system

Abstract

Provide a system. The present invention includes: a means for controlling a camera equipped with an optical center to acquire an image of a plant; A means for analyzing the state of the plant from the acquired image; a means for determining the state of the plant based on the analysis results; a means for notifying the producer of the determination result via a communication means; A system including:

Description

[Technical Field]

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

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

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

[0004] In conventional agriculture, it is extremely important to properly understand the condition of plants and harvest them at the right time, but this method mainly relies on human experience and visual inspection. This can lead to erroneous early or late harvesting, resulting in reduced quality and production efficiency. In addition, constant monitoring of plant conditions requires a lot of labor and time, making efficient production management difficult. [Means for solving the problem]

[0005] The present invention provides a system that automatically captures images of plants using a camera equipped with an optical sensor and determines the condition of the plants through image analysis. Specifically, it includes a means for analyzing the color and sugar content of the plants based on the captured images and determining the appropriate timing for harvesting based on the analysis results. The results are then communicated to the producer via a communication means, enabling timely harvesting. Furthermore, when certain conditions are met, it is possible to automatically send instructions to cultivation machinery to operate the plants. In this way, the present invention significantly reduces human labor and achieves efficient, high-quality production.

[0006] A "camera equipped with an optical center" is a camera that has a built-in light source and the ability to properly illuminate an object and capture an image.

[0007] The term "plant" refers to all plants cultivated in agriculture, etc., and in the present invention, edible plants such as fruits and vegetables are particularly targeted.

[0008] The "means for acquiring images" is a system for automatically capturing and collecting images of plants using a camera equipped with an optical center.

[0009] The "means for analyzing the condition of plants" refers to a system that uses image processing technology to analyze characteristics of plants such as color and sugar content.

[0010] The "means for determining" is a mechanism for evaluating the condition of the plants based on the analysis results and determining the appropriate timing for harvesting.

[0011] "Means for notifying producers via communication means" refers to a system that utilizes communication functions, such as the Internet or mobile networks, to notify producers of the analysis and judgment results in real time.

[0012] The "means for measuring sugar content" is a system for evaluating the sugar content inside a plant using image analysis or other sensor technology.

[0013] The "means for determining the optimal harvest time" is a mechanism for determining the most suitable timing for harvesting based on sugar content and other analytical results.

[0014] The "means for transmitting instructions to the cultivation machine" is a mechanism for transmitting operation instructions to the cultivation machine, such as an irrigation system or a fertilizer spreading device, in order to automate cultivation operations as needed. [Brief explanation of the drawings]

[0015] [Figure 1] 1 is a conceptual diagram showing an example of the configuration of a data processing system according to a first embodiment. [Figure 2] 1 is a conceptual diagram showing an example of main functions of a data processing device and a smart device according to a first embodiment. [Figure 3] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a second embodiment. [Figure 4] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and smart glasses according to a second embodiment. [Figure 5] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a third embodiment. [Figure 6] FIG. 11 is a conceptual diagram showing an example of main functions of a data processing device and a headset-type terminal according to a third embodiment. [Figure 7] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a fourth embodiment. [Figure 8] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and a robot according to a fourth embodiment. [Figure 9] 1 shows an emotion map onto which multiple emotions are mapped. [Figure 10] 1 shows an emotion map onto which multiple emotions are mapped. [Figure 11] FIG. 3 is a sequence diagram showing a processing flow of the data processing system according to the first embodiment. [Figure 12] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in Application Example 1. [Figure 13] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system according to the second embodiment when an emotion engine is combined. [Figure 14] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in Application Example 2 when an emotion engine is combined. DETAILED DESCRIPTION OF THE INVENTION

[0016] An example of an embodiment of a system according to the technology of the present disclosure will be described below with reference to the accompanying drawings.

[0017] First, the terms used in the following description will be explained.

[0018] In the following embodiments, a coded processor (hereinafter simply referred to as a "processor") may be a single arithmetic device or a combination of multiple arithmetic devices. Furthermore, a processor may be a single type of arithmetic device or a combination of multiple types of arithmetic devices. Examples of arithmetic devices include a CPU (Central Processing Unit), a GPU (Graphics Processing Unit), a GPGPU (General-Purpose computing on Graphics Processing Units), and an APU (Accelerated Processing Unit).

[0019] In the following embodiments, a coded RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a working memory by a processor.

[0020] In the following embodiments, the coded storage is one or more non-volatile storage devices that store various programs, various parameters, etc. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), and magnetic tapes.

[0021] In the following embodiments, a communication I / F (Interface) with a symbol is an interface including a communication processor, an antenna, etc. The communication I / F controls communication between multiple computers. Examples of communication standards applied to the communication I / F include wireless communication standards including 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), Bluetooth (registered trademark), etc.

[0022] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." In other words, "A and / or B" means that it may be only A, only B, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" is also applied when three or more things are expressed connected by "and / or."

[0023] [First embodiment]

[0024] FIG. 1 shows an example of the configuration of a data processing system 10 according to the first embodiment.

[0025] 1, a data processing system 10 includes a data processing device 12 and a smart device 14. An example of the data processing device 12 is a server.

[0026] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0027] The smart device 14 includes a computer 36, a reception device 38, an output device 40, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The reception device 38, the output device 40, and the camera 42 are also connected to the bus 52.

[0028] The reception device 38 includes a touch panel 38A, a microphone 38B, and the like, and receives user input. The touch panel 38A detects contact with an indicator (for example, a pen or a finger) to receive user input by the touch of the indicator. The microphone 38B detects the user's voice to receive user input by voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the data indicating the user input.

[0029] 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 of expression that the user 20 can perceive (for example, audio and / or text). The display 40A displays visible information such as text and images in accordance with instructions from the processor 46. The speaker 40B outputs audio in accordance with instructions from the processor 46. The camera 42 is a compact digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0030] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 control the exchange of various information between the processor 46 and the processor 28 via the network 54.

[0031] FIG. 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0032] 2, in the data processing device 12, a specific process is performed by the processor 28. A specific processing program 56 is stored in the storage 32. The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific process is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0033] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0034] In the smart device 14, the processor 46 performs the reception output process. The storage 50 stores a reception output program 60. The reception output program 60 is used in conjunction with the 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 process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0035] Next, a description will be given of the specific processing performed by the specific processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0036] The present invention relates to a system that acquires images of plants using a camera equipped with an optical sensor, analyzes the images to determine the condition of the plants, and notifies the grower at an appropriate time. Furthermore, the system can cooperate with cultivation machinery as needed to perform automatic cultivation operations. Specific examples of the system are described below.

[0037] 1. System Overview

[0038] The main components of the system are as follows:

[0039] Server: Manages all processes of image acquisition, analysis, judgment, and notification.

[0040] Camera terminal: A camera equipped with an optical center is used to capture images of plants.

[0041] User terminal: Used as a communication means for producers to receive notifications.

[0042] Cultivation machinery: machinery for carrying out automated cultivation operations, including irrigation systems and fertilizer application equipment.

[0043] 2. Image Acquisition Process

[0044] The server controls the camera equipped with the optical center to capture images of the plants at regularly scheduled times, for example, at 9:00 a.m. every day, to start the camera and capture new images, which are then saved in local storage for later analysis.

[0045] 3. Image analysis process

[0046] The acquired images are input by the server into the image processing module, which extracts the plant's hue information and calculates the percentage of pixels that fall within a specific range to analyze the plant's coloration. The sugar content is also measured, using an image analysis algorithm.

[0047] 4. Determination and Notification Process

[0048] Based on the analysis results, the server determines the plant's coverage rate and sugar content. This data is used to determine the appropriate time for harvesting. For example, if the coverage rate is 80% or higher and the sugar content is within the appropriate range, it is determined that the time has come to harvest. This determination result is notified to the producer in real time. The notification is sent via the internet to the user's LINE account.

[0049] 5. Automatic cultivation operation process

[0050] If necessary, the server can link with the cultivation machinery to perform automatic cultivation operations. For example, if the plant's condition meets certain conditions, the server can send instructions to the irrigation system to provide appropriate water supply. It can also send instructions to the fertilizer spraying device to automatically manage the supply of fertilizer.

[0051] Specific examples

[0052] A specific scenario is shown below.

[0053] 1. The server controls the camera terminal every morning at 9:00 AM and captures images of mandarin oranges.

[0054] 2. The acquired images are input into an image processing module on the server, where the color and sugar content are analyzed.

[0055] 3. The analysis results show that the coverage rate is 85% and the sugar content is within the appropriate range.

[0056] 4. The server determines that it is time to harvest and notifies the producer of the result via LINE.

[0057] 5. Furthermore, the server plans the next irrigation timing and sends instructions to the irrigation system.

[0058] In this way, the present invention allows for real-time monitoring of plant conditions and efficient and effective production management.

[0059] The processing flow will be explained below.

[0060] Step 1:

[0061] The server sets up a periodic task and sends instructions to activate a camera terminal equipped with a photonic center at a specific time every day to obtain the latest images of the plants to be harvested that day.

[0062] Step 2:

[0063] The device (camera) receives instructions from the server and captures images of the plants. The captured images are temporarily stored in the camera device's local storage.

[0064] Step 3:

[0065] The device (camera) sends the captured image to the server, which receives the file and stores it in a secure location.

[0066] Step 4:

[0067] The server then passes the stored image to an analysis module, which begins processing the image by first converting it to a color space (such as HSV) and extracting pixels that fall into a specific color range.

[0068] Step 5:

[0069] The server evaluates the color of the plants based on the extracted pixel information. Specifically, it calculates the percentage of pixels that fall within a specific range and outputs the result as the coverage rate.

[0070] Step 6:

[0071] The server then runs a sugar content measurement algorithm to calculate an estimated sugar content from the image, which typically uses specific pigment patterns or texture analysis.

[0072] Step 7:

[0073] The server compares the coverage rate and sugar content data to determine the appropriate time for harvesting. For example, if the coverage rate is 80% or higher and the sugar content is within the appropriate range, it determines that it is the best time to harvest.

[0074] Step 8:

[0075] If the server determines that the fruit is suitable for harvesting, it generates a message containing the specific coverage rate and sugar content values.

[0076] Step 9:

[0077] The server sends the generated message to the user (producer) using a communication module. The communication method is via the Internet using the LINE API.

[0078] Step 10:

[0079] The user (producer) checks the harvest timing information received as a LINE message, allowing the user to carry out harvesting work at the appropriate time.

[0080] Step 11:

[0081] If necessary, the server can also send instructions to the farming machine, such as instructions to automatically plan post-harvest irrigation and fertilizer applications.

[0082] Through these steps, the system can monitor the condition of plants in real time, notify producers of the appropriate harvest time, and perform the necessary automated cultivation operations.

[0083] Example 1

[0084] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0085] In conventional plant cultivation methods, monitoring plant conditions and timing of cultivation operations depended on human experience and intuition, making it difficult to make accurate judgments and respond at the appropriate time. Furthermore, manual monitoring and operation required time and effort, making it difficult to improve production efficiency. Furthermore, this approach carried the risk of overlooking changes in plant conditions and the optimal harvest time, which could affect quality and yield.

[0086] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[0087] In this invention, the server includes means for controlling a device that detects the state of a plant using light to acquire an image of the plant, means for processing the acquired image to analyze the state of the plant, means for determining the state of the plant based on the analysis result, and means for notifying the user of the determination result using a communication means. This makes it possible to accurately monitor the state of the plant in real time and automatically perform cultivation operations and harvesting at appropriate times.

[0088] A "device for detecting the condition of a plant using light" is a device for detecting the health condition and components of a plant by utilizing the properties of light.

[0089] "Means for acquiring" refers to the function or process of acquiring images of plants at specified times and under specified conditions.

[0090] "Means for processing images to analyze plant condition" means a process that includes software and algorithms for analyzing acquired images to assess the health and composition of the plant.

[0091] The "means for determining the state of the plant based on the analysis results" is a function that comprehensively evaluates the state of the plant based on the results of image analysis and makes a determination based on certain criteria.

[0092] The "means for notifying the user using a communication means" refers to a communication function for notifying the user of the analysis and determination results, and sends the notification via the Internet or a mobile network.

[0093] "Means for measuring the component content of plants by image processing" refers to a function or process for measuring the components (e.g., sugar content) contained in plants by analyzing image data.

[0094] "Means for determining the optimal harvest time" refers to the function of determining the optimal timing for harvesting based on the ingredient content and other analysis results.

[0095] The "means for automatically sending instructions to cultivation equipment and performing plant cultivation operations" is a function that automatically issues instructions to cultivation equipment based on analysis results and conditions and executes necessary cultivation operations.

[0096] The present invention relates to a system that acquires images of plants using a camera equipped with a device for detecting the state of plants using light, analyzes the images to determine the state of the plants, and notifies the grower of the state at an appropriate time. Furthermore, the system can cooperate with cultivation equipment as needed to perform automatic cultivation operations. Specific examples of the system are described below.

[0097] 1. System Overview

[0098] The main components of this system are as follows:

[0099] Server: Manages all processes of image acquisition, analysis, judgment, and notification.

[0100] Camera terminal: Images of plants are acquired using a camera equipped with a device that uses light to detect the state of the plants.

[0101] User terminal: Used as a communication means for producers to receive notifications.

[0102] Cultivation machinery: machinery for carrying out automated cultivation operations, including irrigation systems and fertilizer application equipment.

[0103] 2. Example of image acquisition

[0104] The server controls the camera terminals at 9:00 AM every day to acquire images of the specified plants. At this time, the camera terminals use light to capture the color and condition of the plants. The acquired images are captured and saved in local storage in JPEG format or other formats. This process is controlled using CRON jobs and HTTP requests.

[0105] 3. Specific examples of image analysis

[0106] The server inputs the acquired images into an image processing module, which uses an open-source image processing library such as OpenCV to analyze the plant's hue information and ingredient content (e.g., sugar content). The hue information is analyzed for each pixel, and the percentage of pixels within a specific color range is calculated.

[0107] 4. Specific examples of judgments and notifications

[0108] The server determines the condition of the plants based on the results of image analysis. For example, if the plant coverage is 80% or more and the sugar content is within the appropriate range, it determines that it is time to harvest. This determination result is sent to the user's LINE account via the Internet. This notification is sent using the LINE API.

[0109] 5. Specific examples of automated cultivation operations

[0110] The server automatically sends instructions to the cultivation machine as needed. For example, if the plant condition is determined to be lacking in water, the server sends instructions to the irrigation system to properly replenish the water. Similarly, if needed, the server sends instructions to the fertilizer application device to automatically apply the appropriate amount of fertilizer.

[0111] Generative AI model and example prompts

[0112] Examples of prompts:

[0113] "Please explain the specific processing steps of a system that acquires images of plants using a camera equipped with a device that uses light to detect the state of the plants, analyzes the images to determine the state of the plants, and notifies the grower at the appropriate time. The system components include a server, camera terminal, user terminal, and cultivation machinery. Please describe the detailed procedures and operations from issuing image acquisition commands to operating the automatic cultivation."

[0114] This allows the generative AI model to explain in detail the specific processing flow of the program.

[0115] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0116] Step 1: Image Acquisition Instructions

[0117] The server sends an instruction to the camera terminal to acquire an image at the scheduled time. This instruction is sent to the camera terminal using an HTTP request.

[0118] Input: Schedule date and time

[0119] Data processing / calculation: Generate an HTTP request at the scheduled time using a CRON job

[0120] Output: Camera start command

[0121] Specific operation: A CRON job on the server is triggered and sends an HTTP request to the specified URL to retrieve the image.

[0122] Step 2: Image acquisition

[0123] The camera device receives instructions from the server to turn on the light source and capture images of the plants. The captured images are saved in local storage and then sent to the server.

[0124] Input: Camera start command

[0125] Data processing / calculation: The camera captures images of the plants and saves them as image files.

[0126] Output: JPEG format image file

[0127] Specific operation: The camera terminal controls the light center, captures images of plants, and saves them in local storage in JPEG format. The images are then sent to the server using an HTTP POST request.

[0128] Step 3: Image analysis

[0129] The server inputs the received images into an image processing module, which uses OpenCV and other tools to analyze the plant's hue information and component content (sugar content).

[0130] Input: JPEG format image file

[0131] Data processing / calculation: Image processing algorithms are used to extract color information and analyze sugar content

[0132] Output: Hue information data, sugar content data

[0133] Specific operation: The server uses an image processing module (e.g., OpenCV) to analyze the image and calculate color information and sugar content. This processing extracts data that can serve as an indicator of the plant's health.

[0134] Step 4: Judgment and Notification

[0135] The server determines the plant coverage rate and sugar content based on the analysis results, and sends a message via the LINE API to notify the user of the results.

[0136] Input: Hue information data, sugar content data

[0137] Data processing / calculation: Executes decision logic and calculates harvest timing

[0138] Output: Harvest timing judgment result

[0139] How it works: The server determines when it's time to harvest based on the plant's coverage and sugar content, and then uses the API to send a notification message to the user's LINE account.

[0140] Step 5: Automatic cultivation operation

[0141] If necessary, the server automatically sends instructions to the cultivation machinery, for example, if there is a lack of water, it will send instructions to the irrigation system to provide adequate water.

[0142] Input: Plant condition data

[0143] Data processing / calculation: condition judgment and corresponding cultivation action determination

[0144] Output: Instructions for irrigation systems and fertilizer application devices

[0145] Specific operation: The server determines the irrigation conditions based on the plant's condition and automatically sends instructions to the irrigation system to supply water. If necessary, it also sends instructions to the fertilizer sprayer, which then automatically sprays fertilizer.

[0146] In this way, the system automates a series of processes, enabling efficient and accurate plant management.

[0147] (Application example 1)

[0148] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0149] In conventional agricultural work, it is difficult to monitor the condition of plants in real time and carry out appropriate cultivation operations. Furthermore, determining the appropriate harvest time requires a high level of experience and knowledge, which places a heavy burden on producers. The present invention aims to solve these problems and provide a system for efficient and effective production management.

[0150] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[0151] In this invention, the server includes means for controlling a camera equipped with an optical sensor to acquire images of plants, means for analyzing the state of the plants from the acquired images, means for determining the state of the plants based on the analysis results, means for notifying the producer of the determination results via communication means, and means for controlling an autonomous machine based on the results of the plant state analysis to perform cultivation operations. This makes it possible to monitor the state of plants in real time and automatically perform cultivation operations such as irrigation and fertilizer application.

[0152] A "camera equipped with an optical sensor" is a camera device equipped with an optical sensor, which is capable of capturing images of plants with high accuracy.

[0153] "Acquired image" refers to image data of a plant captured by a camera equipped with an optical center.

[0154] The "means for analyzing the condition of a plant" is a system that analyzes the hue information and sugar content of a plant based on the acquired image data, and evaluates the health and growth status of the plant.

[0155] "Means for determining the condition of plants" refers to algorithms or programs that use the analysis results to determine whether plants are growing, whether they are diseased, and the appropriate timing for harvesting.

[0156] "Means for notifying producers via communication means" refers to a system for transmitting analysis and judgment results to producers' devices (smartphones, PCs, etc.) via the Internet or mobile phone lines.

[0157] "Autonomous machinery" refers to agricultural machinery that automatically performs plant cultivation operations, and includes tractors and irrigation systems with automatic driving functions.

[0158] "Cultivation operations" refers to all manual work, such as irrigation, fertilizing, weeding, harvesting, etc., performed to promote or protect plant growth.

[0159] This invention relates to a system that uses a camera equipped with an optical sensor to capture images of plants, analyzes the images to determine the condition of the plants, and notifies growers at the appropriate time. It can also work in conjunction with automated driving machines to automatically perform appropriate cultivation operations. Specific embodiments for implementing this invention are described below.

[0160] 1. Main components of the system

[0161] Server: A central control device for managing all processes of image acquisition, analysis, judgment, and notification. It is implemented using a programming language such as Python.

[0162] Camera terminal: A device that captures images of plants using a camera equipped with an optical sensor. This includes general IP cameras and proprietary imaging mechanisms.

[0163] Communication means: Used as a means of communication for producers to receive notifications. Smartphones and PCs are examples of this.

[0164] Autonomous machinery: Agricultural machinery that automatically performs plant cultivation operations. Examples include irrigation systems and fertilizer spreading devices.

[0165] 2. Image Acquisition Process

[0166] The server controls the camera equipped with the optical center to capture images of the plants at regularly scheduled times, for example, at 9:00 a.m. every day, to start the camera and capture new images, which are then saved in local storage for later analysis.

[0167] 3. Image analysis process

[0168] The acquired images are input by the server into the image processing module, which extracts the plant's hue information and calculates the percentage of pixels that fall within a specific range to analyze the plant's coloration. The sugar content is also measured, using an image analysis algorithm.

[0169] 4. Determination and Notification Process

[0170] Based on the analysis results, the server determines the plant's coverage rate and sugar content. This data is used to determine the appropriate time for harvesting. For example, if the coverage rate is 80% or higher and the sugar content is within the appropriate range, it is determined that the time has come to harvest. This determination result is notified to the producer in real time. The notification is sent via the internet to the user's LINE account.

[0171] 5. Automatic cultivation operation process

[0172] If necessary, the server can link with autonomous machines to perform automated cultivation operations. For example, if the plant's condition meets certain conditions, the server can send instructions to the irrigation system to provide adequate water. It can also automatically manage the supply of fertilizer by sending instructions to the fertilizer spraying device.

[0173] Specific examples

[0174] A specific scenario is shown below.

[0175] 1. The server controls the camera terminal every morning at 9:00 a.m. and captures images of tomatoes and mandarin oranges.

[0176] 2. The acquired images are input into an image processing module on the server, where the color and sugar content are analyzed.

[0177] 3. The analysis results show that the coverage rate is 85% and the sugar content is within the appropriate range.

[0178] 4. The server determines that it is time to harvest and notifies the producer of the result via LINE.

[0179] 5. Furthermore, the server plans the next irrigation timing and sends instructions to the irrigation system.

[0180] Examples of prompts to input to a generative AI model:

[0181] "Design a system to monitor the growth of tomatoes and automatically irrigate them. Use a camera to capture images of the plants every day at 9 AM, analyze the images, and calculate the percentage of green pixels. Based on the results, write a program to perform the necessary irrigation operations."

[0182] In this way, the present invention provides a system for monitoring the state of plants in real time and for efficient and effective production management.

[0183] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0184] Step 1:

[0185] The server controls a camera terminal equipped with an optical center to acquire images of plants at regularly scheduled times.

[0186] Input: Scheduled time (e.g. 9:00 AM)

[0187] Output: Images of the plants acquired

[0188] Specific operation: At the scheduled time, the server connects to the camera terminal and sends a command to take a photo. The camera terminal receives the command, captures images of the plants, and sends them to the server.

[0189] Step 2:

[0190] The server stores the acquired images in local storage.

[0191] Input: Plant image

[0192] Output: Saved image data

[0193] Specific operation: The server saves the received image data in the specified folder. The file name is identified by adding date and time information.

[0194] Step 3:

[0195] The server reads the saved images and inputs them into the image processing module for analysis.

[0196] Input: Saved image data

[0197] Output: Analysis results (hue information, sugar content, etc.)

[0198] How it works: The server uses an image processing library (e.g., OpenCV) to extract color information from the image data, calculate the percentage of pixels that fall within a specific range, and then uses image analysis algorithms to measure the sugar content.

[0199] Step 4:

[0200] The server determines the condition of the plant based on the analysis results.

[0201] Input: Image analysis results (hue information, sugar content, etc.)

[0202] Output: Plant condition judgment result (e.g. harvest time, good / bad condition)

[0203] Specific operation: The server runs an algorithm that determines the timing of harvest and necessary cultivation operations based on specific conditions (e.g., coverage rate of 80% or more and sugar content within an appropriate range).

[0204] Step 5:

[0205] The server notifies the producer of the judgment result via a communication means.

[0206] Input: Status determination result

[0207] Output: Notification message to producer

[0208] Specific operation: The server uses a communication API (e.g., LINE API) to send the judgment results to the producer's smartphone or computer.

[0209] Step 6:

[0210] The server controls the automated machinery as needed and performs appropriate cultivation operations.

[0211] Input: Status determination result

[0212] Output: Operation instructions for the automated driving machine

[0213] Specific operation: Based on the status determination results, the server sends instructions to the irrigation system and fertilizer spreading device, which causes the agricultural machinery to automatically perform the necessary tasks.

[0214] Furthermore, an emotion engine that estimates the user's emotion may be combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0215] The present invention relates to a system that acquires images of plants and notifies growers based on the analysis results, and also adds a function to recognize the user's emotions and adjust the notification content and operation accordingly. This system acquires images of plants using a camera equipped with an optical sensor and determines the condition of the plants through image analysis. Furthermore, by combining it with an emotion engine that recognizes the user's (grower's) emotions, the system can adjust the notification content and automatic cultivation operation if the user is experiencing negative emotions such as stress.

[0216] System Overview

[0217] The main components of the system are as follows:

[0218] Server: Manages all processes including image acquisition, analysis, judgment, notification, and emotion recognition.

[0219] Camera terminal: A camera equipped with an optical center is used to capture images of plants.

[0220] User terminal: Used as a communication means for producers to receive notifications and as a data collection means for emotion recognition.

[0221] Cultivation machinery: machinery for carrying out automated cultivation operations, including irrigation systems and fertilizer application equipment.

[0222] 1. Image Acquisition Process

[0223] The server sets a periodic task and sends instructions to activate a camera terminal equipped with an optical sensor at a specific time every day. This instruction is to obtain the latest images of the plants to be harvested that day. The camera terminal receives instructions from the server and captures images of the plants. The obtained images are temporarily stored in the camera terminal's local storage and then sent to the server.

[0224] 2. Image analysis process

[0225] The server then passes the saved image to the analysis module, where image processing begins. This module first converts the image into a color space and extracts pixels that fall within a specific color range. The extracted pixel information is used to evaluate the plant's color development, and then a sugar content measurement algorithm is run to calculate the sugar content estimated from the image. The resulting coverage and sugar content data are used to determine the appropriate time for harvesting.

[0226] 3. Determination and Notification Process

[0227] If the server determines that it is time to harvest, it generates a message indicating this and sends it to the user's device using a communication module. This notification is then sent via the internet to the user's LINE account.

[0228] 4. The process of emotion recognition

[0229] The user device collects the producer's facial expression and voice data and sends it to the server. The server then uses an emotion engine to analyze this data and determine the user's emotions. For example, if the user is feeling stressed, the server can adjust the content of the notification based on that information. Based on the emotion recognition results, the notification can include kinder language or an encouraging message.

[0230] 5. Automatic cultivation operation process

[0231] If necessary, the server can send instructions to the cultivation machine to perform automatic cultivation operations. For example, if the user feels high stress, the server can adjust the frequency of irrigation and fertilizer application to reduce the burden on the user.

[0232] Specific examples

[0233] A specific scenario is shown below.

[0234] 1. The server controls the camera terminal every morning at 9:00 AM and captures images of mandarin oranges.

[0235] 2. The acquired images are input into an image processing module on the server, where the color and sugar content are analyzed.

[0236] 3. The analysis results show that the coverage rate is 85% and the sugar content is within the appropriate range.

[0237] 4. The server determines that it is time to harvest and notifies the user (producer) of the result via LINE.

[0238] 5. The user device collects the user's facial expression and voice data and sends it to the server.

[0239] 6. The server uses an emotion engine to determine the user's emotions and determines that the user is feeling stressed.

[0240] 7. Based on the emotion recognition results, the server resends a notification via LINE containing an encouraging message.

[0241] 8. If necessary, the server sends instructions to the irrigation system to provide adequate watering, and also to the fertilizer application device to automatically manage the supply of fertilizer.

[0242] In this way, the present invention not only monitors the condition of plants in real time and notifies producers at the appropriate time, but also adjusts the content of notifications and automatic cultivation operations according to the user's emotional state, thereby achieving efficient and effective production management.

[0243] The processing flow will be explained below.

[0244] Step 1:

[0245] The server sets up a periodic task and sends instructions to activate a camera terminal equipped with a photonic center at a specific time every day to obtain the latest images of the plants to be harvested that day.

[0246] Step 2:

[0247] The device (camera) receives instructions from the server and captures images of the plants. The captured images are temporarily stored in the camera device's local storage.

[0248] Step 3:

[0249] The device (camera) sends the captured image to the server, which receives the file and stores it in a secure location.

[0250] Step 4:

[0251] The server passes the saved image to the analysis module, which then starts processing the image. First, it converts the image into a color space (such as HSV) and extracts pixels that fall within a specific color range.

[0252] Step 5:

[0253] The server evaluates the color of the plants based on the extracted pixel information. Specifically, it calculates the percentage of pixels that fall within a specific range and outputs the result as the coverage rate.

[0254] Step 6:

[0255] The server runs a sugar content measurement algorithm to calculate the estimated sugar content from the image, which uses specific pigment patterns and texture analysis.

[0256] Step 7:

[0257] The server determines the optimal time for harvesting based on data on coverage and sugar content. For example, if coverage is 80% or higher and sugar content is within the appropriate range, it determines that it is optimal for harvesting.

[0258] Step 8:

[0259] If the server determines that the fruit is suitable for harvesting, it generates a message containing the specific coverage rate and sugar content values.

[0260] Step 9:

[0261] The server sends the generated message to the user (producer) using a communication module. The communication method is via the Internet using the LINE API.

[0262] Step 10:

[0263] The user (producer) can check the harvest timing information received as a LINE message, which allows them to carry out harvesting work at the appropriate time.

[0264] Step 11:

[0265] The user device collects facial and voice data from the user and sends it to the server. For example, data is collected automatically when the user approaches a camera or microphone.

[0266] Step 12:

[0267] The server uses an emotion engine to analyze the received data and determine the user's emotions, for example, using facial expression recognition algorithms and voice emotion analysis techniques.

[0268] Step 13:

[0269] The server then adjusts the notification content based on the emotion recognition results, for example adding a kind comment or encouraging message if the user is feeling stressed.

[0270] Step 14:

[0271] The server then adjusts the message based on the emotion recognition results and sends it back to the user (producer) via LINE.

[0272] Step 15:

[0273] If necessary, the server will send further instructions to the cultivation machine. If the user is feeling high stress, the server will adjust the frequency of irrigation and fertilizer application to reduce the burden on the user.

[0274] Through these steps, the system can monitor the condition of plants in real time, notify farmers of the appropriate harvest time, and adjust the notification content and automated cultivation operations according to the user's emotional state, thereby achieving efficient and high-quality production management.

[0275] Example 2

[0276] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0277] Conventional plant cultivation systems do not automate the assessment of plant conditions or notifications of harvest time, making efficient and accurate management difficult. Furthermore, notifications and operations are performed mechanically without taking into account the emotional state of the grower, resulting in a lack of consideration for reducing stress. Therefore, a system that can properly manage plants and reduce the psychological burden on growers is needed.

[0278] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[0279] In this invention, the server includes means for controlling a camera equipped with an optical center to acquire images of the plants, means for analyzing the state of the plants from the acquired images, means for determining the state of the plants based on the analysis results, means for notifying the determination results to the grower via communication means, means for recognizing the user's emotions by analyzing facial expression data and voice data acquired from the user terminal, and means for adjusting the content of notifications and cultivation operations based on the emotion recognition results. This enables appropriate notifications and operations to be performed taking into consideration the emotional state of the grower while accurately understanding the state of the plants.

[0280] A "camera equipped with a light center" is a camera device that incorporates a light sensor that measures the wavelength and intensity of light, and is used to capture images of plants with high precision.

[0281] The "means for acquiring images" refers to a part consisting of a mechanism and software for controlling the camera, taking images of plants, and saving them as digital data.

[0282] The "analysis means" refers to software and algorithms for processing the acquired image data and quantifying or evaluating the condition and characteristics of the plants.

[0283] The "means for determining" refers to logic and programs for determining the health of plants and the time for harvesting based on the results of image analysis.

[0284] "Communication means" refers to a mechanism including a communication interface and protocol for notifying the results of analysis and judgment to producers and other systems.

[0285] "Facial expression data" refers to the facial expressions of a user captured using a camera or other sensor and converted into digital data.

[0286] "Voice data" refers to the user's voice and speaking style collected by an acoustic sensor such as a microphone and stored as digital data.

[0287] "Means for recognizing emotions" refers to algorithms and software for analyzing facial expression data and voice data to identify the user's emotional state (e.g., stress, joy, fatigue, etc.).

[0288] The "means for adjusting cultivation operations" is a system for automatically adjusting operations related to plant cultivation, such as watering and fertilizer application, based on the emotion recognition results.

[0289] "Cultivation machinery" refers to all machinery and equipment used in plant cultivation, such as irrigation systems and fertilizer spreading devices.

[0290] MODE FOR CARRYING OUT THE INVENTION

[0291] The present invention relates to a system that acquires images of plants and notifies growers based on the analysis results, and also adds a function that recognizes the user's emotions and adjusts the notification content and operation. This system includes the following main components:

[0292] 1. Server

[0293] The server plays a central role in managing the entire image acquisition, analysis, judgment, notification, and emotion recognition process. Specifically, the server has the following functions:

[0294] Control of image acquisition: The server controls the camera terminal equipped with the optical center and instructs it to acquire images of plants at specific times.

[0295] Image analysis: The acquired images are passed to the analysis module, where color space conversion and pixel extraction are performed to evaluate the condition of the plant (e.g., color development, sugar content).

[0296] Judgment and notification: The harvest time is determined based on the analysis results, and the results are notified to producers via LINE or other communication means.

[0297] Emotion recognition: Analyzes facial expression and voice data sent from the user's device to determine the producer's emotional state. If the producer is feeling stressed, the content of notifications will be adjusted.

[0298] Automating cultivation operations: Sending instructions to cultivation machinery as needed to adjust the frequency of irrigation and fertilization.

[0299] 2. Camera terminal

[0300] The camera terminal is a device that captures images of plants using a high-resolution camera equipped with an optical sensor. The camera terminal receives instructions from the server, captures images at specific times, and sends the data to the server. It also has the function of temporarily storing data in local storage.

[0301] 3. User Device

[0302] The user terminal is a means for producers to receive notifications and also functions as a means for collecting data for emotion recognition. Specifically, it plays the following roles:

[0303] Receiving notifications: Receive notifications sent from the server using LINE or other communication methods.

[0304] Data collection: Collects the user's facial expression and voice data and sends it to the server.

[0305] 4. Cultivation machinery

[0306] The cultivation machine is a mechanical facility for automatically operating the cultivation of plants, such as an irrigation system or a fertilizer spraying device, and performs appropriate cultivation operations based on instructions from the server.

[0307] Specific examples

[0308] The specific operating scenario of the system is shown below.

[0309] 1. The server controls the camera terminal every morning at 9:00 AM and captures images of mandarin oranges.

[0310] 2. The acquired images are input into an image processing module on the server, where the color and sugar content are analyzed.

[0311] 3. The analysis results show that the coverage rate is 85% and the sugar content is within the appropriate range.

[0312] 4. The server determines that it is time to harvest and notifies the user (producer) of the result via LINE.

[0313] 5. The user device collects the user's facial expression and voice data and sends it to the server.

[0314] 6. The server uses an emotion engine to determine the user's emotions and determines that the user is feeling stressed.

[0315] 7. Based on the emotion recognition results, the server resends a notification via LINE containing an encouraging message.

[0316] 8. If necessary, the server sends instructions to the irrigation system to provide adequate watering, and also to the fertilizer application device to automatically manage the supply of fertilizer.

[0317] Prompt Sentence Examples

[0318] What does this system do?: This system captures images of plants and notifies growers based on the analysis results. It also recognizes the user's emotions and adjusts operations and notifications accordingly.

[0319] The above is an embodiment of the present invention.

[0320] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0321] Specific processing flow of the program

[0322] Step 1:

[0323] The server sets an instruction to start up the camera terminal equipped with the optical center at a fixed time every day as a scheduled task.

[0324] Input: Schedule setting (e.g. 9:00 AM)

[0325] Output: Instructions to start the camera device

[0326] Step 2:

[0327] The camera terminal receives instructions from the server and activates the camera equipped with the optical center.

[0328] Specific actions: Turn on the camera and put it into a ready state.

[0329] Input: Start command from the server

[0330] Output: The camera is armed and ready to capture images.

[0331] Step 3:

[0332] The camera terminal captures high-resolution images of a target plant (e.g., a mandarin orange).

[0333] Specific actions: Activate the camera's shutter button and generate image data.

[0334] Input: Plant photography target position

[0335] Output: Captured image data

[0336] Step 4:

[0337] The camera terminal temporarily stores the captured images in local storage.

[0338] Specific operation: Saves the image data as a temporary file.

[0339] Input: Captured image data

[0340] Output: Image file saved in local storage

[0341] Step 5:

[0342] The camera terminal transmits the stored image data to a server via the Internet.

[0343] Specific operation: Sends image data over the Internet.

[0344] Input: Image file stored in local storage

[0345] Output: Image data transferred to the server

[0346] Step 6:

[0347] The server receives the images sent from the camera terminal.

[0348] Specific operation: The received image data is stored in the server's temporary storage area.

[0349] Input: Image data from the camera device

[0350] Output: Image data stored in a temporary storage area on the server

[0351] Step 7:

[0352] The server passes the image to the image processing module to begin analysis.

[0353] What it does: Invokes image processing algorithms to perform color space conversion and pixel extraction.

[0354] Input: Image data saved in temporary storage area

[0355] Output: Processed pixel data

[0356] Step 8:

[0357] The server applies a sugar content measurement algorithm based on the analysis results to calculate the sugar content of the plant.

[0358] Specific operation: Runs an algorithm that calculates sugar content using pixel data as input.

[0359] Input: Processed pixel data

[0360] Output: Sugar content data of plants

[0361] Step 9:

[0362] The server determines the appropriate time to harvest based on the coverage rate and sugar content obtained as a result of the analysis.

[0363] Specific operation: Compare with the set threshold and calculate the harvest time.

[0364] Input: Coverage data, sugar content data

[0365] Output: Harvest timing judgment result

[0366] Step 10:

[0367] The server generates a notification message based on the result of the judgment and sends it to the user's LINE account using the LINE API.

[0368] Specific operation: A message is generated based on the judgment result and sent via LINE.

[0369] Input: Harvest timing judgment result

[0370] Output: Notification message sent to the user's LINE

[0371] Step 11:

[0372] The user terminal collects facial expression data and voice data of the user during daily operations.

[0373] Specific actions: Record data using a camera or microphone.

[0374] Input: User's face and voice

[0375] Output: facial expression data, voice data

[0376] Step 12:

[0377] The user terminal transmits the collected facial expression data and voice data to the server.

[0378] Specific operation: Sends data to the server.

[0379] Input: facial expression data, voice data

[0380] Output: Facial expression data and voice data sent to the server

[0381] Step 13:

[0382] The server uses an emotion engine to analyze the facial expression data and voice data to determine the user's emotion.

[0383] What it does: Runs emotion recognition algorithms to identify the user's emotional state.

[0384] Input: facial expression data, voice data

[0385] Output: User's emotional state

[0386] Step 14:

[0387] If the server determines that the user is in a stressed state, it adjusts the content of the notification and resends it.

[0388] Specific behavior: Create a new message and send it again via LINE.

[0389] Input: Emotion recognition results

[0390] Output: The adjusted notification message

[0391] Step 15:

[0392] The server sends instructions to cultivation machinery (such as irrigation systems and fertilizer spreading devices) as needed to perform automatic cultivation operations.

[0393] Specific operation: Issues operational instructions to the machine based on set conditions.

[0394] Input: Emotion recognition results, plant state data

[0395] Output: Operation instructions for cultivation machinery

[0396] Step 16:

[0397] The cultivation machine performs operations such as watering and spreading fertilizer according to instructions from the server.

[0398] Specific actions: Water and fertilize according to instructions.

[0399] Input: Operation instructions from the server

[0400] Output: Feedback of the results of the cultivation operations performed

[0401] (Application example 2)

[0402] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0403] Conventional plant cultivation systems have limited functionality for monitoring plant conditions and notifying appropriate cultivation timing. Furthermore, they simply provide instructions for harvesting and cultivation operations without considering the producer's emotional state, placing a heavy burden on the producer. The present invention aims to achieve effective and efficient production management by providing a system that not only accurately monitors plant conditions and notifies producers at the appropriate time, but also adjusts notification content and operations taking into account the producer's emotional state.

[0404] The specific processing by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes means for controlling a camera equipped with an optical center to acquire images of plants, means for analyzing the state of the plants from the acquired images, means for determining the state of the plants based on the analysis results, means for notifying the determination result to the producer via communication means, means for recognizing the user's emotions and adjusting the notification content and operation, and means for sending instructions to the automated cultivation machine. This not only enables real-time monitoring of the state of plants, but also enables optimal notifications and automatic cultivation operations according to the producer's emotional state.

[0405] A "light center" is a device that has the function of detecting the intensity and position of light, and is used to obtain clear images of plants.

[0406] A "camera" is a device that takes pictures and videos and records the data.

[0407] A "plant" is a multicellular organism that performs photosynthesis and grows above ground.

[0408] "Means for acquiring images" refers to a device for taking images, such as a camera, and a method for controlling its operation.

[0409] "Means for analysis" refers to methods or devices that process data such as images to extract meaningful information.

[0410] A "means for determining" is a method or device that determines a particular situation or condition based on analyzed data.

[0411] "Communication means" refers to methods and devices for sending and receiving information, including the Internet and mobile networks.

[0412] "Producer" refers to an agricultural worker who grows crops, etc.

[0413] "Means for recognizing a user's emotions" refers to methods and technologies for identifying a user's emotional state from facial expressions, voice, etc.

[0414] The "means for adjusting notification content" refers to a method or device for optimizing the content of information or messages to be conveyed based on the results of emotion analysis.

[0415] "Cultivation machinery" refers to machinery for automatically carrying out plant cultivation work, and includes irrigation systems and fertilizer spraying devices.

[0416] A "means for sending instructions" is a method or device for sending operational commands to a remote device or system.

[0417] The present invention relates to a system for monitoring and managing the condition of plants, and for optimal notification and automatic operation taking into consideration the emotional state of the grower. Hereinafter, specific embodiments of the present invention will be described in detail.

[0418] System Configuration

[0419] The main components are a server, a camera terminal, a user terminal, and a cultivation machine.

[0420] Server: Manages all processes, including image acquisition, analysis, judgment, notification, and emotion recognition. In particular, a high-performance server is required for storing and processing data, and it is desirable to implement it in a programming language such as Python.

[0421] Camera terminal: Images of plants are acquired using a camera equipped with an optical sensor. Specifically, a high-resolution camera such as the Intel RealSense D435 is suitable.

[0422] User terminals: Devices such as smartphones and smart glasses that allow producers to receive notifications. These devices are also used to collect facial expression and voice data.

[0423] Cultivation machinery: Machines for automated cultivation operations, such as irrigation systems and fertilizer spreading devices. Specifically, the UR5e robot arm is a suitable example.

[0424] Image acquisition process

[0425] The server sets a periodic task and sends instructions to start the camera terminal equipped with the optical center at a specific time every day. The camera terminal receives this instruction, captures images of the plants, temporarily stores the acquired images in the camera terminal's local storage, and then sends them to the server. The acquired images are saved in the server's storage and prepared for the next analysis process.

[0426] Image analysis process

[0427] The server passes the saved images to the analysis module to evaluate the condition of the plants. First, it converts the image into a color space using an image processing library (e.g., OpenCV), and then extracts pixels that fall into a specific color range. Based on the extracted pixel information, it evaluates the color development of the plants and runs a sugar content measurement algorithm to calculate the sugar content estimated from the image. The coverage rate and sugar content data obtained as a result of the analysis are then used to determine the appropriate time for harvesting.

[0428] Determination and Notification Process

[0429] When the server determines that it is time to harvest, it generates a message indicating this and sends it to the user's device via the communication module. This notification is then sent via the Internet to the user's smartphone or smart glasses. The user device receives this notification and notifies the producer.

[0430] The process of emotion recognition

[0431] The user device collects the producer's facial expression and voice data and sends it to the server. The server then analyzes this data using an emotion recognition engine (e.g., Microsoft® Azure® Cognitive Services) to determine the user's emotional state. If the user is feeling stressed, the server adjusts the content of notifications based on that information, allowing it to include encouraging messages or kind words.

[0432] Automatic cultivation operation process

[0433] If necessary, the server can send instructions to the cultivation machine to perform automatic cultivation operations. For example, if the user feels high stress, the server can adjust the frequency of irrigation and fertilizer application to reduce the burden on the user.

[0434] Specific examples

[0435] A specific scenario is shown below.

[0436] 1. The server controls the camera terminals every morning at 9:00 AM to acquire images of plants.

[0437] 2. The acquired images are input into an image processing module on the server, where the color and sugar content are analyzed.

[0438] 3. The analysis results show that the coverage rate is 85% and the sugar content is within the appropriate range.

[0439] 4. The server determines that it is time to harvest and notifies the user of the result via their smartphone or smart glasses.

[0440] 5. The user device collects the user's facial expression and voice data and sends it to the server.

[0441] 6. The server uses an emotion engine to determine the user's emotion and if the user is feeling stressed, resends the notification with an encouraging message.

[0442] 7. If necessary, the server sends instructions to the irrigation system and fertilizer application device to automatically manage the appropriate watering and fertilization.

[0443] In this way, the present invention not only monitors the state of plants in real time and notifies growers at the appropriate time, but also adjusts the content of notifications and automated cultivation operations according to the user's emotional state, thereby achieving efficient and effective production management.

[0444] Example prompts:

[0445] "Design a system that automatically monitors medicinal plants in a factory and takes into account the emotional state of workers to provide optimal notifications and actions."

[0446] This will not only allow for more efficient and effective management of plant cultivation, but also enable the creation of a system that takes into account the emotions and well-being of workers.

[0447] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0448] Step 1:

[0449] The server sends instructions to start up a camera terminal equipped with an optical center at a specific time every day. The input is pre-set schedule information. The output is for the camera terminal to start up and capture images of plants. This instruction is sent to the camera terminal via the Internet. Specifically, a command to control the camera is sent, and the camera takes a picture of the target plant.

[0450] Step 2:

[0451] The camera terminal receives instructions to capture images of plants and temporarily store them in local storage. The input is a start-up instruction from the server. The output is the acquired image data of the plants. Specifically, the camera automatically focuses, takes high-resolution images of the plants, and stores the data in local storage.

[0452] Step 3:

[0453] The camera terminal sends the saved image data to the server. The input is the image data saved in the local storage. The output is the image data saved on the server. In concrete terms, the camera terminal performs a process of uploading the image data to the server via the Internet.

[0454] Step 4:

[0455] The server passes the received image data to the analysis module, which analyzes the condition of the plants. The input is the image data sent from the camera terminal. The output is the analysis results. Specifically, an image processing library is used to convert the image into a color space and extract pixels that fall within a specific color range. Based on this information, the coloring and sugar content of the plants are evaluated.

[0456] Step 5:

[0457] The server determines the condition of the plants based on the analysis results. The input is the analysis results obtained from the analysis module. The output is a judgment result indicating the condition of the plants. Specifically, the analysis algorithm evaluates data such as cover rate and sugar content to determine the appropriate time for harvesting.

[0458] Step 6:

[0459] The server notifies the user terminal of the judgment result via the communication module. The input is the judgment result indicating the plant's condition. The output is a notification message delivered to the user's smartphone or smart glasses. Specifically, the notification message is generated and sent to the user terminal via the Internet.

[0460] Step 7:

[0461] The user terminal collects the user's facial expression data and voice data and sends them to the server. The input is facial expression data and voice data collected by a camera and microphone. The output is emotion data sent to the server. Specifically, sensors in the smartphone or smart glasses detect the user's emotions and upload the data to the server.

[0462] Step 8:

[0463] The server uses an emotion recognition engine to analyze the user's emotional state. The input is facial expression data and voice data sent from the user's device. The output is an analysis result indicating the user's emotional state. Specifically, the emotion recognition algorithm analyzes the data and determines indicators such as stress and happiness.

[0464] Step 9:

[0465] The server adjusts the notification content based on the emotion analysis results and sends it back to the user device. The input is the analysis result from the emotion recognition engine. The output is a message containing the adjusted notification content. Specifically, a notification containing words of encouragement or a kind message is generated and sent back to the user device.

[0466] Step 10:

[0467] If necessary, the server sends instructions to the cultivation machine to perform automatic cultivation operations. The input is instruction information based on the analysis results and the user's emotional state. The output is automatic cultivation operations using the irrigation system and fertilizer spraying device. Specific operation instructions are sent to the cultivation machine, which operates to maintain the optimal cultivation environment for the plants.

[0468] This allows for real-time monitoring of plant conditions, timely notifications, and cultivation operations that take into account the grower's emotional state.

[0469] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0470] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> ), Gemini (registered trademark) (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0471] In the above embodiment, an example in which the specific process is performed by the data processing device 12 has been given, but the technology of the present disclosure is not limited to this, and the specific process may be performed by the smart device 14.

[0472] [Second embodiment]

[0473] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0474] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0475] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0476] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0477] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio in accordance with instructions from the processor 46.

[0478] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the surroundings of user 20 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0479] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0480] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0481] The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific 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.

[0482] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0483] In the smart glasses 214, the reception output process is performed by the processor 46. A reception output program 60 is stored in the storage 50. 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 process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0484] Next, a description will be given of the identification process performed by the identification processing unit 290 of the data processing device 12. 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."

[0485] The present invention relates to a system that acquires images of plants using a camera equipped with an optical sensor, analyzes the images to determine the condition of the plants, and notifies the grower at an appropriate time. Furthermore, the system can cooperate with cultivation machinery as needed to perform automatic cultivation operations. Specific examples of the system are described below.

[0486] 1. System Overview

[0487] The main components of the system are as follows:

[0488] Server: Manages all processes of image acquisition, analysis, judgment, and notification.

[0489] Camera terminal: A camera equipped with an optical center is used to capture images of plants.

[0490] User terminal: Used as a communication means for producers to receive notifications.

[0491] Cultivation machinery: machinery for carrying out automated cultivation operations, including irrigation systems and fertilizer application equipment.

[0492] 2. Image Acquisition Process

[0493] The server controls the camera equipped with the optical center to capture images of the plants at regularly scheduled times, for example, at 9:00 a.m. every day, to start the camera and capture new images, which are then saved in local storage for later analysis.

[0494] 3. Image analysis process

[0495] The acquired images are input by the server into the image processing module, which extracts the plant's hue information and calculates the percentage of pixels that fall within a specific range to analyze the plant's coloration. The sugar content is also measured, using an image analysis algorithm.

[0496] 4. Determination and Notification Process

[0497] Based on the analysis results, the server determines the plant's coverage rate and sugar content. This data is used to determine the appropriate time for harvesting. For example, if the coverage rate is 80% or higher and the sugar content is within the appropriate range, it is determined that the time has come to harvest. This determination result is notified to the producer in real time. The notification is sent via the internet to the user's LINE account.

[0498] 5. Automatic cultivation operation process

[0499] If necessary, the server can link with the cultivation machinery to perform automatic cultivation operations. For example, if the plant's condition meets certain conditions, the server can send instructions to the irrigation system to provide appropriate water supply. It can also send instructions to the fertilizer spraying device to automatically manage the supply of fertilizer.

[0500] Specific examples

[0501] A specific scenario is shown below.

[0502] 1. The server controls the camera terminal every morning at 9:00 AM and captures images of mandarin oranges.

[0503] 2. The acquired images are input into an image processing module on the server, where the color and sugar content are analyzed.

[0504] 3. The analysis results show that the coverage rate is 85% and the sugar content is within the appropriate range.

[0505] 4. The server determines that it is time to harvest and notifies the producer of the result via LINE.

[0506] 5. Furthermore, the server plans the next irrigation timing and sends instructions to the irrigation system.

[0507] In this way, the present invention allows for real-time monitoring of plant conditions and efficient and effective production management.

[0508] The processing flow will be explained below.

[0509] Step 1:

[0510] The server sets up a periodic task and sends instructions to activate a camera terminal equipped with a photonic center at a specific time every day to obtain the latest images of the plants to be harvested that day.

[0511] Step 2:

[0512] The device (camera) receives instructions from the server and captures images of the plants. The captured images are temporarily stored in the camera device's local storage.

[0513] Step 3:

[0514] The device (camera) sends the captured image to the server, which receives the file and stores it in a secure location.

[0515] Step 4:

[0516] The server then passes the stored image to an analysis module, which begins processing the image by first converting it to a color space (such as HSV) and extracting pixels that fall into a specific color range.

[0517] Step 5:

[0518] The server evaluates the color of the plants based on the extracted pixel information. Specifically, it calculates the percentage of pixels that fall within a specific range and outputs the result as the coverage rate.

[0519] Step 6:

[0520] The server then runs a sugar content measurement algorithm to calculate an estimated sugar content from the image, which typically uses specific pigment patterns or texture analysis.

[0521] Step 7:

[0522] The server compares the coverage rate and sugar content data to determine the appropriate time for harvesting. For example, if the coverage rate is 80% or higher and the sugar content is within the appropriate range, it determines that it is the best time to harvest.

[0523] Step 8:

[0524] If the server determines that the fruit is suitable for harvesting, it generates a message containing the specific coverage rate and sugar content values.

[0525] Step 9:

[0526] The server sends the generated message to the user (producer) using a communication module. The communication method is via the Internet using the LINE API.

[0527] Step 10:

[0528] The user (producer) checks the harvest timing information received as a LINE message, allowing the user to carry out harvesting work at the appropriate time.

[0529] Step 11:

[0530] If necessary, the server can also send instructions to the farming machine, such as instructions to automatically plan post-harvest irrigation and fertilizer applications.

[0531] Through these steps, the system can monitor the condition of plants in real time, notify producers of the appropriate harvest time, and perform the necessary automated cultivation operations.

[0532] Example 1

[0533] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0534] In conventional plant cultivation methods, monitoring plant conditions and timing of cultivation operations depended on human experience and intuition, making it difficult to make accurate judgments and respond at the appropriate time. Furthermore, manual monitoring and operation required time and effort, making it difficult to improve production efficiency. Furthermore, this approach carried the risk of overlooking changes in plant conditions and the optimal harvest time, which could affect quality and yield.

[0535] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[0536] In this invention, the server includes means for controlling a device that detects the state of a plant using light to acquire an image of the plant, means for processing the acquired image to analyze the state of the plant, means for determining the state of the plant based on the analysis result, and means for notifying the user of the determination result using a communication means. This makes it possible to accurately monitor the state of the plant in real time and automatically perform cultivation operations and harvesting at appropriate times.

[0537] A "device for detecting the condition of a plant using light" is a device for detecting the health condition and components of a plant by utilizing the properties of light.

[0538] "Means for acquiring" refers to the function or process of acquiring images of plants at specified times and under specified conditions.

[0539] "Means for processing images to analyze plant condition" means a process that includes software and algorithms for analyzing acquired images to assess the health and composition of the plant.

[0540] The "means for determining the state of the plant based on the analysis results" is a function that comprehensively evaluates the state of the plant based on the results of image analysis and makes a determination based on certain criteria.

[0541] The "means for notifying the user using a communication means" refers to a communication function for notifying the user of the analysis and determination results, and sends the notification via the Internet or a mobile network.

[0542] "Means for measuring the component content of plants by image processing" refers to a function or process for measuring the components (e.g., sugar content) contained in plants by analyzing image data.

[0543] "Means for determining the optimal harvest time" refers to the function of determining the optimal timing for harvesting based on the ingredient content and other analysis results.

[0544] The "means for automatically sending instructions to cultivation equipment and performing plant cultivation operations" is a function that automatically issues instructions to cultivation equipment based on analysis results and conditions and executes necessary cultivation operations.

[0545] The present invention relates to a system that acquires images of plants using a camera equipped with a device for detecting the state of plants using light, analyzes the images to determine the state of the plants, and notifies the grower of the state at an appropriate time. Furthermore, the system can cooperate with cultivation equipment as needed to perform automatic cultivation operations. Specific examples of the system are described below.

[0546] 1. System Overview

[0547] The main components of this system are as follows:

[0548] Server: Manages all processes of image acquisition, analysis, judgment, and notification.

[0549] Camera terminal: Images of plants are acquired using a camera equipped with a device that uses light to detect the state of the plants.

[0550] User terminal: Used as a communication means for producers to receive notifications.

[0551] Cultivation machinery: machinery for carrying out automated cultivation operations, including irrigation systems and fertilizer application equipment.

[0552] 2. Example of image acquisition

[0553] The server controls the camera terminals at 9:00 AM every day to acquire images of the specified plants. At this time, the camera terminals use light to capture the color and condition of the plants. The acquired images are captured and saved in local storage in JPEG format or other formats. This process is controlled using CRON jobs and HTTP requests.

[0554] 3. Specific examples of image analysis

[0555] The server inputs the acquired images into an image processing module, which uses an open-source image processing library such as OpenCV to analyze the plant's hue information and ingredient content (e.g., sugar content). The hue information is analyzed for each pixel, and the percentage of pixels within a specific color range is calculated.

[0556] 4. Specific examples of judgments and notifications

[0557] The server determines the condition of the plants based on the results of image analysis. For example, if the plant coverage is 80% or more and the sugar content is within the appropriate range, it determines that it is time to harvest. This determination result is sent to the user's LINE account via the Internet. This notification is sent using the LINE API.

[0558] 5. Specific examples of automated cultivation operations

[0559] The server automatically sends instructions to the cultivation machine as needed. For example, if the plant condition is determined to be lacking in water, the server sends instructions to the irrigation system to properly replenish the water. Similarly, if needed, the server sends instructions to the fertilizer application device to automatically apply the appropriate amount of fertilizer.

[0560] Generative AI model and example prompts

[0561] Examples of prompts:

[0562] "Please explain the specific processing steps of a system that acquires images of plants using a camera equipped with a device that uses light to detect the state of the plants, analyzes the images to determine the state of the plants, and notifies the grower at the appropriate time. The system components include a server, camera terminal, user terminal, and cultivation machinery. Please describe the detailed procedures and operations from issuing image acquisition commands to operating the automatic cultivation."

[0563] This allows the generative AI model to explain in detail the specific processing flow of the program.

[0564] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0565] Step 1: Image Acquisition Instructions

[0566] The server sends an instruction to the camera terminal to acquire an image at the scheduled time. This instruction is sent to the camera terminal using an HTTP request.

[0567] Input: Schedule date and time

[0568] Data processing / calculation: Generate an HTTP request at the scheduled time using a CRON job

[0569] Output: Camera start command

[0570] Specific operation: A CRON job on the server is triggered and sends an HTTP request to the specified URL to retrieve the image.

[0571] Step 2: Image acquisition

[0572] The camera device receives instructions from the server to turn on the light source and capture images of the plants. The captured images are saved in local storage and then sent to the server.

[0573] Input: Camera start command

[0574] Data processing / calculation: The camera captures images of the plants and saves them as image files.

[0575] Output: JPEG format image file

[0576] Specific operation: The camera terminal controls the light center, captures images of plants, and saves them in local storage in JPEG format. The images are then sent to the server using an HTTP POST request.

[0577] Step 3: Image analysis

[0578] The server inputs the received images into an image processing module, which uses OpenCV and other tools to analyze the plant's hue information and component content (sugar content).

[0579] Input: JPEG format image file

[0580] Data processing / calculation: Image processing algorithms are used to extract color information and analyze sugar content

[0581] Output: Hue information data, sugar content data

[0582] Specific operation: The server uses an image processing module (e.g., OpenCV) to analyze the image and calculate color information and sugar content. This processing extracts data that can serve as an indicator of the plant's health.

[0583] Step 4: Judgment and Notification

[0584] The server determines the plant coverage rate and sugar content based on the analysis results, and sends a message via the LINE API to notify the user of the results.

[0585] Input: Hue information data, sugar content data

[0586] Data processing / calculation: Executes decision logic and calculates harvest timing

[0587] Output: Harvest timing judgment result

[0588] How it works: The server determines when it's time to harvest based on the plant's coverage and sugar content, and then uses the API to send a notification message to the user's LINE account.

[0589] Step 5: Automatic cultivation operation

[0590] If necessary, the server automatically sends instructions to the cultivation machinery, for example, if there is a lack of water, it will send instructions to the irrigation system to provide adequate water.

[0591] Input: Plant condition data

[0592] Data processing / calculation: condition judgment and corresponding cultivation action determination

[0593] Output: Instructions for irrigation systems and fertilizer application devices

[0594] Specific operation: The server determines the irrigation conditions based on the plant's condition and automatically sends instructions to the irrigation system to supply water. If necessary, it also sends instructions to the fertilizer sprayer, which then automatically sprays fertilizer.

[0595] In this way, the system automates a series of processes, enabling efficient and accurate plant management.

[0596] (Application example 1)

[0597] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0598] In conventional agricultural work, it is difficult to monitor the condition of plants in real time and carry out appropriate cultivation operations. Furthermore, determining the appropriate harvest time requires a high level of experience and knowledge, which places a heavy burden on producers. The present invention aims to solve these problems and provide a system for efficient and effective production management.

[0599] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[0600] In this invention, the server includes means for controlling a camera equipped with an optical sensor to acquire images of plants, means for analyzing the state of the plants from the acquired images, means for determining the state of the plants based on the analysis results, means for notifying the producer of the determination results via communication means, and means for controlling an autonomous machine based on the results of the plant state analysis to perform cultivation operations. This makes it possible to monitor the state of plants in real time and automatically perform cultivation operations such as irrigation and fertilizer application.

[0601] A "camera equipped with an optical sensor" is a camera device equipped with an optical sensor, which is capable of capturing images of plants with high accuracy.

[0602] "Acquired image" refers to image data of a plant captured by a camera equipped with an optical center.

[0603] The "means for analyzing the condition of a plant" is a system that analyzes the hue information and sugar content of a plant based on the acquired image data, and evaluates the health and growth status of the plant.

[0604] "Means for determining the condition of plants" refers to algorithms or programs that use the analysis results to determine whether plants are growing, whether they are diseased, and the appropriate timing for harvesting.

[0605] "Means for notifying producers via communication means" refers to a system for transmitting analysis and judgment results to producers' devices (smartphones, PCs, etc.) via the Internet or mobile phone lines.

[0606] "Autonomous machinery" refers to agricultural machinery that automatically performs plant cultivation operations, and includes tractors and irrigation systems with automatic driving functions.

[0607] "Cultivation operations" refers to all manual work, such as irrigation, fertilizing, weeding, harvesting, etc., performed to promote or protect plant growth.

[0608] This invention relates to a system that uses a camera equipped with an optical sensor to capture images of plants, analyzes the images to determine the condition of the plants, and notifies growers at the appropriate time. It can also work in conjunction with automated driving machines to automatically perform appropriate cultivation operations. Specific embodiments for implementing this invention are described below.

[0609] 1. Main components of the system

[0610] Server: A central control device for managing all processes of image acquisition, analysis, judgment, and notification. It is implemented using a programming language such as Python.

[0611] Camera terminal: A device that captures images of plants using a camera equipped with an optical sensor. This includes general IP cameras and proprietary imaging mechanisms.

[0612] Communication means: Used as a means of communication for producers to receive notifications. Smartphones and PCs are examples of this.

[0613] Autonomous machinery: Agricultural machinery that automatically performs plant cultivation operations. Examples include irrigation systems and fertilizer spreading devices.

[0614] 2. Image Acquisition Process

[0615] The server controls the camera equipped with the optical center to capture images of the plants at regularly scheduled times, for example, at 9:00 a.m. every day, to start the camera and capture new images, which are then saved in local storage for later analysis.

[0616] 3. Image analysis process

[0617] The acquired images are input by the server into the image processing module, which extracts the plant's hue information and calculates the percentage of pixels that fall within a specific range to analyze the plant's coloration. The sugar content is also measured, using an image analysis algorithm.

[0618] 4. Determination and Notification Process

[0619] Based on the analysis results, the server determines the plant's coverage rate and sugar content. This data is used to determine the appropriate time for harvesting. For example, if the coverage rate is 80% or higher and the sugar content is within the appropriate range, it is determined that the time has come to harvest. This determination result is notified to the producer in real time. The notification is sent via the internet to the user's LINE account.

[0620] 5. Automatic cultivation operation process

[0621] If necessary, the server can link with autonomous machines to perform automated cultivation operations. For example, if the plant's condition meets certain conditions, the server can send instructions to the irrigation system to provide adequate water. It can also automatically manage the supply of fertilizer by sending instructions to the fertilizer spraying device.

[0622] Specific examples

[0623] A specific scenario is shown below.

[0624] 1. The server controls the camera terminal every morning at 9:00 a.m. and captures images of tomatoes and mandarin oranges.

[0625] 2. The acquired images are input into an image processing module on the server, where the color and sugar content are analyzed.

[0626] 3. The analysis results show that the coverage rate is 85% and the sugar content is within the appropriate range.

[0627] 4. The server determines that it is time to harvest and notifies the producer of the result via LINE.

[0628] 5. Furthermore, the server plans the next irrigation timing and sends instructions to the irrigation system.

[0629] Examples of prompts to input to a generative AI model:

[0630] "Design a system to monitor the growth of tomatoes and automatically irrigate them. Use a camera to capture images of the plants every day at 9 AM, analyze the images, and calculate the percentage of green pixels. Based on the results, write a program to perform the necessary irrigation operations."

[0631] In this way, the present invention provides a system for monitoring the state of plants in real time and for efficient and effective production management.

[0632] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0633] Step 1:

[0634] The server controls a camera terminal equipped with an optical center to acquire images of plants at regularly scheduled times.

[0635] Input: Scheduled time (e.g. 9:00 AM)

[0636] Output: Images of the plants acquired

[0637] Specific operation: At the scheduled time, the server connects to the camera terminal and sends a command to take a photo. The camera terminal receives the command, captures images of the plants, and sends them to the server.

[0638] Step 2:

[0639] The server stores the acquired images in local storage.

[0640] Input: Plant image

[0641] Output: Saved image data

[0642] Specific operation: The server saves the received image data in the specified folder. The file name is identified by adding date and time information.

[0643] Step 3:

[0644] The server reads the saved images and inputs them into the image processing module for analysis.

[0645] Input: Saved image data

[0646] Output: Analysis results (hue information, sugar content, etc.)

[0647] How it works: The server uses an image processing library (e.g., OpenCV) to extract color information from the image data, calculate the percentage of pixels that fall within a specific range, and then uses image analysis algorithms to measure the sugar content.

[0648] Step 4:

[0649] The server determines the condition of the plant based on the analysis results.

[0650] Input: Image analysis results (hue information, sugar content, etc.)

[0651] Output: Plant condition judgment result (e.g. harvest time, good / bad condition)

[0652] Specific operation: The server runs an algorithm that determines the timing of harvest and necessary cultivation operations based on specific conditions (e.g., coverage rate of 80% or more and sugar content within an appropriate range).

[0653] Step 5:

[0654] The server notifies the producer of the judgment result via a communication means.

[0655] Input: Status determination result

[0656] Output: Notification message to producer

[0657] Specific operation: The server uses a communication API (e.g., LINE API) to send the judgment results to the producer's smartphone or computer.

[0658] Step 6:

[0659] The server controls the automated machinery as needed and performs appropriate cultivation operations.

[0660] Input: Status determination result

[0661] Output: Operation instructions for the automated driving machine

[0662] Specific operation: Based on the status determination results, the server sends instructions to the irrigation system and fertilizer spreading device, which causes the agricultural machinery to automatically perform the necessary tasks.

[0663] Furthermore, an emotion engine that estimates the user's emotion may be further combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59, and perform identification processing using the user's emotion.

[0664] The present invention relates to a system that acquires images of plants and notifies growers based on the analysis results, and also adds a function to recognize the user's emotions and adjust the notification content and operation accordingly. This system acquires images of plants using a camera equipped with an optical sensor and determines the condition of the plants through image analysis. Furthermore, by combining it with an emotion engine that recognizes the user's (grower's) emotions, the system can adjust the notification content and automatic cultivation operation if the user is experiencing negative emotions such as stress.

[0665] System Overview

[0666] The main components of the system are as follows:

[0667] Server: Manages all processes including image acquisition, analysis, judgment, notification, and emotion recognition.

[0668] Camera terminal: A camera equipped with an optical center is used to capture images of plants.

[0669] User terminal: Used as a communication means for producers to receive notifications and as a data collection means for emotion recognition.

[0670] Cultivation machinery: machinery for carrying out automated cultivation operations, including irrigation systems and fertilizer application equipment.

[0671] 1. Image Acquisition Process

[0672] The server sets a periodic task and sends instructions to activate a camera terminal equipped with an optical sensor at a specific time every day. This instruction is to obtain the latest images of the plants to be harvested that day. The camera terminal receives instructions from the server and captures images of the plants. The obtained images are temporarily stored in the camera terminal's local storage and then sent to the server.

[0673] 2. Image analysis process

[0674] The server then passes the saved image to the analysis module, where image processing begins. This module first converts the image into a color space and extracts pixels that fall within a specific color range. The extracted pixel information is used to evaluate the plant's color development, and then a sugar content measurement algorithm is run to calculate the sugar content estimated from the image. The resulting coverage and sugar content data are used to determine the appropriate time for harvesting.

[0675] 3. Determination and Notification Process

[0676] If the server determines that it is time to harvest, it generates a message indicating this and sends it to the user's device using a communication module. This notification is then sent via the internet to the user's LINE account.

[0677] 4. The process of emotion recognition

[0678] The user device collects the producer's facial expression and voice data and sends it to the server. The server then uses an emotion engine to analyze this data and determine the user's emotions. For example, if the user is feeling stressed, the server can adjust the content of the notification based on that information. Based on the emotion recognition results, the notification can include kinder language or an encouraging message.

[0679] 5. Automatic cultivation operation process

[0680] If necessary, the server can send instructions to the cultivation machine to perform automatic cultivation operations. For example, if the user feels high stress, the server can adjust the frequency of irrigation and fertilizer application to reduce the burden on the user.

[0681] Specific examples

[0682] A specific scenario is shown below.

[0683] 1. The server controls the camera terminal every morning at 9:00 AM and captures images of mandarin oranges.

[0684] 2. The acquired images are input into an image processing module on the server, where the color and sugar content are analyzed.

[0685] 3. The analysis results show that the coverage rate is 85% and the sugar content is within the appropriate range.

[0686] 4. The server determines that it is time to harvest and notifies the user (producer) of the result via LINE.

[0687] 5. The user device collects the user's facial expression and voice data and sends it to the server.

[0688] 6. The server uses an emotion engine to determine the user's emotions and determines that the user is feeling stressed.

[0689] 7. Based on the emotion recognition results, the server resends a notification via LINE containing an encouraging message.

[0690] 8. If necessary, the server sends instructions to the irrigation system to provide adequate watering, and also to the fertilizer application device to automatically manage the supply of fertilizer.

[0691] In this way, the present invention not only monitors the condition of plants in real time and notifies producers at the appropriate time, but also adjusts the content of notifications and automatic cultivation operations according to the user's emotional state, thereby achieving efficient and effective production management.

[0692] The processing flow will be explained below.

[0693] Step 1:

[0694] The server sets up a periodic task and sends instructions to activate a camera terminal equipped with a photonic center at a specific time every day to obtain the latest images of the plants to be harvested that day.

[0695] Step 2:

[0696] The device (camera) receives instructions from the server and captures images of the plants. The captured images are temporarily stored in the camera device's local storage.

[0697] Step 3:

[0698] The device (camera) sends the captured image to the server, which receives the file and stores it in a secure location.

[0699] Step 4:

[0700] The server passes the saved image to the analysis module, which then starts processing the image. First, it converts the image into a color space (such as HSV) and extracts pixels that fall within a specific color range.

[0701] Step 5:

[0702] The server evaluates the color of the plants based on the extracted pixel information. Specifically, it calculates the percentage of pixels that fall within a specific range and outputs the result as the coverage rate.

[0703] Step 6:

[0704] The server runs a sugar content measurement algorithm to calculate the estimated sugar content from the image, which uses specific pigment patterns and texture analysis.

[0705] Step 7:

[0706] The server determines the optimal time for harvesting based on data on coverage and sugar content. For example, if coverage is 80% or higher and sugar content is within the appropriate range, it determines that it is optimal for harvesting.

[0707] Step 8:

[0708] If the server determines that the fruit is suitable for harvesting, it generates a message containing the specific coverage rate and sugar content values.

[0709] Step 9:

[0710] The server sends the generated message to the user (producer) using a communication module. The communication method is via the Internet using the LINE API.

[0711] Step 10:

[0712] The user (producer) can check the harvest timing information received as a LINE message, which allows them to carry out harvesting work at the appropriate time.

[0713] Step 11:

[0714] The user device collects facial and voice data from the user and sends it to the server. For example, data is collected automatically when the user approaches a camera or microphone.

[0715] Step 12:

[0716] The server uses an emotion engine to analyze the received data and determine the user's emotions, for example, using facial expression recognition algorithms and voice emotion analysis techniques.

[0717] Step 13:

[0718] The server then adjusts the notification content based on the emotion recognition results, for example adding a kind comment or encouraging message if the user is feeling stressed.

[0719] Step 14:

[0720] The server then adjusts the message based on the emotion recognition results and sends it back to the user (producer) via LINE.

[0721] Step 15:

[0722] If necessary, the server will send further instructions to the cultivation machine. If the user is feeling high stress, the server will adjust the frequency of irrigation and fertilizer application to reduce the burden on the user.

[0723] Through these steps, the system can monitor the condition of plants in real time, notify farmers of the appropriate harvest time, and adjust the notification content and automated cultivation operations according to the user's emotional state, thereby achieving efficient and high-quality production management.

[0724] Example 2

[0725] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0726] Conventional plant cultivation systems do not automate the assessment of plant conditions or notifications of harvest time, making efficient and accurate management difficult. Furthermore, notifications and operations are performed mechanically without taking into account the emotional state of the grower, resulting in a lack of consideration for reducing stress. Therefore, a system that can properly manage plants and reduce the psychological burden on growers is needed.

[0727] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[0728] In this invention, the server includes means for controlling a camera equipped with an optical center to acquire images of the plants, means for analyzing the state of the plants from the acquired images, means for determining the state of the plants based on the analysis results, means for notifying the determination results to the grower via communication means, means for recognizing the user's emotions by analyzing facial expression data and voice data acquired from the user terminal, and means for adjusting the content of notifications and cultivation operations based on the emotion recognition results. This enables appropriate notifications and operations to be performed taking into consideration the emotional state of the grower while accurately understanding the state of the plants.

[0729] A "camera equipped with a light center" is a camera device that incorporates a light sensor that measures the wavelength and intensity of light, and is used to capture images of plants with high precision.

[0730] The "means for acquiring images" refers to a part consisting of a mechanism and software for controlling the camera, taking images of plants, and saving them as digital data.

[0731] The "analysis means" refers to software and algorithms for processing the acquired image data and quantifying or evaluating the condition and characteristics of the plants.

[0732] The "means for determining" refers to logic and programs for determining the health of plants and the time for harvesting based on the results of image analysis.

[0733] "Communication means" refers to a mechanism including a communication interface and protocol for notifying the results of analysis and judgment to producers and other systems.

[0734] "Facial expression data" refers to the facial expressions of a user captured using a camera or other sensor and converted into digital data.

[0735] "Voice data" refers to the user's voice and speaking style collected by an acoustic sensor such as a microphone and stored as digital data.

[0736] "Means for recognizing emotions" refers to algorithms and software for analyzing facial expression data and voice data to identify the user's emotional state (e.g., stress, joy, fatigue, etc.).

[0737] The "means for adjusting cultivation operations" is a system for automatically adjusting operations related to plant cultivation, such as watering and fertilizer application, based on the emotion recognition results.

[0738] "Cultivation machinery" refers to all machinery and equipment used in plant cultivation, such as irrigation systems and fertilizer spreading devices.

[0739] MODE FOR CARRYING OUT THE INVENTION

[0740] The present invention relates to a system that acquires images of plants and notifies growers based on the analysis results, and also adds a function that recognizes the user's emotions and adjusts the notification content and operation. This system includes the following main components:

[0741] 1. Server

[0742] The server plays a central role in managing the entire image acquisition, analysis, judgment, notification, and emotion recognition process. Specifically, the server has the following functions:

[0743] Control of image acquisition: The server controls the camera terminal equipped with the optical center and instructs it to acquire images of plants at specific times.

[0744] Image analysis: The acquired images are passed to the analysis module, where color space conversion and pixel extraction are performed to evaluate the condition of the plant (e.g., color development, sugar content).

[0745] Judgment and notification: The harvest time is determined based on the analysis results, and the results are notified to producers via LINE or other communication means.

[0746] Emotion recognition: Analyzes facial expression and voice data sent from the user's device to determine the producer's emotional state. If the producer is feeling stressed, the content of notifications will be adjusted.

[0747] Automating cultivation operations: Sending instructions to cultivation machinery as needed to adjust the frequency of irrigation and fertilization.

[0748] 2. Camera terminal

[0749] The camera terminal is a device that captures images of plants using a high-resolution camera equipped with an optical sensor. The camera terminal receives instructions from the server, captures images at specific times, and sends the data to the server. It also has the function of temporarily storing data in local storage.

[0750] 3. User Device

[0751] The user terminal is a means for producers to receive notifications and also functions as a means for collecting data for emotion recognition. Specifically, it plays the following roles:

[0752] Receiving notifications: Receive notifications sent from the server using LINE or other communication methods.

[0753] Data collection: Collects the user's facial expression and voice data and sends it to the server.

[0754] 4. Cultivation machinery

[0755] The cultivation machine is a mechanical facility for automatically operating the cultivation of plants, such as an irrigation system or a fertilizer spraying device, and performs appropriate cultivation operations based on instructions from the server.

[0756] Specific examples

[0757] The specific operating scenario of the system is shown below.

[0758] 1. The server controls the camera terminal every morning at 9:00 AM and captures images of mandarin oranges.

[0759] 2. The acquired images are input into an image processing module on the server, where the color and sugar content are analyzed.

[0760] 3. The analysis results show that the coverage rate is 85% and the sugar content is within the appropriate range.

[0761] 4. The server determines that it is time to harvest and notifies the user (producer) of the result via LINE.

[0762] 5. The user device collects the user's facial expression and voice data and sends it to the server.

[0763] 6. The server uses an emotion engine to determine the user's emotions and determines that the user is feeling stressed.

[0764] 7. Based on the emotion recognition results, the server resends a notification via LINE containing an encouraging message.

[0765] 8. If necessary, the server sends instructions to the irrigation system to provide adequate watering, and also to the fertilizer application device to automatically manage the supply of fertilizer.

[0766] Prompt Sentence Examples

[0767] What does this system do?: This system captures images of plants and notifies growers based on the analysis results. It also recognizes the user's emotions and adjusts operations and notifications accordingly.

[0768] The above is an embodiment of the present invention.

[0769] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0770] Specific processing flow of the program

[0771] Step 1:

[0772] The server sets an instruction to start up the camera terminal equipped with the optical center at a fixed time every day as a scheduled task.

[0773] Input: Schedule setting (e.g. 9:00 AM)

[0774] Output: Instructions to start the camera device

[0775] Step 2:

[0776] The camera terminal receives instructions from the server and activates the camera equipped with the optical center.

[0777] Specific actions: Turn on the camera and put it into a ready state.

[0778] Input: Start command from the server

[0779] Output: The camera is armed and ready to capture images.

[0780] Step 3:

[0781] The camera terminal captures high-resolution images of a target plant (e.g., a mandarin orange).

[0782] Specific actions: Activate the camera's shutter button and generate image data.

[0783] Input: Plant photography target position

[0784] Output: Captured image data

[0785] Step 4:

[0786] The camera terminal temporarily stores the captured images in local storage.

[0787] Specific operation: Saves the image data as a temporary file.

[0788] Input: Captured image data

[0789] Output: Image file saved in local storage

[0790] Step 5:

[0791] The camera terminal transmits the stored image data to a server via the Internet.

[0792] Specific operation: Sends image data over the Internet.

[0793] Input: Image file stored in local storage

[0794] Output: Image data transferred to the server

[0795] Step 6:

[0796] The server receives the images sent from the camera terminal.

[0797] Specific operation: The received image data is stored in the server's temporary storage area.

[0798] Input: Image data from the camera device

[0799] Output: Image data stored in a temporary storage area on the server

[0800] Step 7:

[0801] The server passes the image to the image processing module to begin analysis.

[0802] What it does: Invokes image processing algorithms to perform color space conversion and pixel extraction.

[0803] Input: Image data saved in temporary storage area

[0804] Output: Processed pixel data

[0805] Step 8:

[0806] The server applies a sugar content measurement algorithm based on the analysis results to calculate the sugar content of the plant.

[0807] Specific operation: Runs an algorithm that calculates sugar content using pixel data as input.

[0808] Input: Processed pixel data

[0809] Output: Sugar content data of plants

[0810] Step 9:

[0811] The server determines the appropriate time to harvest based on the coverage rate and sugar content obtained as a result of the analysis.

[0812] Specific operation: Compare with the set threshold and calculate the harvest time.

[0813] Input: Coverage data, sugar content data

[0814] Output: Harvest timing judgment result

[0815] Step 10:

[0816] The server generates a notification message based on the result of the judgment and sends it to the user's LINE account using the LINE API.

[0817] Specific operation: A message is generated based on the judgment result and sent via LINE.

[0818] Input: Harvest timing judgment result

[0819] Output: Notification message sent to the user's LINE

[0820] Step 11:

[0821] The user terminal collects facial expression data and voice data of the user during daily operations.

[0822] Specific actions: Record data using a camera or microphone.

[0823] Input: User's face and voice

[0824] Output: facial expression data, voice data

[0825] Step 12:

[0826] The user terminal transmits the collected facial expression data and voice data to the server.

[0827] Specific operation: Sends data to the server.

[0828] Input: facial expression data, voice data

[0829] Output: Facial expression data and voice data sent to the server

[0830] Step 13:

[0831] The server uses an emotion engine to analyze the facial expression data and voice data to determine the user's emotion.

[0832] What it does: Runs emotion recognition algorithms to identify the user's emotional state.

[0833] Input: facial expression data, voice data

[0834] Output: User's emotional state

[0835] Step 14:

[0836] If the server determines that the user is in a stressed state, it adjusts the content of the notification and resends it.

[0837] Specific behavior: Create a new message and send it again via LINE.

[0838] Input: Emotion recognition results

[0839] Output: The adjusted notification message

[0840] Step 15:

[0841] The server sends instructions to cultivation machinery (such as irrigation systems and fertilizer spreading devices) as needed to perform automatic cultivation operations.

[0842] Specific operation: Issues operational instructions to the machine based on set conditions.

[0843] Input: Emotion recognition results, plant state data

[0844] Output: Operation instructions for cultivation machinery

[0845] Step 16:

[0846] The cultivation machine performs operations such as watering and spreading fertilizer according to instructions from the server.

[0847] Specific actions: Water and fertilize according to instructions.

[0848] Input: Operation instructions from the server

[0849] Output: Feedback of the results of the cultivation operations performed

[0850] (Application example 2)

[0851] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0852] Conventional plant cultivation systems have limited functionality for monitoring plant conditions and notifying appropriate cultivation timing. Furthermore, they simply provide instructions for harvesting and cultivation operations without considering the producer's emotional state, placing a heavy burden on the producer. The present invention aims to achieve effective and efficient production management by providing a system that not only accurately monitors plant conditions and notifies producers at the appropriate time, but also adjusts notification content and operations taking into account the producer's emotional state.

[0853] The specific processing by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes means for controlling a camera equipped with an optical center to acquire images of plants, means for analyzing the state of the plants from the acquired images, means for determining the state of the plants based on the analysis results, means for notifying the determination result to the producer via communication means, means for recognizing the user's emotions and adjusting the notification content and operation, and means for sending instructions to the automated cultivation machine. This not only enables real-time monitoring of the state of plants, but also enables optimal notifications and automatic cultivation operations according to the producer's emotional state.

[0854] A "light center" is a device that has the function of detecting the intensity and position of light, and is used to obtain clear images of plants.

[0855] A "camera" is a device that takes pictures and videos and records the data.

[0856] A "plant" is a multicellular organism that performs photosynthesis and grows above ground.

[0857] "Means for acquiring images" refers to a device for taking images, such as a camera, and a method for controlling its operation.

[0858] "Means for analysis" refers to methods or devices that process data such as images to extract meaningful information.

[0859] A "means for determining" is a method or device that determines a particular situation or condition based on analyzed data.

[0860] "Communication means" refers to methods and devices for sending and receiving information, including the Internet and mobile networks.

[0861] "Producer" refers to an agricultural worker who grows crops, etc.

[0862] "Means for recognizing a user's emotions" refers to methods and technologies for identifying a user's emotional state from facial expressions, voice, etc.

[0863] The "means for adjusting notification content" refers to a method or device for optimizing the content of information or messages to be conveyed based on the results of emotion analysis.

[0864] "Cultivation machinery" refers to machinery for automatically carrying out plant cultivation work, and includes irrigation systems and fertilizer spraying devices.

[0865] A "means for sending instructions" is a method or device for sending operational commands to a remote device or system.

[0866] The present invention relates to a system for monitoring and managing the condition of plants, and for optimal notification and automatic operation taking into consideration the emotional state of the grower. Hereinafter, specific embodiments of the present invention will be described in detail.

[0867] System Configuration

[0868] The main components are a server, a camera terminal, a user terminal, and a cultivation machine.

[0869] Server: Manages all processes, including image acquisition, analysis, judgment, notification, and emotion recognition. In particular, a high-performance server is required for storing and processing data, and it is desirable to implement it in a programming language such as Python.

[0870] Camera terminal: Images of plants are acquired using a camera equipped with an optical sensor. Specifically, a high-resolution camera such as the Intel RealSense D435 is suitable.

[0871] User terminals: Devices such as smartphones and smart glasses that allow producers to receive notifications. These devices are also used to collect facial expression and voice data.

[0872] Cultivation machinery: Machines for automated cultivation operations, such as irrigation systems and fertilizer spreading devices. Specifically, the UR5e robot arm is a suitable example.

[0873] Image acquisition process

[0874] The server sets a periodic task and sends instructions to start the camera terminal equipped with the optical center at a specific time every day. The camera terminal receives this instruction, captures images of the plants, temporarily stores the acquired images in the camera terminal's local storage, and then sends them to the server. The acquired images are saved in the server's storage and prepared for the next analysis process.

[0875] Image analysis process

[0876] The server passes the saved images to the analysis module to evaluate the condition of the plants. First, it converts the image into a color space using an image processing library (e.g., OpenCV), and then extracts pixels that fall into a specific color range. Based on the extracted pixel information, it evaluates the color development of the plants and runs a sugar content measurement algorithm to calculate the sugar content estimated from the image. The coverage rate and sugar content data obtained as a result of the analysis are then used to determine the appropriate time for harvesting.

[0877] Determination and Notification Process

[0878] When the server determines that it is time to harvest, it generates a message indicating this and sends it to the user's device via the communication module. This notification is then sent via the Internet to the user's smartphone or smart glasses. The user device receives this notification and notifies the producer.

[0879] The process of emotion recognition

[0880] The user's device collects the producer's facial expression and voice data and sends it to a server. The server then analyzes this data using an emotion recognition engine (e.g., Microsoft Azure Cognitive Services) to determine the user's emotional state. If the user is feeling stressed, the server adjusts the content of the notification based on that information, allowing it to include encouraging messages or kind words.

[0881] Automatic cultivation operation process

[0882] If necessary, the server can send instructions to the cultivation machine to perform automatic cultivation operations. For example, if the user feels high stress, the server can adjust the frequency of irrigation and fertilizer application to reduce the burden on the user.

[0883] Specific examples

[0884] A specific scenario is shown below.

[0885] 1. The server controls the camera terminals every morning at 9:00 AM to acquire images of plants.

[0886] 2. The acquired images are input into an image processing module on the server, where the color and sugar content are analyzed.

[0887] 3. The analysis results show that the coverage rate is 85% and the sugar content is within the appropriate range.

[0888] 4. The server determines that it is time to harvest and notifies the user of the result via their smartphone or smart glasses.

[0889] 5. The user device collects the user's facial expression and voice data and sends it to the server.

[0890] 6. The server uses an emotion engine to determine the user's emotion and if the user is feeling stressed, resends the notification with an encouraging message.

[0891] 7. If necessary, the server sends instructions to the irrigation system and fertilizer application device to automatically manage the appropriate watering and fertilization.

[0892] In this way, the present invention not only monitors the state of plants in real time and notifies growers at the appropriate time, but also adjusts the content of notifications and automated cultivation operations according to the user's emotional state, thereby achieving efficient and effective production management.

[0893] Example prompts:

[0894] "Design a system that automatically monitors medicinal plants in a factory and takes into account the emotional state of workers to provide optimal notifications and actions."

[0895] This will not only allow for more efficient and effective management of plant cultivation, but also enable the creation of a system that takes into account the emotions and well-being of workers.

[0896] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0897] Step 1:

[0898] The server sends instructions to start up a camera terminal equipped with an optical center at a specific time every day. The input is pre-set schedule information. The output is for the camera terminal to start up and capture images of plants. This instruction is sent to the camera terminal via the Internet. Specifically, a command to control the camera is sent, and the camera takes a picture of the target plant.

[0899] Step 2:

[0900] The camera terminal receives instructions to capture images of plants and temporarily store them in local storage. The input is a start-up instruction from the server. The output is the acquired image data of the plants. Specifically, the camera automatically focuses, takes high-resolution images of the plants, and stores the data in local storage.

[0901] Step 3:

[0902] The camera terminal sends the saved image data to the server. The input is the image data saved in the local storage. The output is the image data saved on the server. In concrete terms, the camera terminal performs a process of uploading the image data to the server via the Internet.

[0903] Step 4:

[0904] The server passes the received image data to the analysis module, which analyzes the condition of the plants. The input is the image data sent from the camera terminal. The output is the analysis results. Specifically, an image processing library is used to convert the image into a color space and extract pixels that fall within a specific color range. Based on this information, the coloring and sugar content of the plants are evaluated.

[0905] Step 5:

[0906] The server determines the condition of the plants based on the analysis results. The input is the analysis results obtained from the analysis module. The output is a judgment result indicating the condition of the plants. Specifically, the analysis algorithm evaluates data such as cover rate and sugar content to determine the appropriate time for harvesting.

[0907] Step 6:

[0908] The server notifies the user terminal of the judgment result via the communication module. The input is the judgment result indicating the plant's condition. The output is a notification message delivered to the user's smartphone or smart glasses. Specifically, the notification message is generated and sent to the user terminal via the Internet.

[0909] Step 7:

[0910] The user terminal collects the user's facial expression data and voice data and sends them to the server. The input is facial expression data and voice data collected by a camera and microphone. The output is emotion data sent to the server. Specifically, sensors in the smartphone or smart glasses detect the user's emotions and upload the data to the server.

[0911] Step 8:

[0912] The server uses an emotion recognition engine to analyze the user's emotional state. The input is facial expression data and voice data sent from the user's device. The output is an analysis result indicating the user's emotional state. Specifically, the emotion recognition algorithm analyzes the data and determines indicators such as stress and happiness.

[0913] Step 9:

[0914] The server adjusts the notification content based on the emotion analysis results and sends it back to the user device. The input is the analysis result from the emotion recognition engine. The output is a message containing the adjusted notification content. Specifically, a notification containing words of encouragement or a kind message is generated and sent back to the user device.

[0915] Step 10:

[0916] If necessary, the server sends instructions to the cultivation machine to perform automatic cultivation operations. The input is instruction information based on the analysis results and the user's emotional state. The output is automatic cultivation operations using the irrigation system and fertilizer spraying device. Specific operation instructions are sent to the cultivation machine, which operates to maintain the optimal cultivation environment for the plants.

[0917] This allows for real-time monitoring of plant conditions, timely notifications, and cultivation operations that take into account the grower's emotional state.

[0918] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0919] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0920] In the above embodiment, an example in which the specific processing is performed by the data processing device 12 has been given, but the technology of the present disclosure is not limited to this, and the specific processing may be performed by the smart glasses 214.

[0921] [Third embodiment]

[0922] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0923] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0924] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0925] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[0926] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio in accordance with instructions from the processor 46.

[0927] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the surroundings of user 20 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0928] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0929] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0930] The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific 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.

[0931] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0932] In the headset type terminal 314, a reception output process is performed by the processor 46. A reception output program 60 is stored in the storage 50. 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 process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0933] Next, a description will be given of the identification process performed by the identification processing unit 290 of the data processing device 12. 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."

[0934] The present invention relates to a system that acquires images of plants using a camera equipped with an optical sensor, analyzes the images to determine the condition of the plants, and notifies the grower at an appropriate time. Furthermore, the system can cooperate with cultivation machinery as needed to perform automatic cultivation operations. Specific examples of the system are described below.

[0935] 1. System Overview

[0936] The main components of the system are as follows:

[0937] Server: Manages all processes of image acquisition, analysis, judgment, and notification.

[0938] Camera terminal: A camera equipped with an optical center is used to capture images of plants.

[0939] User terminal: Used as a communication means for producers to receive notifications.

[0940] Cultivation machinery: machinery for carrying out automated cultivation operations, including irrigation systems and fertilizer application equipment.

[0941] 2. Image Acquisition Process

[0942] The server controls the camera equipped with the optical center to capture images of the plants at regularly scheduled times, for example, at 9:00 a.m. every day, to start the camera and capture new images, which are then saved in local storage for later analysis.

[0943] 3. Image analysis process

[0944] The acquired images are input by the server into the image processing module, which extracts the plant's hue information and calculates the percentage of pixels that fall within a specific range to analyze the plant's coloration. The sugar content is also measured, using an image analysis algorithm.

[0945] 4. Determination and Notification Process

[0946] Based on the analysis results, the server determines the plant's coverage rate and sugar content. This data is used to determine the appropriate time for harvesting. For example, if the coverage rate is 80% or higher and the sugar content is within the appropriate range, it is determined that the time has come to harvest. This determination result is notified to the producer in real time. The notification is sent via the internet to the user's LINE account.

[0947] 5. Automatic cultivation operation process

[0948] If necessary, the server can link with the cultivation machinery to perform automatic cultivation operations. For example, if the plant's condition meets certain conditions, the server can send instructions to the irrigation system to provide appropriate water supply. It can also send instructions to the fertilizer spraying device to automatically manage the supply of fertilizer.

[0949] Specific examples

[0950] A specific scenario is shown below.

[0951] 1. The server controls the camera terminal every morning at 9:00 AM and captures images of mandarin oranges.

[0952] 2. The acquired images are input into an image processing module on the server, where the color and sugar content are analyzed.

[0953] 3. The analysis results show that the coverage rate is 85% and the sugar content is within the appropriate range.

[0954] 4. The server determines that it is time to harvest and notifies the producer of the result via LINE.

[0955] 5. Furthermore, the server plans the next irrigation timing and sends instructions to the irrigation system.

[0956] In this way, the present invention allows for real-time monitoring of plant conditions and efficient and effective production management.

[0957] The processing flow will be explained below.

[0958] Step 1:

[0959] The server sets up a periodic task and sends instructions to activate a camera terminal equipped with a photonic center at a specific time every day to obtain the latest images of the plants to be harvested that day.

[0960] Step 2:

[0961] The device (camera) receives instructions from the server and captures images of the plants. The captured images are temporarily stored in the camera device's local storage.

[0962] Step 3:

[0963] The device (camera) sends the captured image to the server, which receives the file and stores it in a secure location.

[0964] Step 4:

[0965] The server then passes the stored image to an analysis module, which begins processing the image by first converting it to a color space (such as HSV) and extracting pixels that fall into a specific color range.

[0966] Step 5:

[0967] The server evaluates the color of the plants based on the extracted pixel information. Specifically, it calculates the percentage of pixels that fall within a specific range and outputs the result as the coverage rate.

[0968] Step 6:

[0969] The server then runs a sugar content measurement algorithm to calculate an estimated sugar content from the image, which typically uses specific pigment patterns or texture analysis.

[0970] Step 7:

[0971] The server compares the coverage rate and sugar content data to determine the appropriate time for harvesting. For example, if the coverage rate is 80% or higher and the sugar content is within the appropriate range, it determines that it is the best time to harvest.

[0972] Step 8:

[0973] If the server determines that the fruit is suitable for harvesting, it generates a message containing the specific coverage rate and sugar content values.

[0974] Step 9:

[0975] The server sends the generated message to the user (producer) using a communication module. The communication method is via the Internet using the LINE API.

[0976] Step 10:

[0977] The user (producer) checks the harvest timing information received as a LINE message, allowing the user to carry out harvesting work at the appropriate time.

[0978] Step 11:

[0979] If necessary, the server can also send instructions to the farming machine, such as instructions to automatically plan post-harvest irrigation and fertilizer applications.

[0980] Through these steps, the system can monitor the condition of plants in real time, notify producers of the appropriate harvest time, and perform the necessary automated cultivation operations.

[0981] Example 1

[0982] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[0983] In conventional plant cultivation methods, monitoring plant conditions and timing of cultivation operations depended on human experience and intuition, making it difficult to make accurate judgments and respond at the appropriate time. Furthermore, manual monitoring and operation required time and effort, making it difficult to improve production efficiency. Furthermore, this approach carried the risk of overlooking changes in plant conditions and the optimal harvest time, which could affect quality and yield.

[0984] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[0985] In this invention, the server includes means for controlling a device that detects the state of a plant using light to acquire an image of the plant, means for processing the acquired image to analyze the state of the plant, means for determining the state of the plant based on the analysis result, and means for notifying the user of the determination result using a communication means. This makes it possible to accurately monitor the state of the plant in real time and automatically perform cultivation operations and harvesting at appropriate times.

[0986] A "device for detecting the condition of a plant using light" is a device for detecting the health condition and components of a plant by utilizing the properties of light.

[0987] "Means for acquiring" refers to the function or process of acquiring images of plants at specified times and under specified conditions.

[0988] "Means for processing images to analyze plant condition" means a process that includes software and algorithms for analyzing acquired images to assess the health and composition of the plant.

[0989] The "means for determining the state of the plant based on the analysis results" is a function that comprehensively evaluates the state of the plant based on the results of image analysis and makes a determination based on certain criteria.

[0990] The "means for notifying the user using a communication means" refers to a communication function for notifying the user of the analysis and determination results, and sends the notification via the Internet or a mobile network.

[0991] "Means for measuring the component content of plants by image processing" refers to a function or process for measuring the components (e.g., sugar content) contained in plants by analyzing image data.

[0992] "Means for determining the optimal harvest time" refers to the function of determining the optimal timing for harvesting based on the ingredient content and other analysis results.

[0993] The "means for automatically sending instructions to cultivation equipment and performing plant cultivation operations" is a function that automatically issues instructions to cultivation equipment based on analysis results and conditions and executes necessary cultivation operations.

[0994] The present invention relates to a system that acquires images of plants using a camera equipped with a device for detecting the state of plants using light, analyzes the images to determine the state of the plants, and notifies the grower of the state at an appropriate time. Furthermore, the system can cooperate with cultivation equipment as needed to perform automatic cultivation operations. Specific examples of the system are described below.

[0995] 1. System Overview

[0996] The main components of this system are as follows:

[0997] Server: Manages all processes of image acquisition, analysis, judgment, and notification.

[0998] Camera terminal: Images of plants are acquired using a camera equipped with a device that uses light to detect the state of the plants.

[0999] User terminal: Used as a communication means for producers to receive notifications.

[1000] Cultivation machinery: machinery for carrying out automated cultivation operations, including irrigation systems and fertilizer application equipment.

[1001] 2. Example of image acquisition

[1002] The server controls the camera terminals at 9:00 AM every day to acquire images of the specified plants. At this time, the camera terminals use light to capture the color and condition of the plants. The acquired images are captured and saved in local storage in JPEG format or other formats. This process is controlled using CRON jobs and HTTP requests.

[1003] 3. Specific examples of image analysis

[1004] The server inputs the acquired images into an image processing module, which uses an open-source image processing library such as OpenCV to analyze the plant's hue information and ingredient content (e.g., sugar content). The hue information is analyzed for each pixel, and the percentage of pixels within a specific color range is calculated.

[1005] 4. Specific examples of judgments and notifications

[1006] The server determines the condition of the plants based on the results of image analysis. For example, if the plant coverage is 80% or more and the sugar content is within the appropriate range, it determines that it is time to harvest. This determination result is sent to the user's LINE account via the Internet. This notification is sent using the LINE API.

[1007] 5. Specific examples of automated cultivation operations

[1008] The server automatically sends instructions to the cultivation machine as needed. For example, if the plant condition is determined to be lacking in water, the server sends instructions to the irrigation system to properly replenish the water. Similarly, if needed, the server sends instructions to the fertilizer application device to automatically apply the appropriate amount of fertilizer.

[1009] Generative AI model and example prompts

[1010] Examples of prompts:

[1011] "Please explain the specific processing steps of a system that acquires images of plants using a camera equipped with a device that uses light to detect the state of the plants, analyzes the images to determine the state of the plants, and notifies the grower at the appropriate time. The system components include a server, camera terminal, user terminal, and cultivation machinery. Please describe the detailed procedures and operations from issuing image acquisition commands to operating the automatic cultivation."

[1012] This allows the generative AI model to explain in detail the specific processing flow of the program.

[1013] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1014] Step 1: Image Acquisition Instructions

[1015] The server sends an instruction to the camera terminal to acquire an image at the scheduled time. This instruction is sent to the camera terminal using an HTTP request.

[1016] Input: Schedule date and time

[1017] Data processing / calculation: Generate an HTTP request at the scheduled time using a CRON job

[1018] Output: Camera start command

[1019] Specific operation: A CRON job on the server is triggered and sends an HTTP request to the specified URL to retrieve the image.

[1020] Step 2: Image acquisition

[1021] The camera device receives instructions from the server to turn on the light source and capture images of the plants. The captured images are saved in local storage and then sent to the server.

[1022] Input: Camera start command

[1023] Data processing / calculation: The camera captures images of the plants and saves them as image files.

[1024] Output: JPEG format image file

[1025] Specific operation: The camera terminal controls the light center, captures images of plants, and saves them in local storage in JPEG format. The images are then sent to the server using an HTTP POST request.

[1026] Step 3: Image analysis

[1027] The server inputs the received images into an image processing module, which uses OpenCV and other tools to analyze the plant's hue information and component content (sugar content).

[1028] Input: JPEG format image file

[1029] Data processing / calculation: Image processing algorithms are used to extract color information and analyze sugar content

[1030] Output: Hue information data, sugar content data

[1031] Specific operation: The server uses an image processing module (e.g., OpenCV) to analyze the image and calculate color information and sugar content. This processing extracts data that can serve as an indicator of the plant's health.

[1032] Step 4: Judgment and Notification

[1033] The server determines the plant coverage rate and sugar content based on the analysis results, and sends a message via the LINE API to notify the user of the results.

[1034] Input: Hue information data, sugar content data

[1035] Data processing / calculation: Executes decision logic and calculates harvest timing

[1036] Output: Harvest timing judgment result

[1037] How it works: The server determines when it's time to harvest based on the plant's coverage and sugar content, and then uses the API to send a notification message to the user's LINE account.

[1038] Step 5: Automatic cultivation operation

[1039] If necessary, the server automatically sends instructions to the cultivation machinery, for example, if there is a lack of water, it will send instructions to the irrigation system to provide adequate water.

[1040] Input: Plant condition data

[1041] Data processing / calculation: condition judgment and corresponding cultivation action determination

[1042] Output: Instructions for irrigation systems and fertilizer application devices

[1043] Specific operation: The server determines the irrigation conditions based on the plant's condition and automatically sends instructions to the irrigation system to supply water. If necessary, it also sends instructions to the fertilizer sprayer, which then automatically sprays fertilizer.

[1044] In this way, the system automates a series of processes, enabling efficient and accurate plant management.

[1045] (Application example 1)

[1046] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[1047] In conventional agricultural work, it is difficult to monitor the condition of plants in real time and carry out appropriate cultivation operations. Furthermore, determining the appropriate harvest time requires a high level of experience and knowledge, which places a heavy burden on producers. The present invention aims to solve these problems and provide a system for efficient and effective production management.

[1048] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[1049] In this invention, the server includes means for controlling a camera equipped with an optical sensor to acquire images of plants, means for analyzing the state of the plants from the acquired images, means for determining the state of the plants based on the analysis results, means for notifying the producer of the determination results via communication means, and means for controlling an autonomous machine based on the results of the plant state analysis to perform cultivation operations. This makes it possible to monitor the state of plants in real time and automatically perform cultivation operations such as irrigation and fertilizer application.

[1050] A "camera equipped with an optical sensor" is a camera device equipped with an optical sensor, which is capable of capturing images of plants with high accuracy.

[1051] "Acquired image" refers to image data of a plant captured by a camera equipped with an optical center.

[1052] The "means for analyzing the condition of a plant" is a system that analyzes the hue information and sugar content of a plant based on the acquired image data, and evaluates the health and growth status of the plant.

[1053] "Means for determining the condition of plants" refers to algorithms or programs that use the analysis results to determine whether plants are growing, whether they are diseased, and the appropriate timing for harvesting.

[1054] "Means for notifying producers via communication means" refers to a system for transmitting analysis and judgment results to producers' devices (smartphones, PCs, etc.) via the Internet or mobile phone lines.

[1055] "Autonomous machinery" refers to agricultural machinery that automatically performs plant cultivation operations, and includes tractors and irrigation systems with automatic driving functions.

[1056] "Cultivation operations" refers to all manual work, such as irrigation, fertilizing, weeding, harvesting, etc., performed to promote or protect plant growth.

[1057] This invention relates to a system that uses a camera equipped with an optical sensor to capture images of plants, analyzes the images to determine the condition of the plants, and notifies growers at the appropriate time. It can also work in conjunction with automated driving machines to automatically perform appropriate cultivation operations. Specific embodiments for implementing this invention are described below.

[1058] 1. Main components of the system

[1059] Server: A central control device for managing all processes of image acquisition, analysis, judgment, and notification. It is implemented using a programming language such as Python.

[1060] Camera terminal: A device that captures images of plants using a camera equipped with an optical sensor. This includes general IP cameras and proprietary imaging mechanisms.

[1061] Communication means: Used as a means of communication for producers to receive notifications. Smartphones and PCs are examples of this.

[1062] Autonomous machinery: Agricultural machinery that automatically performs plant cultivation operations. Examples include irrigation systems and fertilizer spreading devices.

[1063] 2. Image Acquisition Process

[1064] The server controls the camera equipped with the optical center to capture images of the plants at regularly scheduled times, for example, at 9:00 a.m. every day, to start the camera and capture new images, which are then saved in local storage for later analysis.

[1065] 3. Image analysis process

[1066] The acquired images are input by the server into the image processing module, which extracts the plant's hue information and calculates the percentage of pixels that fall within a specific range to analyze the plant's coloration. The sugar content is also measured, using an image analysis algorithm.

[1067] 4. Determination and Notification Process

[1068] Based on the analysis results, the server determines the plant's coverage rate and sugar content. This data is used to determine the appropriate time for harvesting. For example, if the coverage rate is 80% or higher and the sugar content is within the appropriate range, it is determined that the time has come to harvest. This determination result is notified to the producer in real time. The notification is sent via the internet to the user's LINE account.

[1069] 5. Automatic cultivation operation process

[1070] If necessary, the server can link with autonomous machines to perform automated cultivation operations. For example, if the plant's condition meets certain conditions, the server can send instructions to the irrigation system to provide adequate water. It can also automatically manage the supply of fertilizer by sending instructions to the fertilizer spraying device.

[1071] Specific examples

[1072] A specific scenario is shown below.

[1073] 1. The server controls the camera terminal every morning at 9:00 a.m. and captures images of tomatoes and mandarin oranges.

[1074] 2. The acquired images are input into an image processing module on the server, where the color and sugar content are analyzed.

[1075] 3. The analysis results show that the coverage rate is 85% and the sugar content is within the appropriate range.

[1076] 4. The server determines that it is time to harvest and notifies the producer of the result via LINE.

[1077] 5. Furthermore, the server plans the next irrigation timing and sends instructions to the irrigation system.

[1078] Examples of prompts to input to a generative AI model:

[1079] "Design a system to monitor the growth of tomatoes and automatically irrigate them. Use a camera to capture images of the plants every day at 9 AM, analyze the images, and calculate the percentage of green pixels. Based on the results, write a program to perform the necessary irrigation operations."

[1080] In this way, the present invention provides a system for monitoring the state of plants in real time and for efficient and effective production management.

[1081] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1082] Step 1:

[1083] The server controls a camera terminal equipped with an optical center to acquire images of plants at regularly scheduled times.

[1084] Input: Scheduled time (e.g. 9:00 AM)

[1085] Output: Images of the plants acquired

[1086] Specific operation: At the scheduled time, the server connects to the camera terminal and sends a command to take a photo. The camera terminal receives the command, captures images of the plants, and sends them to the server.

[1087] Step 2:

[1088] The server stores the acquired images in local storage.

[1089] Input: Plant image

[1090] Output: Saved image data

[1091] Specific operation: The server saves the received image data in the specified folder. The file name is identified by adding date and time information.

[1092] Step 3:

[1093] The server reads the saved images and inputs them into the image processing module for analysis.

[1094] Input: Saved image data

[1095] Output: Analysis results (hue information, sugar content, etc.)

[1096] How it works: The server uses an image processing library (e.g., OpenCV) to extract color information from the image data, calculate the percentage of pixels that fall within a specific range, and then uses image analysis algorithms to measure the sugar content.

[1097] Step 4:

[1098] The server determines the condition of the plant based on the analysis results.

[1099] Input: Image analysis results (hue information, sugar content, etc.)

[1100] Output: Plant condition judgment result (e.g. harvest time, good / bad condition)

[1101] Specific operation: The server runs an algorithm that determines the timing of harvest and necessary cultivation operations based on specific conditions (e.g., coverage rate of 80% or more and sugar content within an appropriate range).

[1102] Step 5:

[1103] The server notifies the producer of the judgment result via a communication means.

[1104] Input: Status determination result

[1105] Output: Notification message to producer

[1106] Specific operation: The server uses a communication API (e.g., LINE API) to send the judgment results to the producer's smartphone or computer.

[1107] Step 6:

[1108] The server controls the automated machinery as needed and performs appropriate cultivation operations.

[1109] Input: Status determination result

[1110] Output: Operation instructions for the automated driving machine

[1111] Specific operation: Based on the status determination results, the server sends instructions to the irrigation system and fertilizer spreading device, which causes the agricultural machinery to automatically perform the necessary tasks.

[1112] Furthermore, an emotion engine that estimates the user's emotion may be further combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59, and perform identification processing using the user's emotion.

[1113] The present invention relates to a system that acquires images of plants and notifies growers based on the analysis results, and also adds a function to recognize the user's emotions and adjust the notification content and operation accordingly. This system acquires images of plants using a camera equipped with an optical sensor and determines the condition of the plants through image analysis. Furthermore, by combining it with an emotion engine that recognizes the user's (grower's) emotions, the system can adjust the notification content and automatic cultivation operation if the user is experiencing negative emotions such as stress.

[1114] System Overview

[1115] The main components of the system are as follows:

[1116] Server: Manages all processes including image acquisition, analysis, judgment, notification, and emotion recognition.

[1117] Camera terminal: A camera equipped with an optical center is used to capture images of plants.

[1118] User terminal: Used as a communication means for producers to receive notifications and as a data collection means for emotion recognition.

[1119] Cultivation machinery: machinery for carrying out automated cultivation operations, including irrigation systems and fertilizer application equipment.

[1120] 1. Image Acquisition Process

[1121] The server sets a periodic task and sends instructions to activate a camera terminal equipped with an optical sensor at a specific time every day. This instruction is to obtain the latest images of the plants to be harvested that day. The camera terminal receives instructions from the server and captures images of the plants. The obtained images are temporarily stored in the camera terminal's local storage and then sent to the server.

[1122] 2. Image analysis process

[1123] The server then passes the saved image to the analysis module, where image processing begins. This module first converts the image into a color space and extracts pixels that fall within a specific color range. The extracted pixel information is used to evaluate the plant's color development, and then a sugar content measurement algorithm is run to calculate the sugar content estimated from the image. The resulting coverage and sugar content data are used to determine the appropriate time for harvesting.

[1124] 3. Determination and Notification Process

[1125] If the server determines that it is time to harvest, it generates a message indicating this and sends it to the user's device using a communication module. This notification is then sent via the internet to the user's LINE account.

[1126] 4. The process of emotion recognition

[1127] The user device collects the producer's facial expression and voice data and sends it to the server. The server then uses an emotion engine to analyze this data and determine the user's emotions. For example, if the user is feeling stressed, the server can adjust the content of the notification based on that information. Based on the emotion recognition results, the notification can include kinder language or an encouraging message.

[1128] 5. Automatic cultivation operation process

[1129] If necessary, the server can send instructions to the cultivation machine to perform automatic cultivation operations. For example, if the user feels high stress, the server can adjust the frequency of irrigation and fertilizer application to reduce the burden on the user.

[1130] Specific examples

[1131] A specific scenario is shown below.

[1132] 1. The server controls the camera terminal every morning at 9:00 AM and captures images of mandarin oranges.

[1133] 2. The acquired images are input into an image processing module on the server, where the color and sugar content are analyzed.

[1134] 3. The analysis results show that the coverage rate is 85% and the sugar content is within the appropriate range.

[1135] 4. The server determines that it is time to harvest and notifies the user (producer) of the result via LINE.

[1136] 5. The user device collects the user's facial expression and voice data and sends it to the server.

[1137] 6. The server uses an emotion engine to determine the user's emotions and determines that the user is feeling stressed.

[1138] 7. Based on the emotion recognition results, the server resends a notification via LINE containing an encouraging message.

[1139] 8. If necessary, the server sends instructions to the irrigation system to provide adequate watering, and also to the fertilizer application device to automatically manage the supply of fertilizer.

[1140] In this way, the present invention not only monitors the condition of plants in real time and notifies producers at the appropriate time, but also adjusts the content of notifications and automatic cultivation operations according to the user's emotional state, thereby achieving efficient and effective production management.

[1141] The processing flow will be explained below.

[1142] Step 1:

[1143] The server sets up a periodic task and sends instructions to activate a camera terminal equipped with a photonic center at a specific time every day to obtain the latest images of the plants to be harvested that day.

[1144] Step 2:

[1145] The device (camera) receives instructions from the server and captures images of the plants. The captured images are temporarily stored in the camera device's local storage.

[1146] Step 3:

[1147] The device (camera) sends the captured image to the server, which receives the file and stores it in a secure location.

[1148] Step 4:

[1149] The server passes the saved image to the analysis module, which then starts processing the image. First, it converts the image into a color space (such as HSV) and extracts pixels that fall within a specific color range.

[1150] Step 5:

[1151] The server evaluates the color of the plants based on the extracted pixel information. Specifically, it calculates the percentage of pixels that fall within a specific range and outputs the result as the coverage rate.

[1152] Step 6:

[1153] The server runs a sugar content measurement algorithm to calculate the estimated sugar content from the image, which uses specific pigment patterns and texture analysis.

[1154] Step 7:

[1155] The server determines the optimal time for harvesting based on data on coverage and sugar content. For example, if coverage is 80% or higher and sugar content is within the appropriate range, it determines that it is optimal for harvesting.

[1156] Step 8:

[1157] If the server determines that the fruit is suitable for harvesting, it generates a message containing the specific coverage rate and sugar content values.

[1158] Step 9:

[1159] The server sends the generated message to the user (producer) using a communication module. The communication method is via the Internet using the LINE API.

[1160] Step 10:

[1161] The user (producer) can check the harvest timing information received as a LINE message, which allows them to carry out harvesting work at the appropriate time.

[1162] Step 11:

[1163] The user device collects facial and voice data from the user and sends it to the server. For example, data is collected automatically when the user approaches a camera or microphone.

[1164] Step 12:

[1165] The server uses an emotion engine to analyze the received data and determine the user's emotions, for example, using facial expression recognition algorithms and voice emotion analysis techniques.

[1166] Step 13:

[1167] The server then adjusts the notification content based on the emotion recognition results, for example adding a kind comment or encouraging message if the user is feeling stressed.

[1168] Step 14:

[1169] The server then adjusts the message based on the emotion recognition results and sends it back to the user (producer) via LINE.

[1170] Step 15:

[1171] If necessary, the server will send further instructions to the cultivation machine. If the user is feeling high stress, the server will adjust the frequency of irrigation and fertilizer application to reduce the burden on the user.

[1172] Through these steps, the system can monitor the condition of plants in real time, notify farmers of the appropriate harvest time, and adjust the notification content and automated cultivation operations according to the user's emotional state, thereby achieving efficient and high-quality production management.

[1173] Example 2

[1174] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[1175] Conventional plant cultivation systems do not automate the assessment of plant conditions or notifications of harvest time, making efficient and accurate management difficult. Furthermore, notifications and operations are performed mechanically without taking into account the emotional state of the grower, resulting in a lack of consideration for reducing stress. Therefore, a system that can properly manage plants and reduce the psychological burden on growers is needed.

[1176] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[1177] In this invention, the server includes means for controlling a camera equipped with an optical center to acquire images of the plants, means for analyzing the state of the plants from the acquired images, means for determining the state of the plants based on the analysis results, means for notifying the determination results to the grower via communication means, means for recognizing the user's emotions by analyzing facial expression data and voice data acquired from the user terminal, and means for adjusting the content of notifications and cultivation operations based on the emotion recognition results. This enables appropriate notifications and operations to be performed taking into consideration the emotional state of the grower while accurately understanding the state of the plants.

[1178] A "camera equipped with a light center" is a camera device that incorporates a light sensor that measures the wavelength and intensity of light, and is used to capture images of plants with high precision.

[1179] The "means for acquiring images" refers to a part consisting of a mechanism and software for controlling the camera, taking images of plants, and saving them as digital data.

[1180] The "analysis means" refers to software and algorithms for processing the acquired image data and quantifying or evaluating the condition and characteristics of the plants.

[1181] The "means for determining" refers to logic and programs for determining the health of plants and the time for harvesting based on the results of image analysis.

[1182] "Communication means" refers to a mechanism including a communication interface and protocol for notifying the results of analysis and judgment to producers and other systems.

[1183] "Facial expression data" refers to the facial expressions of a user captured using a camera or other sensor and converted into digital data.

[1184] "Voice data" refers to the user's voice and speaking style collected by an acoustic sensor such as a microphone and stored as digital data.

[1185] "Means for recognizing emotions" refers to algorithms and software for analyzing facial expression data and voice data to identify the user's emotional state (e.g., stress, joy, fatigue, etc.).

[1186] The "means for adjusting cultivation operations" is a system for automatically adjusting operations related to plant cultivation, such as watering and fertilizer application, based on the emotion recognition results.

[1187] "Cultivation machinery" refers to all machinery and equipment used in plant cultivation, such as irrigation systems and fertilizer spreading devices.

[1188] MODE FOR CARRYING OUT THE INVENTION

[1189] The present invention relates to a system that acquires images of plants and notifies growers based on the analysis results, and also adds a function that recognizes the user's emotions and adjusts the notification content and operation. This system includes the following main components:

[1190] 1. Server

[1191] The server plays a central role in managing the entire image acquisition, analysis, judgment, notification, and emotion recognition process. Specifically, the server has the following functions:

[1192] Control of image acquisition: The server controls the camera terminal equipped with the optical center and instructs it to acquire images of plants at specific times.

[1193] Image analysis: The acquired images are passed to the analysis module, where color space conversion and pixel extraction are performed to evaluate the condition of the plant (e.g., color development, sugar content).

[1194] Judgment and notification: The harvest time is determined based on the analysis results, and the results are notified to producers via LINE or other communication means.

[1195] Emotion recognition: Analyzes facial expression and voice data sent from the user's device to determine the producer's emotional state. If the producer is feeling stressed, the content of notifications will be adjusted.

[1196] Automating cultivation operations: Sending instructions to cultivation machinery as needed to adjust the frequency of irrigation and fertilization.

[1197] 2. Camera terminal

[1198] The camera terminal is a device that captures images of plants using a high-resolution camera equipped with an optical sensor. The camera terminal receives instructions from the server, captures images at specific times, and sends the data to the server. It also has the function of temporarily storing data in local storage.

[1199] 3. User Device

[1200] The user terminal is a means for producers to receive notifications and also functions as a means for collecting data for emotion recognition. Specifically, it plays the following roles:

[1201] Receiving notifications: Receive notifications sent from the server using LINE or other communication methods.

[1202] Data collection: Collects the user's facial expression and voice data and sends it to the server.

[1203] 4. Cultivation machinery

[1204] The cultivation machine is a mechanical facility for automatically operating the cultivation of plants, such as an irrigation system or a fertilizer spraying device, and performs appropriate cultivation operations based on instructions from the server.

[1205] Specific examples

[1206] The specific operating scenario of the system is shown below.

[1207] 1. The server controls the camera terminal every morning at 9:00 AM and captures images of mandarin oranges.

[1208] 2. The acquired images are input into an image processing module on the server, where the color and sugar content are analyzed.

[1209] 3. The analysis results show that the coverage rate is 85% and the sugar content is within the appropriate range.

[1210] 4. The server determines that it is time to harvest and notifies the user (producer) of the result via LINE.

[1211] 5. The user device collects the user's facial expression and voice data and sends it to the server.

[1212] 6. The server uses an emotion engine to determine the user's emotions and determines that the user is feeling stressed.

[1213] 7. Based on the emotion recognition results, the server resends a notification via LINE containing an encouraging message.

[1214] 8. If necessary, the server sends instructions to the irrigation system to provide adequate watering, and also to the fertilizer application device to automatically manage the supply of fertilizer.

[1215] Prompt Sentence Examples

[1216] What does this system do?: This system captures images of plants and notifies growers based on the analysis results. It also recognizes the user's emotions and adjusts operations and notifications accordingly.

[1217] The above is an embodiment of the present invention.

[1218] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1219] Specific processing flow of the program

[1220] Step 1:

[1221] The server sets an instruction to start up the camera terminal equipped with the optical center at a fixed time every day as a scheduled task.

[1222] Input: Schedule setting (e.g. 9:00 AM)

[1223] Output: Instructions to start the camera device

[1224] Step 2:

[1225] The camera terminal receives instructions from the server and activates the camera equipped with the optical center.

[1226] Specific actions: Turn on the camera and put it into a ready state.

[1227] Input: Start command from the server

[1228] Output: The camera is armed and ready to capture images.

[1229] Step 3:

[1230] The camera terminal captures high-resolution images of a target plant (e.g., a mandarin orange).

[1231] Specific actions: Activate the camera's shutter button and generate image data.

[1232] Input: Plant photography target position

[1233] Output: Captured image data

[1234] Step 4:

[1235] The camera terminal temporarily stores the captured images in local storage.

[1236] Specific operation: Saves the image data as a temporary file.

[1237] Input: Captured image data

[1238] Output: Image file saved in local storage

[1239] Step 5:

[1240] The camera terminal transmits the stored image data to a server via the Internet.

[1241] Specific operation: Sends image data over the Internet.

[1242] Input: Image file stored in local storage

[1243] Output: Image data transferred to the server

[1244] Step 6:

[1245] The server receives the images sent from the camera terminal.

[1246] Specific operation: The received image data is stored in the server's temporary storage area.

[1247] Input: Image data from the camera device

[1248] Output: Image data stored in a temporary storage area on the server

[1249] Step 7:

[1250] The server passes the image to the image processing module to begin analysis.

[1251] What it does: Invokes image processing algorithms to perform color space conversion and pixel extraction.

[1252] Input: Image data saved in temporary storage area

[1253] Output: Processed pixel data

[1254] Step 8:

[1255] The server applies a sugar content measurement algorithm based on the analysis results to calculate the sugar content of the plant.

[1256] Specific operation: Runs an algorithm that calculates sugar content using pixel data as input.

[1257] Input: Processed pixel data

[1258] Output: Sugar content data of plants

[1259] Step 9:

[1260] The server determines the appropriate time to harvest based on the coverage rate and sugar content obtained as a result of the analysis.

[1261] Specific operation: Compare with the set threshold and calculate the harvest time.

[1262] Input: Coverage data, sugar content data

[1263] Output: Harvest timing judgment result

[1264] Step 10:

[1265] The server generates a notification message based on the result of the judgment and sends it to the user's LINE account using the LINE API.

[1266] Specific operation: A message is generated based on the judgment result and sent via LINE.

[1267] Input: Harvest timing judgment result

[1268] Output: Notification message sent to the user's LINE

[1269] Step 11:

[1270] The user terminal collects facial expression data and voice data of the user during daily operations.

[1271] Specific actions: Record data using a camera or microphone.

[1272] Input: User's face and voice

[1273] Output: facial expression data, voice data

[1274] Step 12:

[1275] The user terminal transmits the collected facial expression data and voice data to the server.

[1276] Specific operation: Sends data to the server.

[1277] Input: facial expression data, voice data

[1278] Output: Facial expression data and voice data sent to the server

[1279] Step 13:

[1280] The server uses an emotion engine to analyze the facial expression data and voice data to determine the user's emotion.

[1281] What it does: Runs emotion recognition algorithms to identify the user's emotional state.

[1282] Input: facial expression data, voice data

[1283] Output: User's emotional state

[1284] Step 14:

[1285] If the server determines that the user is in a stressed state, it adjusts the content of the notification and resends it.

[1286] Specific behavior: Create a new message and send it again via LINE.

[1287] Input: Emotion recognition results

[1288] Output: The adjusted notification message

[1289] Step 15:

[1290] The server sends instructions to cultivation machinery (such as irrigation systems and fertilizer spreading devices) as needed to perform automatic cultivation operations.

[1291] Specific operation: Issues operational instructions to the machine based on set conditions.

[1292] Input: Emotion recognition results, plant state data

[1293] Output: Operation instructions for cultivation machinery

[1294] Step 16:

[1295] The cultivation machine performs operations such as watering and spreading fertilizer according to instructions from the server.

[1296] Specific actions: Water and fertilize according to instructions.

[1297] Input: Operation instructions from the server

[1298] Output: Feedback of the results of the cultivation operations performed

[1299] (Application example 2)

[1300] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[1301] Conventional plant cultivation systems have limited functionality for monitoring plant conditions and notifying appropriate cultivation timing. Furthermore, they simply provide instructions for harvesting and cultivation operations without considering the producer's emotional state, placing a heavy burden on the producer. The present invention aims to achieve effective and efficient production management by providing a system that not only accurately monitors plant conditions and notifies producers at the appropriate time, but also adjusts notification content and operations taking into account the producer's emotional state.

[1302] The specific processing by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes means for controlling a camera equipped with an optical center to acquire images of plants, means for analyzing the state of the plants from the acquired images, means for determining the state of the plants based on the analysis results, means for notifying the determination result to the producer via communication means, means for recognizing the user's emotions and adjusting the notification content and operation, and means for sending instructions to the automated cultivation machine. This not only enables real-time monitoring of the state of plants, but also enables optimal notifications and automatic cultivation operations according to the producer's emotional state.

[1303] A "light center" is a device that has the function of detecting the intensity and position of light, and is used to obtain clear images of plants.

[1304] A "camera" is a device that takes pictures and videos and records the data.

[1305] A "plant" is a multicellular organism that performs photosynthesis and grows above ground.

[1306] "Means for acquiring images" refers to a device for taking images, such as a camera, and a method for controlling its operation.

[1307] "Means for analysis" refers to methods or devices that process data such as images to extract meaningful information.

[1308] A "means for determining" is a method or device that determines a particular situation or condition based on analyzed data.

[1309] "Communication means" refers to methods and devices for sending and receiving information, including the Internet and mobile networks.

[1310] "Producer" refers to an agricultural worker who grows crops, etc.

[1311] "Means for recognizing a user's emotions" refers to methods and technologies for identifying a user's emotional state from facial expressions, voice, etc.

[1312] The "means for adjusting notification content" refers to a method or device for optimizing the content of information or messages to be conveyed based on the results of emotion analysis.

[1313] "Cultivation machinery" refers to machinery for automatically carrying out plant cultivation work, and includes irrigation systems and fertilizer spraying devices.

[1314] A "means for sending instructions" is a method or device for sending operational commands to a remote device or system.

[1315] The present invention relates to a system for monitoring and managing the condition of plants, and for optimal notification and automatic operation taking into consideration the emotional state of the grower. Hereinafter, specific embodiments of the present invention will be described in detail.

[1316] System Configuration

[1317] The main components are a server, a camera terminal, a user terminal, and a cultivation machine.

[1318] Server: Manages all processes, including image acquisition, analysis, judgment, notification, and emotion recognition. In particular, a high-performance server is required for storing and processing data, and it is desirable to implement it in a programming language such as Python.

[1319] Camera terminal: Images of plants are acquired using a camera equipped with an optical sensor. Specifically, a high-resolution camera such as the Intel RealSense D435 is suitable.

[1320] User terminals: Devices such as smartphones and smart glasses that allow producers to receive notifications. These devices are also used to collect facial expression and voice data.

[1321] Cultivation machinery: Machines for automated cultivation operations, such as irrigation systems and fertilizer spreading devices. Specifically, the UR5e robot arm is a suitable example.

[1322] Image acquisition process

[1323] The server sets a periodic task and sends instructions to start the camera terminal equipped with the optical center at a specific time every day. The camera terminal receives this instruction, captures images of the plants, temporarily stores the acquired images in the camera terminal's local storage, and then sends them to the server. The acquired images are saved in the server's storage and prepared for the next analysis process.

[1324] Image analysis process

[1325] The server passes the saved images to the analysis module to evaluate the condition of the plants. First, it converts the image into a color space using an image processing library (e.g., OpenCV), and then extracts pixels that fall into a specific color range. Based on the extracted pixel information, it evaluates the color development of the plants and runs a sugar content measurement algorithm to calculate the sugar content estimated from the image. The coverage rate and sugar content data obtained as a result of the analysis are then used to determine the appropriate time for harvesting.

[1326] Determination and Notification Process

[1327] When the server determines that it is time to harvest, it generates a message indicating this and sends it to the user's device via the communication module. This notification is then sent via the Internet to the user's smartphone or smart glasses. The user device receives this notification and notifies the producer.

[1328] The process of emotion recognition

[1329] The user's device collects the producer's facial expression and voice data and sends it to a server. The server then analyzes this data using an emotion recognition engine (e.g., Microsoft Azure Cognitive Services) to determine the user's emotional state. If the user is feeling stressed, the server adjusts the content of the notification based on that information, allowing it to include encouraging messages or kind words.

[1330] Automatic cultivation operation process

[1331] If necessary, the server can send instructions to the cultivation machine to perform automatic cultivation operations. For example, if the user feels high stress, the server can adjust the frequency of irrigation and fertilizer application to reduce the burden on the user.

[1332] Specific examples

[1333] A specific scenario is shown below.

[1334] 1. The server controls the camera terminals every morning at 9:00 AM to acquire images of plants.

[1335] 2. The acquired images are input into an image processing module on the server, where the color and sugar content are analyzed.

[1336] 3. The analysis results show that the coverage rate is 85% and the sugar content is within the appropriate range.

[1337] 4. The server determines that it is time to harvest and notifies the user of the result via their smartphone or smart glasses.

[1338] 5. The user device collects the user's facial expression and voice data and sends it to the server.

[1339] 6. The server uses an emotion engine to determine the user's emotion and if the user is feeling stressed, resends the notification with an encouraging message.

[1340] 7. If necessary, the server sends instructions to the irrigation system and fertilizer application device to automatically manage the appropriate watering and fertilization.

[1341] In this way, the present invention not only monitors the state of plants in real time and notifies growers at the appropriate time, but also adjusts the content of notifications and automated cultivation operations according to the user's emotional state, thereby achieving efficient and effective production management.

[1342] Example prompts:

[1343] "Design a system that automatically monitors medicinal plants in a factory and takes into account the emotional state of workers to provide optimal notifications and actions."

[1344] This will not only allow for more efficient and effective management of plant cultivation, but also enable the creation of a system that takes into account the emotions and well-being of workers.

[1345] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1346] Step 1:

[1347] The server sends instructions to start up a camera terminal equipped with an optical center at a specific time every day. The input is pre-set schedule information. The output is for the camera terminal to start up and capture images of plants. This instruction is sent to the camera terminal via the Internet. Specifically, a command to control the camera is sent, and the camera takes a picture of the target plant.

[1348] Step 2:

[1349] The camera terminal receives instructions to capture images of plants and temporarily store them in local storage. The input is a start-up instruction from the server. The output is the acquired image data of the plants. Specifically, the camera automatically focuses, takes high-resolution images of the plants, and stores the data in local storage.

[1350] Step 3:

[1351] The camera terminal sends the saved image data to the server. The input is the image data saved in the local storage. The output is the image data saved on the server. In concrete terms, the camera terminal performs a process of uploading the image data to the server via the Internet.

[1352] Step 4:

[1353] The server passes the received image data to the analysis module, which analyzes the condition of the plants. The input is the image data sent from the camera terminal. The output is the analysis results. Specifically, an image processing library is used to convert the image into a color space and extract pixels that fall within a specific color range. Based on this information, the coloring and sugar content of the plants are evaluated.

[1354] Step 5:

[1355] The server determines the condition of the plants based on the analysis results. The input is the analysis results obtained from the analysis module. The output is a judgment result indicating the condition of the plants. Specifically, the analysis algorithm evaluates data such as cover rate and sugar content to determine the appropriate time for harvesting.

[1356] Step 6:

[1357] The server notifies the user terminal of the judgment result via the communication module. The input is the judgment result indicating the plant's condition. The output is a notification message delivered to the user's smartphone or smart glasses. Specifically, the notification message is generated and sent to the user terminal via the Internet.

[1358] Step 7:

[1359] The user terminal collects the user's facial expression data and voice data and sends them to the server. The input is facial expression data and voice data collected by a camera and microphone. The output is emotion data sent to the server. Specifically, sensors in the smartphone or smart glasses detect the user's emotions and upload the data to the server.

[1360] Step 8:

[1361] The server uses an emotion recognition engine to analyze the user's emotional state. The input is facial expression data and voice data sent from the user's device. The output is an analysis result indicating the user's emotional state. Specifically, the emotion recognition algorithm analyzes the data and determines indicators such as stress and happiness.

[1362] Step 9:

[1363] The server adjusts the notification content based on the emotion analysis results and sends it back to the user device. The input is the analysis result from the emotion recognition engine. The output is a message containing the adjusted notification content. Specifically, a notification containing words of encouragement or a kind message is generated and sent back to the user device.

[1364] Step 10:

[1365] If necessary, the server sends instructions to the cultivation machine to perform automatic cultivation operations. The input is instruction information based on the analysis results and the user's emotional state. The output is automatic cultivation operations using the irrigation system and fertilizer spraying device. Specific operation instructions are sent to the cultivation machine, which operates to maintain the optimal cultivation environment for the plants.

[1366] This allows for real-time monitoring of plant conditions, timely notifications, and cultivation operations that take into account the grower's emotional state.

[1367] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[1368] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[1369] 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 the present disclosure is not limited to this, and the specific processing may be performed by the headset type terminal 314.

[1370] [Fourth embodiment]

[1371] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[1372] 7, a data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[1373] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[1374] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[1375] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio in accordance with instructions from the processor 46.

[1376] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the surroundings of user 20 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[1377] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[1378] The control object 443 includes a display device, LEDs in the eyes, and motors for driving the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[1379] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[1380] The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific 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.

[1381] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[1382] In the robot 414, the processor 46 performs the reception output process. A reception output program 60 is stored in the storage 50. 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 process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[1383] Next, a description will be given of the specific processing performed by the specific processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1384] The present invention relates to a system that acquires images of plants using a camera equipped with an optical sensor, analyzes the images to determine the condition of the plants, and notifies the grower at an appropriate time. Furthermore, the system can cooperate with cultivation machinery as needed to perform automatic cultivation operations. Specific examples of the system are described below.

[1385] 1. System Overview

[1386] The main components of the system are as follows:

[1387] Server: Manages all processes of image acquisition, analysis, judgment, and notification.

[1388] Camera terminal: A camera equipped with an optical center is used to capture images of plants.

[1389] User terminal: Used as a communication means for producers to receive notifications.

[1390] Cultivation machinery: machinery for carrying out automated cultivation operations, including irrigation systems and fertilizer application equipment.

[1391] 2. Image Acquisition Process

[1392] The server controls the camera equipped with the optical center to capture images of the plants at regularly scheduled times, for example, at 9:00 a.m. every day, to start the camera and capture new images, which are then saved in local storage for later analysis.

[1393] 3. Image analysis process

[1394] The acquired images are input by the server into the image processing module, which extracts the plant's hue information and calculates the percentage of pixels that fall within a specific range to analyze the plant's coloration. The sugar content is also measured, using an image analysis algorithm.

[1395] 4. Determination and Notification Process

[1396] Based on the analysis results, the server determines the plant's coverage rate and sugar content. This data is used to determine the appropriate time for harvesting. For example, if the coverage rate is 80% or higher and the sugar content is within the appropriate range, it is determined that the time has come to harvest. This determination result is notified to the producer in real time. The notification is sent via the internet to the user's LINE account.

[1397] 5. Automatic cultivation operation process

[1398] If necessary, the server can link with the cultivation machinery to perform automatic cultivation operations. For example, if the plant's condition meets certain conditions, the server can send instructions to the irrigation system to provide appropriate water supply. It can also send instructions to the fertilizer spraying device to automatically manage the supply of fertilizer.

[1399] Specific examples

[1400] A specific scenario is shown below.

[1401] 1. The server controls the camera terminal every morning at 9:00 AM and captures images of mandarin oranges.

[1402] 2. The acquired images are input into an image processing module on the server, where the color and sugar content are analyzed.

[1403] 3. The analysis results show that the coverage rate is 85% and the sugar content is within the appropriate range.

[1404] 4. The server determines that it is time to harvest and notifies the producer of the result via LINE.

[1405] 5. Furthermore, the server plans the next irrigation timing and sends instructions to the irrigation system.

[1406] In this way, the present invention allows for real-time monitoring of plant conditions and efficient and effective production management.

[1407] The processing flow will be explained below.

[1408] Step 1:

[1409] The server sets up a periodic task and sends instructions to activate a camera terminal equipped with a photonic center at a specific time every day to obtain the latest images of the plants to be harvested that day.

[1410] Step 2:

[1411] The device (camera) receives instructions from the server and captures images of the plants. The captured images are temporarily stored in the camera device's local storage.

[1412] Step 3:

[1413] The device (camera) sends the captured image to the server, which receives the file and stores it in a secure location.

[1414] Step 4:

[1415] The server then passes the stored image to an analysis module, which begins processing the image by first converting it to a color space (such as HSV) and extracting pixels that fall into a specific color range.

[1416] Step 5:

[1417] The server evaluates the color of the plants based on the extracted pixel information. Specifically, it calculates the percentage of pixels that fall within a specific range and outputs the result as the coverage rate.

[1418] Step 6:

[1419] The server then runs a sugar content measurement algorithm to calculate an estimated sugar content from the image, which typically uses specific pigment patterns or texture analysis.

[1420] Step 7:

[1421] The server compares the coverage rate and sugar content data to determine the appropriate time for harvesting. For example, if the coverage rate is 80% or higher and the sugar content is within the appropriate range, it determines that it is the best time to harvest.

[1422] Step 8:

[1423] If the server determines that the fruit is suitable for harvesting, it generates a message containing the specific coverage rate and sugar content values.

[1424] Step 9:

[1425] The server sends the generated message to the user (producer) using a communication module. The communication method is via the Internet using the LINE API.

[1426] Step 10:

[1427] The user (producer) checks the harvest timing information received as a LINE message, allowing the user to carry out harvesting work at the appropriate time.

[1428] Step 11:

[1429] If necessary, the server can also send instructions to the farming machine, such as instructions to automatically plan post-harvest irrigation and fertilizer applications.

[1430] Through these steps, the system can monitor the condition of plants in real time, notify producers of the appropriate harvest time, and perform the necessary automated cultivation operations.

[1431] Example 1

[1432] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1433] In conventional plant cultivation methods, monitoring plant conditions and timing of cultivation operations depended on human experience and intuition, making it difficult to make accurate judgments and respond at the appropriate time. Furthermore, manual monitoring and operation required time and effort, making it difficult to improve production efficiency. Furthermore, this approach carried the risk of overlooking changes in plant conditions and the optimal harvest time, which could affect quality and yield.

[1434] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[1435] In this invention, the server includes means for controlling a device that detects the state of a plant using light to acquire an image of the plant, means for processing the acquired image to analyze the state of the plant, means for determining the state of the plant based on the analysis result, and means for notifying the user of the determination result using a communication means. This makes it possible to accurately monitor the state of the plant in real time and automatically perform cultivation operations and harvesting at appropriate times.

[1436] A "device for detecting the condition of a plant using light" is a device for detecting the health condition and components of a plant by utilizing the properties of light.

[1437] "Means for acquiring" refers to the function or process of acquiring images of plants at specified times and under specified conditions.

[1438] "Means for processing images to analyze plant condition" means a process that includes software and algorithms for analyzing acquired images to assess the health and composition of the plant.

[1439] The "means for determining the state of the plant based on the analysis results" is a function that comprehensively evaluates the state of the plant based on the results of image analysis and makes a determination based on certain criteria.

[1440] The "means for notifying the user using a communication means" refers to a communication function for notifying the user of the analysis and determination results, and sends the notification via the Internet or a mobile network.

[1441] "Means for measuring the component content of plants by image processing" refers to a function or process for measuring the components (e.g., sugar content) contained in plants by analyzing image data.

[1442] "Means for determining the optimal harvest time" refers to the function of determining the optimal timing for harvesting based on the ingredient content and other analysis results.

[1443] The "means for automatically sending instructions to cultivation equipment and performing plant cultivation operations" is a function that automatically issues instructions to cultivation equipment based on analysis results and conditions and executes necessary cultivation operations.

[1444] The present invention relates to a system that acquires images of plants using a camera equipped with a device for detecting the state of plants using light, analyzes the images to determine the state of the plants, and notifies the grower of the state at an appropriate time. Furthermore, the system can cooperate with cultivation equipment as needed to perform automatic cultivation operations. Specific examples of the system are described below.

[1445] 1. System Overview

[1446] The main components of this system are as follows:

[1447] Server: Manages all processes of image acquisition, analysis, judgment, and notification.

[1448] Camera terminal: Images of plants are acquired using a camera equipped with a device that uses light to detect the state of the plants.

[1449] User terminal: Used as a communication means for producers to receive notifications.

[1450] Cultivation machinery: machinery for carrying out automated cultivation operations, including irrigation systems and fertilizer application equipment.

[1451] 2. Example of image acquisition

[1452] The server controls the camera terminals at 9:00 AM every day to acquire images of the specified plants. At this time, the camera terminals use light to capture the color and condition of the plants. The acquired images are captured and saved in local storage in JPEG format or other formats. This process is controlled using CRON jobs and HTTP requests.

[1453] 3. Specific examples of image analysis

[1454] The server inputs the acquired images into an image processing module, which uses an open-source image processing library such as OpenCV to analyze the plant's hue information and ingredient content (e.g., sugar content). The hue information is analyzed for each pixel, and the percentage of pixels within a specific color range is calculated.

[1455] 4. Specific examples of judgments and notifications

[1456] The server determines the condition of the plants based on the results of image analysis. For example, if the plant coverage is 80% or more and the sugar content is within the appropriate range, it determines that it is time to harvest. This determination result is sent to the user's LINE account via the Internet. This notification is sent using the LINE API.

[1457] 5. Specific examples of automated cultivation operations

[1458] The server automatically sends instructions to the cultivation machine as needed. For example, if the plant condition is determined to be lacking in water, the server sends instructions to the irrigation system to properly replenish the water. Similarly, if needed, the server sends instructions to the fertilizer application device to automatically apply the appropriate amount of fertilizer.

[1459] Generative AI model and example prompts

[1460] Examples of prompts:

[1461] "Please explain the specific processing steps of a system that acquires images of plants using a camera equipped with a device that uses light to detect the state of the plants, analyzes the images to determine the state of the plants, and notifies the grower at the appropriate time. The system components include a server, camera terminal, user terminal, and cultivation machinery. Please describe the detailed procedures and operations from issuing image acquisition commands to operating the automatic cultivation."

[1462] This allows the generative AI model to explain in detail the specific processing flow of the program.

[1463] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1464] Step 1: Image Acquisition Instructions

[1465] The server sends an instruction to the camera terminal to acquire an image at the scheduled time. This instruction is sent to the camera terminal using an HTTP request.

[1466] Input: Schedule date and time

[1467] Data processing / calculation: Generate an HTTP request at the scheduled time using a CRON job

[1468] Output: Camera start command

[1469] Specific operation: A CRON job on the server is triggered and sends an HTTP request to the specified URL to retrieve the image.

[1470] Step 2: Image acquisition

[1471] The camera device receives instructions from the server to turn on the light source and capture images of the plants. The captured images are saved in local storage and then sent to the server.

[1472] Input: Camera start command

[1473] Data processing / calculation: The camera captures images of the plants and saves them as image files.

[1474] Output: JPEG format image file

[1475] Specific operation: The camera terminal controls the light center, captures images of plants, and saves them in local storage in JPEG format. The images are then sent to the server using an HTTP POST request.

[1476] Step 3: Image analysis

[1477] The server inputs the received images into an image processing module, which uses OpenCV and other tools to analyze the plant's hue information and component content (sugar content).

[1478] Input: JPEG format image file

[1479] Data processing / calculation: Image processing algorithms are used to extract color information and analyze sugar content

[1480] Output: Hue information data, sugar content data

[1481] Specific operation: The server uses an image processing module (e.g., OpenCV) to analyze the image and calculate color information and sugar content. This processing extracts data that can serve as an indicator of the plant's health.

[1482] Step 4: Judgment and Notification

[1483] The server determines the plant coverage rate and sugar content based on the analysis results, and sends a message via the LINE API to notify the user of the results.

[1484] Input: Hue information data, sugar content data

[1485] Data processing / calculation: Executes decision logic and calculates harvest timing

[1486] Output: Harvest timing judgment result

[1487] How it works: The server determines when it's time to harvest based on the plant's coverage and sugar content, and then uses the API to send a notification message to the user's LINE account.

[1488] Step 5: Automatic cultivation operation

[1489] If necessary, the server automatically sends instructions to the cultivation machinery, for example, if there is a lack of water, it will send instructions to the irrigation system to provide adequate water.

[1490] Input: Plant condition data

[1491] Data processing / calculation: condition judgment and corresponding cultivation action determination

[1492] Output: Instructions for irrigation systems and fertilizer application devices

[1493] Specific operation: The server determines the irrigation conditions based on the plant's condition and automatically sends instructions to the irrigation system to supply water. If necessary, it also sends instructions to the fertilizer sprayer, which then automatically sprays fertilizer.

[1494] In this way, the system automates a series of processes, enabling efficient and accurate plant management.

[1495] (Application example 1)

[1496] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1497] In conventional agricultural work, it is difficult to monitor the condition of plants in real time and carry out appropriate cultivation operations. Furthermore, determining the appropriate harvest time requires a high level of experience and knowledge, which places a heavy burden on producers. The present invention aims to solve these problems and provide a system for efficient and effective production management.

[1498] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[1499] In this invention, the server includes means for controlling a camera equipped with an optical sensor to acquire images of plants, means for analyzing the state of the plants from the acquired images, means for determining the state of the plants based on the analysis results, means for notifying the producer of the determination results via communication means, and means for controlling an autonomous machine based on the results of the plant state analysis to perform cultivation operations. This makes it possible to monitor the state of plants in real time and automatically perform cultivation operations such as irrigation and fertilizer application.

[1500] A "camera equipped with an optical sensor" is a camera device equipped with an optical sensor, which is capable of capturing images of plants with high accuracy.

[1501] "Acquired image" refers to image data of a plant captured by a camera equipped with an optical center.

[1502] The "means for analyzing the condition of a plant" is a system that analyzes the hue information and sugar content of a plant based on the acquired image data, and evaluates the health and growth status of the plant.

[1503] "Means for determining the condition of plants" refers to algorithms or programs that use the analysis results to determine whether plants are growing, whether they are diseased, and the appropriate timing for harvesting.

[1504] "Means for notifying producers via communication means" refers to a system for transmitting analysis and judgment results to producers' devices (smartphones, PCs, etc.) via the Internet or mobile phone lines.

[1505] "Autonomous machinery" refers to agricultural machinery that automatically performs plant cultivation operations, and includes tractors and irrigation systems with automatic driving functions.

[1506] "Cultivation operations" refers to all manual work, such as irrigation, fertilizing, weeding, harvesting, etc., performed to promote or protect plant growth.

[1507] This invention relates to a system that uses a camera equipped with an optical sensor to capture images of plants, analyzes the images to determine the condition of the plants, and notifies growers at the appropriate time. It can also work in conjunction with automated driving machines to automatically perform appropriate cultivation operations. Specific embodiments for implementing this invention are described below.

[1508] 1. Main components of the system

[1509] Server: A central control device for managing all processes of image acquisition, analysis, judgment, and notification. It is implemented using a programming language such as Python.

[1510] Camera terminal: A device that captures images of plants using a camera equipped with an optical sensor. This includes general IP cameras and proprietary imaging mechanisms.

[1511] Communication means: Used as a means of communication for producers to receive notifications. Smartphones and PCs are examples of this.

[1512] Autonomous machinery: Agricultural machinery that automatically performs plant cultivation operations. Examples include irrigation systems and fertilizer spreading devices.

[1513] 2. Image Acquisition Process

[1514] The server controls the camera equipped with the optical center to capture images of the plants at regularly scheduled times, for example, at 9:00 a.m. every day, to start the camera and capture new images, which are then saved in local storage for later analysis.

[1515] 3. Image analysis process

[1516] The acquired images are input by the server into the image processing module, which extracts the plant's hue information and calculates the percentage of pixels that fall within a specific range to analyze the plant's coloration. The sugar content is also measured, using an image analysis algorithm.

[1517] 4. Determination and Notification Process

[1518] Based on the analysis results, the server determines the plant's coverage rate and sugar content. This data is used to determine the appropriate time for harvesting. For example, if the coverage rate is 80% or higher and the sugar content is within the appropriate range, it is determined that the time has come to harvest. This determination result is notified to the producer in real time. The notification is sent via the internet to the user's LINE account.

[1519] 5. Automatic cultivation operation process

[1520] If necessary, the server can link with autonomous machines to perform automated cultivation operations. For example, if the plant's condition meets certain conditions, the server can send instructions to the irrigation system to provide adequate water. It can also automatically manage the supply of fertilizer by sending instructions to the fertilizer spraying device.

[1521] Specific examples

[1522] A specific scenario is shown below.

[1523] 1. The server controls the camera terminal every morning at 9:00 a.m. and captures images of tomatoes and mandarin oranges.

[1524] 2. The acquired images are input into an image processing module on the server, where the color and sugar content are analyzed.

[1525] 3. The analysis results show that the coverage rate is 85% and the sugar content is within the appropriate range.

[1526] 4. The server determines that it is time to harvest and notifies the producer of the result via LINE.

[1527] 5. Furthermore, the server plans the next irrigation timing and sends instructions to the irrigation system.

[1528] Examples of prompts to input to a generative AI model:

[1529] "Design a system to monitor the growth of tomatoes and automatically irrigate them. Use a camera to capture images of the plants every day at 9 AM, analyze the images, and calculate the percentage of green pixels. Based on the results, write a program to perform the necessary irrigation operations."

[1530] In this way, the present invention provides a system for monitoring the state of plants in real time and for efficient and effective production management.

[1531] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1532] Step 1:

[1533] The server controls a camera terminal equipped with an optical center to acquire images of plants at regularly scheduled times.

[1534] Input: Scheduled time (e.g. 9:00 AM)

[1535] Output: Images of the plants acquired

[1536] Specific operation: At the scheduled time, the server connects to the camera terminal and sends a command to take a photo. The camera terminal receives the command, captures images of the plants, and sends them to the server.

[1537] Step 2:

[1538] The server stores the acquired images in local storage.

[1539] Input: Plant image

[1540] Output: Saved image data

[1541] Specific operation: The server saves the received image data in the specified folder. The file name is identified by adding date and time information.

[1542] Step 3:

[1543] The server reads the saved images and inputs them into the image processing module for analysis.

[1544] Input: Saved image data

[1545] Output: Analysis results (hue information, sugar content, etc.)

[1546] How it works: The server uses an image processing library (e.g., OpenCV) to extract color information from the image data, calculate the percentage of pixels that fall within a specific range, and then uses image analysis algorithms to measure the sugar content.

[1547] Step 4:

[1548] The server determines the condition of the plant based on the analysis results.

[1549] Input: Image analysis results (hue information, sugar content, etc.)

[1550] Output: Plant condition judgment result (e.g. harvest time, good / bad condition)

[1551] Specific operation: The server runs an algorithm that determines the timing of harvest and necessary cultivation operations based on specific conditions (e.g., coverage rate of 80% or more and sugar content within an appropriate range).

[1552] Step 5:

[1553] The server notifies the producer of the judgment result via a communication means.

[1554] Input: Status determination result

[1555] Output: Notification message to producer

[1556] Specific operation: The server uses a communication API (e.g., LINE API) to send the judgment results to the producer's smartphone or computer.

[1557] Step 6:

[1558] The server controls the automated machinery as needed and performs appropriate cultivation operations.

[1559] Input: Status determination result

[1560] Output: Operation instructions for the automated driving machine

[1561] Specific operation: Based on the status determination results, the server sends instructions to the irrigation system and fertilizer spreading device, which causes the agricultural machinery to automatically perform the necessary tasks.

[1562] Furthermore, an emotion engine that estimates the user's emotion may be further combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59, and perform identification processing using the user's emotion.

[1563] The present invention relates to a system that acquires images of plants and notifies growers based on the analysis results, and also adds a function to recognize the user's emotions and adjust the notification content and operation accordingly. This system acquires images of plants using a camera equipped with an optical sensor and determines the condition of the plants through image analysis. Furthermore, by combining it with an emotion engine that recognizes the user's (grower's) emotions, the system can adjust the notification content and automatic cultivation operation if the user is experiencing negative emotions such as stress.

[1564] System Overview

[1565] The main components of the system are as follows:

[1566] Server: Manages all processes including image acquisition, analysis, judgment, notification, and emotion recognition.

[1567] Camera terminal: A camera equipped with an optical center is used to capture images of plants.

[1568] User terminal: Used as a communication means for producers to receive notifications and as a data collection means for emotion recognition.

[1569] Cultivation machinery: machinery for carrying out automated cultivation operations, including irrigation systems and fertilizer application equipment.

[1570] 1. Image Acquisition Process

[1571] The server sets a periodic task and sends instructions to activate a camera terminal equipped with an optical sensor at a specific time every day. This instruction is to obtain the latest images of the plants to be harvested that day. The camera terminal receives instructions from the server and captures images of the plants. The obtained images are temporarily stored in the camera terminal's local storage and then sent to the server.

[1572] 2. Image analysis process

[1573] The server then passes the saved image to the analysis module, where image processing begins. This module first converts the image into a color space and extracts pixels that fall within a specific color range. The extracted pixel information is used to evaluate the plant's color development, and then a sugar content measurement algorithm is run to calculate the sugar content estimated from the image. The resulting coverage and sugar content data are used to determine the appropriate time for harvesting.

[1574] 3. Determination and Notification Process

[1575] If the server determines that it is time to harvest, it generates a message indicating this and sends it to the user's device using a communication module. This notification is then sent via the internet to the user's LINE account.

[1576] 4. The process of emotion recognition

[1577] The user device collects the producer's facial expression and voice data and sends it to the server. The server then uses an emotion engine to analyze this data and determine the user's emotions. For example, if the user is feeling stressed, the server can adjust the content of the notification based on that information. Based on the emotion recognition results, the notification can include kinder language or an encouraging message.

[1578] 5. Automatic cultivation operation process

[1579] If necessary, the server can send instructions to the cultivation machine to perform automatic cultivation operations. For example, if the user feels high stress, the server can adjust the frequency of irrigation and fertilizer application to reduce the burden on the user.

[1580] Specific examples

[1581] A specific scenario is shown below.

[1582] 1. The server controls the camera terminal every morning at 9:00 AM and captures images of mandarin oranges.

[1583] 2. The acquired images are input into an image processing module on the server, where the color and sugar content are analyzed.

[1584] 3. The analysis results show that the coverage rate is 85% and the sugar content is within the appropriate range.

[1585] 4. The server determines that it is time to harvest and notifies the user (producer) of the result via LINE.

[1586] 5. The user device collects the user's facial expression and voice data and sends it to the server.

[1587] 6. The server uses an emotion engine to determine the user's emotions and determines that the user is feeling stressed.

[1588] 7. Based on the emotion recognition results, the server resends a notification via LINE containing an encouraging message.

[1589] 8. If necessary, the server sends instructions to the irrigation system to provide adequate watering, and also to the fertilizer application device to automatically manage the supply of fertilizer.

[1590] In this way, the present invention not only monitors the condition of plants in real time and notifies producers at the appropriate time, but also adjusts the content of notifications and automatic cultivation operations according to the user's emotional state, thereby achieving efficient and effective production management.

[1591] The processing flow will be explained below.

[1592] Step 1:

[1593] The server sets up a periodic task and sends instructions to activate a camera terminal equipped with a photonic center at a specific time every day to obtain the latest images of the plants to be harvested that day.

[1594] Step 2:

[1595] The device (camera) receives instructions from the server and captures images of the plants. The captured images are temporarily stored in the camera device's local storage.

[1596] Step 3:

[1597] The device (camera) sends the captured image to the server, which receives the file and stores it in a secure location.

[1598] Step 4:

[1599] The server passes the saved image to the analysis module, which then starts processing the image. First, it converts the image into a color space (such as HSV) and extracts pixels that fall within a specific color range.

[1600] Step 5:

[1601] The server evaluates the color of the plants based on the extracted pixel information. Specifically, it calculates the percentage of pixels that fall within a specific range and outputs the result as the coverage rate.

[1602] Step 6:

[1603] The server runs a sugar content measurement algorithm to calculate the estimated sugar content from the image, which uses specific pigment patterns and texture analysis.

[1604] Step 7:

[1605] The server determines the optimal time for harvesting based on data on coverage and sugar content. For example, if coverage is 80% or higher and sugar content is within the appropriate range, it determines that it is optimal for harvesting.

[1606] Step 8:

[1607] If the server determines that the fruit is suitable for harvesting, it generates a message containing the specific coverage rate and sugar content values.

[1608] Step 9:

[1609] The server sends the generated message to the user (producer) using a communication module. The communication method is via the Internet using the LINE API.

[1610] Step 10:

[1611] The user (producer) can check the harvest timing information received as a LINE message, which allows them to carry out harvesting work at the appropriate time.

[1612] Step 11:

[1613] The user device collects facial and voice data from the user and sends it to the server. For example, data is collected automatically when the user approaches a camera or microphone.

[1614] Step 12:

[1615] The server uses an emotion engine to analyze the received data and determine the user's emotions, for example, using facial expression recognition algorithms and voice emotion analysis techniques.

[1616] Step 13:

[1617] The server then adjusts the notification content based on the emotion recognition results, for example adding a kind comment or encouraging message if the user is feeling stressed.

[1618] Step 14:

[1619] The server then adjusts the message based on the emotion recognition results and sends it back to the user (producer) via LINE.

[1620] Step 15:

[1621] If necessary, the server will send further instructions to the cultivation machine. If the user is feeling high stress, the server will adjust the frequency of irrigation and fertilizer application to reduce the burden on the user.

[1622] Through these steps, the system can monitor the condition of plants in real time, notify farmers of the appropriate harvest time, and adjust the notification content and automated cultivation operations according to the user's emotional state, thereby achieving efficient and high-quality production management.

[1623] Example 2

[1624] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1625] Conventional plant cultivation systems do not automate the assessment of plant conditions or notifications of harvest time, making efficient and accurate management difficult. Furthermore, notifications and operations are performed mechanically without taking into account the emotional state of the grower, resulting in a lack of consideration for reducing stress. Therefore, a system that can properly manage plants and reduce the psychological burden on growers is needed.

[1626] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[1627] In this invention, the server includes means for controlling a camera equipped with an optical center to acquire images of the plants, means for analyzing the state of the plants from the acquired images, means for determining the state of the plants based on the analysis results, means for notifying the determination results to the grower via communication means, means for recognizing the user's emotions by analyzing facial expression data and voice data acquired from the user terminal, and means for adjusting the content of notifications and cultivation operations based on the emotion recognition results. This enables appropriate notifications and operations to be performed taking into consideration the emotional state of the grower while accurately understanding the state of the plants.

[1628] A "camera equipped with a light center" is a camera device that incorporates a light sensor that measures the wavelength and intensity of light, and is used to capture images of plants with high precision.

[1629] The "means for acquiring images" refers to a part consisting of a mechanism and software for controlling the camera, taking images of plants, and saving them as digital data.

[1630] The "analysis means" refers to software and algorithms for processing the acquired image data and quantifying or evaluating the condition and characteristics of the plants.

[1631] The "means for determining" refers to logic and programs for determining the health of plants and the time for harvesting based on the results of image analysis.

[1632] "Communication means" refers to a mechanism including a communication interface and protocol for notifying the results of analysis and judgment to producers and other systems.

[1633] "Facial expression data" refers to the facial expressions of a user captured using a camera or other sensor and converted into digital data.

[1634] "Voice data" refers to the user's voice and speaking style collected by an acoustic sensor such as a microphone and stored as digital data.

[1635] "Means for recognizing emotions" refers to algorithms and software for analyzing facial expression data and voice data to identify the user's emotional state (e.g., stress, joy, fatigue, etc.).

[1636] The "means for adjusting cultivation operations" is a system for automatically adjusting operations related to plant cultivation, such as watering and fertilizer application, based on the emotion recognition results.

[1637] "Cultivation machinery" refers to all machinery and equipment used in plant cultivation, such as irrigation systems and fertilizer spreading devices.

[1638] MODE FOR CARRYING OUT THE INVENTION

[1639] The present invention relates to a system that acquires images of plants and notifies growers based on the analysis results, and also adds a function that recognizes the user's emotions and adjusts the notification content and operation. This system includes the following main components:

[1640] 1. Server

[1641] The server plays a central role in managing the entire image acquisition, analysis, judgment, notification, and emotion recognition process. Specifically, the server has the following functions:

[1642] Control of image acquisition: The server controls the camera terminal equipped with the optical center and instructs it to acquire images of plants at specific times.

[1643] Image analysis: The acquired images are passed to the analysis module, where color space conversion and pixel extraction are performed to evaluate the condition of the plant (e.g., color development, sugar content).

[1644] Judgment and notification: The harvest time is determined based on the analysis results, and the results are notified to producers via LINE or other communication means.

[1645] Emotion recognition: Analyzes facial expression and voice data sent from the user's device to determine the producer's emotional state. If the producer is feeling stressed, the content of notifications will be adjusted.

[1646] Automating cultivation operations: Sending instructions to cultivation machinery as needed to adjust the frequency of irrigation and fertilization.

[1647] 2. Camera terminal

[1648] The camera terminal is a device that captures images of plants using a high-resolution camera equipped with an optical sensor. The camera terminal receives instructions from the server, captures images at specific times, and sends the data to the server. It also has the function of temporarily storing data in local storage.

[1649] 3. User Device

[1650] The user terminal is a means for producers to receive notifications and also functions as a means for collecting data for emotion recognition. Specifically, it plays the following roles:

[1651] Receiving notifications: Receive notifications sent from the server using LINE or other communication methods.

[1652] Data collection: Collects the user's facial expression and voice data and sends it to the server.

[1653] 4. Cultivation machinery

[1654] The cultivation machine is a mechanical facility for automatically operating the cultivation of plants, such as an irrigation system or a fertilizer spraying device, and performs appropriate cultivation operations based on instructions from the server.

[1655] Specific examples

[1656] The specific operating scenario of the system is shown below.

[1657] 1. The server controls the camera terminal every morning at 9:00 AM and captures images of mandarin oranges.

[1658] 2. The acquired images are input into an image processing module on the server, where the color and sugar content are analyzed.

[1659] 3. The analysis results show that the coverage rate is 85% and the sugar content is within the appropriate range.

[1660] 4. The server determines that it is time to harvest and notifies the user (producer) of the result via LINE.

[1661] 5. The user device collects the user's facial expression and voice data and sends it to the server.

[1662] 6. The server uses an emotion engine to determine the user's emotions and determines that the user is feeling stressed.

[1663] 7. Based on the emotion recognition results, the server resends a notification via LINE containing an encouraging message.

[1664] 8. If necessary, the server sends instructions to the irrigation system to provide adequate watering, and also to the fertilizer application device to automatically manage the supply of fertilizer.

[1665] Prompt Sentence Examples

[1666] What does this system do?: This system captures images of plants and notifies growers based on the analysis results. It also recognizes the user's emotions and adjusts operations and notifications accordingly.

[1667] The above is an embodiment of the present invention.

[1668] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1669] Specific processing flow of the program

[1670] Step 1:

[1671] The server sets an instruction to start up the camera terminal equipped with the optical center at a fixed time every day as a scheduled task.

[1672] Input: Schedule setting (e.g. 9:00 AM)

[1673] Output: Instructions to start the camera device

[1674] Step 2:

[1675] The camera terminal receives instructions from the server and activates the camera equipped with the optical center.

[1676] Specific actions: Turn on the camera and put it into a ready state.

[1677] Input: Start command from the server

[1678] Output: The camera is armed and ready to capture images.

[1679] Step 3:

[1680] The camera terminal captures high-resolution images of a target plant (e.g., a mandarin orange).

[1681] Specific actions: Activate the camera's shutter button and generate image data.

[1682] Input: Plant photography target position

[1683] Output: Captured image data

[1684] Step 4:

[1685] The camera terminal temporarily stores the captured images in local storage.

[1686] Specific operation: Saves the image data as a temporary file.

[1687] Input: Captured image data

[1688] Output: Image file saved in local storage

[1689] Step 5:

[1690] The camera terminal transmits the stored image data to a server via the Internet.

[1691] Specific operation: Sends image data over the Internet.

[1692] Input: Image file stored in local storage

[1693] Output: Image data transferred to the server

[1694] Step 6:

[1695] The server receives the images sent from the camera terminal.

[1696] Specific operation: The received image data is stored in the server's temporary storage area.

[1697] Input: Image data from the camera device

[1698] Output: Image data stored in a temporary storage area on the server

[1699] Step 7:

[1700] The server passes the image to the image processing module to begin analysis.

[1701] What it does: Invokes image processing algorithms to perform color space conversion and pixel extraction.

[1702] Input: Image data saved in temporary storage area

[1703] Output: Processed pixel data

[1704] Step 8:

[1705] The server applies a sugar content measurement algorithm based on the analysis results to calculate the sugar content of the plant.

[1706] Specific operation: Runs an algorithm that calculates sugar content using pixel data as input.

[1707] Input: Processed pixel data

[1708] Output: Sugar content data of plants

[1709] Step 9:

[1710] The server determines the appropriate time to harvest based on the coverage rate and sugar content obtained as a result of the analysis.

[1711] Specific operation: Compare with the set threshold and calculate the harvest time.

[1712] Input: Coverage data, sugar content data

[1713] Output: Harvest timing judgment result

[1714] Step 10:

[1715] The server generates a notification message based on the result of the judgment and sends it to the user's LINE account using the LINE API.

[1716] Specific operation: A message is generated based on the judgment result and sent via LINE.

[1717] Input: Harvest timing judgment result

[1718] Output: Notification message sent to the user's LINE

[1719] Step 11:

[1720] The user terminal collects facial expression data and voice data of the user during daily operations.

[1721] Specific actions: Record data using a camera or microphone.

[1722] Input: User's face and voice

[1723] Output: facial expression data, voice data

[1724] Step 12:

[1725] The user terminal transmits the collected facial expression data and voice data to the server.

[1726] Specific operation: Sends data to the server.

[1727] Input: facial expression data, voice data

[1728] Output: Facial expression data and voice data sent to the server

[1729] Step 13:

[1730] The server uses an emotion engine to analyze the facial expression data and voice data to determine the user's emotion.

[1731] What it does: Runs emotion recognition algorithms to identify the user's emotional state.

[1732] Input: facial expression data, voice data

[1733] Output: User's emotional state

[1734] Step 14:

[1735] If the server determines that the user is in a stressed state, it adjusts the content of the notification and resends it.

[1736] Specific behavior: Create a new message and send it again via LINE.

[1737] Input: Emotion recognition results

[1738] Output: The adjusted notification message

[1739] Step 15:

[1740] The server sends instructions to cultivation machinery (such as irrigation systems and fertilizer spreading devices) as needed to perform automatic cultivation operations.

[1741] Specific operation: Issues operational instructions to the machine based on set conditions.

[1742] Input: Emotion recognition results, plant state data

[1743] Output: Operation instructions for cultivation machinery

[1744] Step 16:

[1745] The cultivation machine performs operations such as watering and spreading fertilizer according to instructions from the server.

[1746] Specific actions: Water and fertilize according to instructions.

[1747] Input: Operation instructions from the server

[1748] Output: Feedback of the results of the cultivation operations performed

[1749] (Application example 2)

[1750] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1751] Conventional plant cultivation systems have limited functionality for monitoring plant conditions and notifying appropriate cultivation timing. Furthermore, they simply provide instructions for harvesting and cultivation operations without considering the producer's emotional state, placing a heavy burden on the producer. The present invention aims to achieve effective and efficient production management by providing a system that not only accurately monitors plant conditions and notifies producers at the appropriate time, but also adjusts notification content and operations taking into account the producer's emotional state.

[1752] The specific processing by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes means for controlling a camera equipped with an optical center to acquire images of plants, means for analyzing the state of the plants from the acquired images, means for determining the state of the plants based on the analysis results, means for notifying the determination result to the producer via communication means, means for recognizing the user's emotions and adjusting the notification content and operation, and means for sending instructions to the automated cultivation machine. This not only enables real-time monitoring of the state of plants, but also enables optimal notifications and automatic cultivation operations according to the producer's emotional state.

[1753] A "light center" is a device that has the function of detecting the intensity and position of light, and is used to obtain clear images of plants.

[1754] A "camera" is a device that takes pictures and videos and records the data.

[1755] A "plant" is a multicellular organism that performs photosynthesis and grows above ground.

[1756] "Means for acquiring images" refers to a device for taking images, such as a camera, and a method for controlling its operation.

[1757] "Means for analysis" refers to methods or devices that process data such as images to extract meaningful information.

[1758] A "means for determining" is a method or device that determines a particular situation or condition based on analyzed data.

[1759] "Communication means" refers to methods and devices for sending and receiving information, including the Internet and mobile networks.

[1760] "Producer" refers to an agricultural worker who grows crops, etc.

[1761] "Means for recognizing a user's emotions" refers to methods and technologies for identifying a user's emotional state from facial expressions, voice, etc.

[1762] The "means for adjusting notification content" refers to a method or device for optimizing the content of information or messages to be conveyed based on the results of emotion analysis.

[1763] "Cultivation machinery" refers to machinery for automatically carrying out plant cultivation work, and includes irrigation systems and fertilizer spraying devices.

[1764] A "means for sending instructions" is a method or device for sending operational commands to a remote device or system.

[1765] The present invention relates to a system for monitoring and managing the condition of plants, and for optimal notification and automatic operation taking into consideration the emotional state of the grower. Hereinafter, specific embodiments of the present invention will be described in detail.

[1766] System Configuration

[1767] The main components are a server, a camera terminal, a user terminal, and a cultivation machine.

[1768] Server: Manages all processes, including image acquisition, analysis, judgment, notification, and emotion recognition. In particular, a high-performance server is required for storing and processing data, and it is desirable to implement it in a programming language such as Python.

[1769] Camera terminal: Images of plants are acquired using a camera equipped with an optical sensor. Specifically, a high-resolution camera such as the Intel RealSense D435 is suitable.

[1770] User terminals: Devices such as smartphones and smart glasses that allow producers to receive notifications. These devices are also used to collect facial expression and voice data.

[1771] Cultivation machinery: Machines for automated cultivation operations, such as irrigation systems and fertilizer spreading devices. Specifically, the UR5e robot arm is a suitable example.

[1772] Image acquisition process

[1773] The server sets a periodic task and sends instructions to start the camera terminal equipped with the optical center at a specific time every day. The camera terminal receives this instruction, captures images of the plants, temporarily stores the acquired images in the camera terminal's local storage, and then sends them to the server. The acquired images are saved in the server's storage and prepared for the next analysis process.

[1774] Image analysis process

[1775] The server passes the saved images to the analysis module to evaluate the condition of the plants. First, it converts the image into a color space using an image processing library (e.g., OpenCV), and then extracts pixels that fall into a specific color range. Based on the extracted pixel information, it evaluates the color development of the plants and runs a sugar content measurement algorithm to calculate the sugar content estimated from the image. The coverage rate and sugar content data obtained as a result of the analysis are then used to determine the appropriate time for harvesting.

[1776] Determination and Notification Process

[1777] When the server determines that it is time to harvest, it generates a message indicating this and sends it to the user's device via the communication module. This notification is then sent via the Internet to the user's smartphone or smart glasses. The user device receives this notification and notifies the producer.

[1778] The process of emotion recognition

[1779] The user's device collects the producer's facial expression and voice data and sends it to a server. The server then analyzes this data using an emotion recognition engine (e.g., Microsoft Azure Cognitive Services) to determine the user's emotional state. If the user is feeling stressed, the server adjusts the content of the notification based on that information, allowing it to include encouraging messages or kind words.

[1780] Automatic cultivation operation process

[1781] If necessary, the server can send instructions to the cultivation machine to perform automatic cultivation operations. For example, if the user feels high stress, the server can adjust the frequency of irrigation and fertilizer application to reduce the burden on the user.

[1782] Specific examples

[1783] A specific scenario is shown below.

[1784] 1. The server controls the camera terminals every morning at 9:00 AM to acquire images of plants.

[1785] 2. The acquired images are input into an image processing module on the server, where the color and sugar content are analyzed.

[1786] 3. The analysis results show that the coverage rate is 85% and the sugar content is within the appropriate range.

[1787] 4. The server determines that it is time to harvest and notifies the user of the result via their smartphone or smart glasses.

[1788] 5. The user device collects the user's facial expression and voice data and sends it to the server.

[1789] 6. The server uses an emotion engine to determine the user's emotion and if the user is feeling stressed, resends the notification with an encouraging message.

[1790] 7. If necessary, the server sends instructions to the irrigation system and fertilizer application device to automatically manage the appropriate watering and fertilization.

[1791] In this way, the present invention not only monitors the state of plants in real time and notifies growers at the appropriate time, but also adjusts the content of notifications and automated cultivation operations according to the user's emotional state, thereby achieving efficient and effective production management.

[1792] Example prompts:

[1793] "Design a system that automatically monitors medicinal plants in a factory and takes into account the emotional state of workers to provide optimal notifications and actions."

[1794] This will not only allow for more efficient and effective management of plant cultivation, but also enable the creation of a system that takes into account the emotions and well-being of workers.

[1795] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1796] Step 1:

[1797] The server sends instructions to start up a camera terminal equipped with an optical center at a specific time every day. The input is pre-set schedule information. The output is for the camera terminal to start up and capture images of plants. This instruction is sent to the camera terminal via the Internet. Specifically, a command to control the camera is sent, and the camera takes a picture of the target plant.

[1798] Step 2:

[1799] The camera terminal receives instructions to capture images of plants and temporarily store them in local storage. The input is a start-up instruction from the server. The output is the acquired image data of the plants. Specifically, the camera automatically focuses, takes high-resolution images of the plants, and stores the data in local storage.

[1800] Step 3:

[1801] The camera terminal sends the saved image data to the server. The input is the image data saved in the local storage. The output is the image data saved on the server. In concrete terms, the camera terminal performs a process of uploading the image data to the server via the Internet.

[1802] Step 4:

[1803] The server passes the received image data to the analysis module, which analyzes the condition of the plants. The input is the image data sent from the camera terminal. The output is the analysis results. Specifically, an image processing library is used to convert the image into a color space and extract pixels that fall within a specific color range. Based on this information, the coloring and sugar content of the plants are evaluated.

[1804] Step 5:

[1805] The server determines the condition of the plants based on the analysis results. The input is the analysis results obtained from the analysis module. The output is a judgment result indicating the condition of the plants. Specifically, the analysis algorithm evaluates data such as cover rate and sugar content to determine the appropriate time for harvesting.

[1806] Step 6:

[1807] The server notifies the user terminal of the judgment result via the communication module. The input is the judgment result indicating the plant's condition. The output is a notification message delivered to the user's smartphone or smart glasses. Specifically, the notification message is generated and sent to the user terminal via the Internet.

[1808] Step 7:

[1809] The user terminal collects the user's facial expression data and voice data and sends them to the server. The input is facial expression data and voice data collected by a camera and microphone. The output is emotion data sent to the server. Specifically, sensors in the smartphone or smart glasses detect the user's emotions and upload the data to the server.

[1810] Step 8:

[1811] The server uses an emotion recognition engine to analyze the user's emotional state. The input is facial expression data and voice data sent from the user's device. The output is an analysis result indicating the user's emotional state. Specifically, the emotion recognition algorithm analyzes the data and determines indicators such as stress and happiness.

[1812] Step 9:

[1813] The server adjusts the notification content based on the emotion analysis results and sends it back to the user device. The input is the analysis result from the emotion recognition engine. The output is a message containing the adjusted notification content. Specifically, a notification containing words of encouragement or a kind message is generated and sent back to the user device.

[1814] Step 10:

[1815] If necessary, the server sends instructions to the cultivation machine to perform automatic cultivation operations. The input is instruction information based on the analysis results and the user's emotional state. The output is automatic cultivation operations using the irrigation system and fertilizer spraying device. Specific operation instructions are sent to the cultivation machine, which operates to maintain the optimal cultivation environment for the plants.

[1816] This allows for real-time monitoring of plant conditions, timely notifications, and cultivation operations that take into account the grower's emotional state.

[1817] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[1818] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[1819] 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 the present disclosure is not limited to this, and the specific processing may be performed by the robot 414.

[1820] The emotion identification model 59 as an emotion engine may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[1821] FIG. 9 is a diagram illustrating an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and actions arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion includes both affect and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[1822] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[1823] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[1824] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is indicated, and when they approach the ideal, a state of pleasure is indicated. Emotions can also be created for robots, automobiles, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is indicated, and when they approach the ideal, a state of pleasure is indicated. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on Voice Emotion Recognition and Emotional Brain Physiological Signal Analysis Systems, Tokushima University, Doctoral Dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the "reaction" domain, where sensation is dominant. The right half of the emotion map lists emotions belonging to the "situation" domain, where situational awareness is dominant.

[1825] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[1826] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[1827] The system according to the present disclosure has been described above mainly with respect to the functions of the data processing device 12, but the system according to the present disclosure is not necessarily implemented on a server. The system according to the present disclosure may be implemented as a general information processing system. The present disclosure may be implemented, for example, as a software program running on a personal computer or an application running on a smartphone, etc. The method according to the present disclosure may be provided to users in the form of SaaS (Software as a Service).

[1828] In the above embodiment, an example was given in which the specific processing is performed by one computer 22, but the technology of the present disclosure is not limited to this, and the specific processing may be distributed and performed by a plurality of computers including the computer 22. For example, the data generation model 58 may be provided in an external device of the data processing device 12, and data may be generated in the external device in accordance with input data.

[1829] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[1830] 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.

[1831] It is not necessary to store all of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[1832] The hardware resource for executing a specific process can be any of the following processors: An example of a processor is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. Another example of a processor is a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[1833] The hardware resource that executes the specific processing may be configured with one of these various processors, or may be configured with a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific processing may be a single processor.

[1834] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[1835] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[1836] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[1837] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference.

[1838] The following is further disclosed regarding the above embodiment.

[1839] (Claim 1)

[1840] means for controlling a camera equipped with an optical center to acquire images of the plant;

[1841] A means for analyzing the state of the plant from the acquired image;

[1842] a means for determining the state of the plant based on the analysis results;

[1843] a means for notifying the producer of the determination result via a communication means;

[1844] A system including:

[1845] (Claim 2)

[1846] 10. The system of claim 1, further comprising means for measuring sugar content of the plant by image analysis and determining the optimal time for harvesting based on the measurement results.

[1847] (Claim 3)

[1848] 10. The system according to claim 1, further comprising means for automatically transmitting instructions to the cultivation machine to perform plant cultivation operations when a certain condition is met.

[1849] "Example 1"

[1850] (Claim 1)

[1851] a means for controlling a device for detecting the state of a plant using light to acquire an image of the plant;

[1852] a means for processing the acquired images to analyze the state of the plants;

[1853] A means for determining the state of the plant based on the analysis results;

[1854] a means for notifying a user of the result of the determination using a communication means;

[1855] A system including:

[1856] (Claim 2)

[1857] 10. The system of claim 1, further comprising means for measuring the content of elements in a plant by image processing and determining the optimal harvest time based on the measurement results.

[1858] (Claim 3)

[1859] 10. The system according to claim 1, further comprising means for automatically transmitting instructions to the cultivation equipment to perform plant cultivation operations when a certain condition is met.

[1860] "Application Example 1"

[1861] (Claim 1)

[1862] means for controlling a camera equipped with an optical center to acquire images of the plant;

[1863] A means for analyzing the state of the plant from the acquired image;

[1864] a means for determining the state of the plant based on the analysis results;

[1865] a means for notifying the producer of the determination result via a communication means;

[1866] a means for controlling the automatic operation machine based on the result of the plant state analysis and performing cultivation operations;

[1867] A system including:

[1868] (Claim 2)

[1869] 10. The system of claim 1, further comprising means for measuring sugar content of the plant by image analysis and determining the optimal time for harvesting based on the measurement results.

[1870] (Claim 3)

[1871] 10. The system according to claim 1, further comprising means for automatically transmitting instructions to the cultivation machine to perform plant cultivation operations when a certain condition is met.

[1872] "Example 2: Combining Emotion Engines"

[1873] (Claim 1)

[1874] means for controlling a camera equipped with an optical center to acquire images of the plant;

[1875] A means for analyzing the state of the plant from the acquired image;

[1876] a means for determining the state of the plant based on the analysis results;

[1877] a means for notifying the producer of the determination result via a communication means;

[1878] means for recognizing a user's emotions by analyzing facial expression data and voice data acquired from the user terminal;

[1879] A means for adjusting notification content and cultivation operations based on the emotion recognition result;

[1880] A system including:

[1881] (Claim 2)

[1882] 10. The system of claim 1, further comprising means for measuring sugar content of the plant by image analysis and determining the optimal time for harvesting based on the measurement results.

[1883] (Claim 3)

[1884] 10. The system according to claim 1, further comprising means for automatically transmitting instructions to the cultivation machine to perform plant cultivation operations when a certain condition is met.

[1885] "Application example 2 when combining emotion engines"

[1886] (Claim 1)

[1887] means for controlling a camera equipped with an optical center to acquire images of the plant;

[1888] A means for analyzing the state of the plant from the acquired image;

[1889] a means for determining the state of the plant based on the analysis results;

[1890] a means for n...

Claims

1. means for controlling a camera equipped with an optical center to acquire images of the plant; A means for analyzing the state of the plant from the acquired image; A means for determining the state of the plant based on the analysis results; a means for notifying the producer of the determination result via a communication means; A system including:

2. The system according to claim 1 , further comprising means for measuring the sugar content of the plant by image analysis and determining the optimum time for harvesting based on the measurement results.

3. 2. The system according to claim 1, further comprising means for automatically transmitting instructions to the cultivation machine to perform plant cultivation operations when a certain condition is met.

Images