the optimal amount of fertilizer and water to use. The input data is soil characteristics, and the output is a proposal for optimized material usage. This proposal is notified to the user via a terminal.

[0369] Step 2:

[0370] The server obtains weather information from meteorological agencies via APIs. This data includes temperature, precipitation, wind speed, and other factors. Based on this information, the server calculates the optimal timing for planting and harvesting. The input data is weather conditions, and the output is the recommended timing for farm work. Users visually confirm this information through their terminals.

[0371] Step 3:

[0372] The user acquires image data of the farm using an unmanned aerial vehicle. The server receives this image data and analyzes it using OpenCV. The data analysis targets the health status of plants in the images, and if abnormalities are detected, control measures are suggested. The input data is image data, and the output is a health assessment and necessary control measures.

[0373] Step 4:

[0374] The server collects market information and analyzes it using the R programming language. The data includes market supply and demand, and price trends. Based on this, the server develops an optimal sales strategy. The input data is market conditions, and the output is the recommended sales strategy. Users can view this via their terminal.

[0375] Step 5:

[0376] The device records the user's voice and facial expressions via a microphone and camera. The server uses this data to analyze emotions using a generative AI model based on TensorFlow. The input data is the user's voice and facial expression information, and the output is an evaluation of their emotional state. Based on this evaluation, the server adjusts the information delivery method to reduce the burden on the user.

[0377] (Application Example 2)

[0378] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal."

[0379] In recent years, with the development of urban agriculture, farmers need to quickly and accurately utilize various environmental, meteorological, and market information to maintain efficient production activities. However, it is difficult to comprehensively analyze this information and formulate appropriate agricultural plans, and especially in urban areas where sudden climate changes and market fluctuations are common, flexible and adaptive responses are crucial. Furthermore, farmers experience high levels of mental stress, and emotional support is needed.

[0380] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0381] In this invention, the server includes means for acquiring and analyzing soil information to calculate and present the optimal amount of fertilizer and water to be used; means for acquiring weather information and suggesting the optimal timing for sowing and harvesting based on it; means for analyzing images to evaluate the health of crops and suggesting control measures if abnormalities are detected; and means for monitoring the emotional state of the user and adjusting the information provided. This makes it possible to improve production efficiency and reduce mental burden in urban agriculture.

[0382] "Soil information" refers to information about the physical and chemical properties of land, obtained in order to optimize the management of fertilizers and water necessary for agricultural activities.

[0383] "Weather information" refers to data on weather conditions such as temperature, precipitation, humidity, and wind speed, which is necessary when formulating agricultural plans.

[0384] "Image" refers to still images or video data captured and used to visually represent the health of crops.

[0385] "Market information" refers to data on price trends, supply and demand balance, and consumer needs that are necessary for formulating sales strategies for agricultural products.

[0386] "User emotional state" refers to the emotional and psychological state of individuals engaged in agricultural activities, and is a factor used to adjust the provision of information.

[0387] A "flying robot" is a type of unmanned aerial vehicle used to collect and analyze information while moving through the air.

[0388] The system for implementing this invention consists of a server, terminals, various sensors, and a flying robot. Users receive information through the terminals, aiming to improve the efficiency of agricultural activities and reduce mental stress.

[0389] The server analyzes soil information obtained from multiple soil sensors to calculate the optimal amount of fertilizer and water to use. This information is provided in real time to the user's device. Furthermore, the server receives weather information from meteorological agencies and suggests the optimal timing for planting and harvesting for the environment. A cloud-based weather forecasting system is used to aggregate and analyze weather data for weather analysis.

[0390] The flying robot periodically takes images of crops and transmits the data to a server. The server uses image analysis technology to evaluate the health of the crops, and if any abnormalities are detected, it quickly presents control measures to the user. Computer vision technology is used for this image analysis.

[0391] For market information analysis, the server uses market trend data from a large-scale database to generate and propose the latest sales strategies to users. This allows for the development of flexible sales strategies that adapt to market fluctuations.

[0392] Furthermore, the device's built-in emotion recognition sensor and generative AI model analyze the user's voice and facial expressions to evaluate their emotional state. If the user is experiencing stress, the system adjusts how information is presented to make it easier to understand. In this example prompt, it would be used in the form of, "Please tell me how you would like information presented to help alleviate the stress the user is experiencing. This system will provide information tailored to the user."

[0393] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0394] Step 1:

[0395] The server acquires soil information in real time from soil sensors. The main inputs are soil moisture, temperature, and pH value. This data is analyzed to calculate the optimal amount of fertilizer and water to use. An algorithm is used for the analysis, and the calculation results are sent to the terminal. The output is a fertilizer and water usage instruction presented to the user.

[0396] Step 2:

[0397] The server receives weather data provided by meteorological agencies. Inputs include information such as temperature, precipitation, wind speed, and humidity. This data is analyzed comprehensively to predict the optimal timing for planting and harvesting. The output is a suggested message regarding that timing, which is sent to the user via a terminal.

[0398] Step 3:

[0399] The flying robot periodically takes images of the crops. This image data is sent to a server. The server uses image analysis software to evaluate the health of the crops. The input is the image data, and the output is an evaluation of whether the crops are healthy and any control measures if abnormalities are detected. This is also sent to the terminal.

[0400] Step 4:

[0401] The server retrieves and analyzes market information from an online database. Inputs include current prices, demand trends, and inventory data. Based on the analysis results, the server generates an optimal sales strategy and outputs a message to the user proposing a sales plan.

[0402] Step 5:

[0403] The emotion recognition sensor built into the device captures the user's voice and facial expressions. The sensor's measurement data is used as input, and a generative AI model is used to determine emotions. Based on the determination result, the server adjusts how the information is displayed to make it easily understandable. The output is information and suggestions optimized for the user's state.

[0404] 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.

[0405] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. 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. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0406] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the smart glasses 214.

[0407] [Third Embodiment]

[0408] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0409] 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.

[0410] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. 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 (Wide Area Network) and / or a LAN (Local Area Network).

[0411] 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.

[0412] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, 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.

[0413] 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, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0414] 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.

[0415] 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.

[0416] The specific processing program 56 is an example of a "program" relating 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 in accordance with the specific processing program 56 executed on the RAM 30.

[0417] The 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.

[0418] In the headset terminal 314, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0419] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the headset terminal 314 will be referred to as the "terminal".

[0420] This invention provides a system for optimizing and achieving sustainability in agricultural production. This system consists of a server, terminals, and users, and provides comprehensive support to agricultural workers.

[0421] The server first receives soil data transmitted from soil sensors. Based on this data, it analyzes the soil's moisture, nutrients, and temperature, and calculates the appropriate amount of fertilizer and water to be used. The calculation results are then provided to the user via a terminal.

[0422] Furthermore, the server obtains real-time weather information from meteorological agencies. Based on this, it analyzes the optimal timing for planting and harvesting and notifies the user. For example, it may suggest whether to harvest earlier based on future rainfall forecasts.

[0423] In addition, the server receives and analyzes images of crops taken by drones. This analysis assesses the health of the crops and detects signs of pests and diseases. If an abnormality is detected early, the server suggests control measures to the user via the terminal. For example, it may recommend the use of specific pesticides on crops showing signs of pests or diseases.

[0424] The server also collects domestic and international market data and analyzes price fluctuations and changes in demand. This allows it to present users with market-specific sales strategies to maximize profits. For example, it might suggest opportunities to increase profits by exporting specific crops at specific times.

[0425] In summary, this system enables agricultural workers to make data-driven decisions and, as a result, improve agricultural production in an efficient and sustainable manner.

[0426] The following describes the processing flow.

[0427] Step 1:

[0428] The server receives soil data from soil sensors. This data includes soil moisture content, temperature, and nutrient levels, which are used for subsequent analysis.

[0429] Step 2:

[0430] The server analyzes the received soil data and calculates the optimal amount of fertilizer and water to use for each crop. The analysis utilizes machine learning models and statistical methods, and compares the current data with historical data.

[0431] Step 3:

[0432] The server generates calculation results and sends the optimal amount of fertilizer and water to the terminal. The terminal visualizes this information for the user, providing guidance for planning actual fertilization and watering.

[0433] Step 4:

[0434] The server retrieves real-time weather data from meteorological agencies. This data, including short-term and long-term weather forecasts, is used for scheduling agricultural work.

[0435] Step 5:

[0436] The server analyzes the acquired weather data and calculates the optimal timing for planting and harvesting. Based on the analysis results and predicted weather conditions, it notifies the user and suggests necessary actions.

[0437] Step 6:

[0438] The server receives images of crops taken by drones and analyzes them. Computer vision technology is used for image analysis to assess the health of the crops based on their leaf color and shape.

[0439] Step 7:

[0440] The server evaluates the health of the analyzed crops and, if any abnormalities in pests or diseases are detected, sends the details to the terminal. The terminal then suggests control measures and pesticide usage methods to the user, urging them to take prompt action.

[0441] Step 8:

[0442] The server collects market price and demand data from the market database. The collected data serves as foundational information for developing crop sales strategies.

[0443] Step 9:

[0444] The server analyzes collected market data to formulate the optimal sales timing and pricing strategy. The terminal communicates this to the user, supporting management decisions to maximize profits.

[0445] (Example 1)

[0446] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0447] Modern agriculture demands maximizing production efficiency while flexibly responding to environmental changes and market demands. However, traditional methods involve managing soil and weather conditions, crop health, and market trends individually, making it difficult to optimize these factors comprehensively. Solving this problem is essential to achieving sustainable agriculture and improving profitability.

[0448] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0449] This invention includes a server that acquires and analyzes soil information to calculate and present the optimal amount of agricultural materials and liquids to be used, a server that acquires information on natural phenomena and proposes the optimal timing for cultivation and harvesting based on this information, and a server that analyzes video to evaluate the health of plants and presents preventive measures if abnormalities are detected. This enables agricultural workers to make data-driven decisions, making efficient and environmentally friendly agricultural production possible.

[0450] "Soil information" refers to data about the physical and chemical properties of soil, including moisture, nutrients, and temperature.

[0451] "Natural phenomena information" refers to real-time and forecast data such as weather, temperature, precipitation, and wind speed, obtained from meteorological agencies and other sources.

[0452] "Agricultural materials and liquids" refer to chemicals and water used in agricultural activities, such as fertilizers and water, that are used to promote plant growth.

[0453] "Video" refers to visual data acquired by drones and other filming devices, which is used to understand the condition of crops and the state of the field.

[0454] "Plant health" refers to an indicator of a crop's growth status, and is judged based on visual information such as the plant's appearance, color, and shape.

[0455] "Distribution information" refers to economic data related to the sale of agricultural products, such as domestic and international market trends, price fluctuations, and demand forecasts.

[0456] "Data-driven processing" is an approach that involves comprehensively analyzing large amounts of collected data and making decisions based on the results, and is useful for optimizing agricultural production.

[0457] This invention provides a system for optimizing agricultural production based on data. This system primarily consists of a server, terminals, and users, each with a specific function.

[0458] The server receives data from soil sensors, drone cameras, weather agencies, and market databases. Soil data is primarily used to monitor soil moisture, nutrients, and temperature. This utilizes sensor technology and data analysis libraries (e.g., Python and R). For image analysis, generative AI models such as TensorFlow are used based on data acquired by drones to detect crop health and abnormalities.

[0459] The server retrieves real-time weather information via API and analyzes weather forecasts and their impact using statistical methods and machine learning algorithms. This allows it to suggest optimal planting and harvesting times to users. Market data is obtained from online market databases, and price and demand trends are analyzed. This is done using database management systems and analytical software.

[0460] The terminal receives analytical data provided by the server and displays it to the user in a visually easy-to-understand format. This enables users to make quick decisions on-site.

[0461] Based on the information provided by the system, users select and implement specific agricultural measures. This includes appropriate input of agricultural materials and adjustment of harvest timing.

[0462] As a concrete example, a server might analyze weather information and, based on rainfall forecasts, notify the user whether or not they should harvest earlier. An example of a prompt message would be, "Please tell me the data needed to determine the next harvest timing and how to analyze it."

[0463] This system supports users in improving agricultural efficiency and achieving sustainable production by comprehensively analyzing various types of data.

[0464] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0465] Step 1:

[0466] The server receives soil information from soil sensors. Specifically, raw data regarding soil moisture, nutrients, and temperature is input. The server processes this data using analysis software to calculate the optimal amount of fertilizer and water to use. The data obtained from this calculation is output, allowing for the development of an efficient plan for the application of agricultural materials.

[0467] Step 2:

[0468] The server acquires weather information. It inputs weather data from meteorological agencies' APIs and calculates the optimal timing for planting and harvesting based on that data. It analyzes weather trends using statistical methods and predicts future weather. This forecast information is output, and the user is offered a suggestion for the optimal work schedule.

[0469] Step 3:

[0470] The server receives video data of crops captured by drones. It takes image data as input and uses a generative AI model for analysis. The server outputs the results of an evaluation of the crops' health and signs of pests and diseases. If an abnormality is detected, the user is notified of appropriate control measures.

[0471] Step 4:

[0472] The server collects market data and analyzes distribution information. It inputs price and demand data from online market databases and analyzes market trends. As a result of the trend analysis, data suggesting the optimal sales strategy is output and notified to the user.

[0473] Step 5:

[0474] The terminal receives analysis results from the server and provides them to the user. Specifically, it visualizes the analysis data and outputs it in a visually easy-to-understand format. Based on this output, the user adjusts their agricultural activities.

[0475] Step 6:

[0476] Users develop specific agricultural activities based on the information provided. They apply materials according to calculated fertilizer and water usage amounts and schedule appropriate work based on weather forecasts. This enables data-driven decision-making and promotes efficient and sustainable agriculture.

[0477] (Application Example 1)

[0478] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0479] With the advancement of urbanization in modern society, urban agriculture is attracting attention, but there is a lack of appropriate information and technology for developing efficient cultivation management and profitable market strategies. As a result, it is difficult for individual users to obtain the optimal agricultural experience in their home gardens or community farms.

[0480] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0481] This invention includes a server that includes means for acquiring and analyzing soil information to calculate and present the optimal amounts of fertilizer and irrigation to be used; means for acquiring weather information and suggesting the optimal timing for cultivation and harvesting based on it; means for analyzing images to evaluate the health of plants and suggesting control measures if abnormalities are detected; means for analyzing economic data and suggesting the optimal sales strategy; and means for providing users with the optimal cultivation conditions and management methods for urban agriculture. This enables comprehensive support for practicing efficient and sustainable agriculture even in urban areas.

[0482] "Soil information" refers to data about the conditions below the soil surface, which are fundamental to plant growth. Specifically, it includes moisture content, nutrient levels, temperature, and other factors.

[0483] "Weather information" refers to data about atmospheric conditions, including elements such as temperature, precipitation, humidity, and wind speed, and has an impact on agricultural activities.

[0484] "Fertilization" refers to the act of providing the soil with nutrients necessary to promote plant growth, and mainly involves using fertilizers containing nitrogen, phosphorus, and potassium.

[0485] "Irrigation" is the act of supplying plants with the water they need, and is especially common in dry climates and soils lacking moisture.

[0486] "Image analysis" is a computer processing technique that uses digital images, and its purpose is to detect the health status and abnormalities of plants.

[0487] "Control measures" refer to actions taken to prevent or mitigate damage to plants from diseases, pests, and extreme weather conditions, and include, for example, the use of specific pesticides and the introduction of appropriate management techniques.

[0488] "Economic data" refers to information related to supply and demand in the market, including data on price fluctuations and competing products.

[0489] "Urban agriculture" refers to agricultural activities carried out in urban areas, including crop cultivation in home gardens and community farms.

[0490] "Cultivation conditions" refer to the environmental factors necessary for plant growth, including light, water, temperature, and soil nutrients.

[0491] A "sales strategy" is a plan or policy for efficiently selling a product in the market, and includes initiatives aimed at maximizing profits.

[0492] The system used to realize this application is a complex information processing system designed to support urban agriculture. The server communicates with sensors to collect soil information and analyzes that data. AWS Lambda is used for the analysis, evaluating soil moisture, nutrients, and temperature in real time, and calculating the optimal amount of fertilizer and irrigation. The OpenWeather API is also used to obtain weather information, suggesting optimal planting and harvesting times based on local weather conditions.

[0493] The terminal is the user's smartphone, which sends images of plants taken with its camera to the Google Cloud Vision API to evaluate the plant's health. Based on these results, if an abnormality is detected, the system can instruct the user on appropriate control measures. For economic data analysis, the Python libraries Pandas and BeautifulSoup are used to analyze trends and provide the user with better sales strategies.

[0494] Furthermore, the system provides users with appropriate cultivation conditions and management methods for urban agriculture, supporting them in practicing efficient and sustainable agricultural activities. Such a system makes agricultural activities in urban environments easy and effective. An example of a prompt message would be: "This app provides optimal growing and sales strategies based on soil data, weather information, and market fluctuations to improve the efficiency of urban home gardens. Specifically, please tell me which crops to grow and how to grow them at what time of year."

[0495] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0496] Step 1:

[0497] The server acquires soil information from soil sensors and analyzes it in real time using AWS Lambda. This analysis evaluates soil moisture content, nutrients, and temperature, calculates the optimal amount of fertilizer and irrigation to be used, and sends this information to the terminal. The input is soil sensor data, and the output is the optimal amount of fertilizer and irrigation to be used.

[0498] Step 2:

[0499] The server retrieves weather data via the OpenWeather API. Based on this data, it calculates the optimal timing for planting and harvesting and notifies the user of the results. The input is weather data, and the output is the recommended planting and harvesting times.

[0500] Step 3:

[0501] The user sends an image of a plant taken with their smartphone camera to the device, which then sends this image to the Google Cloud Vision API. The server analyzes the image to assess the plant's health and, if it detects signs of pests or diseases, instructs the user on appropriate control measures. The input is an image of the plant, and the output is the plant's health status and recommended control measures.

[0502] Step 4:

[0503] The server collects economic data using Python's Pandas and BeautifulSoup libraries and analyzes trends. The results of this analysis are then presented to the user as an optimal sales strategy. The input is market trends and economic data, and the output is the optimal sales strategy.

[0504] Step 5:

[0505] Users receive reminders and information on cultivation methods related to various farming tasks through their devices. This supports efficient and sustainable urban farming practices. Inputs are information on cultivation and management conditions, and outputs are specific reminders and cultivation methods corresponding to those conditions.

[0506] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0507] This invention is an integrated agricultural support system based on soil data, weather information, and market trends, aimed at improving the production efficiency and reducing the mental stress of agricultural workers. Furthermore, this system incorporates an emotion engine that monitors and adjusts the user's emotional state.

[0508] The server receives soil data from soil sensors, analyzes it, and calculates the optimal amount of fertilizer and water to use. The calculated information is provided to the user via a terminal. The terminal presents the information in a visually easy-to-understand format to support specific agricultural activities.

[0509] Regarding weather information acquisition, the server retrieves the latest data from meteorological agencies and suggests planting and harvesting timings. This allows the system to present users with agricultural plans that are suitable for changing weather conditions.

[0510] Drones are used to acquire images of crops, and a server uses this image data to assess the health of the crops. If signs of pests or diseases are found, the server suggests control measures to the user via a terminal, supporting a rapid response to protect the crops.

[0511] Market data analysis involves servers monitoring domestic and international market trends and formulating sales strategies tailored to fluctuating market conditions. Users can access these strategies through their terminals to develop profitable sales plans.

[0512] The newly added emotion engine uses the device's sensors to detect emotions from the user's voice, facial expressions, and other data, and adjusts the information and notifications provided according to the user's emotional state. For example, if the user is feeling stressed, it helps the user understand by presenting complex information in a concise manner. Furthermore, the emotion engine continuously learns from the user's feedback, enabling more accurate emotion analysis.

[0513] Thus, the present invention is designed to enable agricultural workers to perform their agricultural activities efficiently and comfortably by combining data analysis and emotion recognition technology.

[0514] The following describes the processing flow.

[0515] Step 1:

[0516] The server collects soil data received from soil sensors. This includes information such as soil moisture content, pH level, and nutrient content. The server processes this data in real time and stores it in a database.

[0517] Step 2:

[0518] The server analyzes the collected soil data and calculates the optimal amount of fertilizer and water for crop growth. This calculation uses machine learning algorithms and compares the current data with historical data.

[0519] Step 3:

[0520] The server sends the results of optimized fertilizer and water usage to the terminal. The terminal visualizes this information for the user and presents an automatically adjusted replenishment plan. The user then adjusts their daily farming activities based on this information.

[0521] Step 4:

[0522] The server periodically retrieves weather data from external meteorological agencies. This data includes factors that affect agriculture, such as temperature, precipitation, and wind speed.

[0523] Step 5:

[0524] The server analyzes the acquired weather data and calculates the optimal timing for planting and harvesting. The analysis results are notified to the user via the terminal, and necessary actions are suggested.

[0525] Step 6:

[0526] The server receives images of crops transmitted from the drone. The received images are analyzed using computer vision technology to assess the health of the crops based on the condition of their leaves and stems.

[0527] Step 7:

[0528] Based on the image analysis results, the server calculates countermeasures if abnormalities are found in the crops and sends them to the terminal. The terminal then issues a warning to the user and suggests appropriate control measures.

[0529] Step 8:

[0530] The server collects domestic and international market data and forecasts agricultural product price trends and demand. Based on the analysis results, it formulates the optimal sales strategy.

[0531] Step 9:

[0532] The terminal notifies the user of sales strategy proposals from the server, assisting with specific sales timing and pricing. The user then uses this information to adjust their market activities.

[0533] Step 10:

[0534] The emotion engine recognizes emotions through the user's voice and facial expressions and adjusts how information is presented on the device. If the user is stressed, the emotion engine simplifies the information and presents it to the user in a more easily understandable way.

[0535] Step 11:

[0536] Users provide feedback on the information and suggestions offered through their devices. The emotion engine learns from this feedback and improves the accuracy of future emotion recognition.

[0537] (Example 2)

[0538] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0539] The aim is to solve the challenges faced by agricultural workers in improving production efficiency and reducing mental stress. Specifically, it aims to reduce the burden on workers by acquiring and analyzing various types of information necessary in the agricultural field, thereby formulating optimal agricultural plans and sales strategies. In addition, it aims to alleviate mental burden and support smooth production activities by providing information tailored to the emotional state of workers.

[0540] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0541] In this invention, the server includes means for acquiring and analyzing soil information to calculate and present the optimal amount of fertilizer and water to be used; means for acquiring weather information and proposing the optimal timing for cultivation and harvesting based on it; and means for analyzing images to evaluate the health of plants and presenting control measures if abnormalities are detected. This enables users to accurately acquire the information necessary to carry out production activities efficiently. Furthermore, profitability can be improved through means for analyzing market information and proposing the optimal sales strategy. In addition, by providing means for detecting the user's emotional state and adjusting the information provided based on it, it is possible to reduce mental stress.

[0542] "Soil information" refers to data that shows soil characteristics related to crop growth, such as pH, humidity, and nutrient content of farmland.

[0543] "Weather information" refers to data on meteorological conditions that affect agricultural activities, such as temperature, precipitation, and wind speed.

[0544] "Image information" refers to digital image data captured to visually understand the condition of crops.

[0545] An "unmanned aerial vehicle" refers to a drone that is remotely controlled or flies autonomously and is used for the purpose of acquiring data from the air.

[0546] A "user" refers to an individual or group that uses the system to conduct agricultural activities.

[0547] "Emotional state" refers to the psychological and emotional state of a user, obtained by analyzing their voice, facial expressions, and other data.

[0548] A "server" is a centralized computer system that receives and analyzes data and provides information to users.

[0549] "Plant health" refers to the state of a crop that shows signs of disease or pest infestation, or abnormalities in its growth.

[0550] This invention relates to an agricultural support system that aims to improve agricultural production efficiency and reduce mental stress through data communication and analysis between a server, terminal, and user.

[0551] The server plays a central role in receiving and analyzing various data necessary for agriculture. Soil information is collected from soil sensors installed in the fields and analyzed on the server. A data analysis algorithm using Python is applied to the analysis, which calculates the optimal amount of fertilizer and water to be used. Weather information is obtained through APIs provided by meteorological agencies, and the optimal timing for cultivation and harvesting is calculated on the server.

[0552] The user provides image information obtained by the unmanned aerial vehicle to the server. The server analyzes this image and uses it to assess the health of the plants. If signs of pests or diseases are detected, the server develops appropriate control measures and notifies the user via the terminal.

[0553] The server also collects and analyzes market information and presents users with sales strategies tailored to fluctuating market conditions. The R programming language is used for time-series analysis. Furthermore, the server incorporates a generative AI model that monitors the user's emotional state through the terminal. A TensorFlow-based model is used for emotion analysis, evaluating the user's psychological state from their voice and facial expressions. If stress is detected, the server adjusts the information it provides to reduce the user's burden.

[0554] A terminal is a device that receives data from a server and presents information to the user visually. An intuitive interface is employed here. For example, weather forecasts and soil analysis results could be displayed in graph format.

[0555] For example, if a user prompts, "Analyze the soil pH data and tell me the appropriate type and amount of fertilizer," the server will calculate the optimal fertilizer based on the soil information and notify the terminal. When plant images are provided to the server, a prompt such as, "Identify signs of pests and diseases from the crop images and tell me how to control them," can be used.

[0556] Thus, the present invention utilizes data analysis and emotion recognition technology to enable agricultural workers to perform their agricultural activities efficiently and comfortably.

[0557] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0558] Step 1:

[0559] The server acquires soil information from soil sensors. This data includes pH, humidity, and nutrient content. The server analyzes this information using a data analysis algorithm based on Python to calculate the optimal amount of fertilizer and water to use. The input data is soil characteristics, and the output is a proposal for optimized material usage. This proposal is notified to the user via a terminal.

[0560] Step 2:

[0561] The server obtains weather information from meteorological agencies via APIs. This data includes temperature, precipitation, wind speed, and other factors. Based on this information, the server calculates the optimal timing for planting and harvesting. The input data is weather conditions, and the output is the recommended timing for farm work. Users visually confirm this information through their terminals.

[0562] Step 3:

[0563] The user acquires image data of the farm using an unmanned aerial vehicle. The server receives this image data and analyzes it using OpenCV. The data analysis targets the health status of plants in the images, and if abnormalities are detected, control measures are suggested. The input data is image data, and the output is a health assessment and necessary control measures.

[0564] Step 4:

[0565] The server collects market information and analyzes it using the R programming language. The data includes market supply and demand, and price trends. Based on this, the server develops an optimal sales strategy. The input data is market conditions, and the output is the recommended sales strategy. Users can view this via their terminal.

[0566] Step 5:

[0567] The device records the user's voice and facial expressions via a microphone and camera. The server uses this data to analyze emotions using a generative AI model based on TensorFlow. The input data is the user's voice and facial expression information, and the output is an evaluation of their emotional state. Based on this evaluation, the server adjusts the information delivery method to reduce the burden on the user.

[0568] (Application Example 2)

[0569] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0570] In recent years, with the development of urban agriculture, farmers need to quickly and accurately utilize various environmental, meteorological, and market information to maintain efficient production activities. However, it is difficult to comprehensively analyze this information and formulate appropriate agricultural plans, and especially in urban areas where sudden climate changes and market fluctuations are common, flexible and adaptive responses are crucial. Furthermore, farmers experience high levels of mental stress, and emotional support is needed.

[0571] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0572] In this invention, the server includes means for acquiring and analyzing soil information to calculate and present the optimal amount of fertilizer and water to be used; means for acquiring weather information and suggesting the optimal timing for sowing and harvesting based on it; means for analyzing images to evaluate the health of crops and suggesting control measures if abnormalities are detected; and means for monitoring the emotional state of the user and adjusting the information provided. This makes it possible to improve production efficiency and reduce mental burden in urban agriculture.

[0573] "Soil information" refers to information about the physical and chemical properties of land, obtained in order to optimize the management of fertilizers and water necessary for agricultural activities.

[0574] "Weather information" refers to data on weather conditions such as temperature, precipitation, humidity, and wind speed, which is necessary when formulating agricultural plans.

[0575] "Image" refers to still images or video data captured and used to visually represent the health of crops.

[0576] "Market information" refers to data on price trends, supply and demand balance, and consumer needs that are necessary for formulating sales strategies for agricultural products.

[0577] "User emotional state" refers to the emotional and psychological state of individuals engaged in agricultural activities, and is a factor used to adjust the provision of information.

[0578] A "flying robot" is a type of unmanned aerial vehicle used to collect and analyze information while moving through the air.

[0579] The system for implementing this invention consists of a server, terminals, various sensors, and a flying robot. Users receive information through the terminals, aiming to improve the efficiency of agricultural activities and reduce mental stress.

[0580] The server analyzes soil information obtained from multiple soil sensors to calculate the optimal amount of fertilizer and water to use. This information is provided in real time to the user's device. Furthermore, the server receives weather information from meteorological agencies and suggests the optimal timing for planting and harvesting for the environment. A cloud-based weather forecasting system is used to aggregate and analyze weather data for weather analysis.

[0581] The flying robot periodically takes images of crops and transmits the data to a server. The server uses image analysis technology to evaluate the health of the crops, and if any abnormalities are detected, it quickly presents control measures to the user. Computer vision technology is used for this image analysis.

[0582] For market information analysis, the server uses market trend data from a large-scale database to generate and propose the latest sales strategies to users. This allows for the development of flexible sales strategies that adapt to market fluctuations.

[0583] Furthermore, the device's built-in emotion recognition sensor and generative AI model analyze the user's voice and facial expressions to evaluate their emotional state. If the user is experiencing stress, the system adjusts how information is presented to make it easier to understand. In this example prompt, it would be used in the form of, "Please tell me how you would like information presented to help alleviate the stress the user is experiencing. This system will provide information tailored to the user."

[0584] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0585] Step 1:

[0586] The server acquires soil information in real time from soil sensors. The main inputs are soil moisture, temperature, and pH value. This data is analyzed to calculate the optimal amount of fertilizer and water to use. An algorithm is used for the analysis, and the calculation results are sent to the terminal. The output is a fertilizer and water usage instruction presented to the user.

[0587] Step 2:

[0588] The server receives weather data provided by meteorological agencies. Inputs include information such as temperature, precipitation, wind speed, and humidity. This data is analyzed comprehensively to predict the optimal timing for planting and harvesting. The output is a suggested message regarding that timing, which is sent to the user via a terminal.

[0589] Step 3:

[0590] The flying robot periodically takes images of the crops. This image data is sent to a server. The server uses image analysis software to evaluate the health of the crops. The input is the image data, and the output is an evaluation of whether the crops are healthy and any control measures if abnormalities are detected. This is also sent to the terminal.

[0591] Step 4:

[0592] The server retrieves and analyzes market information from an online database. Inputs include current prices, demand trends, and inventory data. Based on the analysis results, the server generates an optimal sales strategy and outputs a message to the user proposing a sales plan.

[0593] Step 5:

[0594] The emotion recognition sensor built into the device captures the user's voice and facial expressions. The sensor's measurement data is used as input, and a generative AI model is used to determine emotions. Based on the determination result, the server adjusts how the information is displayed to make it easily understandable. The output is information and suggestions optimized for the user's state.

[0595] 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.

[0596] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. 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. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0597] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and specific processing may also be performed by the headset terminal 314.

[0598] [Fourth Embodiment]

[0599] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0600] 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.

[0601] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. 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 (Wide Area Network) and / or a LAN (Local Area Network).

[0602] 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.

[0603] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, 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.

[0604] 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, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0605] 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.

[0606] 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. Furthermore, the robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0607] 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.

[0608] The specific processing program 56 is an example of a "program" relating 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 in accordance with the specific processing program 56 executed on the RAM 30.

[0609] The 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.

[0610] In robot 414, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0611] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0612] This invention provides a system for optimizing and achieving sustainability in agricultural production. This system consists of a server, terminals, and users, and provides comprehensive support to agricultural workers.

[0613] The server first receives soil data transmitted from soil sensors. Based on this data, it analyzes the soil's moisture, nutrients, and temperature, and calculates the appropriate amount of fertilizer and water to be used. The calculation results are then provided to the user via a terminal.

[0614] Furthermore, the server obtains real-time weather information from meteorological agencies. Based on this, it analyzes the optimal timing for planting and harvesting and notifies the user. For example, it may suggest whether to harvest earlier based on future rainfall forecasts.

[0615] In addition, the server receives and analyzes images of crops taken by drones. This analysis assesses the health of the crops and detects signs of pests and diseases. If an abnormality is detected early, the server suggests control measures to the user via the terminal. For example, it may recommend the use of specific pesticides on crops showing signs of pests or diseases.

[0616] The server also collects domestic and international market data and analyzes price fluctuations and changes in demand. This allows it to present users with market-specific sales strategies to maximize profits. For example, it might suggest opportunities to increase profits by exporting specific crops at specific times.

[0617] In summary, this system enables agricultural workers to make data-driven decisions and, as a result, improve agricultural production in an efficient and sustainable manner.

[0618] The following describes the processing flow.

[0619] Step 1:

[0620] The server receives soil data from soil sensors. This data includes soil moisture content, temperature, and nutrient levels, which are used for subsequent analysis.

[0621] Step 2:

[0622] The server analyzes the received soil data and calculates the optimal amount of fertilizer and water to use for each crop. The analysis utilizes machine learning models and statistical methods, and compares the current data with historical data.

[0623] Step 3:

[0624] The server generates calculation results and sends the optimal amount of fertilizer and water to the terminal. The terminal visualizes this information for the user, providing guidance for planning actual fertilization and watering.

[0625] Step 4:

[0626] The server retrieves real-time weather data from meteorological agencies. This data, including short-term and long-term weather forecasts, is used for scheduling agricultural work.

[0627] Step 5:

[0628] The server analyzes the acquired weather data and calculates the optimal timing for planting and harvesting. Based on the analysis results and predicted weather conditions, it notifies the user and suggests necessary actions.

[0629] Step 6:

[0630] The server receives images of crops taken by drones and analyzes them. Computer vision technology is used for image analysis to assess the health of the crops based on their leaf color and shape.

[0631] Step 7:

[0632] The server evaluates the health of the analyzed crops and, if any abnormalities in pests or diseases are detected, sends the details to the terminal. The terminal then suggests control measures and pesticide usage methods to the user, urging them to take prompt action.

[0633] Step 8:

[0634] The server collects market price and demand data from the market database. The collected data serves as foundational information for developing crop sales strategies.

[0635] Step 9:

[0636] The server analyzes collected market data to formulate the optimal sales timing and pricing strategy. The terminal communicates this to the user, supporting management decisions to maximize profits.

[0637] (Example 1)

[0638] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0639] Modern agriculture demands maximizing production efficiency while flexibly responding to environmental changes and market demands. However, traditional methods involve managing soil and weather conditions, crop health, and market trends individually, making it difficult to optimize these factors comprehensively. Solving this problem is essential to achieving sustainable agriculture and improving profitability.

[0640] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0641] This invention includes a server that acquires and analyzes soil information to calculate and present the optimal amount of agricultural materials and liquids to be used, a server that acquires information on natural phenomena and proposes the optimal timing for cultivation and harvesting based on this information, and a server that analyzes video to evaluate the health of plants and presents preventive measures if abnormalities are detected. This enables agricultural workers to make data-driven decisions, making efficient and environmentally friendly agricultural production possible.

[0642] "Soil information" refers to data about the physical and chemical properties of soil, including moisture, nutrients, and temperature.

[0643] "Natural phenomena information" refers to real-time and forecast data such as weather, temperature, precipitation, and wind speed, obtained from meteorological agencies and other sources.

[0644] "Agricultural materials and liquids" refer to chemicals and water used in agricultural activities, such as fertilizers and water, that are used to promote plant growth.

[0645] "Video" refers to visual data acquired by drones and other filming devices, which is used to understand the condition of crops and the state of the field.

[0646] "Plant health" refers to an indicator of a crop's growth status, and is judged based on visual information such as the plant's appearance, color, and shape.

[0647] "Distribution information" refers to economic data related to the sale of agricultural products, such as domestic and international market trends, price fluctuations, and demand forecasts.

[0648] "Data-driven processing" is an approach that involves comprehensively analyzing large amounts of collected data and making decisions based on the results, and is useful for optimizing agricultural production.

[0649] This invention provides a system for optimizing agricultural production based on data. This system primarily consists of a server, terminals, and users, each with a specific function.

[0650] The server receives data from soil sensors, drone cameras, weather agencies, and market databases. Soil data is primarily used to monitor soil moisture, nutrients, and temperature. This utilizes sensor technology and data analysis libraries (e.g., Python and R). For image analysis, generative AI models such as TensorFlow are used based on data acquired by drones to detect crop health and abnormalities.

[0651] The server retrieves real-time weather information via API and analyzes weather forecasts and their impact using statistical methods and machine learning algorithms. This allows it to suggest optimal planting and harvesting times to users. Market data is obtained from online market databases, and price and demand trends are analyzed. This is done using database management systems and analytical software.

[0652] The terminal receives analytical data provided by the server and displays it to the user in a visually easy-to-understand format. This enables users to make quick decisions on-site.

[0653] Based on the information provided by the system, users select and implement specific agricultural measures. This includes appropriate input of agricultural materials and adjustment of harvest timing.

[0654] As a concrete example, a server might analyze weather information and, based on rainfall forecasts, notify the user whether or not they should harvest earlier. An example of a prompt message would be, "Please tell me the data needed to determine the next harvest timing and how to analyze it."

[0655] This system supports users in improving agricultural efficiency and achieving sustainable production by comprehensively analyzing various types of data.

[0656] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0657] Step 1:

[0658] The server receives soil information from soil sensors. Specifically, raw data regarding soil moisture, nutrients, and temperature is input. The server processes this data using analysis software to calculate the optimal amount of fertilizer and water to use. The data obtained from this calculation is output, allowing for the development of an efficient plan for the application of agricultural materials.

[0659] Step 2:

[0660] The server acquires weather information. It inputs weather data from meteorological agencies' APIs and calculates the optimal timing for planting and harvesting based on that data. It analyzes weather trends using statistical methods and predicts future weather. This forecast information is output, and the user is offered a suggestion for the optimal work schedule.

[0661] Step 3:

[0662] The server receives video data of crops captured by drones. It takes image data as input and uses a generative AI model for analysis. The server outputs the results of an evaluation of the crops' health and signs of pests and diseases. If an abnormality is detected, the user is notified of appropriate control measures.

[0663] Step 4:

[0664] The server collects market data and analyzes distribution information. It inputs price and demand data from online market databases and analyzes market trends. As a result of the trend analysis, data suggesting the optimal sales strategy is output and notified to the user.

[0665] Step 5:

[0666] The terminal receives analysis results from the server and provides them to the user. Specifically, it visualizes the analysis data and outputs it in a visually easy-to-understand format. Based on this output, the user adjusts their agricultural activities.

[0667] Step 6:

[0668] Users develop specific agricultural activities based on the information provided. They apply materials according to calculated fertilizer and water usage amounts and schedule appropriate work based on weather forecasts. This enables data-driven decision-making and promotes efficient and sustainable agriculture.

[0669] (Application Example 1)

[0670] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0671] With the advancement of urbanization in modern society, urban agriculture is attracting attention, but there is a lack of appropriate information and technology for developing efficient cultivation management and profitable market strategies. As a result, it is difficult for individual users to obtain the optimal agricultural experience in their home gardens or community farms.

[0672] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0673] This invention includes a server that includes means for acquiring and analyzing soil information to calculate and present the optimal amounts of fertilizer and irrigation to be used; means for acquiring weather information and suggesting the optimal timing for cultivation and harvesting based on it; means for analyzing images to evaluate the health of plants and suggesting control measures if abnormalities are detected; means for analyzing economic data and suggesting the optimal sales strategy; and means for providing users with the optimal cultivation conditions and management methods for urban agriculture. This enables comprehensive support for practicing efficient and sustainable agriculture even in urban areas.

[0674] "Soil information" refers to data about the conditions below the soil surface, which are fundamental to plant growth. Specifically, it includes moisture content, nutrient levels, temperature, and other factors.

[0675] "Weather information" refers to data about atmospheric conditions, including elements such as temperature, precipitation, humidity, and wind speed, and has an impact on agricultural activities.

[0676] "Fertilization" refers to the act of providing the soil with nutrients necessary to promote plant growth, and mainly involves using fertilizers containing nitrogen, phosphorus, and potassium.

[0677] "Irrigation" is the act of supplying plants with the water they need, and is especially common in dry climates and soils lacking moisture.

[0678] "Image analysis" is a computer processing technique that uses digital images, and its purpose is to detect the health status and abnormalities of plants.

[0679] "Control measures" refer to actions taken to prevent or mitigate damage to plants from diseases, pests, and extreme weather conditions, and include, for example, the use of specific pesticides and the introduction of appropriate management techniques.

[0680] "Economic data" refers to information related to supply and demand in the market, including data on price fluctuations and competing products.

[0681] "Urban agriculture" refers to agricultural activities carried out in urban areas, including crop cultivation in home gardens and community farms.

[0682] "Cultivation conditions" refer to the environmental factors necessary for plant growth, including light, water, temperature, and soil nutrients.

[0683] A "sales strategy" is a plan or policy for efficiently selling a product in the market, and includes initiatives aimed at maximizing profits.

[0684] The system used to realize this application is a complex information processing system designed to support urban agriculture. The server communicates with sensors to collect soil information and analyzes that data. AWS Lambda is used for the analysis, evaluating soil moisture, nutrients, and temperature in real time, and calculating the optimal amount of fertilizer and irrigation. The OpenWeather API is also used to obtain weather information, suggesting optimal planting and harvesting times based on local weather conditions.

[0685] The terminal is the user's smartphone, which sends images of plants taken with its camera to the Google Cloud Vision API to evaluate the plant's health. Based on these results, if an abnormality is detected, the system can instruct the user on appropriate control measures. For economic data analysis, the Python libraries Pandas and BeautifulSoup are used to analyze trends and provide the user with better sales strategies.

[0686] Furthermore, the system provides users with appropriate cultivation conditions and management methods for urban agriculture, supporting them in practicing efficient and sustainable agricultural activities. Such a system makes agricultural activities in urban environments easy and effective. An example of a prompt message would be: "This app provides optimal growing and sales strategies based on soil data, weather information, and market fluctuations to improve the efficiency of urban home gardens. Specifically, please tell me which crops to grow and how to grow them at what time of year."

[0687] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0688] Step 1:

[0689] The server acquires soil information from soil sensors and analyzes it in real time using AWS Lambda. This analysis evaluates soil moisture content, nutrients, and temperature, calculates the optimal amount of fertilizer and irrigation to be used, and sends this information to the terminal. The input is soil sensor data, and the output is the optimal amount of fertilizer and irrigation to be used.

[0690] Step 2:

[0691] The server retrieves weather data via the OpenWeather API. Based on this data, it calculates the optimal timing for planting and harvesting and notifies the user of the results. The input is weather data, and the output is the recommended planting and harvesting times.

[0692] Step 3:

[0693] The user sends an image of a plant taken with their smartphone camera to the device, which then sends this image to the Google Cloud Vision API. The server analyzes the image to assess the plant's health and, if it detects signs of pests or diseases, instructs the user on appropriate control measures. The input is an image of the plant, and the output is the plant's health status and recommended control measures.

[0694] Step 4:

[0695] The server collects economic data using Python's Pandas and BeautifulSoup libraries and analyzes trends. The results of this analysis are then presented to the user as an optimal sales strategy. The input is market trends and economic data, and the output is the optimal sales strategy.

[0696] Step 5:

[0697] Users receive reminders and information on cultivation methods related to various farming tasks through their devices. This supports efficient and sustainable urban farming practices. Inputs are information on cultivation and management conditions, and outputs are specific reminders and cultivation methods corresponding to those conditions.

[0698] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0699] This invention is an integrated agricultural support system based on soil data, weather information, and market trends, aimed at improving the production efficiency and reducing the mental stress of agricultural workers. Furthermore, this system incorporates an emotion engine that monitors and adjusts the user's emotional state.

[0700] The server receives soil data from soil sensors, analyzes it, and calculates the optimal amount of fertilizer and water to use. The calculated information is provided to the user via a terminal. The terminal presents the information in a visually easy-to-understand format to support specific agricultural activities.

[0701] Regarding weather information acquisition, the server retrieves the latest data from meteorological agencies and suggests planting and harvesting timings. This allows the system to present users with agricultural plans that are suitable for changing weather conditions.

[0702] Drones are used to acquire images of crops, and a server uses this image data to assess the health of the crops. If signs of pests or diseases are found, the server suggests control measures to the user via a terminal, supporting a rapid response to protect the crops.

[0703] Market data analysis involves servers monitoring domestic and international market trends and formulating sales strategies tailored to fluctuating market conditions. Users can access these strategies through their terminals to develop profitable sales plans.

[0704] The newly added emotion engine uses the device's sensors to detect emotions from the user's voice, facial expressions, and other data, and adjusts the information and notifications provided according to the user's emotional state. For example, if the user is feeling stressed, it helps the user understand by presenting complex information in a concise manner. Furthermore, the emotion engine continuously learns from the user's feedback, enabling more accurate emotion analysis.

[0705] Thus, the present invention is designed to enable agricultural workers to perform their agricultural activities efficiently and comfortably by combining data analysis and emotion recognition technology.

[0706] The following describes the processing flow.

[0707] Step 1:

[0708] The server collects soil data received from soil sensors. This includes information such as soil moisture content, pH level, and nutrient content. The server processes this data in real time and stores it in a database.

[0709] Step 2:

[0710] The server analyzes the collected soil data and calculates the optimal amount of fertilizer and water for crop growth. This calculation uses machine learning algorithms and compares the current data with historical data.

[0711] Step 3:

[0712] The server sends the results of optimized fertilizer and water usage to the terminal. The terminal visualizes this information for the user and presents an automatically adjusted replenishment plan. The user then adjusts their daily farming activities based on this information.

[0713] Step 4:

[0714] The server periodically retrieves weather data from external meteorological agencies. This data includes factors that affect agriculture, such as temperature, precipitation, and wind speed.

[0715] Step 5:

[0716] The server analyzes the acquired weather data and calculates the optimal timing for planting and harvesting. The analysis results are notified to the user via the terminal, and necessary actions are suggested.

[0717] Step 6:

[0718] The server receives images of crops transmitted from the drone. The received images are analyzed using computer vision technology to assess the health of the crops based on the condition of their leaves and stems.

[0719] Step 7:

[0720] Based on the image analysis results, the server calculates countermeasures if abnormalities are found in the crops and sends them to the terminal. The terminal then issues a warning to the user and suggests appropriate control measures.

[0721] Step 8:

[0722] The server collects domestic and international market data and forecasts agricultural product price trends and demand. Based on the analysis results, it formulates the optimal sales strategy.

[0723] Step 9:

[0724] The terminal notifies the user of sales strategy proposals from the server, assisting with specific sales timing and pricing. The user then uses this information to adjust their market activities.

[0725] Step 10:

[0726] The emotion engine recognizes emotions through the user's voice and facial expressions and adjusts how information is presented on the device. If the user is stressed, the emotion engine simplifies the information and presents it to the user in a more easily understandable way.

[0727] Step 11:

[0728] Users provide feedback on the information and suggestions offered through their devices. The emotion engine learns from this feedback and improves the accuracy of future emotion recognition.

[0729] (Example 2)

[0730] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0731] The aim is to solve the challenges faced by agricultural workers in improving production efficiency and reducing mental stress. Specifically, it aims to reduce the burden on workers by acquiring and analyzing various types of information necessary in the agricultural field, thereby formulating optimal agricultural plans and sales strategies. In addition, it aims to alleviate mental burden and support smooth production activities by providing information tailored to the emotional state of workers.

[0732] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0733] In this invention, the server includes means for acquiring and analyzing soil information to calculate and present the optimal amount of fertilizer and water to be used; means for acquiring weather information and proposing the optimal timing for cultivation and harvesting based on it; and means for analyzing images to evaluate the health of plants and presenting control measures if abnormalities are detected. This enables users to accurately acquire the information necessary to carry out production activities efficiently. Furthermore, profitability can be improved through means for analyzing market information and proposing the optimal sales strategy. In addition, by providing means for detecting the user's emotional state and adjusting the information provided based on it, it is possible to reduce mental stress.

[0734] "Soil information" refers to data that shows soil characteristics related to crop growth, such as pH, humidity, and nutrient content of farmland.

[0735] "Weather information" refers to data on meteorological conditions that affect agricultural activities, such as temperature, precipitation, and wind speed.

[0736] "Image information" refers to digital image data captured to visually understand the condition of crops.

[0737] An "unmanned aerial vehicle" refers to a drone that is remotely controlled or flies autonomously and is used for the purpose of acquiring data from the air.

[0738] A "user" refers to an individual or group that uses the system to conduct agricultural activities.

[0739] "Emotional state" refers to the psychological and emotional state of a user, obtained by analyzing their voice, facial expressions, and other data.

[0740] A "server" is a centralized computer system that receives and analyzes data and provides information to users.

[0741] "Plant health" refers to the state of a crop that shows signs of disease or pest infestation, or abnormalities in its growth.

[0742] This invention relates to an agricultural support system that aims to improve agricultural production efficiency and reduce mental stress through data communication and analysis between a server, terminal, and user.

[0743] The server plays a central role in receiving and analyzing various data necessary for agriculture. Soil information is collected from soil sensors installed in the fields and analyzed on the server. A data analysis algorithm using Python is applied to the analysis, which calculates the optimal amount of fertilizer and water to be used. Weather information is obtained through APIs provided by meteorological agencies, and the optimal timing for cultivation and harvesting is calculated on the server.

[0744] The user provides image information obtained by the unmanned aerial vehicle to the server. The server analyzes this image and uses it to assess the health of the plants. If signs of pests or diseases are detected, the server develops appropriate control measures and notifies the user via the terminal.

[0745] The server also collects and analyzes market information and presents users with sales strategies tailored to fluctuating market conditions. The R programming language is used for time-series analysis. Furthermore, the server incorporates a generative AI model that monitors the user's emotional state through the terminal. A TensorFlow-based model is used for emotion analysis, evaluating the user's psychological state from their voice and facial expressions. If stress is detected, the server adjusts the information it provides to reduce the user's burden.

[0746] A terminal is a device that receives data from a server and presents information to the user visually. An intuitive interface is employed here. For example, weather forecasts and soil analysis results could be displayed in graph format.

[0747] For example, if a user prompts, "Analyze the soil pH data and tell me the appropriate type and amount of fertilizer," the server will calculate the optimal fertilizer based on the soil information and notify the terminal. When plant images are provided to the server, a prompt such as, "Identify signs of pests and diseases from the crop images and tell me how to control them," can be used.

[0748] Thus, the present invention utilizes data analysis and emotion recognition technology to enable agricultural workers to perform their agricultural activities efficiently and comfortably.

[0749] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0750] Step 1:

[0751] The server acquires soil information from soil sensors. This data includes pH, humidity, and nutrient content. The server analyzes this information using a data analysis algorithm based on Python to calculate the optimal amount of fertilizer and water to use. The input data is soil characteristics, and the output is a proposal for optimized material usage. This proposal is notified to the user via a terminal.

[0752] Step 2:

[0753] The server obtains weather information from meteorological agencies via APIs. This data includes temperature, precipitation, wind speed, and other factors. Based on this information, the server calculates the optimal timing for planting and harvesting. The input data is weather conditions, and the output is the recommended timing for farm work. Users visually confirm this information through their terminals.

[0754] Step 3:

[0755] The user acquires image data of the farm using an unmanned aerial vehicle. The server receives this image data and analyzes it using OpenCV. The data analysis targets the health status of plants in the images, and if abnormalities are detected, control measures are suggested. The input data is image data, and the output is a health assessment and necessary control measures.

[0756] Step 4:

[0757] The server collects market information and analyzes it using the R programming language. The data includes market supply and demand, and price trends. Based on this, the server develops an optimal sales strategy. The input data is market conditions, and the output is the recommended sales strategy. Users can view this via their terminal.

[0758] Step 5:

[0759] The device records the user's voice and facial expressions via a microphone and camera. The server uses this data to analyze emotions using a generative AI model based on TensorFlow. The input data is the user's voice and facial expression information, and the output is an evaluation of their emotional state. Based on this evaluation, the server adjusts the information delivery method to reduce the burden on the user.

[0760] (Application Example 2)

[0761] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0762] In recent years, with the development of urban agriculture, farmers need to quickly and accurately utilize various environmental, meteorological, and market information to maintain efficient production activities. However, it is difficult to comprehensively analyze this information and formulate appropriate agricultural plans, and especially in urban areas where sudden climate changes and market fluctuations are common, flexible and adaptive responses are crucial. Furthermore, farmers experience high levels of mental stress, and emotional support is needed.

[0763] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0764] In this invention, the server includes means for acquiring and analyzing soil information to calculate and present the optimal amount of fertilizer and water to be used; means for acquiring weather information and suggesting the optimal timing for sowing and harvesting based on it; means for analyzing images to evaluate the health of crops and suggesting control measures if abnormalities are detected; and means for monitoring the emotional state of the user and adjusting the information provided. This makes it possible to improve production efficiency and reduce mental burden in urban agriculture.

[0765] "Soil information" refers to information about the physical and chemical properties of land, obtained in order to optimize the management of fertilizers and water necessary for agricultural activities.

[0766] "Weather information" refers to data on weather conditions such as temperature, precipitation, humidity, and wind speed, which is necessary when formulating agricultural plans.

[0767] "Image" refers to still images or video data captured and used to visually represent the health of crops.

[0768] "Market information" refers to data on price trends, supply and demand balance, and consumer needs that are necessary for formulating sales strategies for agricultural products.

[0769] "User emotional state" refers to the emotional and psychological state of individuals engaged in agricultural activities, and is a factor used to adjust the provision of information.

[0770] A "flying robot" is a type of unmanned aerial vehicle used to collect and analyze information while moving through the air.

[0771] The system for implementing this invention consists of a server, terminals, various sensors, and a flying robot. Users receive information through the terminals, aiming to improve the efficiency of agricultural activities and reduce mental stress.

[0772] The server analyzes soil information obtained from multiple soil sensors to calculate the optimal amount of fertilizer and water to use. This information is provided in real time to the user's device. Furthermore, the server receives weather information from meteorological agencies and suggests the optimal timing for planting and harvesting for the environment. A cloud-based weather forecasting system is used to aggregate and analyze weather data for weather analysis.

[0773] The flying robot periodically takes images of crops and transmits the data to a server. The server uses image analysis technology to evaluate the health of the crops, and if any abnormalities are detected, it quickly presents control measures to the user. Computer vision technology is used for this image analysis.

[0774] For market information analysis, the server uses market trend data from a large-scale database to generate and propose the latest sales strategies to users. This allows for the development of flexible sales strategies that adapt to market fluctuations.

[0775] Furthermore, the device's built-in emotion recognition sensor and generative AI model analyze the user's voice and facial expressions to evaluate their emotional state. If the user is experiencing stress, the system adjusts how information is presented to make it easier to understand. In this example prompt, it would be used in the form of, "Please tell me how you would like information presented to help alleviate the stress the user is experiencing. This system will provide information tailored to the user."

[0776] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0777] Step 1:

[0778] The server acquires soil information in real time from soil sensors. The main inputs are soil moisture, temperature, and pH value. This data is analyzed to calculate the optimal amount of fertilizer and water to use. An algorithm is used for the analysis, and the calculation results are sent to the terminal. The output is a fertilizer and water usage instruction presented to the user.

[0779] Step 2:

[0780] The server receives weather data provided by meteorological agencies. Inputs include information such as temperature, precipitation, wind speed, and humidity. This data is analyzed comprehensively to predict the optimal timing for planting and harvesting. The output is a suggested message regarding that timing, which is sent to the user via a terminal.

[0781] Step 3:

[0782] The flying robot periodically takes images of the crops. This image data is sent to a server. The server uses image analysis software to evaluate the health of the crops. The input is the image data, and the output is an evaluation of whether the crops are healthy and any control measures if abnormalities are detected. This is also sent to the terminal.

[0783] Step 4:

[0784] The server retrieves and analyzes market information from an online database. Inputs include current prices, demand trends, and inventory data. Based on the analysis results, the server generates an optimal sales strategy and outputs a message to the user proposing a sales plan.

[0785] Step 5:

[0786] The emotion recognition sensor built into the device captures the user's voice and facial expressions. The sensor's measurement data is used as input, and a generative AI model is used to determine emotions. Based on the determination result, the server adjusts how the information is displayed to make it easily understandable. The output is information and suggestions optimized for the user's state.

[0787] 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.

[0788] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. 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. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0789] In the above embodiment, an example was given in which the specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the robot 414.

[0790] 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.

[0791] Figure 9 shows an 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.

[0792] 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.

[0793] 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.

[0794] 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, motorcycles, etc., 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, for example, based 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.

[0795] 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."

[0796] 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.

[0797] The above description primarily focuses on the functions of the data processing device 12 in relation to this disclosure. However, the system related to this disclosure is not necessarily implemented on a server. The system related to this disclosure may be implemented as a general information processing system. This disclosure may be implemented, for example, as a software program that runs on a personal computer or as an application that runs on a smartphone. The method related to this disclosure may be provided to users in SaaS (Software as a Service) format.

[0798] 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 of the specific process may be performed by multiple computers, including computer 22. For example, a data generation model 58 may be provided in an external device of the data processing device 12, and the external device may generate data according to the input data.

[0799] 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.

[0800] 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.

[0801] 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.

[0802] 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.

[0803] 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.

[0804] 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.

[0805] 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.

[0806] 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 the like 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.

[0807] 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.

[0808] The following is further disclosed regarding the embodiments described above.

[0809] (Claim 1)

[0810] A means of acquiring soil data, analyzing it, and calculating and presenting the optimal amount of fertilizer and water to be used,

[0811] A means for acquiring weather information and proposing the optimal timing for planting and harvesting based on it,

[0812] A means of analyzing images, evaluating the health of crops, and suggesting control measures if abnormalities are detected,

[0813] A means of analyzing market data and proposing the optimal sales strategy,

[0814] A system that includes this.

[0815] (Claim 2)

[0816] The system according to claim 1, further comprising means for proposing agricultural plans adapted to environmental changes by integrating and analyzing soil data and weather information.

[0817] (Claim 3)

[0818] The system according to claim 1, wherein the means for analyzing the health status of crops is to use image data obtained from a drone.

[0819] "Example 1"

[0820] (Claim 1)

[0821] A means for acquiring soil information, analyzing it, and calculating and presenting the optimal amount of agricultural materials and liquids to be used,

[0822] A means of acquiring information on natural phenomena and proposing the optimal timing for cultivation and harvesting based on this information,

[0823] A means of analyzing video footage, evaluating the health of plants, and suggesting preventative measures if abnormalities are detected,

[0824] A means of analyzing distribution information and proposing the optimal sales strategy,

[0825] A means of providing agricultural plans adapted to environmental changes through data-driven processing,

[0826] A system that includes this.

[0827] (Claim 2)

[0828] The system according to claim 1, wherein the means for analyzing the health status of plants is to use video information obtained from a remotely controlled device.

[0829] (Claim 3)

[0830] The system according to claim 1, further comprising means for obtaining the latest natural phenomenon information and distribution information in real time using an automated information gathering process.

[0831] "Application Example 1"

[0832] (Claim 1)

[0833] A means for acquiring soil information, analyzing it, and calculating and presenting the optimal amounts of fertilizer and irrigation to be used,

[0834] A means of acquiring weather information and proposing the optimal timing for cultivation and harvesting based on it,

[0835] A means of analyzing images, evaluating the health of plants, and suggesting control measures if abnormalities are detected,

[0836] A means of analyzing economic data and proposing the optimal sales strategy,

[0837] A means of providing users with optimal cultivation conditions and management methods for urban agriculture,

[0838] A system that includes this.

[0839] (Claim 2)

[0840] The system according to claim 1, further comprising means for proposing agricultural plans adapted to environmental changes, and means for proposing market strategies aimed at maximizing profits for urban agriculture, by integrating and analyzing soil information and weather information.

[0841] (Claim 3)

[0842] The system according to claim 1, wherein the means for analyzing the health status of plants is to use image data obtained from an unmanned aerial vehicle, and the diagnostic results are provided using a mobile terminal held by the user.

[0843] "Example 2 of combining an emotion engine"

[0844] (Claim 1)

[0845] A means for acquiring soil information, analyzing it, and calculating and presenting the optimal amount of fertilizer and water to be used,

[0846] A means of acquiring weather information and proposing the optimal timing for cultivation and harvesting based on it,

[0847] A means of analyzing images, evaluating the health of plants, and suggesting control measures if abnormalities are detected,

[0848] A means of analyzing market information and proposing the optimal sales strategy,

[0849] A means for detecting the user's emotional state and adjusting the information provided based on that state,

[0850] A system that includes this.

[0851] (Claim 2)

[0852] The system according to claim 1, further comprising means for proposing an agricultural plan adapted to environmental changes by integrating and analyzing soil information and weather information.

[0853] (Claim 3)

[0854] The system according to claim 1, wherein the means for analyzing the health status of plants is to use image information obtained from an unmanned aerial vehicle.

[0855] "Application example 2 when combining with an emotional engine"

[0856] (Claim 1)

[0857] A means for acquiring soil information, analyzing it, and calculating and presenting the optimal amount of fertilizer and water to be used,

[0858] A means for acquiring weather information and proposing the optimal timing for sowing and harvesting based on it,

[0859] A means of analyzing images, evaluating the health of crops, and suggesting control measures if abnormalities are detected,

[0860] A means of analyzing market information and proposing the optimal sales strategy,

[0861] A means of monitoring the emotional state of users and adjusting the information provided,

[0862] A system that includes this.

[0863] (Claim 2)

[0864] The system according to claim 1, further comprising means for proposing an agricultural plan adapted to environmental changes by integrating and analyzing soil information and weather information.

[0865] (Claim 3)

[0866] The system according to claim 1, wherein the means for analyzing the health status of crops is to use image information obtained from a flying robot. [Explanation of Symbols]

[0867] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots< / url:> < / url:> < / url:> < / url:>

Claims

1. A means for acquiring soil information, analyzing it, and calculating and presenting the optimal amounts of fertilizer and irrigation to be used, A means of acquiring weather information and proposing the optimal timing for cultivation and harvesting based on it, A means of analyzing images, evaluating the health of plants, and suggesting control measures if abnormalities are detected, A means of analyzing economic data and proposing the optimal sales strategy, A means of providing users with optimal cultivation conditions and management methods for urban agriculture, A system that includes this.

2. The system according to claim 1, further comprising means for proposing agricultural plans adapted to environmental changes, and means for proposing market strategies aimed at maximizing profits for urban agriculture, by integrating and analyzing soil information and weather information.

3. The system according to claim 1, wherein the means for analyzing the health status of plants is to use image data obtained from an unmanned aerial vehicle, and the diagnostic results are provided using a mobile terminal held by the user.

Images