System

Abstract

A system is provided.SOLUTION: A system includes means for acquiring an image, means for analyzing the acquired image to evaluate a health state of a plant, means for inputting data regarding a growth environment, means for generating a care plan on the basis of an analysis result and the growth environment data, means for transmitting a reminder in accordance with the generated care plan, and means for providing a preventive measure and a coping method regarding a change of seasons and a disease specific to the plant.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

[0002] Patent document 1 discloses a persona chatbot control method performed by at least one processor, the method including the steps of receiving a user utterance, adding the user utterance to a prompt including 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] Plant lovers need to provide proper care to keep their plants healthy, but this requires a lot of knowledge and experience. They also face the problem of forgetting to care for their plants in their busy daily lives. Furthermore, dealing with plant-specific diseases and seasonal changes takes time and effort. To solve these challenges, a system is needed that automatically provides personalized plant care. [Means for solving the problem]

[0005] The present invention provides a system including a means for acquiring images, a means for analyzing the acquired images to evaluate the health of plants, a means for inputting data related to the growing environment, a means for generating a care plan based on the analysis results and the growing environment data, a means for sending reminders according to the generated care plan, and a means for providing preventive measures and remedies for seasonal changes and plant-specific diseases. This allows plant lovers to provide appropriate care at the right time and efficiently maintain the health of their plants.

[0006] "Acquiring an image" refers to the act of acquiring image data to display the state of the plant.

[0007] "Analyzing acquired images to assess the health of a plant" refers to the process of analyzing acquired images and diagnosing the health of a plant.

[0008] "Inputting data on the growing environment" refers to the act of inputting information on the growing environment of a plant into the system.

[0009] "Generating a care plan based on the analysis results and growth environment data" refers to the process of creating a specific plan for plant care based on the analyzed data and the input growth environment data.

[0010] "Sending a reminder according to the generated care plan" refers to sending a notification to the user at an appropriate time based on the generated care plan.

[0011] "Providing preventive measures and treatments for seasonal changes and plant-specific diseases" is the process of providing users with preventive measures and treatments for seasonal changes and specific diseases.

[0012] "Device" refers to the electronic device that the user uses at hand, specifically a smartphone or tablet.

[0013] A "server" is a remote computing device that processes and stores data and acts as the central nerve center of the system.

[0014] "User" refers to plant enthusiasts and gardeners who use the System. [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 is a system that allows plant lovers to effectively care for their plants. The system uses a smartphone camera to capture images of plants and analyzes those images to assess the plant's health. The system also uses user input data about the plant's growing environment, allowing the system to generate a personalized care plan based on the analysis results and the growing environment data. The system also sends reminders to the user based on the care plan, providing preventative and treatment measures for seasonal changes and plant-specific diseases.

[0037] Program processing overview

[0038] 1. Acquire images of plants

[0039] First, the user opens the app on their smartphone and takes a picture of a plant with their camera. The device temporarily stores the image and prepares it to be sent to the server.

[0040] 2. Analyze the images

[0041] When the device sends the captured image to the server, the server activates the image analysis module. The server evaluates the plant's health based on the received image. Specifically, it extracts features such as leaf color, shape, and the presence or absence of disease spots, and diagnoses whether the plant is healthy or diseased.

[0042] 3. Enter the habitat data

[0043] Within the app, users input information about the plant's growing environment, including the type of plant, its location, sunlight hours, temperature, humidity, etc. The device then sends this data to a server.

[0044] 4. Generate a care plan

[0045] Based on the image analysis results and the growing environment data entered by the user, the server generates an optimal care plan for maintaining the health of the plant, including watering frequency, the appropriate type and amount of fertilizer, disease prevention measures, and more.

[0046] 5. Send a reminder

[0047] The server sets reminders based on the care plan generated by the server. Taking into account the user's daily rhythm, the server sends reminders to the device at appropriate times. The device then displays notifications to the user, encouraging them to carry out the care.

[0048] 6. Provide disease prevention measures

[0049] In addition, the server generates preventative and treatment measures for seasonal changes and specific illnesses, and this information is also sent to the device and notified to the user.

[0050] Specific examples

[0051] For example, let's say a user is growing a houseplant. Every morning, the user opens the app and takes a photo of the plant. The device sends the image to a server, which analyzes the plant's health. The analysis reveals that some of the leaves are yellowing, suggesting a lack of water. The user enters data such as the plant's location (a sunny living room) and the number of hours of sunlight (approximately 6 hours) into the app, and the server takes this information and the analysis results into consideration to generate a care plan that calls for watering the plant once a week and administering liquid fertilizer once a month.

[0052] Based on this care plan, the server sets a watering reminder every Wednesday morning at 8:00 a.m. and sends it to the device. The user receives the reminder at 8:00 a.m. and waters the plants. The server also sends advice on pest prevention as the seasons change, ensuring the user remembers to perform the necessary care.

[0053] In this way, the system of the present invention personalizes proper plant care, making it easy for even busy users to keep their plants healthy.

[0054] The processing flow will be explained below.

[0055] Program processing flow

[0056] Step 1:

[0057] The user opens the app on their smartphone. The user selects the camera function and takes a photo of a plant. The device temporarily stores the image and prepares it to be sent to the server.

[0058] Step 2:

[0059] The device sends the stored image data to the server, which then stores the received image data in a database.

[0060] Step 3:

[0061] The server launches the image analysis module. The server applies image processing algorithms to recognize plant parts in the image. The server extracts features such as leaf color, shape, and the presence of disease spots to assess the plant's health.

[0062] Step 4:

[0063] The user enters environmental information such as the type of plant, location, and sunlight hours into a form within the app. The device sends the entered data to the server, which then stores the received environmental data in a database.

[0064] Step 5:

[0065] The server creates an optimal care plan based on environmental data and image analysis results. The server generates specific care instructions, such as the appropriate watering frequency, type of fertilizer, and timing. The server sends the created care plan to the device. The device notifies the user of the received care plan.

[0066] Step 6:

[0067] The server sets the reminder schedule based on the care plan. The server determines the date and time of the reminder, taking into account the user's daily routine. The device sets the reminder and sends a notification at the specified date and time.

[0068] Step 7:

[0069] The server analyzes data on seasonal changes and plant-specific diseases. The server generates appropriate disease prevention and treatment methods. The server sends this information to the device. The device notifies the user of the received information.

[0070] Example 1

[0071] 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."

[0072] Conventional plant care systems have struggled to properly assess the health of plants and provide optimal care plans for individual plants. Users often forget to care for their plants in their busy daily lives, making it difficult to maintain their plants' health. Furthermore, there were no systems that accurately provided preventive measures and treatments for seasonal and plant-specific diseases.

[0073] 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.

[0074] In this invention, the server includes a means for capturing images of plants using a photography function, a means for analyzing the captured images to evaluate the health of the plants, and a means for inputting information about the growing environment. This allows a user to capture images of plants and analyze the images to accurately evaluate their health. A means for generating a care plan based on the analysis results and growing environment information can provide an optimal care plan for each individual plant. A means for sending reminders according to the generated care plan ensures that the user does not forget to care for the plants. Furthermore, a means for providing preventive measures and treatments for seasonal changes and plant-specific diseases provides comprehensive care to keep the plants healthy.

[0075] The "photography function" is a function that allows a user to take an image of a plant using the smartphone camera.

[0076] "Analyzing images to evaluate the health of plants" refers to the process of extracting characteristics such as leaf color, shape, and the presence or absence of disease spots from acquired plant images, and diagnosing the health of the plant.

[0077] "Entering information about the growing environment" refers to the act of the user entering data such as the type of plant, installation location, hours of sunlight, temperature, and humidity into the app.

[0078] "Generating a care plan based on the analysis results and growth environment information" refers to the process of creating a care plan suitable for maintaining the health of plants based on the results of image analysis and growth environment information entered by the user.

[0079] "Sending a reminder according to a care plan" refers to the act of sending a notification to a user to perform care at an appropriate time based on the generated care plan.

[0080] "Providing preventive measures and treatments for seasonal changes and plant-specific diseases" is a process of guiding users to specific preventive measures and treatments to deal with seasonal climate changes and plant-specific diseases.

[0081] This invention is a system that allows plant lovers to effectively care for their plants. The system uses a smartphone camera to capture images of plants and analyzes those images to assess the plant's health. The system also uses user input information about the plant's growing environment. Based on the analysis results and the growing environment information, the system generates a personalized care plan and sends reminders according to the plan. It also provides preventative measures and treatments for seasonal changes and plant-specific diseases.

[0082] Specifically, the following hardware and software are used.

[0083] Hardware and software used

[0084] 1. Smartphone: A mobile information device held by a user.

[0085] 2. Camera: The built-in photography function of a smartphone.

[0086] 3. Server: A computer with high processing power that performs image analysis and data storage.

[0087] 4. Application software: Apps installed on smartphones.

[0088] 5. Image analysis module: Uses libraries such as Python, OpenCV, and TensorFlow.

[0089] 6. Database: Storage for habitat information and care plans.

[0090] Operation procedures and examples

[0091] Acquire images of plants

[0092] The user launches the app on their smartphone and uses the camera to take a picture of the plant. The device temporarily stores the image and prepares to send it to the server.

[0093] Analyzing the image

[0094] When the device sends the captured image to the server, the server launches an image analysis module and evaluates the plant's health based on the received image. For example, OpenCV is used to extract leaf color, shape, and the presence or absence of disease spots, and TensorFlow is used to diagnose the plant's health.

[0095] Entering the habitat data

[0096] The user enters information about the plant's growing environment in the app, including the type of plant, its location, sunlight hours, temperature, humidity, etc. The device converts this data into JSON format and sends it to the server.

[0097] Generate a care plan

[0098] The server generates an optimal care plan based on the image analysis results and growing environment information, for example, by using a database knowledge base and machine learning algorithms to determine the optimal watering frequency and type and amount of fertilizer for the user's plants.

[0099] Send a reminder

[0100] The server sets reminders based on the care plan generated by the server. Taking into account the user's daily rhythm, the server sends reminders to the device at appropriate times. The device then notifies the user of the reminders and encourages them to care for their plants.

[0101] Providing disease prevention measures

[0102] Additionally, the server generates preventative measures and treatments for seasonal changes and specific illnesses, which are then sent to the device and notified to the user.

[0103] Specific operation example

[0104] For example, consider a user who is caring for a houseplant. Every morning, the user opens the app and takes a photo of the plant. The device sends the captured image to the server, which analyzes the plant's health. The analysis reveals that some of the leaves are yellowing, suggesting a lack of water. The user inputs data such as the plant's location (a sunny living room) and the amount of sunlight (approximately 6 hours), and the server generates a care plan for watering once a week and liquid fertilizer once a month. Based on this care plan, the server sets a watering reminder every Wednesday at 8:00 a.m. and sends it to the device. The user receives the reminder at 8:00 a.m. and waters the plant. The server also sends pest prevention measures in early spring, helping the user remember to take care of the plant.

[0105] Prompt Sentence Examples

[0106] "After a user opens the app on their smartphone and takes a picture of their houseplant, the server analyzes the image and evaluates the plant's health. Please also explain in detail how the system generates an optimal care plan and sends reminders based on growing environment information entered by the user, such as the installation location and sunlight hours. Please also emphasize that seasonal disease prevention measures are also provided."

[0107] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0108] Step 1:

[0109] The user launches the smartphone app and takes a picture of the plant using the camera function. The device temporarily saves this image in its internal storage. At this stage, the input is the captured image, and the output is the temporarily saved image file. Specifically, the user taps the camera button in the app, frames the entire plant, and presses the shutter button.

[0110] Step 2:

[0111] The device retrieves the temporarily saved image and sends it to the server. Here, the input is the temporarily saved image file, and the output is the image data sent to the server. Specifically, the device checks the network connection and uses an HTTP request to upload the image file to the specified endpoint on the server.

[0112] Step 3:

[0113] The server analyzes the received image data. It launches an image analysis module and uses OpenCV and TensorFlow to extract features such as leaf color, shape, and the presence or absence of disease spots. The input is the image data sent to the server, and the output is an assessment result regarding the plant's health. Specifically, the server reads the image data, analyzes the pigment information of each pixel, and evaluates the health by detecting specific patterns.

[0114] Step 4:

[0115] The user enters information about the plant's growing environment in the app. This includes the type of plant, location, sunlight hours, temperature, humidity, etc. The device converts this data into JSON format and sends it to the server. The input is the growing environment information entered by the user, and the output is the JSON data sent to the server. Specifically, the user manually enters the required information into the app's input form and clicks the "Submit" button.

[0116] Step 5:

[0117] The server receives the image analysis results and growing environment information and generates an optimal care plan. Using the database's knowledge base and machine learning algorithms, the care plan is created based on the analysis results and input information. The input is the image analysis results and growing environment information, and the output is the generated care plan. The server calculates this and creates a plan that includes specific watering frequency, type and amount of fertilizer, and disease prevention measures.

[0118] Step 6:

[0119] Reminders are set based on the care plan generated by the server. Reminders are sent to the device at appropriate times, taking into account the user's daily rhythm. The input is the generated care plan, and the output is the reminder notification sent to the device. Specifically, the server takes into account the user's time zone settings and sets a reminder, for example, "Water the plants every Wednesday at 8am," and sends a push notification to the device.

[0120] Step 7:

[0121] The server generates preventive measures and treatment methods according to seasonal changes and specific diseases. This information is also sent to the terminal and notified to the user. The input is external seasonal information and plant condition information, and the output is the generated preventive measures and treatment methods. Specifically, the server regularly collects local weather data and disease occurrence information, and based on that, creates specific advice such as "use pest prevention spray in early spring" and sends it to the terminal.

[0122] Through the above processing steps, the system provides personalized plant care to users and provides comprehensive support for keeping plants healthy.

[0123] (Application example 1)

[0124] 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."

[0125] Conventional plant care systems are specialized in managing plant health, but are unable to address food freshness and quality control. For busy modern people, managing food storage methods and consumption timings is particularly difficult, resulting in food waste. The present invention aims to solve these problems and enable the appropriate management of both plants and food.

[0126] 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.

[0127] In this invention, the server includes [means for acquiring images], [means for analyzing the acquired images to evaluate the health of the plant], and [means for inputting data related to the growth environment. This enables plant health management. In addition, by including [means for generating a care plan based on the analysis results and growth environment data], [means for sending reminders in accordance with the generated care plan], and [means for providing preventive measures and treatments for seasonal changes and plant-specific diseases], the efficiency of plant care is improved. Furthermore, by adding [means for analyzing the freshness and quality of food and suggesting storage methods] and [means for sending reminders based on the storage method], it becomes possible to manage the freshness of food and ensure its appropriate storage and consumption, which is expected to reduce food waste.

[0128] "Means for acquiring images" is a function that allows a user to take images of plants or food with a camera and send the images to the server.

[0129] The "means for analyzing acquired images to evaluate the health of plants" refers to a function that analyzes images received by the server and diagnoses the health of the plant based on the color and shape of the leaves, the presence or absence of disease spots, etc.

[0130] The "means for inputting data on the growing environment" is a function that allows the user to input information on the growing environment, such as the type of plant, installation location, hours of sunlight, temperature, and humidity, and send it to the server.

[0131] "Means for generating a care plan based on the analysis results and growth environment data" is a function that enables the server to create a care plan optimal for maintaining the health of plants based on the image analysis results and the input growth environment data.

[0132] The "means for sending a reminder in accordance with the generated care plan" is a function in which the server sends a reminder to the user at an appropriate time based on the generated care plan.

[0133] "Means for providing preventive measures and remedies for seasonal changes and plant-specific diseases" is a function in which the server generates preventive measures and remedies according to seasonal changes and plant-specific diseases and notifies the user.

[0134] "Means for analyzing food freshness and quality and suggesting storage methods" is a function that analyzes photographed images of food, evaluates its freshness and quality, and suggests the optimal storage method.

[0135] The "means for sending a reminder based on the storage method" is a function for sending a reminder to the user at an appropriate time based on the proposed storage method.

[0136] This invention is a system for effectively managing plants and food. The system captures images of plants and food ingredients using a camera on a user device and evaluates their health and freshness through image analysis. It then generates a personalized care plan or storage method based on the growth or storage environment data entered by the user, and sends appropriate reminders.

[0137] Hardware and Software Configuration

[0138] This system is implemented using the following hardware and software.

[0139] 1. User device: A mobile device with a camera function, such as a smartphone or tablet, that has an interface that allows users to take images of plants or food and provide input data.

[0140] 2. Server: A computer device with high-performance data processing capabilities that receives images and data sent from user devices, analyzes them, and generates care plans.

[0141] 3. Image analysis module: Software that uses image processing libraries (e.g., OpenCV, TensorFlow) to assess the health of plants or the freshness of food from images.

[0142] 4. Database: A database that manages the status of plants and food for each user, generated care plans, and reminder information.

[0143] 5. Communication module: Internet communication function for transferring data between user terminals and servers.

[0144] Operation explanation

[0145] 1. Image acquisition and transmission:

[0146] Users take pictures of plants or food using a smartphone application, which temporarily stores the images and prepares them for transmission to the server.

[0147] 2. Image Analysis:

[0148] The server uses an image analysis module to evaluate the health of plants and the freshness of food based on the received images, analyzing characteristics such as leaf color, shape, and the presence or absence of disease spots.

[0149] 3. Enter environmental data:

[0150] Within the app, users input data on the growing environment, such as the type of plant, installation location, sunlight hours, temperature, and humidity, as well as the type of food, purchase date, storage location, and storage temperature, etc. This data is sent to the server.

[0151] 4. Generate a care plan and save it:

[0152] Based on the results of image analysis and data entered by the user, the server generates optimal care plans for maintaining plant health and food storage methods, including watering frequency, appropriate fertilizer type and amount, and disease prevention measures, as well as suggestions for refrigeration and freezing methods and cooking and consumption timing.

[0153] 5. Send reminders:

[0154] The server sends reminders to the device at appropriate times based on the generated care plan and storage method, so that the user does not miss important care or consumption opportunities.

[0155] 6. Providing preventative and remedial measures:

[0156] Depending on the change of seasons and the time when certain plant diseases are more likely to occur, the server generates preventive measures and treatments and notifies the user.

[0157] Specific examples

[0158] For example, let's say a user is growing a houseplant. Every morning, the user opens the app and takes a photo of the plant, and the device sends the image to the server. The server analyzes the plant's health and detects that some of the leaves are yellowing, suggesting a lack of water. When the user inputs data such as the plant's location and sunlight hours, the server generates a care plan for watering the plant once a week and liquid fertilizing it once a month. Based on this care plan, the server sends a watering reminder every Wednesday morning at 8:00 a.m. The server also sends advice on pest prevention as the seasons change.

[0159] In the case of food management, a user takes a picture of an apple, and the server analyzes it, resulting in a freshness score of 70. In this case, the system suggests storing the apple in the refrigerator for five days, and sends a reminder when the expiration date approaches. An example of a specific prompt is, "Store the apple with a freshness score of 70 in the refrigerator and set a reminder to consume it in five days."

[0160] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0161] Step 1:

[0162] A user launches a smartphone application and takes a picture of a plant or food with the camera. The input data is the plant or food image taken with the camera, and the output is a temporarily saved image file. The specific operation is to capture the image using the camera function and temporarily save it in local storage.

[0163] Step 2:

[0164] The terminal sends the temporarily saved image to the server. The input data is the image file saved on the user terminal, and the output is the image data transferred to the server. The specific operation is to upload the image file to the server via network communication.

[0165] Step 3:

[0166] The server launches an image analysis module to analyze the images it receives. The input data is the image file received by the server, and the output is the evaluation results for the health of plants and the freshness of food. Specifically, it uses libraries such as OpenCV and TensorFlow to extract features from the images and apply analysis algorithms to make an evaluation.

[0167] Step 4:

[0168] The user inputs data on the plant's growing environment or food storage environment within the app. The input data includes the type of plant, installation location, sunlight hours, temperature, humidity, type of food, purchase date, storage location, storage temperature, etc., and the output is the growing environment data or storage environment data sent to the server. The specific operation is that the user inputs data via the user interface and sends the data to the server.

[0169] Step 5:

[0170] The server generates optimal care plans and food preservation methods for maintaining plant health based on the image analysis results and data entered by the user. The input data is the image analysis results and growth environment data or preservation environment data, and the output is the generated care plans and preservation methods. Specifically, the server uses past data and algorithms stored in the database to calculate and generate optimal plans and methods.

[0171] Step 6:

[0172] The server sends reminders to the user's device at appropriate times based on the generated care plan and storage method. The input data is the generated care plan or storage method and reminder setting information, and the output is the reminder notification sent to the user's device. The specific operation is to set a reminder schedule and send the notification at the set time.

[0173] Step 7:

[0174] The server generates preventive measures and countermeasures according to the change of seasons and the time when a particular plant disease is likely to occur, and notifies the user. The input data includes the season, disease information, plant type, etc., and the output is a notification of the preventive measures and countermeasures sent to the user. The specific operation is to generate appropriate advice based on plant type and seasonal information, and notify the user as necessary.

[0175] 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.

[0176] This invention is a system that combines an emotion engine that recognizes the user's emotions to help plant lovers efficiently care for their plants. The system captures images of plants using a smartphone camera, analyzes the images to evaluate the plant's health, and analyzes the user's emotions using the emotion engine. This generates a personalized care plan and provides reminders based on the user's emotions. It also provides preventative measures and treatments for seasonal changes and plant-specific diseases.

[0177] Program processing overview

[0178] 1. Acquire images of plants

[0179] First, the user opens the app on their smartphone and takes a picture of a plant with their camera. The device temporarily stores the image and prepares it to be sent to the server.

[0180] 2. Analyze the images

[0181] When the device sends the captured image to the server, the server activates the image analysis module. The server evaluates the plant's health based on the received image. Specifically, it extracts features such as leaf color, shape, and the presence or absence of disease spots, and diagnoses whether the plant is healthy or diseased.

[0182] 3. Enter the habitat data

[0183] Within the app, users input information about the plant's growing environment, including the type of plant, its location, sunlight hours, temperature, humidity, etc. The device then sends this data to a server.

[0184] 4. Emotion Recognition by Emotion Engine

[0185] The device takes a picture of the user's face with a camera, and the device or server analyzes the user's emotions using an emotion engine. The analyzed emotion data is also sent to the server.

[0186] 5. Generate a care plan

[0187] The server generates an optimal care plan for maintaining plant health based on the image analysis results, growth environment data, and emotional data. Specifically, the plan includes watering frequency, appropriate fertilizer type and amount, disease prevention measures, etc. The care plan is also adjusted based on the user's emotional state.

[0188] 6. Send a reminder

[0189] The server sets reminders based on the care plan generated by the server. Taking into account the user's daily rhythm and emotional state, the server sends reminders to the device with appropriate timing and content. The device then displays notifications to the user, encouraging them to carry out the care.

[0190] 7. Provide disease prevention measures

[0191] In addition, the server generates preventative and treatment measures for seasonal changes and specific illnesses, and this information is also sent to the device and notified to the user.

[0192] Specific examples

[0193] For example, let's say a user is growing a houseplant. Every day, the user opens the app and takes a photo of the plant. The device sends the captured image to a server, which analyzes the plant's health. The analysis reveals that some of the leaves are yellowing, suggesting a lack of water. The user enters data such as the plant's location (a sunny living room) and the number of hours of sunlight (approximately 6 hours) into the app, and the server takes this information and the analysis results into consideration to generate a care plan that calls for watering the plant once a week and administering liquid fertilizer once a month.

[0194] The device also uses the user's emotion engine to analyze the user's facial expressions when taking a photo. For example, if the emotion engine recognizes that the user looks busy and stressed, the system will simplify the care plan and adjust it to reduce the user's burden.

[0195] Based on this care plan, the server sets a watering reminder every Wednesday morning at 8:00 a.m. and sends it to the device. The user receives the reminder at 8:00 a.m. and waters the plants. The server also sends advice on pest prevention as the seasons change, ensuring the user remembers to perform the necessary care.

[0196] In this way, the system of the present invention personalizes appropriate plant care, allowing even busy users to easily keep their plants healthy. Furthermore, by taking the user's emotions into consideration, it is possible to provide more flexible and effective care plans.

[0197] The processing flow will be explained below.

[0198] Program processing flow

[0199] Step 1:

[0200] The user opens the app on their smartphone. The user selects the camera function and takes a photo of a plant. The device temporarily stores the image and prepares it to be sent to the server.

[0201] Step 2:

[0202] The device sends the stored image data to the server, which then stores the received image data in a database.

[0203] Step 3:

[0204] The server launches the image analysis module. The server applies image processing algorithms to recognize plant parts in the image. The server extracts features such as leaf color, shape, and the presence of disease spots to assess the plant's health.

[0205] Step 4:

[0206] The user enters environmental information such as the type of plant, location, and sunlight hours into a form within the app. The device sends the entered data to the server, which then stores the received environmental data in a database.

[0207] Step 5:

[0208] The device takes a picture of the user's face with a camera. The device or server analyzes the user's emotions using an emotion engine. The analyzed emotion data is also sent to the server.

[0209] Step 6:

[0210] The server creates an optimal care plan based on environmental data, image analysis results, and emotional data. The server then generates specific care instructions, such as the appropriate watering frequency, type of fertilizer, and timing. The care plan is also adjusted based on the user's emotions.

[0211] Step 7:

[0212] The server sends the generated care plan to the terminal, and the terminal notifies the user of the received care plan.

[0213] Step 8:

[0214] The server sets a reminder schedule based on the care plan. The server determines the date, time, and content of the reminder, taking into account the user's daily rhythm and emotional state. The device sets the reminder and notifies the user at the specified date and time.

[0215] Step 9:

[0216] The server analyzes data on seasonal changes and plant-specific diseases. The server generates appropriate disease prevention and treatment methods. The server sends this information to the device. The device notifies the user of the received information.

[0217] Example 2

[0218] 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."

[0219] Plant care is time-consuming, especially for users with busy lifestyles. It is difficult to accurately grasp the health status of plants and continue appropriate care. Furthermore, there is a lack of methods to provide personalized care plans that take the user's emotions into account. Therefore, there is a strong need for a system that evaluates the health status of plants and provides appropriate care based on the user's emotions.

[0220] The identification process by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means. In this invention, the server includes: [means for acquiring video;] [means for analyzing the acquired video to evaluate the health condition of the plant;] [means for inputting data related to the growing environment;] [means for recognizing the user's emotions and acquiring emotion data;] [means for generating a care plan based on the analysis results, the growing environment data, and the emotion data;] [means for sending notifications according to the generated care plan; and [means for providing preventive measures and remedies for seasonal changes and plant-specific diseases.] This makes it possible to efficiently care for plants and provide a personalized care plan based on the user's emotions. Furthermore, by providing appropriate care and preventive measures based on the plant's health condition, the plant can be kept healthy.

[0221] The "means for acquiring images" refers to a means by which a user records images of plants using a photographing device such as a smartphone or tablet.

[0222] The "means for analyzing acquired images to evaluate the health of plants" refers to a means for analyzing recorded images of plants and diagnosing the health of plants based on information such as leaf color, shape, and the presence or absence of disease spots.

[0223] The "means for inputting data on the growing environment" is a means by which the user inputs information on the growing environment, such as the type of plant, installation location, hours of sunlight, temperature, and humidity.

[0224] The "means for recognizing the user's emotions and acquiring emotion data" refers to a means for taking a picture of the user's face with a camera, analyzing the user's emotional state using an emotion engine, and acquiring the data.

[0225] The "means for generating a care plan based on the analysis results, growth environment data, and emotional data" refers to a means for integrating the analysis results of the plant's health condition, data related to the growth environment, and the user's emotional data to generate an optimal plant care plan.

[0226] The "means for sending a notification in accordance with the generated care plan" refers to a means for sending a notification of care plan execution to a user at a specific timing based on the generated care plan.

[0227] The "means for providing preventive measures and remedies for seasonal changes and diseases specific to plants" is a means for providing users with preventive measures and remedies for seasonal changes and diseases specific to specific plants.

[0228] The "means for providing the generated care plan to the terminal" refers to a means for displaying or notifying the contents of the generated care plan on the user's terminal.

[0229] The "means for encrypting the acquired image and transmitting it to the server" is a means for encrypting the acquired image of the plant as a security measure and transmitting it to the server.

[0230] This invention is a system that analyzes the health of plants and the user's emotional state to provide personalized care plans to help plant lovers efficiently care for their plants. This system uses hardware and software such as a mobile device such as a smartphone, a server, and an emotion engine.

[0231] The process begins when a user launches the app on their smartphone and takes a picture of a plant with their camera. The device acquires the image and temporarily stores it. The device then compresses and encrypts the image before sending it to the server.

[0232] The server launches an AI-based image analysis module based on the images it receives. The server analyzes the color, shape, and presence of disease spots on the plant's leaves to assess the plant's health. The assessed information is then recorded in a database.

[0233] Next, the user enters information about the plant's growing environment in the app, including the type of plant, its location, sunlight hours, temperature, humidity, etc. The device also sends this data to the server.

[0234] The device then takes a photo of the user's face with a camera and activates an emotion engine to analyze the user's emotions. The analyzed emotion data is also sent to the server, which then generates a comprehensive care plan based on the results of the image analysis, the growth environment data, and the user's emotion data.

[0235] The generated care plan will suggest specific actions needed to maintain the plant's health, such as watering frequency, the type and amount of fertilizer to use, and disease prevention measures. Furthermore, the care plan's contents are adjusted according to the user's emotional state. For example, if the user is busy and stressed, measures such as reducing the frequency of care will be taken.

[0236] The server sets reminders based on the care plan and sends them to the device. The reminders are sent at times that take into account the user's daily rhythm. The device displays the reminder notification, encouraging the user to perform the care.

[0237] Finally, the server automatically generates preventative and treatment measures for seasonal changes and specific illnesses, and this information is also sent to the device and notified to the user.

[0238] Specific examples

[0239] For example, let's say a user is growing a houseplant. Every day, the user opens the app and takes a photo of the plant. The device compresses and encrypts the captured image and sends it to the server. The server then launches an AI-based image analysis module to analyze the leaves for yellowing or disease spots. Based on the analysis results, it is determined that the plant is lacking water.

[0240] The user enters information such as the installation location (a sunny living room) and the number of hours of sunlight (approximately 6 hours) into a form within the app and submits it. The device then sends this data to the server. The server then uses an emotion recognition engine to analyze the user's emotional state, taking a photo of the user's face and analyzing their emotions. For example, if the user is feeling stressed, the system will simplify the care plan and adjust it to reduce the user's burden.

[0241] Based on the care plan generated by the server, a watering reminder is set every Wednesday morning at 8:00 and sent to the device. The device then displays the reminder to the user at the appropriate time, prompting the user to water the plants. The server also generates pest prevention advice according to the change of seasons and notifies the user.

[0242] Prompt Sentence Examples

[0243] "I want to know if my houseplants look healthy. Please tell me the best way to care for them while taking my feelings into consideration."

[0244] "What is the watering and fertilizing schedule for the houseplants in my sunny living room?"

[0245] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0246] Step 1: Get an image of your plant

[0247] The user opens the smartphone app and takes a picture of a plant with the camera. The device activates the camera module, and the user presses the capture button to capture the image. The input is the plant image taken by the user, and the output is the captured image data. The device temporarily saves this image and prepares to send it to the server.

[0248] Step 2: Send the image to the server

[0249] The device sends the image of the plant that it previously saved to the server. The device compresses the image data and encrypts it for security. The input is the acquired image data, and the output is the compressed and encrypted image data. This is then sent to the server.

[0250] Step 3: Analyze the image on the server

[0251] The server launches an image analysis module and analyzes the received plant images. Specifically, it uses an AI-based image recognition algorithm to analyze the color, shape, and presence of disease spots on the plant leaves. The input is the encrypted and transmitted image data, and the output is plant health data based on the analysis results. This data is stored in a database on the server.

[0252] Step 4: Enter habitat data

[0253] The user enters information about the plant's growing environment into a form within the app. This includes details such as the plant's type, location, sunlight hours, temperature, and humidity. The device then sends this data to the server. The input is the growing environment data entered by the user, and the output is the growing environment data sent to the server.

[0254] Step 5: Photograph the user's face and recognize their emotions

[0255] The device takes a picture of the user's face with a camera and activates the emotion engine to analyze the user's emotions. The input is the user's face image taken with the camera, and the output is the analyzed emotion data. This emotion data is also sent to the server.

[0256] Step 6: Generate a care plan

[0257] The server generates an optimal care plan based on the results of image analysis, growth environment data, and emotional data. Using an AI algorithm, the server determines the frequency of watering, the optimal type and amount of fertilizer, disease prevention measures, etc. The inputs are the results of image analysis, growth environment data, and emotional data, and the output is the generated care plan.

[0258] Step 7: Set and send reminders

[0259] The server sets a reminder based on the care plan generated. When the server completes setting the reminder, it sends a notification to the device. The device displays the reminder to the user at the appropriate time. The input is the generated care plan, and the output is the reminder.

[0260] Step 8: Providing preventative and remedial measures

[0261] The server then generates preventive measures and treatments according to the change of seasons and specific diseases. This information is also sent to the terminal and notified to the user. The input is data about the seasons and diseases, and the output is the generated preventive measures and treatments.

[0262] (Application example 2)

[0263] 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."

[0264] Conventional plant care assistant systems provide uniform care plans without considering the user's emotions, placing a heavy burden on the user and often resulting in ineffective care. Furthermore, the image analysis function for accurately grasping the plant's health status is insufficient, making it difficult to provide appropriate care methods. Furthermore, the inability to provide appropriate countermeasures for seasonal changes and disease prevention is also an issue.

[0265] 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 acquiring images]; [means for analyzing the acquired images to evaluate the health status of the plant]; [means for inputting data related to the growth environment]; [means for generating a care plan based on the analysis results, the growth environment data, and the user's emotional data]; [means for sending reminders according to the generated care plan]; [means for providing preventive measures and remedies for seasonal changes and plant-specific diseases]; and [means for adjusting the care plan according to the user's emotions]. This allows for the provision of a personalized care plan taking the user's emotions into consideration, enabling appropriate plant care. It also makes it possible to accurately grasp the health status of the plant and provide appropriate countermeasures according to the season and disease prevention.

[0266] "Means for acquiring images" refers to the ability of a user to take images of plants using a smartphone or other device.

[0267] "Means for analyzing acquired images to evaluate the health of plants" refers to a technology in which a server analyzes images of plants, extracts characteristics such as leaf color and shape, and the presence or absence of disease spots, and evaluates the health of the plants.

[0268] "Means for inputting data on the growing environment" refers to a function that allows users to input information about the growing environment, such as the type of plant, installation location, hours of sunlight, temperature, and humidity, within the app.

[0269] "Means for generating a care plan based on analysis results, growth environment data, and user emotional data" refers to a technology in which a server generates an optimal plant care plan based on the analysis results of the plant's health, growth environment data, and user emotional data.

[0270] The "means for sending reminders in accordance with the generated care plan" is a technology for sending care reminders to the user at appropriate times based on the care plan generated by the server.

[0271] The "means for providing preventive measures and remedies for seasonal changes and plant-specific diseases" is a technology in which a server generates preventive measures and remedies for seasonal changes and plant-specific diseases and notifies the user.

[0272] The "means for adjusting the care plan according to the user's emotions" is a technology that analyzes the user's emotional data and makes adjustments such as simplifying the care plan when the user is feeling stressed.

[0273] The "means for distributing the generated care plan to the user terminal" is a function for distributing the care plan generated by the server to the user's smartphone or other device.

[0274] The "means for encrypting the acquired user emotion data and image and transmitting them to the server" is a technology for encrypting the acquired user emotion data and plant image and transmitting them securely to the server.

[0275] The present invention provides a system for enabling a user to properly care for plants, the system comprising the following means:

[0276] 1. Image acquisition method:

[0277] Users use their smartphone, smart glasses, or other devices to take images of plants, which are temporarily stored on the device and prepared for transmission to the server.

[0278] 2. Analyzing captured images to assess plant health:

[0279] The server uses an image analysis module (e.g., OpenCV) to analyze the received images. Image analysis extracts features such as leaf color, shape, and the presence or absence of disease spots, and evaluates the plant's health.

[0280] 3. Means for inputting habitat data:

[0281] Through the application, users input information about the plant's growing environment, such as the type of plant, its location, hours of sunlight, temperature, and humidity, which is then sent from the device to the server.

[0282] 4. A method for generating a care plan based on the analysis results, growth environment data, and user emotion data:

[0283] The server generates an optimal plant care plan based on the results of the plant's health analysis, growing environment data, and the user's emotional data (obtained using an emotion analysis engine such as DeepFace). The plan includes information such as watering frequency and type of fertilizer.

[0284] 5. Means of sending reminders according to the generated care plan:

[0285] The server then sends reminders to the user at appropriate times based on the generated care plan, taking into account the user's daily rhythm and emotional state.

[0286] 6. Means of providing preventative and remedial measures for seasonal and plant-specific diseases:

[0287] The server generates and notifies users of preventive and remedial measures for seasonal changes and specific diseases, allowing them to take the necessary care in a timely manner.

[0288] 7. How to adjust care plans based on user emotions:

[0289] The server adjusts the care plan based on the user's emotional data, for example by simplifying it if the user is feeling stressed, thereby reducing the burden on the user.

[0290] As a concrete example, suppose a user is tending a houseplant. The user opens the app and takes a photo of the plant every day. The device sends the captured image to the server, which analyzes the plant's health. The analysis reveals that some of the leaves are yellowing, suggesting a lack of water. The user enters data such as the plant's location (a sunny living room) and the amount of sunlight (approximately six hours) into the app. The server then takes this information and the analysis results into account to generate a care plan for watering the plant once a week and providing liquid fertilizer once a month. The device also uses the user's emotion engine to analyze the user's facial expression when taking the photo. For example, if the emotion engine recognizes that the user appears busy and stressed, the system simplifies the care plan to reduce the user's burden. Based on this care plan, the server sets a watering reminder for every Wednesday at 8:00 a.m. and sends it to the device. The user receives the reminder at 8:00 a.m. and waters the plant. The server also sends pest prevention advice as the seasons change, ensuring the user remembers to perform the necessary care.

[0291] An example of a prompt for the generative AI model is as follows:

[0292] "Generate plant care advice for a cactus plant in a living room with 6 hours of sunshine, 24°C temperature, and 50% humidity. The user is stressed and overwhelmed at work."

[0293] "Analyze the health of your plant and suggest an appropriate care plan. The plant is a Dracaena, the location is the kitchen, the sunshine hours are 7 hours, the temperature is 22 degrees, the humidity is 60%. The user is relaxed."

[0294] In accordance with the above description, the present invention provides a system that provides efficient and personalized plant care for plant lovers.

[0295] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0296] Step 1:

[0297] (Image acquisition)

[0298] Users can take pictures of their plants using the camera on their smartphone or smart glasses, which allows them to visually record the latest status of their plants. The images are temporarily stored on the device.

[0299] Input: A plant image taken by the user

[0300] Output: Plant image data temporarily saved on the device

[0301] Step 2:

[0302] (Image sending)

[0303] The device sends the stored plant images to the server, where they are encrypted for privacy purposes.

[0304] Input: Plant image data stored on the device

[0305] Output: Encrypted image data sent to the server

[0306] Step 3:

[0307] (Image analysis)

[0308] The server uses an image analysis module (e.g., OpenCV) to analyze the images of the plants sent to it, extracting features such as leaf color, shape, and the presence or absence of disease spots to assess the plant's health.

[0309] Input: Encrypted image data sent to the server

[0310] Output: Plant health assessment data

[0311] Step 4:

[0312] (Growth environment data input)

[0313] Users enter information about the plant's growing environment, such as the type of plant, its location, sunlight hours, temperature, humidity, etc., into the app, and this data is sent from the device to the server.

[0314] Input: User-entered data about the habitat

[0315] Output: Habitat data sent to the server

[0316] Step 5:

[0317] (Emotion data acquisition)

[0318] To perform emotion analysis, a user takes a facial image using a camera on their smartphone or smart glasses. The device or server then analyzes the user's emotions using an emotion analysis engine such as DeepFace and sends the data to the server.

[0319] Input: A face image taken by the user

[0320] Output: Sentiment analysis data sent to the server

[0321] Step 6:

[0322] (Care plan generation)

[0323] The server generates an optimal plant care plan based on the results of plant image analysis, growing environment data, and user emotional data, including watering frequency, appropriate type and amount of fertilizer, and disease prevention measures.

[0324] Input: Plant health assessment data, growth environment data, emotion analysis data

[0325] Output: Generated care plan

[0326] Step 7:

[0327] (Reminder sent)

[0328] Based on the care plan generated by the server, care reminders are sent to the user at appropriate times, taking into account the user's daily rhythm and emotional state.

[0329] Input: Generated Care Plan

[0330] Output: Reminder notification sent to device

[0331] Step 8:

[0332] (Providing preventive measures)

[0333] The server generates and notifies users of preventive and remedial measures for seasonal changes and plant-specific diseases, allowing them to take the necessary care in a timely manner.

[0334] Input: Seasonal information, plant-specific disease data

[0335] Output: Preventive measures and action notifications sent to the device

[0336] Step 9:

[0337] (Adjusting the care plan)

[0338] The server adjusts the care plan based on the user's emotional data, for example simplifying the care plan if the user is feeling stressed.

[0339] Input: User sentiment analysis data

[0340] Output: Coordinated care plan

[0341] The above are the specific processing steps of the system that realizes the application example, which allows users to perform efficient and personalized plant care.

[0342] 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.

[0343] 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.

[0344] 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.

[0345] [Second embodiment]

[0346] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0347] 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.

[0348] 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).

[0349] 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.

[0350] 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.

[0351] 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).

[0352] 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.

[0353] 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.

[0354] 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.

[0355] 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.

[0356] 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.

[0357] 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."

[0358] The present invention is a system that allows plant lovers to effectively care for their plants. The system uses a smartphone camera to capture images of plants and analyzes those images to assess the plant's health. The system also uses user input data about the plant's growing environment, allowing the system to generate a personalized care plan based on the analysis results and the growing environment data. The system also sends reminders to the user based on the care plan, providing preventative and treatment measures for seasonal changes and plant-specific diseases.

[0359] Program processing overview

[0360] 1. Acquire images of plants

[0361] First, the user opens the app on their smartphone and takes a picture of a plant with their camera. The device temporarily stores the image and prepares it to be sent to the server.

[0362] 2. Analyze the images

[0363] When the device sends the captured image to the server, the server activates the image analysis module. The server evaluates the plant's health based on the received image. Specifically, it extracts features such as leaf color, shape, and the presence or absence of disease spots, and diagnoses whether the plant is healthy or diseased.

[0364] 3. Enter the habitat data

[0365] Within the app, users input information about the plant's growing environment, including the type of plant, its location, sunlight hours, temperature, humidity, etc. The device then sends this data to a server.

[0366] 4. Generate a care plan

[0367] Based on the image analysis results and the growing environment data entered by the user, the server generates an optimal care plan for maintaining the health of the plant, including watering frequency, the appropriate type and amount of fertilizer, disease prevention measures, and more.

[0368] 5. Send a reminder

[0369] The server sets reminders based on the care plan generated by the server. Taking into account the user's daily rhythm, the server sends reminders to the device at appropriate times. The device then displays notifications to the user, encouraging them to carry out the care.

[0370] 6. Provide disease prevention measures

[0371] In addition, the server generates preventative and treatment measures for seasonal changes and specific illnesses, and this information is also sent to the device and notified to the user.

[0372] Specific examples

[0373] For example, let's say a user is growing a houseplant. Every morning, the user opens the app and takes a photo of the plant. The device sends the image to a server, which analyzes the plant's health. The analysis reveals that some of the leaves are yellowing, suggesting a lack of water. The user enters data such as the plant's location (a sunny living room) and the number of hours of sunlight (approximately 6 hours) into the app, and the server takes this information and the analysis results into consideration to generate a care plan that calls for watering the plant once a week and administering liquid fertilizer once a month.

[0374] Based on this care plan, the server sets a watering reminder every Wednesday morning at 8:00 a.m. and sends it to the device. The user receives the reminder at 8:00 a.m. and waters the plants. The server also sends advice on pest prevention as the seasons change, ensuring the user remembers to perform the necessary care.

[0375] In this way, the system of the present invention personalizes proper plant care, making it easy for even busy users to keep their plants healthy.

[0376] The processing flow will be explained below.

[0377] Program processing flow

[0378] Step 1:

[0379] The user opens the app on their smartphone. The user selects the camera function and takes a photo of a plant. The device temporarily stores the image and prepares it to be sent to the server.

[0380] Step 2:

[0381] The device sends the stored image data to the server, which then stores the received image data in a database.

[0382] Step 3:

[0383] The server launches the image analysis module. The server applies image processing algorithms to recognize plant parts in the image. The server extracts features such as leaf color, shape, and the presence of disease spots to assess the plant's health.

[0384] Step 4:

[0385] The user enters environmental information such as the type of plant, location, and sunlight hours into a form within the app. The device sends the entered data to the server, which then stores the received environmental data in a database.

[0386] Step 5:

[0387] The server creates an optimal care plan based on environmental data and image analysis results. The server generates specific care instructions, such as the appropriate watering frequency, type of fertilizer, and timing. The server sends the created care plan to the device. The device notifies the user of the received care plan.

[0388] Step 6:

[0389] The server sets the reminder schedule based on the care plan. The server determines the date and time of the reminder, taking into account the user's daily routine. The device sets the reminder and sends a notification at the specified date and time.

[0390] Step 7:

[0391] The server analyzes data on seasonal changes and plant-specific diseases. The server generates appropriate disease prevention and treatment methods. The server sends this information to the device. The device notifies the user of the received information.

[0392] Example 1

[0393] 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."

[0394] Conventional plant care systems have struggled to properly assess the health of plants and provide optimal care plans for individual plants. Users often forget to care for their plants in their busy daily lives, making it difficult to maintain their plants' health. Furthermore, there were no systems that accurately provided preventive measures and treatments for seasonal and plant-specific diseases.

[0395] 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.

[0396] In this invention, the server includes a means for capturing images of plants using a photography function, a means for analyzing the captured images to evaluate the health of the plants, and a means for inputting information about the growing environment. This allows a user to capture images of plants and analyze the images to accurately evaluate their health. A means for generating a care plan based on the analysis results and growing environment information can provide an optimal care plan for each individual plant. A means for sending reminders according to the generated care plan ensures that the user does not forget to care for the plants. Furthermore, a means for providing preventive measures and treatments for seasonal changes and plant-specific diseases provides comprehensive care to keep the plants healthy.

[0397] The "photography function" is a function that allows a user to take an image of a plant using the smartphone camera.

[0398] "Analyzing images to evaluate the health of plants" refers to the process of extracting characteristics such as leaf color, shape, and the presence or absence of disease spots from acquired plant images, and diagnosing the health of the plant.

[0399] "Entering information about the growing environment" refers to the act of the user entering data such as the type of plant, installation location, hours of sunlight, temperature, and humidity into the app.

[0400] "Generating a care plan based on the analysis results and growth environment information" refers to the process of creating a care plan suitable for maintaining the health of plants based on the results of image analysis and growth environment information entered by the user.

[0401] "Sending a reminder according to a care plan" refers to the act of sending a notification to a user to perform care at an appropriate time based on the generated care plan.

[0402] "Providing preventive measures and treatments for seasonal changes and plant-specific diseases" is a process of guiding users to specific preventive measures and treatments to deal with seasonal climate changes and plant-specific diseases.

[0403] This invention is a system that allows plant lovers to effectively care for their plants. The system uses a smartphone camera to capture images of plants and analyzes those images to assess the plant's health. The system also uses user input information about the plant's growing environment. Based on the analysis results and the growing environment information, the system generates a personalized care plan and sends reminders according to the plan. It also provides preventative measures and treatments for seasonal changes and plant-specific diseases.

[0404] Specifically, the following hardware and software are used.

[0405] Hardware and software used

[0406] 1. Smartphone: A mobile information device held by a user.

[0407] 2. Camera: The built-in photography function of a smartphone.

[0408] 3. Server: A computer with high processing power that performs image analysis and data storage.

[0409] 4. Application software: Apps installed on smartphones.

[0410] 5. Image analysis module: Uses libraries such as Python, OpenCV, and TensorFlow.

[0411] 6. Database: Storage for habitat information and care plans.

[0412] Operation procedures and examples

[0413] Acquire images of plants

[0414] The user launches the app on their smartphone and uses the camera to take a picture of the plant. The device temporarily stores the image and prepares to send it to the server.

[0415] Analyzing the image

[0416] When the device sends the captured image to the server, the server launches an image analysis module and evaluates the plant's health based on the received image. For example, OpenCV is used to extract leaf color, shape, and the presence or absence of disease spots, and TensorFlow is used to diagnose the plant's health.

[0417] Entering the habitat data

[0418] The user enters information about the plant's growing environment in the app, including the type of plant, its location, sunlight hours, temperature, humidity, etc. The device converts this data into JSON format and sends it to the server.

[0419] Generate a care plan

[0420] The server generates an optimal care plan based on the image analysis results and growing environment information, for example, by using a database knowledge base and machine learning algorithms to determine the optimal watering frequency and type and amount of fertilizer for the user's plants.

[0421] Send a reminder

[0422] The server sets reminders based on the care plan generated by the server. Taking into account the user's daily rhythm, the server sends reminders to the device at appropriate times. The device then notifies the user of the reminders and encourages them to care for their plants.

[0423] Providing disease prevention measures

[0424] Additionally, the server generates preventative measures and treatments for seasonal changes and specific illnesses, which are then sent to the device and notified to the user.

[0425] Specific operation example

[0426] For example, consider a user who is caring for a houseplant. Every morning, the user opens the app and takes a photo of the plant. The device sends the captured image to the server, which analyzes the plant's health. The analysis reveals that some of the leaves are yellowing, suggesting a lack of water. The user inputs data such as the plant's location (a sunny living room) and the amount of sunlight (approximately 6 hours), and the server generates a care plan for watering once a week and liquid fertilizer once a month. Based on this care plan, the server sets a watering reminder every Wednesday at 8:00 a.m. and sends it to the device. The user receives the reminder at 8:00 a.m. and waters the plant. The server also sends pest prevention measures in early spring, helping the user remember to take care of the plant.

[0427] Prompt Sentence Examples

[0428] "After a user opens the app on their smartphone and takes a picture of their houseplant, the server analyzes the image and evaluates the plant's health. Please also explain in detail how the system generates an optimal care plan and sends reminders based on growing environment information entered by the user, such as the installation location and sunlight hours. Please also emphasize that seasonal disease prevention measures are also provided."

[0429] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0430] Step 1:

[0431] The user launches the smartphone app and takes a picture of the plant using the camera function. The device temporarily saves this image in its internal storage. At this stage, the input is the captured image, and the output is the temporarily saved image file. Specifically, the user taps the camera button in the app, frames the entire plant, and presses the shutter button.

[0432] Step 2:

[0433] The device retrieves the temporarily saved image and sends it to the server. Here, the input is the temporarily saved image file, and the output is the image data sent to the server. Specifically, the device checks the network connection and uses an HTTP request to upload the image file to the specified endpoint on the server.

[0434] Step 3:

[0435] The server analyzes the received image data. It launches an image analysis module and uses OpenCV and TensorFlow to extract features such as leaf color, shape, and the presence or absence of disease spots. The input is the image data sent to the server, and the output is an assessment result regarding the plant's health. Specifically, the server reads the image data, analyzes the pigment information of each pixel, and evaluates the health by detecting specific patterns.

[0436] Step 4:

[0437] The user enters information about the plant's growing environment in the app. This includes the type of plant, location, sunlight hours, temperature, humidity, etc. The device converts this data into JSON format and sends it to the server. The input is the growing environment information entered by the user, and the output is the JSON data sent to the server. Specifically, the user manually enters the required information into the app's input form and clicks the "Submit" button.

[0438] Step 5:

[0439] The server receives the image analysis results and growing environment information and generates an optimal care plan. Using the database's knowledge base and machine learning algorithms, the care plan is created based on the analysis results and input information. The input is the image analysis results and growing environment information, and the output is the generated care plan. The server calculates this and creates a plan that includes specific watering frequency, type and amount of fertilizer, and disease prevention measures.

[0440] Step 6:

[0441] Reminders are set based on the care plan generated by the server. Reminders are sent to the device at appropriate times, taking into account the user's daily rhythm. The input is the generated care plan, and the output is the reminder notification sent to the device. Specifically, the server takes into account the user's time zone settings and sets a reminder, for example, "Water the plants every Wednesday at 8am," and sends a push notification to the device.

[0442] Step 7:

[0443] The server generates preventive measures and treatment methods according to seasonal changes and specific diseases. This information is also sent to the terminal and notified to the user. The input is external seasonal information and plant condition information, and the output is the generated preventive measures and treatment methods. Specifically, the server regularly collects local weather data and disease occurrence information, and based on that, creates specific advice such as "use pest prevention spray in early spring" and sends it to the terminal.

[0444] Through the above processing steps, the system provides personalized plant care to users and provides comprehensive support for keeping plants healthy.

[0445] (Application example 1)

[0446] 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."

[0447] Conventional plant care systems are specialized in managing plant health, but are unable to address food freshness and quality control. For busy modern people, managing food storage methods and consumption timings is particularly difficult, resulting in food waste. The present invention aims to solve these problems and enable the appropriate management of both plants and food.

[0448] 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.

[0449] In this invention, the server includes [means for acquiring images], [means for analyzing the acquired images to evaluate the health of the plant], and [means for inputting data related to the growth environment. This enables plant health management. In addition, by including [means for generating a care plan based on the analysis results and growth environment data], [means for sending reminders in accordance with the generated care plan], and [means for providing preventive measures and treatments for seasonal changes and plant-specific diseases], the efficiency of plant care is improved. Furthermore, by adding [means for analyzing the freshness and quality of food and suggesting storage methods] and [means for sending reminders based on the storage method], it becomes possible to manage the freshness of food and ensure its appropriate storage and consumption, which is expected to reduce food waste.

[0450] "Means for acquiring images" is a function that allows a user to take images of plants or food with a camera and send the images to the server.

[0451] The "means for analyzing acquired images to evaluate the health of plants" refers to a function that analyzes images received by the server and diagnoses the health of the plant based on the color and shape of the leaves, the presence or absence of disease spots, etc.

[0452] The "means for inputting data on the growing environment" is a function that allows the user to input information on the growing environment, such as the type of plant, installation location, hours of sunlight, temperature, and humidity, and send it to the server.

[0453] "Means for generating a care plan based on the analysis results and growth environment data" is a function that enables the server to create a care plan optimal for maintaining the health of plants based on the image analysis results and the input growth environment data.

[0454] The "means for sending a reminder in accordance with the generated care plan" is a function in which the server sends a reminder to the user at an appropriate time based on the generated care plan.

[0455] "Means for providing preventive measures and remedies for seasonal changes and plant-specific diseases" is a function in which the server generates preventive measures and remedies according to seasonal changes and plant-specific diseases and notifies the user.

[0456] "Means for analyzing food freshness and quality and suggesting storage methods" is a function that analyzes photographed images of food, evaluates its freshness and quality, and suggests the optimal storage method.

[0457] The "means for sending a reminder based on the storage method" is a function for sending a reminder to the user at an appropriate time based on the proposed storage method.

[0458] This invention is a system for effectively managing plants and food. The system captures images of plants and food ingredients using a camera on a user device and evaluates their health and freshness through image analysis. It then generates a personalized care plan or storage method based on the growth or storage environment data entered by the user, and sends appropriate reminders.

[0459] Hardware and Software Configuration

[0460] This system is implemented using the following hardware and software.

[0461] 1. User device: A mobile device with a camera function, such as a smartphone or tablet, that has an interface that allows users to take images of plants or food and provide input data.

[0462] 2. Server: A computer device with high-performance data processing capabilities that receives images and data sent from user devices, analyzes them, and generates care plans.

[0463] 3. Image analysis module: Software that uses image processing libraries (e.g., OpenCV, TensorFlow) to assess the health of plants or the freshness of food from images.

[0464] 4. Database: A database that manages the status of plants and food for each user, generated care plans, and reminder information.

[0465] 5. Communication module: Internet communication function for transferring data between user terminals and servers.

[0466] Operation explanation

[0467] 1. Image acquisition and transmission:

[0468] Users take pictures of plants or food using a smartphone application, which temporarily stores the images and prepares them for transmission to the server.

[0469] 2. Image Analysis:

[0470] The server uses an image analysis module to evaluate the health of plants and the freshness of food based on the received images, analyzing characteristics such as leaf color, shape, and the presence or absence of disease spots.

[0471] 3. Enter environmental data:

[0472] Within the app, users input data on the growing environment, such as the type of plant, installation location, sunlight hours, temperature, and humidity, as well as the type of food, purchase date, storage location, and storage temperature, etc. This data is sent to the server.

[0473] 4. Generate a care plan and save it:

[0474] Based on the results of image analysis and data entered by the user, the server generates optimal care plans for maintaining plant health and food storage methods, including watering frequency, appropriate fertilizer type and amount, and disease prevention measures, as well as suggestions for refrigeration and freezing methods and cooking and consumption timing.

[0475] 5. Send reminders:

[0476] The server sends reminders to the device at appropriate times based on the generated care plan and storage method, so that the user does not miss important care or consumption opportunities.

[0477] 6. Providing preventative and remedial measures:

[0478] Depending on the change of seasons and the time when certain plant diseases are more likely to occur, the server generates preventive measures and treatments and notifies the user.

[0479] Specific examples

[0480] For example, let's say a user is growing a houseplant. Every morning, the user opens the app and takes a photo of the plant, and the device sends the image to the server. The server analyzes the plant's health and detects that some of the leaves are yellowing, suggesting a lack of water. When the user inputs data such as the plant's location and sunlight hours, the server generates a care plan for watering the plant once a week and liquid fertilizing it once a month. Based on this care plan, the server sends a watering reminder every Wednesday morning at 8:00 a.m. The server also sends advice on pest prevention as the seasons change.

[0481] In the case of food management, a user takes a picture of an apple, and the server analyzes it, resulting in a freshness score of 70. In this case, the system suggests storing the apple in the refrigerator for five days, and sends a reminder when the expiration date approaches. An example of a specific prompt is, "Store the apple with a freshness score of 70 in the refrigerator and set a reminder to consume it in five days."

[0482] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0483] Step 1:

[0484] A user launches a smartphone application and takes a picture of a plant or food with the camera. The input data is the plant or food image taken with the camera, and the output is a temporarily saved image file. The specific operation is to capture the image using the camera function and temporarily save it in local storage.

[0485] Step 2:

[0486] The terminal sends the temporarily saved image to the server. The input data is the image file saved on the user terminal, and the output is the image data transferred to the server. The specific operation is to upload the image file to the server via network communication.

[0487] Step 3:

[0488] The server launches an image analysis module to analyze the images it receives. The input data is the image file received by the server, and the output is the evaluation results for the health of plants and the freshness of food. Specifically, it uses libraries such as OpenCV and TensorFlow to extract features from the images and apply analysis algorithms to make an evaluation.

[0489] Step 4:

[0490] The user inputs data on the plant's growing environment or food storage environment within the app. The input data includes the type of plant, installation location, sunlight hours, temperature, humidity, type of food, purchase date, storage location, storage temperature, etc., and the output is the growing environment data or storage environment data sent to the server. The specific operation is that the user inputs data via the user interface and sends the data to the server.

[0491] Step 5:

[0492] The server generates optimal care plans and food preservation methods for maintaining plant health based on the image analysis results and data entered by the user. The input data is the image analysis results and growth environment data or preservation environment data, and the output is the generated care plans and preservation methods. Specifically, the server uses past data and algorithms stored in the database to calculate and generate optimal plans and methods.

[0493] Step 6:

[0494] The server sends reminders to the user's device at appropriate times based on the generated care plan and storage method. The input data is the generated care plan or storage method and reminder setting information, and the output is the reminder notification sent to the user's device. The specific operation is to set a reminder schedule and send the notification at the set time.

[0495] Step 7:

[0496] The server generates preventive measures and countermeasures according to the change of seasons and the time when a particular plant disease is likely to occur, and notifies the user. The input data includes the season, disease information, plant type, etc., and the output is a notification of the preventive measures and countermeasures sent to the user. The specific operation is to generate appropriate advice based on plant type and seasonal information, and notify the user as necessary.

[0497] 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.

[0498] This invention is a system that combines an emotion engine that recognizes the user's emotions to help plant lovers efficiently care for their plants. The system captures images of plants using a smartphone camera, analyzes the images to evaluate the plant's health, and analyzes the user's emotions using the emotion engine. This generates a personalized care plan and provides reminders based on the user's emotions. It also provides preventative measures and treatments for seasonal changes and plant-specific diseases.

[0499] Program processing overview

[0500] 1. Acquire images of plants

[0501] First, the user opens the app on their smartphone and takes a picture of a plant with their camera. The device temporarily stores the image and prepares it to be sent to the server.

[0502] 2. Analyze the images

[0503] When the device sends the captured image to the server, the server activates the image analysis module. The server evaluates the plant's health based on the received image. Specifically, it extracts features such as leaf color, shape, and the presence or absence of disease spots, and diagnoses whether the plant is healthy or diseased.

[0504] 3. Enter the habitat data

[0505] Within the app, users input information about the plant's growing environment, including the type of plant, its location, sunlight hours, temperature, humidity, etc. The device then sends this data to a server.

[0506] 4. Emotion Recognition by Emotion Engine

[0507] The device takes a picture of the user's face with a camera, and the device or server analyzes the user's emotions using an emotion engine. The analyzed emotion data is also sent to the server.

[0508] 5. Generate a care plan

[0509] The server generates an optimal care plan for maintaining plant health based on the image analysis results, growth environment data, and emotional data. Specifically, the plan includes watering frequency, appropriate fertilizer type and amount, disease prevention measures, etc. The care plan is also adjusted based on the user's emotional state.

[0510] 6. Send a reminder

[0511] The server sets reminders based on the care plan generated by the server. Taking into account the user's daily rhythm and emotional state, the server sends reminders to the device with appropriate timing and content. The device then displays notifications to the user, encouraging them to carry out the care.

[0512] 7. Provide disease prevention measures

[0513] In addition, the server generates preventative and treatment measures for seasonal changes and specific illnesses, and this information is also sent to the device and notified to the user.

[0514] Specific examples

[0515] For example, let's say a user is growing a houseplant. Every day, the user opens the app and takes a photo of the plant. The device sends the captured image to a server, which analyzes the plant's health. The analysis reveals that some of the leaves are yellowing, suggesting a lack of water. The user enters data such as the plant's location (a sunny living room) and the number of hours of sunlight (approximately 6 hours) into the app, and the server takes this information and the analysis results into consideration to generate a care plan that calls for watering the plant once a week and administering liquid fertilizer once a month.

[0516] The device also uses the user's emotion engine to analyze the user's facial expressions when taking a photo. For example, if the emotion engine recognizes that the user looks busy and stressed, the system will simplify the care plan and adjust it to reduce the user's burden.

[0517] Based on this care plan, the server sets a watering reminder every Wednesday morning at 8:00 a.m. and sends it to the device. The user receives the reminder at 8:00 a.m. and waters the plants. The server also sends advice on pest prevention as the seasons change, ensuring the user remembers to perform the necessary care.

[0518] In this way, the system of the present invention personalizes appropriate plant care, allowing even busy users to easily keep their plants healthy. Furthermore, by taking the user's emotions into consideration, it is possible to provide more flexible and effective care plans.

[0519] The processing flow will be explained below.

[0520] Program processing flow

[0521] Step 1:

[0522] The user opens the app on their smartphone. The user selects the camera function and takes a photo of a plant. The device temporarily stores the image and prepares it to be sent to the server.

[0523] Step 2:

[0524] The device sends the stored image data to the server, which then stores the received image data in a database.

[0525] Step 3:

[0526] The server launches the image analysis module. The server applies image processing algorithms to recognize plant parts in the image. The server extracts features such as leaf color, shape, and the presence of disease spots to assess the plant's health.

[0527] Step 4:

[0528] The user enters environmental information such as the type of plant, location, and sunlight hours into a form within the app. The device sends the entered data to the server, which then stores the received environmental data in a database.

[0529] Step 5:

[0530] The device takes a picture of the user's face with a camera. The device or server analyzes the user's emotions using an emotion engine. The analyzed emotion data is also sent to the server.

[0531] Step 6:

[0532] The server creates an optimal care plan based on environmental data, image analysis results, and emotional data. The server then generates specific care instructions, such as the appropriate watering frequency, type of fertilizer, and timing. The care plan is also adjusted based on the user's emotions.

[0533] Step 7:

[0534] The server sends the generated care plan to the terminal, and the terminal notifies the user of the received care plan.

[0535] Step 8:

[0536] The server sets a reminder schedule based on the care plan. The server determines the date, time, and content of the reminder, taking into account the user's daily rhythm and emotional state. The device sets the reminder and notifies the user at the specified date and time.

[0537] Step 9:

[0538] The server analyzes data on seasonal changes and plant-specific diseases. The server generates appropriate disease prevention and treatment methods. The server sends this information to the device. The device notifies the user of the received information.

[0539] Example 2

[0540] 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."

[0541] Plant care is time-consuming, especially for users with busy lifestyles. It is difficult to accurately grasp the health status of plants and continue appropriate care. Furthermore, there is a lack of methods to provide personalized care plans that take the user's emotions into account. Therefore, there is a strong need for a system that evaluates the health status of plants and provides appropriate care based on the user's emotions.

[0542] The identification process by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means. In this invention, the server includes: [means for acquiring video;] [means for analyzing the acquired video to evaluate the health condition of the plant;] [means for inputting data related to the growing environment;] [means for recognizing the user's emotions and acquiring emotion data;] [means for generating a care plan based on the analysis results, the growing environment data, and the emotion data;] [means for sending notifications according to the generated care plan; and [means for providing preventive measures and remedies for seasonal changes and plant-specific diseases.] This makes it possible to efficiently care for plants and provide a personalized care plan based on the user's emotions. Furthermore, by providing appropriate care and preventive measures based on the plant's health condition, the plant can be kept healthy.

[0543] The "means for acquiring images" refers to a means by which a user records images of plants using a photographing device such as a smartphone or tablet.

[0544] The "means for analyzing acquired images to evaluate the health of plants" refers to a means for analyzing recorded images of plants and diagnosing the health of plants based on information such as leaf color, shape, and the presence or absence of disease spots.

[0545] The "means for inputting data on the growing environment" is a means by which the user inputs information on the growing environment, such as the type of plant, installation location, hours of sunlight, temperature, and humidity.

[0546] The "means for recognizing the user's emotions and acquiring emotion data" refers to a means for taking a picture of the user's face with a camera, analyzing the user's emotional state using an emotion engine, and acquiring the data.

[0547] The "means for generating a care plan based on the analysis results, growth environment data, and emotional data" refers to a means for integrating the analysis results of the plant's health condition, data related to the growth environment, and the user's emotional data to generate an optimal plant care plan.

[0548] The "means for sending a notification in accordance with the generated care plan" refers to a means for sending a notification of care plan execution to a user at a specific timing based on the generated care plan.

[0549] The "means for providing preventive measures and remedies for seasonal changes and diseases specific to plants" is a means for providing users with preventive measures and remedies for seasonal changes and diseases specific to specific plants.

[0550] The "means for providing the generated care plan to the terminal" refers to a means for displaying or notifying the contents of the generated care plan on the user's terminal.

[0551] The "means for encrypting the acquired image and transmitting it to the server" is a means for encrypting the acquired image of the plant as a security measure and transmitting it to the server.

[0552] This invention is a system that analyzes the health of plants and the user's emotional state to provide personalized care plans to help plant lovers efficiently care for their plants. This system uses hardware and software such as a mobile device such as a smartphone, a server, and an emotion engine.

[0553] The process begins when a user launches the app on their smartphone and takes a picture of a plant with their camera. The device acquires the image and temporarily stores it. The device then compresses and encrypts the image before sending it to the server.

[0554] The server launches an AI-based image analysis module based on the images it receives. The server analyzes the color, shape, and presence of disease spots on the plant's leaves to assess the plant's health. The assessed information is then recorded in a database.

[0555] Next, the user enters information about the plant's growing environment in the app, including the type of plant, its location, sunlight hours, temperature, humidity, etc. The device also sends this data to the server.

[0556] The device then takes a photo of the user's face with a camera and activates an emotion engine to analyze the user's emotions. The analyzed emotion data is also sent to the server, which then generates a comprehensive care plan based on the results of the image analysis, the growth environment data, and the user's emotion data.

[0557] The generated care plan will suggest specific actions needed to maintain the plant's health, such as watering frequency, the type and amount of fertilizer to use, and disease prevention measures. Furthermore, the care plan's contents are adjusted according to the user's emotional state. For example, if the user is busy and stressed, measures such as reducing the frequency of care will be taken.

[0558] The server sets reminders based on the care plan and sends them to the device. The reminders are sent at times that take into account the user's daily rhythm. The device displays the reminder notification, encouraging the user to perform the care.

[0559] Finally, the server automatically generates preventative and treatment measures for seasonal changes and specific illnesses, and this information is also sent to the device and notified to the user.

[0560] Specific examples

[0561] For example, let's say a user is growing a houseplant. Every day, the user opens the app and takes a photo of the plant. The device compresses and encrypts the captured image and sends it to the server. The server then launches an AI-based image analysis module to analyze the leaves for yellowing or disease spots. Based on the analysis results, it is determined that the plant is lacking water.

[0562] The user enters information such as the installation location (a sunny living room) and the number of hours of sunlight (approximately 6 hours) into a form within the app and submits it. The device then sends this data to the server. The server then uses an emotion recognition engine to analyze the user's emotional state, taking a photo of the user's face and analyzing their emotions. For example, if the user is feeling stressed, the system will simplify the care plan and adjust it to reduce the user's burden.

[0563] Based on the care plan generated by the server, a watering reminder is set every Wednesday morning at 8:00 and sent to the device. The device then displays the reminder to the user at the appropriate time, prompting the user to water the plants. The server also generates pest prevention advice according to the change of seasons and notifies the user.

[0564] Prompt Sentence Examples

[0565] "I want to know if my houseplants look healthy. Please tell me the best way to care for them while taking my feelings into consideration."

[0566] "What is the watering and fertilizing schedule for the houseplants in my sunny living room?"

[0567] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0568] Step 1: Get an image of your plant

[0569] The user opens the smartphone app and takes a picture of a plant with the camera. The device activates the camera module, and the user presses the capture button to capture the image. The input is the plant image taken by the user, and the output is the captured image data. The device temporarily saves this image and prepares to send it to the server.

[0570] Step 2: Send the image to the server

[0571] The device sends the image of the plant that it previously saved to the server. The device compresses the image data and encrypts it for security. The input is the acquired image data, and the output is the compressed and encrypted image data. This is then sent to the server.

[0572] Step 3: Analyze the image on the server

[0573] The server launches an image analysis module and analyzes the received plant images. Specifically, it uses an AI-based image recognition algorithm to analyze the color, shape, and presence of disease spots on the plant leaves. The input is the encrypted and transmitted image data, and the output is plant health data based on the analysis results. This data is stored in a database on the server.

[0574] Step 4: Enter habitat data

[0575] The user enters information about the plant's growing environment into a form within the app. This includes details such as the plant's type, location, sunlight hours, temperature, and humidity. The device then sends this data to the server. The input is the growing environment data entered by the user, and the output is the growing environment data sent to the server.

[0576] Step 5: Photograph the user's face and recognize their emotions

[0577] The device takes a picture of the user's face with a camera and activates the emotion engine to analyze the user's emotions. The input is the user's face image taken with the camera, and the output is the analyzed emotion data. This emotion data is also sent to the server.

[0578] Step 6: Generate a care plan

[0579] The server generates an optimal care plan based on the results of image analysis, growth environment data, and emotional data. Using an AI algorithm, the server determines the frequency of watering, the optimal type and amount of fertilizer, disease prevention measures, etc. The inputs are the results of image analysis, growth environment data, and emotional data, and the output is the generated care plan.

[0580] Step 7: Set and send reminders

[0581] The server sets a reminder based on the care plan generated. When the server completes setting the reminder, it sends a notification to the device. The device displays the reminder to the user at the appropriate time. The input is the generated care plan, and the output is the reminder.

[0582] Step 8: Providing preventative and remedial measures

[0583] The server then generates preventive measures and treatments according to the change of seasons and specific diseases. This information is also sent to the terminal and notified to the user. The input is data about the seasons and diseases, and the output is the generated preventive measures and treatments.

[0584] (Application example 2)

[0585] 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."

[0586] Conventional plant care assistant systems provide uniform care plans without considering the user's emotions, placing a heavy burden on the user and often resulting in ineffective care. Furthermore, the image analysis function for accurately grasping the plant's health status is insufficient, making it difficult to provide appropriate care methods. Furthermore, the inability to provide appropriate countermeasures for seasonal changes and disease prevention is also an issue.

[0587] 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 acquiring images]; [means for analyzing the acquired images to evaluate the health status of the plant]; [means for inputting data related to the growth environment]; [means for generating a care plan based on the analysis results, the growth environment data, and the user's emotional data]; [means for sending reminders according to the generated care plan]; [means for providing preventive measures and remedies for seasonal changes and plant-specific diseases]; and [means for adjusting the care plan according to the user's emotions]. This allows for the provision of a personalized care plan taking the user's emotions into consideration, enabling appropriate plant care. It also makes it possible to accurately grasp the health status of the plant and provide appropriate countermeasures according to the season and disease prevention.

[0588] "Means for acquiring images" refers to the ability of a user to take images of plants using a smartphone or other device.

[0589] "Means for analyzing acquired images to evaluate the health of plants" refers to a technology in which a server analyzes images of plants, extracts characteristics such as leaf color and shape, and the presence or absence of disease spots, and evaluates the health of the plants.

[0590] "Means for inputting data on the growing environment" refers to a function that allows users to input information about the growing environment, such as the type of plant, installation location, hours of sunlight, temperature, and humidity, within the app.

[0591] "Means for generating a care plan based on analysis results, growth environment data, and user emotional data" refers to a technology in which a server generates an optimal plant care plan based on the analysis results of the plant's health, growth environment data, and user emotional data.

[0592] The "means for sending reminders in accordance with the generated care plan" is a technology for sending care reminders to the user at appropriate times based on the care plan generated by the server.

[0593] The "means for providing preventive measures and remedies for seasonal changes and plant-specific diseases" is a technology in which a server generates preventive measures and remedies for seasonal changes and plant-specific diseases and notifies the user.

[0594] The "means for adjusting the care plan according to the user's emotions" is a technology that analyzes the user's emotional data and makes adjustments such as simplifying the care plan when the user is feeling stressed.

[0595] The "means for distributing the generated care plan to the user terminal" is a function for distributing the care plan generated by the server to the user's smartphone or other device.

[0596] The "means for encrypting the acquired user emotion data and image and transmitting them to the server" is a technology for encrypting the acquired user emotion data and plant image and transmitting them securely to the server.

[0597] The present invention provides a system for enabling a user to properly care for plants, the system comprising the following means:

[0598] 1. Image acquisition method:

[0599] Users use their smartphone, smart glasses, or other devices to take images of plants, which are temporarily stored on the device and prepared for transmission to the server.

[0600] 2. Analyzing captured images to assess plant health:

[0601] The server uses an image analysis module (e.g., OpenCV) to analyze the received images. Image analysis extracts features such as leaf color, shape, and the presence or absence of disease spots, and evaluates the plant's health.

[0602] 3. Means for inputting habitat data:

[0603] Through the application, users input information about the plant's growing environment, such as the type of plant, its location, hours of sunlight, temperature, and humidity, which is then sent from the device to the server.

[0604] 4. A method for generating a care plan based on the analysis results, growth environment data, and user emotion data:

[0605] The server generates an optimal plant care plan based on the results of the plant's health analysis, growing environment data, and the user's emotional data (obtained using an emotion analysis engine such as DeepFace). The plan includes information such as watering frequency and type of fertilizer.

[0606] 5. Means of sending reminders according to the generated care plan:

[0607] The server then sends reminders to the user at appropriate times based on the generated care plan, taking into account the user's daily rhythm and emotional state.

[0608] 6. Means of providing preventative and remedial measures for seasonal and plant-specific diseases:

[0609] The server generates and notifies users of preventive and remedial measures for seasonal changes and specific diseases, allowing them to take the necessary care in a timely manner.

[0610] 7. How to adjust care plans based on user emotions:

[0611] The server adjusts the care plan based on the user's emotional data, for example by simplifying it if the user is feeling stressed, thereby reducing the burden on the user.

[0612] As a concrete example, suppose a user is tending a houseplant. The user opens the app and takes a photo of the plant every day. The device sends the captured image to the server, which analyzes the plant's health. The analysis reveals that some of the leaves are yellowing, suggesting a lack of water. The user enters data such as the plant's location (a sunny living room) and the amount of sunlight (approximately six hours) into the app. The server then takes this information and the analysis results into account to generate a care plan for watering the plant once a week and providing liquid fertilizer once a month. The device also uses the user's emotion engine to analyze the user's facial expression when taking the photo. For example, if the emotion engine recognizes that the user appears busy and stressed, the system simplifies the care plan to reduce the user's burden. Based on this care plan, the server sets a watering reminder for every Wednesday at 8:00 a.m. and sends it to the device. The user receives the reminder at 8:00 a.m. and waters the plant. The server also sends pest prevention advice as the seasons change, ensuring the user remembers to perform the necessary care.

[0613] An example of a prompt for the generative AI model is as follows:

[0614] "Generate plant care advice for a cactus plant in a living room with 6 hours of sunshine, 24°C temperature, and 50% humidity. The user is stressed and overwhelmed at work."

[0615] "Analyze the health of your plant and suggest an appropriate care plan. The plant is a Dracaena, the location is the kitchen, the sunshine hours are 7 hours, the temperature is 22 degrees, the humidity is 60%. The user is relaxed."

[0616] In accordance with the above description, the present invention provides a system that provides efficient and personalized plant care for plant lovers.

[0617] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0618] Step 1:

[0619] (Image acquisition)

[0620] Users can take pictures of their plants using the camera on their smartphone or smart glasses, which allows them to visually record the latest status of their plants. The images are temporarily stored on the device.

[0621] Input: A plant image taken by the user

[0622] Output: Plant image data temporarily saved on the device

[0623] Step 2:

[0624] (Image sending)

[0625] The device sends the stored plant images to the server, where they are encrypted for privacy purposes.

[0626] Input: Plant image data stored on the device

[0627] Output: Encrypted image data sent to the server

[0628] Step 3:

[0629] (Image analysis)

[0630] The server uses an image analysis module (e.g., OpenCV) to analyze the images of the plants sent to it, extracting features such as leaf color, shape, and the presence or absence of disease spots to assess the plant's health.

[0631] Input: Encrypted image data sent to the server

[0632] Output: Plant health assessment data

[0633] Step 4:

[0634] (Growth environment data input)

[0635] Users enter information about the plant's growing environment, such as the type of plant, its location, sunlight hours, temperature, humidity, etc., into the app, and this data is sent from the device to the server.

[0636] Input: User-entered data about the habitat

[0637] Output: Habitat data sent to the server

[0638] Step 5:

[0639] (Emotion data acquisition)

[0640] To perform emotion analysis, a user takes a facial image using a camera on their smartphone or smart glasses. The device or server then analyzes the user's emotions using an emotion analysis engine such as DeepFace and sends the data to the server.

[0641] Input: A face image taken by the user

[0642] Output: Sentiment analysis data sent to the server

[0643] Step 6:

[0644] (Care plan generation)

[0645] The server generates an optimal plant care plan based on the results of plant image analysis, growing environment data, and user emotional data, including watering frequency, appropriate type and amount of fertilizer, and disease prevention measures.

[0646] Input: Plant health assessment data, growth environment data, emotion analysis data

[0647] Output: Generated care plan

[0648] Step 7:

[0649] (Reminder sent)

[0650] Based on the care plan generated by the server, care reminders are sent to the user at appropriate times, taking into account the user's daily rhythm and emotional state.

[0651] Input: Generated Care Plan

[0652] Output: Reminder notification sent to device

[0653] Step 8:

[0654] (Providing preventive measures)

[0655] The server generates and notifies users of preventive and remedial measures for seasonal changes and plant-specific diseases, allowing them to take the necessary care in a timely manner.

[0656] Input: Seasonal information, plant-specific disease data

[0657] Output: Preventive measures and action notifications sent to the device

[0658] Step 9:

[0659] (Adjusting the care plan)

[0660] The server adjusts the care plan based on the user's emotional data, for example simplifying the care plan if the user is feeling stressed.

[0661] Input: User sentiment analysis data

[0662] Output: Coordinated care plan

[0663] The above are the specific processing steps of the system that realizes the application example, which allows users to perform efficient and personalized plant care.

[0664] 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.

[0665] 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.

[0666] 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.

[0667] [Third embodiment]

[0668] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0669] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0670] 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).

[0671] 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.

[0672] 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.

[0673] 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).

[0674] 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.

[0675] 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.

[0676] 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.

[0677] 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.

[0678] 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.

[0679] 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."

[0680] The present invention is a system that allows plant lovers to effectively care for their plants. The system uses a smartphone camera to capture images of plants and analyzes those images to assess the plant's health. The system also uses user input data about the plant's growing environment, allowing the system to generate a personalized care plan based on the analysis results and the growing environment data. The system also sends reminders to the user based on the care plan, providing preventative and treatment measures for seasonal changes and plant-specific diseases.

[0681] Program processing overview

[0682] 1. Acquire images of plants

[0683] First, the user opens the app on their smartphone and takes a picture of a plant with their camera. The device temporarily stores the image and prepares it to be sent to the server.

[0684] 2. Analyze the images

[0685] When the device sends the captured image to the server, the server activates the image analysis module. The server evaluates the plant's health based on the received image. Specifically, it extracts features such as leaf color, shape, and the presence or absence of disease spots, and diagnoses whether the plant is healthy or diseased.

[0686] 3. Enter the habitat data

[0687] Within the app, users input information about the plant's growing environment, including the type of plant, its location, sunlight hours, temperature, humidity, etc. The device then sends this data to a server.

[0688] 4. Generate a care plan

[0689] Based on the image analysis results and the growing environment data entered by the user, the server generates an optimal care plan for maintaining the health of the plant, including watering frequency, the appropriate type and amount of fertilizer, disease prevention measures, and more.

[0690] 5. Send a reminder

[0691] The server sets reminders based on the care plan generated by the server. Taking into account the user's daily rhythm, the server sends reminders to the device at appropriate times. The device then displays notifications to the user, encouraging them to carry out the care.

[0692] 6. Provide disease prevention measures

[0693] In addition, the server generates preventative and treatment measures for seasonal changes and specific illnesses, and this information is also sent to the device and notified to the user.

[0694] Specific examples

[0695] For example, let's say a user is growing a houseplant. Every morning, the user opens the app and takes a photo of the plant. The device sends the image to a server, which analyzes the plant's health. The analysis reveals that some of the leaves are yellowing, suggesting a lack of water. The user enters data such as the plant's location (a sunny living room) and the number of hours of sunlight (approximately 6 hours) into the app, and the server takes this information and the analysis results into consideration to generate a care plan that calls for watering the plant once a week and administering liquid fertilizer once a month.

[0696] Based on this care plan, the server sets a watering reminder every Wednesday morning at 8:00 a.m. and sends it to the device. The user receives the reminder at 8:00 a.m. and waters the plants. The server also sends advice on pest prevention as the seasons change, ensuring the user remembers to perform the necessary care.

[0697] In this way, the system of the present invention personalizes proper plant care, making it easy for even busy users to keep their plants healthy.

[0698] The processing flow will be explained below.

[0699] Program processing flow

[0700] Step 1:

[0701] The user opens the app on their smartphone. The user selects the camera function and takes a photo of a plant. The device temporarily stores the image and prepares it to be sent to the server.

[0702] Step 2:

[0703] The device sends the stored image data to the server, which then stores the received image data in a database.

[0704] Step 3:

[0705] The server launches the image analysis module. The server applies image processing algorithms to recognize plant parts in the image. The server extracts features such as leaf color, shape, and the presence of disease spots to assess the plant's health.

[0706] Step 4:

[0707] The user enters environmental information such as the type of plant, location, and sunlight hours into a form within the app. The device sends the entered data to the server, which then stores the received environmental data in a database.

[0708] Step 5:

[0709] The server creates an optimal care plan based on environmental data and image analysis results. The server generates specific care instructions, such as the appropriate watering frequency, type of fertilizer, and timing. The server sends the created care plan to the device. The device notifies the user of the received care plan.

[0710] Step 6:

[0711] The server sets the reminder schedule based on the care plan. The server determines the date and time of the reminder, taking into account the user's daily routine. The device sets the reminder and sends a notification at the specified date and time.

[0712] Step 7:

[0713] The server analyzes data on seasonal changes and plant-specific diseases. The server generates appropriate disease prevention and treatment methods. The server sends this information to the device. The device notifies the user of the received information.

[0714] Example 1

[0715] 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."

[0716] Conventional plant care systems have struggled to properly assess the health of plants and provide optimal care plans for individual plants. Users often forget to care for their plants in their busy daily lives, making it difficult to maintain their plants' health. Furthermore, there were no systems that accurately provided preventive measures and treatments for seasonal and plant-specific diseases.

[0717] 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.

[0718] In this invention, the server includes a means for capturing images of plants using a photography function, a means for analyzing the captured images to evaluate the health of the plants, and a means for inputting information about the growing environment. This allows a user to capture images of plants and analyze the images to accurately evaluate their health. A means for generating a care plan based on the analysis results and growing environment information can provide an optimal care plan for each individual plant. A means for sending reminders according to the generated care plan ensures that the user does not forget to care for the plants. Furthermore, a means for providing preventive measures and treatments for seasonal changes and plant-specific diseases provides comprehensive care to keep the plants healthy.

[0719] The "photography function" is a function that allows a user to take an image of a plant using the smartphone camera.

[0720] "Analyzing images to evaluate the health of plants" refers to the process of extracting characteristics such as leaf color, shape, and the presence or absence of disease spots from acquired plant images, and diagnosing the health of the plant.

[0721] "Entering information about the growing environment" refers to the act of the user entering data such as the type of plant, installation location, hours of sunlight, temperature, and humidity into the app.

[0722] "Generating a care plan based on the analysis results and growth environment information" refers to the process of creating a care plan suitable for maintaining the health of plants based on the results of image analysis and growth environment information entered by the user.

[0723] "Sending a reminder according to a care plan" refers to the act of sending a notification to a user to perform care at an appropriate time based on the generated care plan.

[0724] "Providing preventive measures and treatments for seasonal changes and plant-specific diseases" is a process of guiding users to specific preventive measures and treatments to deal with seasonal climate changes and plant-specific diseases.

[0725] This invention is a system that allows plant lovers to effectively care for their plants. The system uses a smartphone camera to capture images of plants and analyzes those images to assess the plant's health. The system also uses user input information about the plant's growing environment. Based on the analysis results and the growing environment information, the system generates a personalized care plan and sends reminders according to the plan. It also provides preventative measures and treatments for seasonal changes and plant-specific diseases.

[0726] Specifically, the following hardware and software are used.

[0727] Hardware and software used

[0728] 1. Smartphone: A mobile information device held by a user.

[0729] 2. Camera: The built-in photography function of a smartphone.

[0730] 3. Server: A computer with high processing power that performs image analysis and data storage.

[0731] 4. Application software: Apps installed on smartphones.

[0732] 5. Image analysis module: Uses libraries such as Python, OpenCV, and TensorFlow.

[0733] 6. Database: Storage for habitat information and care plans.

[0734] Operation procedures and examples

[0735] Acquire images of plants

[0736] The user launches the app on their smartphone and uses the camera to take a picture of the plant. The device temporarily stores the image and prepares to send it to the server.

[0737] Analyzing the image

[0738] When the device sends the captured image to the server, the server launches an image analysis module and evaluates the plant's health based on the received image. For example, OpenCV is used to extract leaf color, shape, and the presence or absence of disease spots, and TensorFlow is used to diagnose the plant's health.

[0739] Entering the habitat data

[0740] The user enters information about the plant's growing environment in the app, including the type of plant, its location, sunlight hours, temperature, humidity, etc. The device converts this data into JSON format and sends it to the server.

[0741] Generate a care plan

[0742] The server generates an optimal care plan based on the image analysis results and growing environment information, for example, by using a database knowledge base and machine learning algorithms to determine the optimal watering frequency and type and amount of fertilizer for the user's plants.

[0743] Send a reminder

[0744] The server sets reminders based on the care plan generated by the server. Taking into account the user's daily rhythm, the server sends reminders to the device at appropriate times. The device then notifies the user of the reminders and encourages them to care for their plants.

[0745] Providing disease prevention measures

[0746] Additionally, the server generates preventative measures and treatments for seasonal changes and specific illnesses, which are then sent to the device and notified to the user.

[0747] Specific operation example

[0748] For example, consider a user who is caring for a houseplant. Every morning, the user opens the app and takes a photo of the plant. The device sends the captured image to the server, which analyzes the plant's health. The analysis reveals that some of the leaves are yellowing, suggesting a lack of water. The user inputs data such as the plant's location (a sunny living room) and the amount of sunlight (approximately 6 hours), and the server generates a care plan for watering once a week and liquid fertilizer once a month. Based on this care plan, the server sets a watering reminder every Wednesday at 8:00 a.m. and sends it to the device. The user receives the reminder at 8:00 a.m. and waters the plant. The server also sends pest prevention measures in early spring, helping the user remember to take care of the plant.

[0749] Prompt Sentence Examples

[0750] "After a user opens the app on their smartphone and takes a picture of their houseplant, the server analyzes the image and evaluates the plant's health. Please also explain in detail how the system generates an optimal care plan and sends reminders based on growing environment information entered by the user, such as the installation location and sunlight hours. Please also emphasize that seasonal disease prevention measures are also provided."

[0751] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0752] Step 1:

[0753] The user launches the smartphone app and takes a picture of the plant using the camera function. The device temporarily saves this image in its internal storage. At this stage, the input is the captured image, and the output is the temporarily saved image file. Specifically, the user taps the camera button in the app, frames the entire plant, and presses the shutter button.

[0754] Step 2:

[0755] The device retrieves the temporarily saved image and sends it to the server. Here, the input is the temporarily saved image file, and the output is the image data sent to the server. Specifically, the device checks the network connection and uses an HTTP request to upload the image file to the specified endpoint on the server.

[0756] Step 3:

[0757] The server analyzes the received image data. It launches an image analysis module and uses OpenCV and TensorFlow to extract features such as leaf color, shape, and the presence or absence of disease spots. The input is the image data sent to the server, and the output is an assessment result regarding the plant's health. Specifically, the server reads the image data, analyzes the pigment information of each pixel, and evaluates the health by detecting specific patterns.

[0758] Step 4:

[0759] The user enters information about the plant's growing environment in the app. This includes the type of plant, location, sunlight hours, temperature, humidity, etc. The device converts this data into JSON format and sends it to the server. The input is the growing environment information entered by the user, and the output is the JSON data sent to the server. Specifically, the user manually enters the required information into the app's input form and clicks the "Submit" button.

[0760] Step 5:

[0761] The server receives the image analysis results and growing environment information and generates an optimal care plan. Using the database's knowledge base and machine learning algorithms, the care plan is created based on the analysis results and input information. The input is the image analysis results and growing environment information, and the output is the generated care plan. The server calculates this and creates a plan that includes specific watering frequency, type and amount of fertilizer, and disease prevention measures.

[0762] Step 6:

[0763] Reminders are set based on the care plan generated by the server. Reminders are sent to the device at appropriate times, taking into account the user's daily rhythm. The input is the generated care plan, and the output is the reminder notification sent to the device. Specifically, the server takes into account the user's time zone settings and sets a reminder, for example, "Water the plants every Wednesday at 8am," and sends a push notification to the device.

[0764] Step 7:

[0765] The server generates preventive measures and treatment methods according to seasonal changes and specific diseases. This information is also sent to the terminal and notified to the user. The input is external seasonal information and plant condition information, and the output is the generated preventive measures and treatment methods. Specifically, the server regularly collects local weather data and disease occurrence information, and based on that, creates specific advice such as "use pest prevention spray in early spring" and sends it to the terminal.

[0766] Through the above processing steps, the system provides personalized plant care to users and provides comprehensive support for keeping plants healthy.

[0767] (Application example 1)

[0768] 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."

[0769] Conventional plant care systems are specialized in managing plant health, but are unable to address food freshness and quality control. For busy modern people, managing food storage methods and consumption timings is particularly difficult, resulting in food waste. The present invention aims to solve these problems and enable the appropriate management of both plants and food.

[0770] 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.

[0771] In this invention, the server includes [means for acquiring images], [means for analyzing the acquired images to evaluate the health of the plant], and [means for inputting data related to the growth environment. This enables plant health management. In addition, by including [means for generating a care plan based on the analysis results and growth environment data], [means for sending reminders in accordance with the generated care plan], and [means for providing preventive measures and treatments for seasonal changes and plant-specific diseases], the efficiency of plant care is improved. Furthermore, by adding [means for analyzing the freshness and quality of food and suggesting storage methods] and [means for sending reminders based on the storage method], it becomes possible to manage the freshness of food and ensure its appropriate storage and consumption, which is expected to reduce food waste.

[0772] "Means for acquiring images" is a function that allows a user to take images of plants or food with a camera and send the images to the server.

[0773] The "means for analyzing acquired images to evaluate the health of plants" refers to a function that analyzes images received by the server and diagnoses the health of the plant based on the color and shape of the leaves, the presence or absence of disease spots, etc.

[0774] The "means for inputting data on the growing environment" is a function that allows the user to input information on the growing environment, such as the type of plant, installation location, hours of sunlight, temperature, and humidity, and send it to the server.

[0775] "Means for generating a care plan based on the analysis results and growth environment data" is a function that enables the server to create a care plan optimal for maintaining the health of plants based on the image analysis results and the input growth environment data.

[0776] The "means for sending a reminder in accordance with the generated care plan" is a function in which the server sends a reminder to the user at an appropriate time based on the generated care plan.

[0777] "Means for providing preventive measures and remedies for seasonal changes and plant-specific diseases" is a function in which the server generates preventive measures and remedies according to seasonal changes and plant-specific diseases and notifies the user.

[0778] "Means for analyzing food freshness and quality and suggesting storage methods" is a function that analyzes photographed images of food, evaluates its freshness and quality, and suggests the optimal storage method.

[0779] The "means for sending a reminder based on the storage method" is a function for sending a reminder to the user at an appropriate time based on the proposed storage method.

[0780] This invention is a system for effectively managing plants and food. The system captures images of plants and food ingredients using a camera on a user device and evaluates their health and freshness through image analysis. It then generates a personalized care plan or storage method based on the growth or storage environment data entered by the user, and sends appropriate reminders.

[0781] Hardware and Software Configuration

[0782] This system is implemented using the following hardware and software.

[0783] 1. User device: A mobile device with a camera function, such as a smartphone or tablet, that has an interface that allows users to take images of plants or food and provide input data.

[0784] 2. Server: A computer device with high-performance data processing capabilities that receives images and data sent from user devices, analyzes them, and generates care plans.

[0785] 3. Image analysis module: Software that uses image processing libraries (e.g., OpenCV, TensorFlow) to assess the health of plants or the freshness of food from images.

[0786] 4. Database: A database that manages the status of plants and food for each user, generated care plans, and reminder information.

[0787] 5. Communication module: Internet communication function for transferring data between user terminals and servers.

[0788] Operation explanation

[0789] 1. Image acquisition and transmission:

[0790] Users take pictures of plants or food using a smartphone application, which temporarily stores the images and prepares them for transmission to the server.

[0791] 2. Image Analysis:

[0792] The server uses an image analysis module to evaluate the health of plants and the freshness of food based on the received images, analyzing characteristics such as leaf color, shape, and the presence or absence of disease spots.

[0793] 3. Enter environmental data:

[0794] Within the app, users input data on the growing environment, such as the type of plant, installation location, sunlight hours, temperature, and humidity, as well as the type of food, purchase date, storage location, and storage temperature, etc. This data is sent to the server.

[0795] 4. Generate a care plan and save it:

[0796] Based on the results of image analysis and data entered by the user, the server generates optimal care plans for maintaining plant health and food storage methods, including watering frequency, appropriate fertilizer type and amount, and disease prevention measures, as well as suggestions for refrigeration and freezing methods and cooking and consumption timing.

[0797] 5. Send reminders:

[0798] The server sends reminders to the device at appropriate times based on the generated care plan and storage method, so that the user does not miss important care or consumption opportunities.

[0799] 6. Providing preventative and remedial measures:

[0800] Depending on the change of seasons and the time when certain plant diseases are more likely to occur, the server generates preventive measures and treatments and notifies the user.

[0801] Specific examples

[0802] For example, let's say a user is growing a houseplant. Every morning, the user opens the app and takes a photo of the plant, and the device sends the image to the server. The server analyzes the plant's health and detects that some of the leaves are yellowing, suggesting a lack of water. When the user inputs data such as the plant's location and sunlight hours, the server generates a care plan for watering the plant once a week and liquid fertilizing it once a month. Based on this care plan, the server sends a watering reminder every Wednesday morning at 8:00 a.m. The server also sends advice on pest prevention as the seasons change.

[0803] In the case of food management, a user takes a picture of an apple, and the server analyzes it, resulting in a freshness score of 70. In this case, the system suggests storing the apple in the refrigerator for five days, and sends a reminder when the expiration date approaches. An example of a specific prompt is, "Store the apple with a freshness score of 70 in the refrigerator and set a reminder to consume it in five days."

[0804] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0805] Step 1:

[0806] A user launches a smartphone application and takes a picture of a plant or food with the camera. The input data is the plant or food image taken with the camera, and the output is a temporarily saved image file. The specific operation is to capture the image using the camera function and temporarily save it in local storage.

[0807] Step 2:

[0808] The terminal sends the temporarily saved image to the server. The input data is the image file saved on the user terminal, and the output is the image data transferred to the server. The specific operation is to upload the image file to the server via network communication.

[0809] Step 3:

[0810] The server launches an image analysis module to analyze the images it receives. The input data is the image file received by the server, and the output is the evaluation results for the health of plants and the freshness of food. Specifically, it uses libraries such as OpenCV and TensorFlow to extract features from the images and apply analysis algorithms to make an evaluation.

[0811] Step 4:

[0812] The user inputs data on the plant's growing environment or food storage environment within the app. The input data includes the type of plant, installation location, sunlight hours, temperature, humidity, type of food, purchase date, storage location, storage temperature, etc., and the output is the growing environment data or storage environment data sent to the server. The specific operation is that the user inputs data via the user interface and sends the data to the server.

[0813] Step 5:

[0814] The server generates optimal care plans and food preservation methods for maintaining plant health based on the image analysis results and data entered by the user. The input data is the image analysis results and growth environment data or preservation environment data, and the output is the generated care plans and preservation methods. Specifically, the server uses past data and algorithms stored in the database to calculate and generate optimal plans and methods.

[0815] Step 6:

[0816] The server sends reminders to the user's device at appropriate times based on the generated care plan and storage method. The input data is the generated care plan or storage method and reminder setting information, and the output is the reminder notification sent to the user's device. The specific operation is to set a reminder schedule and send the notification at the set time.

[0817] Step 7:

[0818] The server generates preventive measures and countermeasures according to the change of seasons and the time when a particular plant disease is likely to occur, and notifies the user. The input data includes the season, disease information, plant type, etc., and the output is a notification of the preventive measures and countermeasures sent to the user. The specific operation is to generate appropriate advice based on plant type and seasonal information, and notify the user as necessary.

[0819] 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.

[0820] This invention is a system that combines an emotion engine that recognizes the user's emotions to help plant lovers efficiently care for their plants. The system captures images of plants using a smartphone camera, analyzes the images to evaluate the plant's health, and analyzes the user's emotions using the emotion engine. This generates a personalized care plan and provides reminders based on the user's emotions. It also provides preventative measures and treatments for seasonal changes and plant-specific diseases.

[0821] Program processing overview

[0822] 1. Acquire images of plants

[0823] First, the user opens the app on their smartphone and takes a picture of a plant with their camera. The device temporarily stores the image and prepares it to be sent to the server.

[0824] 2. Analyze the images

[0825] When the device sends the captured image to the server, the server activates the image analysis module. The server evaluates the plant's health based on the received image. Specifically, it extracts features such as leaf color, shape, and the presence or absence of disease spots, and diagnoses whether the plant is healthy or diseased.

[0826] 3. Enter the habitat data

[0827] Within the app, users input information about the plant's growing environment, including the type of plant, its location, sunlight hours, temperature, humidity, etc. The device then sends this data to a server.

[0828] 4. Emotion Recognition by Emotion Engine

[0829] The device takes a picture of the user's face with a camera, and the device or server analyzes the user's emotions using an emotion engine. The analyzed emotion data is also sent to the server.

[0830] 5. Generate a care plan

[0831] The server generates an optimal care plan for maintaining plant health based on the image analysis results, growth environment data, and emotional data. Specifically, the plan includes watering frequency, appropriate fertilizer type and amount, disease prevention measures, etc. The care plan is also adjusted based on the user's emotional state.

[0832] 6. Send a reminder

[0833] The server sets reminders based on the care plan generated by the server. Taking into account the user's daily rhythm and emotional state, the server sends reminders to the device with appropriate timing and content. The device then displays notifications to the user, encouraging them to carry out the care.

[0834] 7. Provide disease prevention measures

[0835] In addition, the server generates preventative and treatment measures for seasonal changes and specific illnesses, and this information is also sent to the device and notified to the user.

[0836] Specific examples

[0837] For example, let's say a user is growing a houseplant. Every day, the user opens the app and takes a photo of the plant. The device sends the captured image to a server, which analyzes the plant's health. The analysis reveals that some of the leaves are yellowing, suggesting a lack of water. The user enters data such as the plant's location (a sunny living room) and the number of hours of sunlight (approximately 6 hours) into the app, and the server takes this information and the analysis results into consideration to generate a care plan that calls for watering the plant once a week and administering liquid fertilizer once a month.

[0838] The device also uses the user's emotion engine to analyze the user's facial expressions when taking a photo. For example, if the emotion engine recognizes that the user looks busy and stressed, the system will simplify the care plan and adjust it to reduce the user's burden.

[0839] Based on this care plan, the server sets a watering reminder every Wednesday morning at 8:00 a.m. and sends it to the device. The user receives the reminder at 8:00 a.m. and waters the plants. The server also sends advice on pest prevention as the seasons change, ensuring the user remembers to perform the necessary care.

[0840] In this way, the system of the present invention personalizes appropriate plant care, allowing even busy users to easily keep their plants healthy. Furthermore, by taking the user's emotions into consideration, it is possible to provide more flexible and effective care plans.

[0841] The processing flow will be explained below.

[0842] Program processing flow

[0843] Step 1:

[0844] The user opens the app on their smartphone. The user selects the camera function and takes a photo of a plant. The device temporarily stores the image and prepares it to be sent to the server.

[0845] Step 2:

[0846] The device sends the stored image data to the server, which then stores the received image data in a database.

[0847] Step 3:

[0848] The server launches the image analysis module. The server applies image processing algorithms to recognize plant parts in the image. The server extracts features such as leaf color, shape, and the presence of disease spots to assess the plant's health.

[0849] Step 4:

[0850] The user enters environmental information such as the type of plant, location, and sunlight hours into a form within the app. The device sends the entered data to the server, which then stores the received environmental data in a database.

[0851] Step 5:

[0852] The device takes a picture of the user's face with a camera. The device or server analyzes the user's emotions using an emotion engine. The analyzed emotion data is also sent to the server.

[0853] Step 6:

[0854] The server creates an optimal care plan based on environmental data, image analysis results, and emotional data. The server then generates specific care instructions, such as the appropriate watering frequency, type of fertilizer, and timing. The care plan is also adjusted based on the user's emotions.

[0855] Step 7:

[0856] The server sends the generated care plan to the terminal, and the terminal notifies the user of the received care plan.

[0857] Step 8:

[0858] The server sets a reminder schedule based on the care plan. The server determines the date, time, and content of the reminder, taking into account the user's daily rhythm and emotional state. The device sets the reminder and notifies the user at the specified date and time.

[0859] Step 9:

[0860] The server analyzes data on seasonal changes and plant-specific diseases. The server generates appropriate disease prevention and treatment methods. The server sends this information to the device. The device notifies the user of the received information.

[0861] Example 2

[0862] 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."

[0863] Plant care is time-consuming, especially for users with busy lifestyles. It is difficult to accurately grasp the health status of plants and continue appropriate care. Furthermore, there is a lack of methods to provide personalized care plans that take the user's emotions into account. Therefore, there is a strong need for a system that evaluates the health status of plants and provides appropriate care based on the user's emotions.

[0864] The identification process by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means. In this invention, the server includes: [means for acquiring video;] [means for analyzing the acquired video to evaluate the health condition of the plant;] [means for inputting data related to the growing environment;] [means for recognizing the user's emotions and acquiring emotion data;] [means for generating a care plan based on the analysis results, the growing environment data, and the emotion data;] [means for sending notifications according to the generated care plan; and [means for providing preventive measures and remedies for seasonal changes and plant-specific diseases.] This makes it possible to efficiently care for plants and provide a personalized care plan based on the user's emotions. Furthermore, by providing appropriate care and preventive measures based on the plant's health condition, the plant can be kept healthy.

[0865] The "means for acquiring images" refers to a means by which a user records images of plants using a photographing device such as a smartphone or tablet.

[0866] The "means for analyzing acquired images to evaluate the health of plants" refers to a means for analyzing recorded images of plants and diagnosing the health of plants based on information such as leaf color, shape, and the presence or absence of disease spots.

[0867] The "means for inputting data on the growing environment" is a means by which the user inputs information on the growing environment, such as the type of plant, installation location, hours of sunlight, temperature, and humidity.

[0868] The "means for recognizing the user's emotions and acquiring emotion data" refers to a means for taking a picture of the user's face with a camera, analyzing the user's emotional state using an emotion engine, and acquiring the data.

[0869] The "means for generating a care plan based on the analysis results, growth environment data, and emotional data" refers to a means for integrating the analysis results of the plant's health condition, data related to the growth environment, and the user's emotional data to generate an optimal plant care plan.

[0870] The "means for sending a notification in accordance with the generated care plan" refers to a means for sending a notification of care plan execution to a user at a specific timing based on the generated care plan.

[0871] The "means for providing preventive measures and remedies for seasonal changes and diseases specific to plants" is a means for providing users with preventive measures and remedies for seasonal changes and diseases specific to specific plants.

[0872] The "means for providing the generated care plan to the terminal" refers to a means for displaying or notifying the contents of the generated care plan on the user's terminal.

[0873] The "means for encrypting the acquired image and transmitting it to the server" is a means for encrypting the acquired image of the plant as a security measure and transmitting it to the server.

[0874] This invention is a system that analyzes the health of plants and the user's emotional state to provide personalized care plans to help plant lovers efficiently care for their plants. This system uses hardware and software such as a mobile device such as a smartphone, a server, and an emotion engine.

[0875] The process begins when a user launches the app on their smartphone and takes a picture of a plant with their camera. The device acquires the image and temporarily stores it. The device then compresses and encrypts the image before sending it to the server.

[0876] The server launches an AI-based image analysis module based on the images it receives. The server analyzes the color, shape, and presence of disease spots on the plant's leaves to assess the plant's health. The assessed information is then recorded in a database.

[0877] Next, the user enters information about the plant's growing environment in the app, including the type of plant, its location, sunlight hours, temperature, humidity, etc. The device also sends this data to the server.

[0878] The device then takes a photo of the user's face with a camera and activates an emotion engine to analyze the user's emotions. The analyzed emotion data is also sent to the server, which then generates a comprehensive care plan based on the results of the image analysis, the growth environment data, and the user's emotion data.

[0879] The generated care plan will suggest specific actions needed to maintain the plant's health, such as watering frequency, the type and amount of fertilizer to use, and disease prevention measures. Furthermore, the care plan's contents are adjusted according to the user's emotional state. For example, if the user is busy and stressed, measures such as reducing the frequency of care will be taken.

[0880] The server sets reminders based on the care plan and sends them to the device. The reminders are sent at times that take into account the user's daily rhythm. The device displays the reminder notification, encouraging the user to perform the care.

[0881] Finally, the server automatically generates preventative and treatment measures for seasonal changes and specific illnesses, and this information is also sent to the device and notified to the user.

[0882] Specific examples

[0883] For example, let's say a user is growing a houseplant. Every day, the user opens the app and takes a photo of the plant. The device compresses and encrypts the captured image and sends it to the server. The server then launches an AI-based image analysis module to analyze the leaves for yellowing or disease spots. Based on the analysis results, it is determined that the plant is lacking water.

[0884] The user enters information such as the installation location (a sunny living room) and the number of hours of sunlight (approximately 6 hours) into a form within the app and submits it. The device then sends this data to the server. The server then uses an emotion recognition engine to analyze the user's emotional state, taking a photo of the user's face and analyzing their emotions. For example, if the user is feeling stressed, the system will simplify the care plan and adjust it to reduce the user's burden.

[0885] Based on the care plan generated by the server, a watering reminder is set every Wednesday morning at 8:00 and sent to the device. The device then displays the reminder to the user at the appropriate time, prompting the user to water the plants. The server also generates pest prevention advice according to the change of seasons and notifies the user.

[0886] Prompt Sentence Examples

[0887] "I want to know if my houseplants look healthy. Please tell me the best way to care for them while taking my feelings into consideration."

[0888] "What is the watering and fertilizing schedule for the houseplants in my sunny living room?"

[0889] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0890] Step 1: Get an image of your plant

[0891] The user opens the smartphone app and takes a picture of a plant with the camera. The device activates the camera module, and the user presses the capture button to capture the image. The input is the plant image taken by the user, and the output is the captured image data. The device temporarily saves this image and prepares to send it to the server.

[0892] Step 2: Send the image to the server

[0893] The device sends the image of the plant that it previously saved to the server. The device compresses the image data and encrypts it for security. The input is the acquired image data, and the output is the compressed and encrypted image data. This is then sent to the server.

[0894] Step 3: Analyze the image on the server

[0895] The server launches an image analysis module and analyzes the received plant images. Specifically, it uses an AI-based image recognition algorithm to analyze the color, shape, and presence of disease spots on the plant leaves. The input is the encrypted and transmitted image data, and the output is plant health data based on the analysis results. This data is stored in a database on the server.

[0896] Step 4: Enter habitat data

[0897] The user enters information about the plant's growing environment into a form within the app. This includes details such as the plant's type, location, sunlight hours, temperature, and humidity. The device then sends this data to the server. The input is the growing environment data entered by the user, and the output is the growing environment data sent to the server.

[0898] Step 5: Photograph the user's face and recognize their emotions

[0899] The device takes a picture of the user's face with a camera and activates the emotion engine to analyze the user's emotions. The input is the user's face image taken with the camera, and the output is the analyzed emotion data. This emotion data is also sent to the server.

[0900] Step 6: Generate a care plan

[0901] The server generates an optimal care plan based on the results of image analysis, growth environment data, and emotional data. Using an AI algorithm, the server determines the frequency of watering, the optimal type and amount of fertilizer, disease prevention measures, etc. The inputs are the results of image analysis, growth environment data, and emotional data, and the output is the generated care plan.

[0902] Step 7: Set and send reminders

[0903] The server sets a reminder based on the care plan generated. When the server completes setting the reminder, it sends a notification to the device. The device displays the reminder to the user at the appropriate time. The input is the generated care plan, and the output is the reminder.

[0904] Step 8: Providing preventative and remedial measures

[0905] The server then generates preventive measures and treatments according to the change of seasons and specific diseases. This information is also sent to the terminal and notified to the user. The input is data about the seasons and diseases, and the output is the generated preventive measures and treatments.

[0906] (Application example 2)

[0907] 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."

[0908] Conventional plant care assistant systems provide uniform care plans without considering the user's emotions, placing a heavy burden on the user and often resulting in ineffective care. Furthermore, the image analysis function for accurately grasping the plant's health status is insufficient, making it difficult to provide appropriate care methods. Furthermore, the inability to provide appropriate countermeasures for seasonal changes and disease prevention is also an issue.

[0909] 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 acquiring images]; [means for analyzing the acquired images to evaluate the health status of the plant]; [means for inputting data related to the growth environment]; [means for generating a care plan based on the analysis results, the growth environment data, and the user's emotional data]; [means for sending reminders according to the generated care plan]; [means for providing preventive measures and remedies for seasonal changes and plant-specific diseases]; and [means for adjusting the care plan according to the user's emotions]. This allows for the provision of a personalized care plan taking the user's emotions into consideration, enabling appropriate plant care. It also makes it possible to accurately grasp the health status of the plant and provide appropriate countermeasures according to the season and disease prevention.

[0910] "Means for acquiring images" refers to the ability of a user to take images of plants using a smartphone or other device.

[0911] "Means for analyzing acquired images to evaluate the health of plants" refers to a technology in which a server analyzes images of plants, extracts characteristics such as leaf color and shape, and the presence or absence of disease spots, and evaluates the health of the plants.

[0912] "Means for inputting data on the growing environment" refers to a function that allows users to input information about the growing environment, such as the type of plant, installation location, hours of sunlight, temperature, and humidity, within the app.

[0913] "Means for generating a care plan based on analysis results, growth environment data, and user emotional data" refers to a technology in which a server generates an optimal plant care plan based on the analysis results of the plant's health, growth environment data, and user emotional data.

[0914] The "means for sending reminders in accordance with the generated care plan" is a technology for sending care reminders to the user at appropriate times based on the care plan generated by the server.

[0915] The "means for providing preventive measures and remedies for seasonal changes and plant-specific diseases" is a technology in which a server generates preventive measures and remedies for seasonal changes and plant-specific diseases and notifies the user.

[0916] The "means for adjusting the care plan according to the user's emotions" is a technology that analyzes the user's emotional data and makes adjustments such as simplifying the care plan when the user is feeling stressed.

[0917] The "means for distributing the generated care plan to the user terminal" is a function for distributing the care plan generated by the server to the user's smartphone or other device.

[0918] The "means for encrypting the acquired user emotion data and image and transmitting them to the server" is a technology for encrypting the acquired user emotion data and plant image and transmitting them securely to the server.

[0919] The present invention provides a system for enabling a user to properly care for plants, the system comprising the following means:

[0920] 1. Image acquisition method:

[0921] Users use their smartphone, smart glasses, or other devices to take images of plants, which are temporarily stored on the device and prepared for transmission to the server.

[0922] 2. Analyzing captured images to assess plant health:

[0923] The server uses an image analysis module (e.g., OpenCV) to analyze the received images. Image analysis extracts features such as leaf color, shape, and the presence or absence of disease spots, and evaluates the plant's health.

[0924] 3. Means for inputting habitat data:

[0925] Through the application, users input information about the plant's growing environment, such as the type of plant, its location, hours of sunlight, temperature, and humidity, which is then sent from the device to the server.

[0926] 4. A method for generating a care plan based on the analysis results, growth environment data, and user emotion data:

[0927] The server generates an optimal plant care plan based on the results of the plant's health analysis, growing environment data, and the user's emotional data (obtained using an emotion analysis engine such as DeepFace). The plan includes information such as watering frequency and type of fertilizer.

[0928] 5. Means of sending reminders according to the generated care plan:

[0929] The server then sends reminders to the user at appropriate times based on the generated care plan, taking into account the user's daily rhythm and emotional state.

[0930] 6. Means of providing preventative and remedial measures for seasonal and plant-specific diseases:

[0931] The server generates and notifies users of preventive and remedial measures for seasonal changes and specific diseases, allowing them to take the necessary care in a timely manner.

[0932] 7. How to adjust care plans based on user emotions:

[0933] The server adjusts the care plan based on the user's emotional data, for example by simplifying it if the user is feeling stressed, thereby reducing the burden on the user.

[0934] As a concrete example, suppose a user is tending a houseplant. The user opens the app and takes a photo of the plant every day. The device sends the captured image to the server, which analyzes the plant's health. The analysis reveals that some of the leaves are yellowing, suggesting a lack of water. The user enters data such as the plant's location (a sunny living room) and the amount of sunlight (approximately six hours) into the app. The server then takes this information and the analysis results into account to generate a care plan for watering the plant once a week and providing liquid fertilizer once a month. The device also uses the user's emotion engine to analyze the user's facial expression when taking the photo. For example, if the emotion engine recognizes that the user appears busy and stressed, the system simplifies the care plan to reduce the user's burden. Based on this care plan, the server sets a watering reminder for every Wednesday at 8:00 a.m. and sends it to the device. The user receives the reminder at 8:00 a.m. and waters the plant. The server also sends pest prevention advice as the seasons change, ensuring the user remembers to perform the necessary care.

[0935] An example of a prompt for the generative AI model is as follows:

[0936] "Generate plant care advice for a cactus plant in a living room with 6 hours of sunshine, 24°C temperature, and 50% humidity. The user is stressed and overwhelmed at work."

[0937] "Analyze the health of your plant and suggest an appropriate care plan. The plant is a Dracaena, the location is the kitchen, the sunshine hours are 7 hours, the temperature is 22 degrees, the humidity is 60%. The user is relaxed."

[0938] In accordance with the above description, the present invention provides a system that provides efficient and personalized plant care for plant lovers.

[0939] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0940] Step 1:

[0941] (Image acquisition)

[0942] Users can take pictures of their plants using the camera on their smartphone or smart glasses, which allows them to visually record the latest status of their plants. The images are temporarily stored on the device.

[0943] Input: A plant image taken by the user

[0944] Output: Plant image data temporarily saved on the device

[0945] Step 2:

[0946] (Image sending)

[0947] The device sends the stored plant images to the server, where they are encrypted for privacy purposes.

[0948] Input: Plant image data stored on the device

[0949] Output: Encrypted image data sent to the server

[0950] Step 3:

[0951] (Image analysis)

[0952] The server uses an image analysis module (e.g., OpenCV) to analyze the images of the plants sent to it, extracting features such as leaf color, shape, and the presence or absence of disease spots to assess the plant's health.

[0953] Input: Encrypted image data sent to the server

[0954] Output: Plant health assessment data

[0955] Step 4:

[0956] (Growth environment data input)

[0957] Users enter information about the plant's growing environment, such as the type of plant, its location, sunlight hours, temperature, humidity, etc., into the app, and this data is sent from the device to the server.

[0958] Input: User-entered data about the habitat

[0959] Output: Habitat data sent to the server

[0960] Step 5:

[0961] (Emotion data acquisition)

[0962] To perform emotion analysis, a user takes a facial image using a camera on their smartphone or smart glasses. The device or server then analyzes the user's emotions using an emotion analysis engine such as DeepFace and sends the data to the server.

[0963] Input: A face image taken by the user

[0964] Output: Sentiment analysis data sent to the server

[0965] Step 6:

[0966] (Care plan generation)

[0967] The server generates an optimal plant care plan based on the results of plant image analysis, growing environment data, and user emotional data, including watering frequency, appropriate type and amount of fertilizer, and disease prevention measures.

[0968] Input: Plant health assessment data, growth environment data, emotion analysis data

[0969] Output: Generated care plan

[0970] Step 7:

[0971] (Reminder sent)

[0972] Based on the care plan generated by the server, care reminders are sent to the user at appropriate times, taking into account the user's daily rhythm and emotional state.

[0973] Input: Generated Care Plan

[0974] Output: Reminder notification sent to device

[0975] Step 8:

[0976] (Providing preventive measures)

[0977] The server generates and notifies users of preventive and remedial measures for seasonal changes and plant-specific diseases, allowing them to take the necessary care in a timely manner.

[0978] Input: Seasonal information, plant-specific disease data

[0979] Output: Preventive measures and action notifications sent to the device

[0980] Step 9:

[0981] (Adjusting the care plan)

[0982] The server adjusts the care plan based on the user's emotional data, for example simplifying the care plan if the user is feeling stressed.

[0983] Input: User sentiment analysis data

[0984] Output: Coordinated care plan

[0985] The above are the specific processing steps of the system that realizes the application example, which allows users to perform efficient and personalized plant care.

[0986] 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.

[0987] 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.

[0988] 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.

[0989] [Fourth embodiment]

[0990] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0991] 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.

[0992] 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).

[0993] 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.

[0994] 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.

[0995] 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).

[0996] 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.

[0997] 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.

[0998] 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.

[0999] 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.

[1000] 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.

[1001] 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.

[1002] 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."

[1003] The present invention is a system that allows plant lovers to effectively care for their plants. The system uses a smartphone camera to capture images of plants and analyzes those images to assess the plant's health. The system also uses user input data about the plant's growing environment, allowing the system to generate a personalized care plan based on the analysis results and the growing environment data. The system also sends reminders to the user based on the care plan, providing preventative and treatment measures for seasonal changes and plant-specific diseases.

[1004] Program processing overview

[1005] 1. Acquire images of plants

[1006] First, the user opens the app on their smartphone and takes a picture of a plant with their camera. The device temporarily stores the image and prepares it to be sent to the server.

[1007] 2. Analyze the images

[1008] When the device sends the captured image to the server, the server activates the image analysis module. The server evaluates the plant's health based on the received image. Specifically, it extracts features such as leaf color, shape, and the presence or absence of disease spots, and diagnoses whether the plant is healthy or diseased.

[1009] 3. Enter the habitat data

[1010] Within the app, users input information about the plant's growing environment, including the type of plant, its location, sunlight hours, temperature, humidity, etc. The device then sends this data to a server.

[1011] 4. Generate a care plan

[1012] Based on the image analysis results and the growing environment data entered by the user, the server generates an optimal care plan for maintaining the health of the plant, including watering frequency, the appropriate type and amount of fertilizer, disease prevention measures, and more.

[1013] 5. Send a reminder

[1014] The server sets reminders based on the care plan generated by the server. Taking into account the user's daily rhythm, the server sends reminders to the device at appropriate times. The device then displays notifications to the user, encouraging them to carry out the care.

[1015] 6. Provide disease prevention measures

[1016] In addition, the server generates preventative and treatment measures for seasonal changes and specific illnesses, and this information is also sent to the device and notified to the user.

[1017] Specific examples

[1018] For example, let's say a user is growing a houseplant. Every morning, the user opens the app and takes a photo of the plant. The device sends the image to a server, which analyzes the plant's health. The analysis reveals that some of the leaves are yellowing, suggesting a lack of water. The user enters data such as the plant's location (a sunny living room) and the number of hours of sunlight (approximately 6 hours) into the app, and the server takes this information and the analysis results into consideration to generate a care plan that calls for watering the plant once a week and administering liquid fertilizer once a month.

[1019] Based on this care plan, the server sets a watering reminder every Wednesday morning at 8:00 a.m. and sends it to the device. The user receives the reminder at 8:00 a.m. and waters the plants. The server also sends advice on pest prevention as the seasons change, ensuring the user remembers to perform the necessary care.

[1020] In this way, the system of the present invention personalizes proper plant care, making it easy for even busy users to keep their plants healthy.

[1021] The processing flow will be explained below.

[1022] Program processing flow

[1023] Step 1:

[1024] The user opens the app on their smartphone. The user selects the camera function and takes a photo of a plant. The device temporarily stores the image and prepares it to be sent to the server.

[1025] Step 2:

[1026] The device sends the stored image data to the server, which then stores the received image data in a database.

[1027] Step 3:

[1028] The server launches the image analysis module. The server applies image processing algorithms to recognize plant parts in the image. The server extracts features such as leaf color, shape, and the presence of disease spots to assess the plant's health.

[1029] Step 4:

[1030] The user enters environmental information such as the type of plant, location, and sunlight hours into a form within the app. The device sends the entered data to the server, which then stores the received environmental data in a database.

[1031] Step 5:

[1032] The server creates an optimal care plan based on environmental data and image analysis results. The server generates specific care instructions, such as the appropriate watering frequency, type of fertilizer, and timing. The server sends the created care plan to the device. The device notifies the user of the received care plan.

[1033] Step 6:

[1034] The server sets the reminder schedule based on the care plan. The server determines the date and time of the reminder, taking into account the user's daily routine. The device sets the reminder and sends a notification at the specified date and time.

[1035] Step 7:

[1036] The server analyzes data on seasonal changes and plant-specific diseases. The server generates appropriate disease prevention and treatment methods. The server sends this information to the device. The device notifies the user of the received information.

[1037] Example 1

[1038] 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."

[1039] Conventional plant care systems have struggled to properly assess the health of plants and provide optimal care plans for individual plants. Users often forget to care for their plants in their busy daily lives, making it difficult to maintain their plants' health. Furthermore, there were no systems that accurately provided preventive measures and treatments for seasonal and plant-specific diseases.

[1040] 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.

[1041] In this invention, the server includes a means for capturing images of plants using a photography function, a means for analyzing the captured images to evaluate the health of the plants, and a means for inputting information about the growing environment. This allows a user to capture images of plants and analyze the images to accurately evaluate their health. A means for generating a care plan based on the analysis results and growing environment information can provide an optimal care plan for each individual plant. A means for sending reminders according to the generated care plan ensures that the user does not forget to care for the plants. Furthermore, a means for providing preventive measures and treatments for seasonal changes and plant-specific diseases provides comprehensive care to keep the plants healthy.

[1042] The "photography function" is a function that allows a user to take an image of a plant using the smartphone camera.

[1043] "Analyzing images to evaluate the health of plants" refers to the process of extracting characteristics such as leaf color, shape, and the presence or absence of disease spots from acquired plant images, and diagnosing the health of the plant.

[1044] "Entering information about the growing environment" refers to the act of the user entering data such as the type of plant, installation location, hours of sunlight, temperature, and humidity into the app.

[1045] "Generating a care plan based on the analysis results and growth environment information" refers to the process of creating a care plan suitable for maintaining the health of plants based on the results of image analysis and growth environment information entered by the user.

[1046] "Sending a reminder according to a care plan" refers to the act of sending a notification to a user to perform care at an appropriate time based on the generated care plan.

[1047] "Providing preventive measures and treatments for seasonal changes and plant-specific diseases" is a process of guiding users to specific preventive measures and treatments to deal with seasonal climate changes and plant-specific diseases.

[1048] This invention is a system that allows plant lovers to effectively care for their plants. The system uses a smartphone camera to capture images of plants and analyzes those images to assess the plant's health. The system also uses user input information about the plant's growing environment. Based on the analysis results and the growing environment information, the system generates a personalized care plan and sends reminders according to the plan. It also provides preventative measures and treatments for seasonal changes and plant-specific diseases.

[1049] Specifically, the following hardware and software are used.

[1050] Hardware and software used

[1051] 1. Smartphone: A mobile information device held by a user.

[1052] 2. Camera: The built-in photography function of a smartphone.

[1053] 3. Server: A computer with high processing power that performs image analysis and data storage.

[1054] 4. Application software: Apps installed on smartphones.

[1055] 5. Image analysis module: Uses libraries such as Python, OpenCV, and TensorFlow.

[1056] 6. Database: Storage for habitat information and care plans.

[1057] Operation procedures and examples

[1058] Acquire images of plants

[1059] The user launches the app on their smartphone and uses the camera to take a picture of the plant. The device temporarily stores the image and prepares to send it to the server.

[1060] Analyzing the image

[1061] When the device sends the captured image to the server, the server launches an image analysis module and evaluates the plant's health based on the received image. For example, OpenCV is used to extract leaf color, shape, and the presence or absence of disease spots, and TensorFlow is used to diagnose the plant's health.

[1062] Entering the habitat data

[1063] The user enters information about the plant's growing environment in the app, including the type of plant, its location, sunlight hours, temperature, humidity, etc. The device converts this data into JSON format and sends it to the server.

[1064] Generate a care plan

[1065] The server generates an optimal care plan based on the image analysis results and growing environment information, for example, by using a database knowledge base and machine learning algorithms to determine the optimal watering frequency and type and amount of fertilizer for the user's plants.

[1066] Send a reminder

[1067] The server sets reminders based on the care plan generated by the server. Taking into account the user's daily rhythm, the server sends reminders to the device at appropriate times. The device then notifies the user of the reminders and encourages them to care for their plants.

[1068] Providing disease prevention measures

[1069] Additionally, the server generates preventative measures and treatments for seasonal changes and specific illnesses, which are then sent to the device and notified to the user.

[1070] Specific operation example

[1071] For example, consider a user who is caring for a houseplant. Every morning, the user opens the app and takes a photo of the plant. The device sends the captured image to the server, which analyzes the plant's health. The analysis reveals that some of the leaves are yellowing, suggesting a lack of water. The user inputs data such as the plant's location (a sunny living room) and the amount of sunlight (approximately 6 hours), and the server generates a care plan for watering once a week and liquid fertilizer once a month. Based on this care plan, the server sets a watering reminder every Wednesday at 8:00 a.m. and sends it to the device. The user receives the reminder at 8:00 a.m. and waters the plant. The server also sends pest prevention measures in early spring, helping the user remember to take care of the plant.

[1072] Prompt Sentence Examples

[1073] "After a user opens the app on their smartphone and takes a picture of their houseplant, the server analyzes the image and evaluates the plant's health. Please also explain in detail how the system generates an optimal care plan and sends reminders based on growing environment information entered by the user, such as the installation location and sunlight hours. Please also emphasize that seasonal disease prevention measures are also provided."

[1074] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1075] Step 1:

[1076] The user launches the smartphone app and takes a picture of the plant using the camera function. The device temporarily saves this image in its internal storage. At this stage, the input is the captured image, and the output is the temporarily saved image file. Specifically, the user taps the camera button in the app, frames the entire plant, and presses the shutter button.

[1077] Step 2:

[1078] The device retrieves the temporarily saved image and sends it to the server. Here, the input is the temporarily saved image file, and the output is the image data sent to the server. Specifically, the device checks the network connection and uses an HTTP request to upload the image file to the specified endpoint on the server.

[1079] Step 3:

[1080] The server analyzes the received image data. It launches an image analysis module and uses OpenCV and TensorFlow to extract features such as leaf color, shape, and the presence or absence of disease spots. The input is the image data sent to the server, and the output is an assessment result regarding the plant's health. Specifically, the server reads the image data, analyzes the pigment information of each pixel, and evaluates the health by detecting specific patterns.

[1081] Step 4:

[1082] The user enters information about the plant's growing environment in the app. This includes the type of plant, location, sunlight hours, temperature, humidity, etc. The device converts this data into JSON format and sends it to the server. The input is the growing environment information entered by the user, and the output is the JSON data sent to the server. Specifically, the user manually enters the required information into the app's input form and clicks the "Submit" button.

[1083] Step 5:

[1084] The server receives the image analysis results and growing environment information and generates an optimal care plan. Using the database's knowledge base and machine learning algorithms, the care plan is created based on the analysis results and input information. The input is the image analysis results and growing environment information, and the output is the generated care plan. The server calculates this and creates a plan that includes specific watering frequency, type and amount of fertilizer, and disease prevention measures.

[1085] Step 6:

[1086] Reminders are set based on the care plan generated by the server. Reminders are sent to the device at appropriate times, taking into account the user's daily rhythm. The input is the generated care plan, and the output is the reminder notification sent to the device. Specifically, the server takes into account the user's time zone settings and sets a reminder, for example, "Water the plants every Wednesday at 8am," and sends a push notification to the device.

[1087] Step 7:

[1088] The server generates preventive measures and treatment methods according to seasonal changes and specific diseases. This information is also sent to the terminal and notified to the user. The input is external seasonal information and plant condition information, and the output is the generated preventive measures and treatment methods. Specifically, the server regularly collects local weather data and disease occurrence information, and based on that, creates specific advice such as "use pest prevention spray in early spring" and sends it to the terminal.

[1089] Through the above processing steps, the system provides personalized plant care to users and provides comprehensive support for keeping plants healthy.

[1090] (Application example 1)

[1091] 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."

[1092] Conventional plant care systems are specialized in managing plant health, but are unable to address food freshness and quality control. For busy modern people, managing food storage methods and consumption timings is particularly difficult, resulting in food waste. The present invention aims to solve these problems and enable the appropriate management of both plants and food.

[1093] 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.

[1094] In this invention, the server includes [means for acquiring images], [means for analyzing the acquired images to evaluate the health of the plant], and [means for inputting data related to the growth environment. This enables plant health management. In addition, by including [means for generating a care plan based on the analysis results and growth environment data], [means for sending reminders in accordance with the generated care plan], and [means for providing preventive measures and treatments for seasonal changes and plant-specific diseases], the efficiency of plant care is improved. Furthermore, by adding [means for analyzing the freshness and quality of food and suggesting storage methods] and [means for sending reminders based on the storage method], it becomes possible to manage the freshness of food and ensure its appropriate storage and consumption, which is expected to reduce food waste.

[1095] "Means for acquiring images" is a function that allows a user to take images of plants or food with a camera and send the images to the server.

[1096] The "means for analyzing acquired images to evaluate the health of plants" refers to a function that analyzes images received by the server and diagnoses the health of the plant based on the color and shape of the leaves, the presence or absence of disease spots, etc.

[1097] The "means for inputting data on the growing environment" is a function that allows the user to input information on the growing environment, such as the type of plant, installation location, hours of sunlight, temperature, and humidity, and send it to the server.

[1098] "Means for generating a care plan based on the analysis results and growth environment data" is a function that enables the server to create a care plan optimal for maintaining the health of plants based on the image analysis results and the input growth environment data.

[1099] The "means for sending a reminder in accordance with the generated care plan" is a function in which the server sends a reminder to the user at an appropriate time based on the generated care plan.

[1100] "Means for providing preventive measures and remedies for seasonal changes and plant-specific diseases" is a function in which the server generates preventive measures and remedies according to seasonal changes and plant-specific diseases and notifies the user.

[1101] "Means for analyzing food freshness and quality and suggesting storage methods" is a function that analyzes photographed images of food, evaluates its freshness and quality, and suggests the optimal storage method.

[1102] The "means for sending a reminder based on the storage method" is a function for sending a reminder to the user at an appropriate time based on the proposed storage method.

[1103] This invention is a system for effectively managing plants and food. The system captures images of plants and food ingredients using a camera on a user device and evaluates their health and freshness through image analysis. It then generates a personalized care plan or storage method based on the growth or storage environment data entered by the user, and sends appropriate reminders.

[1104] Hardware and Software Configuration

[1105] This system is implemented using the following hardware and software.

[1106] 1. User device: A mobile device with a camera function, such as a smartphone or tablet, that has an interface that allows users to take images of plants or food and provide input data.

[1107] 2. Server: A computer device with high-performance data processing capabilities that receives images and data sent from user devices, analyzes them, and generates care plans.

[1108] 3. Image analysis module: Software that uses image processing libraries (e.g., OpenCV, TensorFlow) to assess the health of plants or the freshness of food from images.

[1109] 4. Database: A database that manages the status of plants and food for each user, generated care plans, and reminder information.

[1110] 5. Communication module: Internet communication function for transferring data between user terminals and servers.

[1111] Operation explanation

[1112] 1. Image acquisition and transmission:

[1113] Users take pictures of plants or food using a smartphone application, which temporarily stores the images and prepares them for transmission to the server.

[1114] 2. Image Analysis:

[1115] The server uses an image analysis module to evaluate the health of plants and the freshness of food based on the received images, analyzing characteristics such as leaf color, shape, and the presence or absence of disease spots.

[1116] 3. Enter environmental data:

[1117] Within the app, users input data on the growing environment, such as the type of plant, installation location, sunlight hours, temperature, and humidity, as well as the type of food, purchase date, storage location, and storage temperature, etc. This data is sent to the server.

[1118] 4. Generate a care plan and save it:

[1119] Based on the results of image analysis and data entered by the user, the server generates optimal care plans for maintaining plant health and food storage methods, including watering frequency, appropriate fertilizer type and amount, and disease prevention measures, as well as suggestions for refrigeration and freezing methods and cooking and consumption timing.

[1120] 5. Send reminders:

[1121] The server sends reminders to the device at appropriate times based on the generated care plan and storage method, so that the user does not miss important care or consumption opportunities.

[1122] 6. Providing preventative and remedial measures:

[1123] Depending on the change of seasons and the time when certain plant diseases are more likely to occur, the server generates preventive measures and treatments and notifies the user.

[1124] Specific examples

[1125] For example, let's say a user is growing a houseplant. Every morning, the user opens the app and takes a photo of the plant, and the device sends the image to the server. The server analyzes the plant's health and detects that some of the leaves are yellowing, suggesting a lack of water. When the user inputs data such as the plant's location and sunlight hours, the server generates a care plan for watering the plant once a week and liquid fertilizing it once a month. Based on this care plan, the server sends a watering reminder every Wednesday morning at 8:00 a.m. The server also sends advice on pest prevention as the seasons change.

[1126] In the case of food management, a user takes a picture of an apple, and the server analyzes it, resulting in a freshness score of 70. In this case, the system suggests storing the apple in the refrigerator for five days, and sends a reminder when the expiration date approaches. An example of a specific prompt is, "Store the apple with a freshness score of 70 in the refrigerator and set a reminder to consume it in five days."

[1127] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1128] Step 1:

[1129] A user launches a smartphone application and takes a picture of a plant or food with the camera. The input data is the plant or food image taken with the camera, and the output is a temporarily saved image file. The specific operation is to capture the image using the camera function and temporarily save it in local storage.

[1130] Step 2:

[1131] The terminal sends the temporarily saved image to the server. The input data is the image file saved on the user terminal, and the output is the image data transferred to the server. The specific operation is to upload the image file to the server via network communication.

[1132] Step 3:

[1133] The server launches an image analysis module to analyze the images it receives. The input data is the image file received by the server, and the output is the evaluation results for the health of plants and the freshness of food. Specifically, it uses libraries such as OpenCV and TensorFlow to extract features from the images and apply analysis algorithms to make an evaluation.

[1134] Step 4:

[1135] The user inputs data on the plant's growing environment or food storage environment within the app. The input data includes the type of plant, installation location, sunlight hours, temperature, humidity, type of food, purchase date, storage location, storage temperature, etc., and the output is the growing environment data or storage environment data sent to the server. The specific operation is that the user inputs data via the user interface and sends the data to the server.

[1136] Step 5:

[1137] The server generates optimal care plans and food preservation methods for maintaining plant health based on the image analysis results and data entered by the user. The input data is the image analysis results and growth environment data or preservation environment data, and the output is the generated care plans and preservation methods. Specifically, the server uses past data and algorithms stored in the database to calculate and generate optimal plans and methods.

[1138] Step 6:

[1139] The server sends reminders to the user's device at appropriate times based on the generated care plan and storage method. The input data is the generated care plan or storage method and reminder setting information, and the output is the reminder notification sent to the user's device. The specific operation is to set a reminder schedule and send the notification at the set time.

[1140] Step 7:

[1141] The server generates preventive measures and countermeasures according to the change of seasons and the time when a particular plant disease is likely to occur, and notifies the user. The input data includes the season, disease information, plant type, etc., and the output is a notification of the preventive measures and countermeasures sent to the user. The specific operation is to generate appropriate advice based on plant type and seasonal information, and notify the user as necessary.

[1142] 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.

[1143] This invention is a system that combines an emotion engine that recognizes the user's emotions to help plant lovers efficiently care for their plants. The system captures images of plants using a smartphone camera, analyzes the images to evaluate the plant's health, and analyzes the user's emotions using the emotion engine. This generates a personalized care plan and provides reminders based on the user's emotions. It also provides preventative measures and treatments for seasonal changes and plant-specific diseases.

[1144] Program processing overview

[1145] 1. Acquire images of plants

[1146] First, the user opens the app on their smartphone and takes a picture of a plant with their camera. The device temporarily stores the image and prepares it to be sent to the server.

[1147] 2. Analyze the images

[1148] When the device sends the captured image to the server, the server activates the image analysis module. The server evaluates the plant's health based on the received image. Specifically, it extracts features such as leaf color, shape, and the presence or absence of disease spots, and diagnoses whether the plant is healthy or diseased.

[1149] 3. Enter the habitat data

[1150] Within the app, users input information about the plant's growing environment, including the type of plant, its location, sunlight hours, temperature, humidity, etc. The device then sends this data to a server.

[1151] 4. Emotion Recognition by Emotion Engine

[1152] The device takes a picture of the user's face with a camera, and the device or server analyzes the user's emotions using an emotion engine. The analyzed emotion data is also sent to the server.

[1153] 5. Generate a care plan

[1154] The server generates an optimal care plan for maintaining plant health based on the image analysis results, growth environment data, and emotional data. Specifically, the plan includes watering frequency, appropriate fertilizer type and amount, disease prevention measures, etc. The care plan is also adjusted based on the user's emotional state.

[1155] 6. Send a reminder

[1156] The server sets reminders based on the care plan generated by the server. Taking into account the user's daily rhythm and emotional state, the server sends reminders to the device with appropriate timing and content. The device then displays notifications to the user, encouraging them to carry out the care.

[1157] 7. Provide disease prevention measures

[1158] In addition, the server generates preventative and treatment measures for seasonal changes and specific illnesses, and this information is also sent to the device and notified to the user.

[1159] Specific examples

[1160] For example, let's say a user is growing a houseplant. Every day, the user opens the app and takes a photo of the plant. The device sends the captured image to a server, which analyzes the plant's health. The analysis reveals that some of the leaves are yellowing, suggesting a lack of water. The user enters data such as the plant's location (a sunny living room) and the number of hours of sunlight (approximately 6 hours) into the app, and the server takes this information and the analysis results into consideration to generate a care plan that calls for watering the plant once a week and administering liquid fertilizer once a month.

[1161] The device also uses the user's emotion engine to analyze the user's facial expressions when taking a photo. For example, if the emotion engine recognizes that the user looks busy and stressed, the system will simplify the care plan and adjust it to reduce the user's burden.

[1162] Based on this care plan, the server sets a watering reminder every Wednesday morning at 8:00 a.m. and sends it to the device. The user receives the reminder at 8:00 a.m. and waters the plants. The server also sends advice on pest prevention as the seasons change, ensuring the user remembers to perform the necessary care.

[1163] In this way, the system of the present invention personalizes appropriate plant care, allowing even busy users to easily keep their plants healthy. Furthermore, by taking the user's emotions into consideration, it is possible to provide more flexible and effective care plans.

[1164] The processing flow will be explained below.

[1165] Program processing flow

[1166] Step 1:

[1167] The user opens the app on their smartphone. The user selects the camera function and takes a photo of a plant. The device temporarily stores the image and prepares it to be sent to the server.

[1168] Step 2:

[1169] The device sends the stored image data to the server, which then stores the received image data in a database.

[1170] Step 3:

[1171] The server launches the image analysis module. The server applies image processing algorithms to recognize plant parts in the image. The server extracts features such as leaf color, shape, and the presence of disease spots to assess the plant's health.

[1172] Step 4:

[1173] The user enters environmental information such as the type of plant, location, and sunlight hours into a form within the app. The device sends the entered data to the server, which then stores the received environmental data in a database.

[1174] Step 5:

[1175] The device takes a picture of the user's face with a camera. The device or server analyzes the user's emotions using an emotion engine. The analyzed emotion data is also sent to the server.

[1176] Step 6:

[1177] The server creates an optimal care plan based on environmental data, image analysis results, and emotional data. The server then generates specific care instructions, such as the appropriate watering frequency, type of fertilizer, and timing. The care plan is also adjusted based on the user's emotions.

[1178] Step 7:

[1179] The server sends the generated care plan to the terminal, and the terminal notifies the user of the received care plan.

[1180] Step 8:

[1181] The server sets a reminder schedule based on the care plan. The server determines the date, time, and content of the reminder, taking into account the user's daily rhythm and emotional state. The device sets the reminder and notifies the user at the specified date and time.

[1182] Step 9:

[1183] The server analyzes data on seasonal changes and plant-specific diseases. The server generates appropriate disease prevention and treatment methods. The server sends this information to the device. The device notifies the user of the received information.

[1184] Example 2

[1185] 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."

[1186] Plant care is time-consuming, especially for users with busy lifestyles. It is difficult to accurately grasp the health status of plants and continue appropriate care. Furthermore, there is a lack of methods to provide personalized care plans that take the user's emotions into account. Therefore, there is a strong need for a system that evaluates the health status of plants and provides appropriate care based on the user's emotions.

[1187] The identification process by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means. In this invention, the server includes: [means for acquiring video;] [means for analyzing the acquired video to evaluate the health condition of the plant;] [means for inputting data related to the growing environment;] [means for recognizing the user's emotions and acquiring emotion data;] [means for generating a care plan based on the analysis results, the growing environment data, and the emotion data;] [means for sending notifications according to the generated care plan; and [means for providing preventive measures and remedies for seasonal changes and plant-specific diseases.] This makes it possible to efficiently care for plants and provide a personalized care plan based on the user's emotions. Furthermore, by providing appropriate care and preventive measures based on the plant's health condition, the plant can be kept healthy.

[1188] The "means for acquiring images" refers to a means by which a user records images of plants using a photographing device such as a smartphone or tablet.

[1189] The "means for analyzing acquired images to evaluate the health of plants" refers to a means for analyzing recorded images of plants and diagnosing the health of plants based on information such as leaf color, shape, and the presence or absence of disease spots.

[1190] The "means for inputting data on the growing environment" is a means by which the user inputs information on the growing environment, such as the type of plant, installation location, hours of sunlight, temperature, and humidity.

[1191] The "means for recognizing the user's emotions and acquiring emotion data" refers to a means for taking a picture of the user's face with a camera, analyzing the user's emotional state using an emotion engine, and acquiring the data.

[1192] The "means for generating a care plan based on the analysis results, growth environment data, and emotional data" refers to a means for integrating the analysis results of the plant's health condition, data related to the growth environment, and the user's emotional data to generate an optimal plant care plan.

[1193] The "means for sending a notification in accordance with the generated care plan" refers to a means for sending a notification of care plan execution to a user at a specific timing based on the generated care plan.

[1194] The "means for providing preventive measures and remedies for seasonal changes and diseases specific to plants" is a means for providing users with preventive measures and remedies for seasonal changes and diseases specific to specific plants.

[1195] The "means for providing the generated care plan to the terminal" refers to a means for displaying or notifying the contents of the generated care plan on the user's terminal.

[1196] The "means for encrypting the acquired image and transmitting it to the server" is a means for encrypting the acquired image of the plant as a security measure and transmitting it to the server.

[1197] This invention is a system that analyzes the health of plants and the user's emotional state to provide personalized care plans to help plant lovers efficiently care for their plants. This system uses hardware and software such as a mobile device such as a smartphone, a server, and an emotion engine.

[1198] The process begins when a user launches the app on their smartphone and takes a picture of a plant with their camera. The device acquires the image and temporarily stores it. The device then compresses and encrypts the image before sending it to the server.

[1199] The server launches an AI-based image analysis module based on the images it receives. The server analyzes the color, shape, and presence of disease spots on the plant's leaves to assess the plant's health. The assessed information is then recorded in a database.

[1200] Next, the user enters information about the plant's growing environment in the app, including the type of plant, its location, sunlight hours, temperature, humidity, etc. The device also sends this data to the server.

[1201] The device then takes a photo of the user's face with a camera and activates an emotion engine to analyze the user's emotions. The analyzed emotion data is also sent to the server, which then generates a comprehensive care plan based on the results of the image analysis, the growth environment data, and the user's emotion data.

[1202] The generated care plan will suggest specific actions needed to maintain the plant's health, such as watering frequency, the type and amount of fertilizer to use, and disease prevention measures. Furthermore, the care plan's contents are adjusted according to the user's emotional state. For example, if the user is busy and stressed, measures such as reducing the frequency of care will be taken.

[1203] The server sets reminders based on the care plan and sends them to the device. The reminders are sent at times that take into account the user's daily rhythm. The device displays the reminder notification, encouraging the user to perform the care.

[1204] Finally, the server automatically generates preventative and treatment measures for seasonal changes and specific illnesses, and this information is also sent to the device and notified to the user.

[1205] Specific examples

[1206] For example, let's say a user is growing a houseplant. Every day, the user opens the app and takes a photo of the plant. The device compresses and encrypts the captured image and sends it to the server. The server then launches an AI-based image analysis module to analyze the leaves for yellowing or disease spots. Based on the analysis results, it is determined that the plant is lacking water.

[1207] The user enters information such as the installation location (a sunny living room) and the number of hours of sunlight (approximately 6 hours) into a form within the app and submits it. The device then sends this data to the server. The server then uses an emotion recognition engine to analyze the user's emotional state, taking a photo of the user's face and analyzing their emotions. For example, if the user is feeling stressed, the system will simplify the care plan and adjust it to reduce the user's burden.

[1208] Based on the care plan generated by the server, a watering reminder is set every Wednesday morning at 8:00 and sent to the device. The device then displays the reminder to the user at the appropriate time, prompting the user to water the plants. The server also generates pest prevention advice according to the change of seasons and notifies the user.

[1209] Prompt Sentence Examples

[1210] "I want to know if my houseplants look healthy. Please tell me the best way to care for them while taking my feelings into consideration."

[1211] "What is the watering and fertilizing schedule for the houseplants in my sunny living room?"

[1212] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1213] Step 1: Get an image of your plant

[1214] The user opens the smartphone app and takes a picture of a plant with the camera. The device activates the camera module, and the user presses the capture button to capture the image. The input is the plant image taken by the user, and the output is the captured image data. The device temporarily saves this image and prepares to send it to the server.

[1215] Step 2: Send the image to the server

[1216] The device sends the image of the plant that it previously saved to the server. The device compresses the image data and encrypts it for security. The input is the acquired image data, and the output is the compressed and encrypted image data. This is then sent to the server.

[1217] Step 3: Analyze the image on the server

[1218] The server launches an image analysis module and analyzes the received plant images. Specifically, it uses an AI-based image recognition algorithm to analyze the color, shape, and presence of disease spots on the plant leaves. The input is the encrypted and transmitted image data, and the output is plant health data based on the analysis results. This data is stored in a database on the server.

[1219] Step 4: Enter habitat data

[1220] The user enters information about the plant's growing environment into a form within the app. This includes details such as the plant's type, location, sunlight hours, temperature, and humidity. The device then sends this data to the server. The input is the growing environment data entered by the user, and the output is the growing environment data sent to the server.

[1221] Step 5: Photograph the user's face and recognize their emotions

[1222] The device takes a picture of the user's face with a camera and activates the emotion engine to analyze the user's emotions. The input is the user's face image taken with the camera, and the output is the analyzed emotion data. This emotion data is also sent to the server.

[1223] Step 6: Generate a care plan

[1224] The server generates an optimal care plan based on the results of image analysis, growth environment data, and emotional data. Using an AI algorithm, the server determines the frequency of watering, the optimal type and amount of fertilizer, disease prevention measures, etc. The inputs are the results of image analysis, growth environment data, and emotional data, and the output is the generated care plan.

[1225] Step 7: Set and send reminders

[1226] The server sets a reminder based on the care plan generated. When the server completes setting the reminder, it sends a notification to the device. The device displays the reminder to the user at the appropriate time. The input is the generated care plan, and the output is the reminder.

[1227] Step 8: Providing preventative and remedial measures

[1228] The server then generates preventive measures and treatments according to the change of seasons and specific diseases. This information is also sent to the terminal and notified to the user. The input is data about the seasons and diseases, and the output is the generated preventive measures and treatments.

[1229] (Application example 2)

[1230] 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."

[1231] Conventional plant care assistant systems provide uniform care plans without considering the user's emotions, placing a heavy burden on the user and often resulting in ineffective care. Furthermore, the image analysis function for accurately grasping the plant's health status is insufficient, making it difficult to provide appropriate care methods. Furthermore, the inability to provide appropriate countermeasures for seasonal changes and disease prevention is also an issue.

[1232] 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 acquiring images]; [means for analyzing the acquired images to evaluate the health status of the plant]; [means for inputting data related to the growth environment]; [means for generating a care plan based on the analysis results, the growth environment data, and the user's emotional data]; [means for sending reminders according to the generated care plan]; [means for providing preventive measures and remedies for seasonal changes and plant-specific diseases]; and [means for adjusting the care plan according to the user's emotions]. This allows for the provision of a personalized care plan taking the user's emotions into consideration, enabling appropriate plant care. It also makes it possible to accurately grasp the health status of the plant and provide appropriate countermeasures according to the season and disease prevention.

[1233] "Means for acquiring images" refers to the ability of a user to take images of plants using a smartphone or other device.

[1234] "Means for analyzing acquired images to evaluate the health of plants" refers to a technology in which a server analyzes images of plants, extracts characteristics such as leaf color and shape, and the presence or absence of disease spots, and evaluates the health of the plants.

[1235] "Means for inputting data on the growing environment" refers to a function that allows users to input information about the growing environment, such as the type of plant, installation location, hours of sunlight, temperature, and humidity, within the app.

[1236] "Means for generating a care plan based on analysis results, growth environment data, and user emotional data" refers to a technology in which a server generates an optimal plant care plan based on the analysis results of the plant's health, growth environment data, and user emotional data.

[1237] The "means for sending reminders in accordance with the generated care plan" is a technology for sending care reminders to the user at appropriate times based on the care plan generated by the server.

[1238] The "means for providing preventive measures and remedies for seasonal changes and plant-specific diseases" is a technology in which a server generates preventive measures and remedies for seasonal changes and plant-specific diseases and notifies the user.

[1239] The "means for adjusting the care plan according to the user's emotions" is a technology that analyzes the user's emotional data and makes adjustments such as simplifying the care plan when the user is feeling stressed.

[1240] The "means for distributing the generated care plan to the user terminal" is a function for distributing the care plan generated by the server to the user's smartphone or other device.

[1241] The "means for encrypting the acquired user emotion data and image and transmitting them to the server" is a technology for encrypting the acquired user emotion data and plant image and transmitting them securely to the server.

[1242] The present invention provides a system for enabling a user to properly care for plants, the system comprising the following means:

[1243] 1. Image acquisition method:

[1244] Users use their smartphone, smart glasses, or other devices to take images of plants, which are temporarily stored on the device and prepared for transmission to the server.

[1245] 2. Analyzing captured images to assess plant health:

[1246] The server uses an image analysis module (e.g., OpenCV) to analyze the received images. Image analysis extracts features such as leaf color, shape, and the presence or absence of disease spots, and evaluates the plant's health.

[1247] 3. Means for inputting habitat data:

[1248] Through the application, users input information about the plant's growing environment, such as the type of plant, its location, hours of sunlight, temperature, and humidity, which is then sent from the device to the server.

[1249] 4. A method for generating a care plan based on the analysis results, growth environment data, and user emotion data:

[1250] The server generates an optimal plant care plan based on the results of the plant's health analysis, growing environment data, and the user's emotional data (obtained using an emotion analysis engine such as DeepFace). The plan includes information such as watering frequency and type of fertilizer.

[1251] 5. Means of sending reminders according to the generated care plan:

[1252] The server then sends reminders to the user at appropriate times based on the generated care plan, taking into account the user's daily rhythm and emotional state.

[1253] 6. Means of providing preventative and remedial measures for seasonal and plant-specific diseases:

[1254] The server generates and notifies users of preventive and remedial measures for seasonal changes and specific diseases, allowing them to take the necessary care in a timely manner.

[1255] 7. How to adjust care plans based on user emotions:

[1256] The server adjusts the care plan based on the user's emotional data, for example by simplifying it if the user is feeling stressed, thereby reducing the burden on the user.

[1257] As a concrete example, suppose a user is tending a houseplant. The user opens the app and takes a photo of the plant every day. The device sends the captured image to the server, which analyzes the plant's health. The analysis reveals that some of the leaves are yellowing, suggesting a lack of water. The user enters data such as the plant's location (a sunny living room) and the amount of sunlight (approximately six hours) into the app. The server then takes this information and the analysis results into account to generate a care plan for watering the plant once a week and providing liquid fertilizer once a month. The device also uses the user's emotion engine to analyze the user's facial expression when taking the photo. For example, if the emotion engine recognizes that the user appears busy and stressed, the system simplifies the care plan to reduce the user's burden. Based on this care plan, the server sets a watering reminder for every Wednesday at 8:00 a.m. and sends it to the device. The user receives the reminder at 8:00 a.m. and waters the plant. The server also sends pest prevention advice as the seasons change, ensuring the user remembers to perform the necessary care.

[1258] An example of a prompt for the generative AI model is as follows:

[1259] "Generate plant care advice for a cactus plant in a living room with 6 hours of sunshine, 24°C temperature, and 50% humidity. The user is stressed and overwhelmed at work."

[1260] "Analyze the health of your plant and suggest an appropriate care plan. The plant is a Dracaena, the location is the kitchen, the sunshine hours are 7 hours, the temperature is 22 degrees, the humidity is 60%. The user is relaxed."

[1261] In accordance with the above description, the present invention provides a system that provides efficient and personalized plant care for plant lovers.

[1262] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1263] Step 1:

[1264] (Image acquisition)

[1265] Users can take pictures of their plants using the camera on their smartphone or smart glasses, which allows them to visually record the latest status of their plants. The images are temporarily stored on the device.

[1266] Input: A plant image taken by the user

[1267] Output: Plant image data temporarily saved on the device

[1268] Step 2:

[1269] (Image sending)

[1270] The device sends the stored plant images to the server, where they are encrypted for privacy purposes.

[1271] Input: Plant image data stored on the device

[1272] Output: Encrypted image data sent to the server

[1273] Step 3:

[1274] (Image analysis)

[1275] The server uses an image analysis module (e.g., OpenCV) to analyze the images of the plants sent to it, extracting features such as leaf color, shape, and the presence or absence of disease spots to assess the plant's health.

[1276] Input: Encrypted image data sent to the server

[1277] Output: Plant health assessment data

[1278] Step 4:

[1279] (Growth environment data input)

[1280] Users enter information about the plant's growing environment, such as the type of plant, its location, sunlight hours, temperature, humidity, etc., into the app, and this data is sent from the device to the server.

[1281] Input: User-entered data about the habitat

[1282] Output: Habitat data sent to the server

[1283] Step 5:

[1284] (Emotion data acquisition)

[1285] To perform emotion analysis, a user takes a facial image using a camera on their smartphone or smart glasses. The device or server then analyzes the user's emotions using an emotion analysis engine such as DeepFace and sends the data to the server.

[1286] Input: A face image taken by the user

[1287] Output: Sentiment analysis data sent to the server

[1288] Step 6:

[1289] (Care plan generation)

[1290] The server generates an optimal plant care plan based on the results of plant image analysis, growing environment data, and user emotional data, including watering frequency, appropriate type and amount of fertilizer, and disease prevention measures.

[1291] Input: Plant health assessment data, growth environment data, emotion analysis data

[1292] Output: Generated care plan

[1293] Step 7:

[1294] (Reminder sent)

[1295] Based on the care plan generated by the server, care reminders are sent to the user at appropriate times, taking into account the user's daily rhythm and emotional state.

[1296] Input: Generated Care Plan

[1297] Output: Reminder notification sent to device

[1298] Step 8:

[1299] (Providing preventive measures)

[1300] The server generates and notifies users of preventive and remedial measures for seasonal changes and plant-specific diseases, allowing them to take the necessary care in a timely manner.

[1301] Input: Seasonal information, plant-specific disease data

[1302] Output: Preventive measures and action notifications sent to the device

[1303] Step 9:

[1304] (Adjusting the care plan)

[1305] The server adjusts the care plan based on the user's emotional data, for example simplifying the care plan if the user is feeling stressed.

[1306] Input: User sentiment analysis data

[1307] Output: Coordinated care plan

[1308] The above are the specific processing steps of the system that realizes the application example, which allows users to perform efficient and personalized plant care.

[1309] 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.

[1310] 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.

[1311] 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.

[1312] 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.

[1313] 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.

[1314] 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.

[1315] 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).

[1316] 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.

[1317] 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."

[1318] 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.

[1319] 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).

[1320] 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.

[1321] 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.

[1322] 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.

[1323] 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.

[1324] 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.

[1325] 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.

[1326] 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.

[1327] 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.

[1328] 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.

[1329] 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.

[1330] The following is further disclosed regarding the above embodiment.

[1331] (Claim 1)

[1332] a means for acquiring an image;

[1333] means for analyzing the acquired images to assess the health of the plant;

[1334] a means for inputting data relating to the growing environment;

[1335] A means for generating a care plan based on the analysis results and growth environment data;

[1336] means for sending reminders according to the generated care plan;

[1337] A means of providing preventative and treatment measures for seasonal and plant-specific diseases;

[1338] A system including:

[1339] (Claim 2)

[1340] 10. The system of claim 1, further comprising means for delivering the generated care plan to a terminal.

[1341] (Claim 3)

[1342] 10. The system of claim 1, further comprising means for encrypting the captured image and transmitting it to the server.

[1343] "Example 1"

[1344] (Claim 1)

[1345] a means for acquiring an image of the plant using a photographing function;

[1346] means for analyzing the acquired images to assess the health of the plant;

[1347] a means for inputting information about the growing environment;

[1348] A means for generating a care plan based on the analysis results and growth environment information;

[1349] means for sending reminders according to the generated care plan;

[1350] A means of providing preventative and treatment measures for seasonal and plant-specific diseases;

[1351] A system including:

[1352] (Claim 2)

[1353] The system of claim 1, wherein the generated care plan is delivered to an information terminal.

[1354] (Claim 3)

[1355] The system according to claim 1, wherein the acquired image is encrypted and transmitted to the information server.

[1356] "Application Example 1"

[1357] (Claim 1)

[1358] a means for acquiring an image;

[1359] means for analyzing the acquired images to assess the health of the plant;

[1360] a means for inputting data relating to the growing environment;

[1361] A means for generating a care plan based on the analysis results and growth environment data;

[1362] means for sending reminders according to the generated care plan;

[1363] A means of providing preventative and treatment measures for seasonal and plant-specific diseases;

[1364] A means of analyzing food freshness and quality and suggesting preservation methods,

[1365] means for sending reminders based on the storage method;

[1366] A system including:

[1367] (Claim 2)

[1368] The system of claim 1, wherein the generated care plan is delivered to the terminal.

[1369] (Claim 3)

[1370] The system of claim 1, wherein the acquired image is encrypted and transmitted to the server.

[1371] "Example 2: Combining Emotion Engines"

[1372] (Claim 1)

[1373] a means for acquiring the image;

[1374] a means for analyzing the acquired video to assess the health of the plant;

[1375] a means for inputting data relating to the growing environment;

[1376] means for recognizing a user's emotion and acquiring emotion data;

[1377] A means for generating a care plan based on the analysis results, growth environment data, and emotion data;

[1378] means for sending notifications in accordance with the generated care plan;

[1379] A means of providing preventative and treatment measures for seasonal and plant-specific diseases;

[1380] A system including:

[1381] (Claim 2)

[1382] 10. The system of claim 1, further comprising means for providing the generated care plan to a terminal.

[1383] (Claim 3)

[1384] 10. The system of claim 1, further comprising means for encrypting the captured video and transmitting it to the server.

[1385] "Application example 2 when combining emotion engines"

[1386] (Claim 1)

[1387] a means for acquiring an image;

[1388] means for analyzing the acquired images to assess the health of the plant;

[1389] a means for inputting data relating to the growing environment;

[1390] A means for generating a care plan based on the analysis results, growth environment data, and user emotion data;

[1391] means for sending reminders according to the generated care plan;

[1392] A means of providing preventative and treatment measures for seasonal and plant-specific diseases;

[1393] a means for adjusting a care plan in response to a user's emotions;

[1394] A system including:

[1395] (Claim 2)

[1396] 10. The system of claim 1, further comprising means for delivering the generated care plan to a user terminal.

[1397] (Claim 3)

[1398] 10. The system according to claim 1, further comprising means for encrypting the acquired user emotion data and image and transmitting the encrypted data to the server. [Explanation of symbols]

[1399] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot< / url:> < / url:> < / url:> < / url:>

Claims

1. a means for acquiring an image; means for analyzing the acquired images to assess the health of the plant; a means for inputting data relating to the growing environment; A means for generating a care plan based on the analysis results and growth environment data; means for sending reminders according to the generated care plan; A means of providing preventative and treatment measures for seasonal and plant-specific diseases; A system including:

2. The system of claim 1 further comprising means for delivering the generated care plan to a terminal.

3. 10. The system of claim 1, further comprising means for encrypting the captured image and transmitting it to the server.

Images