A plant maintenance method, device, equipment and storage medium

Abstract

This invention discloses a plant care method, apparatus, device, and storage medium. The method includes: using an XR camera to acquire multiple frames of images of a target scene; for each frame, using a plant recognition model to determine several plant objects contained in the image, and using XR technology to determine the anchor point coordinates of each plant object; for each plant object, using a species recognition model to determine the target species, using a health detection model to determine the health status, and combining the target species and health status to determine care information; and through the XR camera's visual interface, based on the anchor point coordinates, responding to the user with the target species of the plant object, its health status, and care information. This solution provides users with rich and diverse plant care information in an intelligent, efficient, and accurate manner, and leverages XR technology to provide intuitive and convenient 3D interactive responses to relevant content, thereby effectively improving the user experience while ensuring efficiency and accuracy.

Description

Technical Field

[0001] One or more embodiments of the present invention relate to the field of intelligent plant care technology, and more particularly to a plant care method, apparatus, equipment and storage medium. Background Technology

[0002] XR (Extended Reality) technology, represented by AR (Augmented Reality), VR (Virtual Reality), and MR (Mixed Reality), can merge the virtual world constructed by computers with the real physical world and present it to users through smart devices. It is a manifestation of the shift from 2D to 3D interaction between humans and machines.

[0003] Thanks to the development of related technologies, XR technology can now be combined with image recognition, natural language processing and other technologies in some applications to provide richer functional services and improve the user experience. Summary of the Invention

[0004] In view of the above, one or more embodiments of the present invention provide a plant care method, apparatus, device and storage medium.

[0005] To achieve the above objectives, one or more embodiments of the present invention provide the following technical solutions:

[0006] According to a first aspect of one or more embodiments of the present invention, a plant care method is provided, the method comprising:

[0007] Use an XR camera to acquire multiple frames of images to be identified in the target scene;

[0008] For each frame of the image to be identified, a trained plant recognition model is used to determine several plant objects contained in the image, and then XR technology is combined to determine the anchor point coordinates of each plant object.

[0009] For each plant object, a trained species identification model is used to determine the target species to which the plant object belongs, and a trained health detection model is used to determine the health status of the plant object. Then, by combining the target species and the health status, the maintenance information of the plant object is determined.

[0010] Through the XR camera's visualization interface, based on the anchor point coordinates of each plant object, the system responds to the user with the target species to which the plant object belongs, the health status of the plant object, and the maintenance information of the plant object.

[0011] In one alternative implementation, the health status of each plant object includes the disease information of the plant object;

[0012] The determination of maintenance information corresponding to the plant object includes:

[0013] If the plant is found to have an infectious disease based on the disease information, the prevention and control tasks for the plant are determined by combining the target species with the disease information; wherein the prevention and control tasks include one or more of the following: protection tasks and treatment tasks.

[0014] In one alternative implementation, the step of responding to the user with maintenance information corresponding to the plant object includes:

[0015] Through the XR camera's visualization interface, based on the anchor point coordinates of the plant object and the distance information in the protection task, the protection task responds to the user with the protection task of the plant object; wherein, the protection task uses different colors to distinguish between healthy plant objects and diseased plant objects, and uses a distance scale to indicate the safe distance between healthy plant objects and diseased plant objects.

[0016] And / or, through the visualization interface of the XR camera, based on the anchor point coordinates of the plant object, a rescue task for the plant object is responded to the user; wherein the rescue task indicates rescue measures corresponding to the plant object.

[0017] In one alternative implementation, determining the maintenance information corresponding to the plant object includes:

[0018] The maintenance status of the plant object is determined by using a trained maintenance detection model, and then the maintenance task of the plant object is determined by combining the target species, the health status and the maintenance status.

[0019] In the case where the plant is a soil-grown plant, the maintenance status includes one or more of the following: flowerpot information and soil information; and the maintenance task includes one or more of the following: repotting task, watering task, and fertilizing task.

[0020] When the plant is a hydroponic plant, the maintenance status includes one or more of the following: container information, water quality information, and water level information. The maintenance task includes one or more of the following: container replacement task, water change task, and nutrient solution application task.

[0021] In one alternative implementation, the step of responding to the user with maintenance information corresponding to the plant object includes:

[0022] Within the timeframe of each maintenance task, the content of the maintenance task is periodically pushed to the user based on the execution frequency of the maintenance task.

[0023] In one alternative implementation, the method further includes:

[0024] The trained environmental detection model is used to determine the illumination and airflow distribution in the target scene.

[0025] For each plant object, based on the light distribution and airflow distribution, combined with the target species to which the plant object belongs and the health status of the plant object, the ideal coordinates of the plant object in the target scene are determined. Then, the migration task of the plant object is determined by the anchor point coordinates and the ideal coordinates of the plant object.

[0026] Through the visualization interface of the XR camera, the system responds to the user with the migration task of the plant objects based on the coordinate information of each plant object in the migration task.

[0027] In one alternative implementation, the method further includes:

[0028] For the plant objects selected by the user, the beautification task of the plant objects is determined by combining one or more of the target species to which each plant object belongs and the health status of each plant object.

[0029] Through the XR camera's visualization interface, based on the coordinate information in the beautification task, the beautification task of the several plant objects is responded to the user; wherein, the beautification task indicates the deployment of the beautified positions of the several plant objects in the target scene.

[0030] In one alternative implementation, the method further includes:

[0031] For each plant object, the pruning task of the plant object is determined by combining one or more of the target species to which the plant object belongs, the health status of the plant object, and the current season information, using a trained pruning recommendation model.

[0032] Through the XR camera's visualization interface, based on the coordinate information in the cropping scheme, a cropping task for the plant object is responded to the user; wherein, the cropping task indicates the target part of the plant object to be cropped and the corresponding cropping method.

[0033] In one alternative implementation, the method further includes:

[0034] For each plant object, an information card for the plant object is determined, and the information card for the plant object is responded to the user through the visualization interface of the XR camera based on the anchor point coordinates of the plant object; wherein, the information card displays one or more of the following in a thumbnail format: target species, health status, maintenance information, migration task, beautification task, and pruning task.

[0035] In response to the user's checkbox operation, the XR camera's visual interface displays a sub-information card corresponding to the checkbox operation; wherein the sub-information card displays the complete content of any one of the following: target species, health status, maintenance information, migration task, beautification task, or cropping task.

[0036] In one alternative implementation, the method further includes:

[0037] When responding to information through the visualization interface of the XR camera, several related plant objects are displayed together.

[0038] In one alternative implementation, the method further includes:

[0039] When responding to information through the XR camera's visual interface, guidance information from third-party experts or other online users is also displayed together.

[0040] According to a second aspect of one or more embodiments of the present invention, a plant maintenance device is provided, the device comprising an image acquisition unit, an anchor point determination unit, an information determination unit, and an information response unit; wherein:

[0041] The image acquisition unit is used to call the XR camera to acquire multiple frames of images to be identified in the target scene;

[0042] The anchor point determination unit is used to determine several plant objects contained in each frame of the image to be identified using a trained plant recognition model, and then combine XR technology to determine the anchor point coordinates of each plant object.

[0043] The information determination unit is used to determine the target species to which the plant object belongs using a trained species identification model and to determine the health status of the plant object using a trained health detection model for each plant object. Then, by combining the target species and the health status, the maintenance information of the plant object is determined.

[0044] The information response unit is used to respond to the user, based on the anchor point coordinates of each plant object, through the visualization interface of the XR camera, the target species to which the plant object belongs, the health status of the plant object, and the maintenance information of the plant object.

[0045] According to a third aspect of one or more embodiments of the present invention, an electronic device is provided, comprising:

[0046] The processor, and memory for storing processor-executable instructions;

[0047] The processor implements the steps in the method described in the first aspect by running the executable instructions.

[0048] According to a fourth aspect of one or more embodiments of the present invention, a computer-readable storage medium is provided having a computer program stored thereon, which, when executed by a processor, implements the steps of the method described in the first aspect above.

[0049] As can be seen from the above description, after the present invention calls the XR camera to collect multiple frames of images of the target scene, it first combines image recognition technology and XR technology to determine several plant objects and their anchor point coordinates in the target scene. Then, it uses a trained model to obtain relevant data such as the species and health status of each plant object to comprehensively determine the maintenance information of each plant object. Subsequently, it responds to the user with the maintenance information required for maintaining each plant object in the target scene through the XR camera's visualization interface in a 3D interactive manner.

[0050] This solution combines XR, image recognition, and other technologies in plant care applications. On the one hand, it provides users with rich and diverse plant care information in an intelligent and efficient manner. On the other hand, it leverages XR technology to respond to relevant content in a 3D interactive way, making it more intuitive and convenient. Thus, while ensuring the efficiency and accuracy of the solution, it effectively improves the user experience. Attached Figure Description

[0051] Figure 1 This is a schematic diagram illustrating an XR interactive interface for plant care, as shown in an exemplary embodiment.

[0052] Figure 2 A flowchart of a plant care method provided as an exemplary embodiment.

[0053] Figure 3 A flowchart illustrating a method for determining plant care information, as shown in an exemplary embodiment.

[0054] Figure 4 A flowchart illustrating a method for determining plant care information, as shown in another exemplary embodiment.

[0055] Figure 5 A flowchart illustrating a method for determining a plant migration task, as shown in an exemplary embodiment.

[0056] Figure 6A flowchart illustrating a method for determining a plant beautification task, as shown in an exemplary embodiment.

[0057] Figure 7 A flowchart illustrating a method for determining a plant pruning task, as shown in an exemplary embodiment.

[0058] Figure 8 A flowchart illustrating a method for responding to plant care information, as shown in an exemplary embodiment.

[0059] Figure 9 A flowchart illustrating a method for responding to plant care information, as shown in another exemplary embodiment.

[0060] Figure 10 A flowchart illustrating a method for responding to a plant information card, as shown in an exemplary embodiment.

[0061] Figure 11 A flowchart illustrating a method for responding to associated plant information, as shown in an exemplary embodiment.

[0062] Figure 12 A flowchart illustrating a method for responding to plant guidance information, as shown in an exemplary embodiment.

[0063] Figure 13 This is a schematic diagram of the structure of an electronic device containing a plant care device, provided as an exemplary embodiment.

[0064] Figure 14 A block diagram of a plant care device provided as an exemplary embodiment. Detailed Implementation

[0065] Exemplary embodiments will now be described in detail, examples of which are illustrated in the accompanying drawings. When the following description relates to the drawings, unless otherwise indicated, the same numerals in different drawings denote the same or similar elements. The embodiments described in the following exemplary embodiments do not represent all embodiments consistent with one or more embodiments of the present invention. Rather, they are merely examples of apparatuses and methods consistent with some aspects of one or more embodiments of the present invention as detailed in the appended claims.

[0066] It should be noted that the steps of the corresponding methods in other embodiments are not necessarily performed in the order shown and described in this invention. In some other embodiments, the methods may include more or fewer steps than those described in this invention. Furthermore, a single step described in this invention may be broken down into multiple steps in other embodiments; and multiple steps described in this invention may be combined into a single step in other embodiments.

[0067] XR (Extended Reality) technology is a collective term for AR (Augmented Reality), VR (Virtual Reality), and MR (Mixed Reality) technologies. It is an important manifestation of the shift in human-machine interaction from 2D to 3D interaction.

[0068] AR technology mainly overlays specific image information onto the real world through devices such as smartphones, while VR technology simulates the real world in front of the user through wearable devices such as smart glasses. MR technology lies between the two, enabling a complex real-time interaction between the user, the device, and the real world.

[0069] Thanks to the rapid development of related technologies, in some application scenarios, XR technology can be further combined with technologies such as image recognition and natural language processing to achieve more diverse functional services, thereby bringing users a better user experience.

[0070] Taking intelligent plant care as an example, conventional solutions generally require users to take and upload plant images, and then the device will provide some simple knowledge about plant care at once. These solutions cannot reflect the care information of multiple plants in the scene at the same time, and the care information provided is monotonous and limited. In addition, if users need to learn more about other content after taking and uploading images, they are required to take and upload images again, which is a complicated and tedious process, resulting in inefficiency and boredom.

[0071] In view of this, the present invention proposes a plant care method that combines XR, image recognition and other technologies in the application scenario of plant care. It has many advantages such as accurate information, high efficiency, intuitive display and convenient interaction, which can effectively improve user experience and ensure the survival rate of plant objects.

[0072] Please refer to Figure 1 , Figure 1 The diagram shown is a schematic representation of an XR interactive interface for plant care, as illustrated in an exemplary embodiment.

[0073] The plant care method described herein can be applied to various electronic devices such as smartphones, tablets, personal computers, or wearable smart devices through multiple methods, including apps, mini-programs, and web pages. Specifically, when the method is run via an app, mini-program, or web page, the electronic device executing the method can be the smartphone, tablet, personal computer, or wearable smart device, or it can be a server that interacts with the smartphone, tablet, personal computer, or wearable smart device, etc. The present invention does not impose specific limitations on this.

[0074] Please refer to Figure 2 , Figure 2 The diagram shown is a flowchart of a plant care method provided by an exemplary embodiment of the present invention.

[0075] The plant care method may include the following specific steps:

[0076] Step 202: Use an XR camera to capture multiple frames of images to be identified in the target scene.

[0077] In this embodiment, when a user launches a plant care APP, mini-program, or webpage on an electronic device, the XR camera can first capture multiple frames of images to be identified in the target scene. Based on these multiple frames of images, the user can then construct a three-dimensional representation of the real world on the electronic device before proceeding with subsequent steps.

[0078] The target scene can be the user's current real-world scene, which generally includes several plant objects that need to be maintained. The specific scene is not limited. The number of images to be identified can be determined by the number and spatial distribution of the plant objects to be maintained in the target scene, and is not specifically limited.

[0079] The specific process of constructing a 3D model of the real world based on the acquired images can be found in relevant technologies, which may include feature point recognition, matrix calibration, etc., and will not be elaborated here.

[0080] Step 204: For each frame of the image to be identified, a trained plant recognition model is used to determine several plant objects contained in the image to be identified, and then XR technology is used to determine the anchor point coordinates of each plant object.

[0081] In this embodiment, for each frame of the image to be identified captured by the XR camera in the target scene, the image to be identified can be used as input to the trained plant recognition model. Based on the output of the plant recognition model, several plant objects contained in the image to be identified can be determined. Then, the anchor point coordinates of each plant object are determined by combining XR technology.

[0082] Specifically, the output of the plant recognition model can yield several object boxes contained in an input frame of the image to be recognized. Each object box can contain one plant object to be identified and maintained, and the specific number of object boxes in a frame of the image to be recognized is determined by the specific number of plant objects contained in the image.

[0083] The center point of each object frame is the anchor point of the plant object contained within that object frame. Using XR technology, calibration transformations and other methods can determine the coordinates of these anchor points. The anchor point coordinates of the same plant object may move within the image as the shooting perspective changes, but they can all be mapped to the fixed location of the plant object in the real world.

[0084] The specific algorithm used to implement the plant recognition model is not limited, but the method for determining plant objects based on the model's output is consistent with the specific algorithm used to implement the model. For example, the original plant recognition model can be trained in a supervised manner using a sample image set in which the bounding boxes of plant objects are already labeled. If the plant recognition model is a region recognition model, the bounding boxes and center points of each plant object in the image can be determined from the pixel coordinate data in the output.

[0085] Step 206: For each plant object, a trained species identification model is used to determine the target species to which the plant object belongs, and a trained health detection model is used to determine the health status of the plant object. Then, the maintenance information of the plant object is determined by combining the target species and the health status.

[0086] In this embodiment, after determining each plant object contained in each frame of the image to be identified based on the plant recognition model, the trained model can be used to determine data such as the species and health status of each plant object. Then, the maintenance information required to maintain the plant object can be determined by combining the data such as the species and health status of the plant object.

[0087] Specifically, the image of the plant object, such as the image of the object frame where the plant object is located, can be used as input to the trained species recognition model, and the target species to which the plant object belongs can be determined based on the output of the species recognition model.

[0088] The specific algorithm used in the species identification model is not limited, but the method for determining the species of a plant object based on the model's output is consistent with the specific algorithm used in the model implementation. For example, the original species identification model can be trained in a supervised manner using a sample image set whose plant species have been labeled. If the species identification model is a classification model, the species with the highest confidence in its output is the target species to which the current plant object belongs.

[0089] Optionally, before using a trained model to identify plant species, based on the distance between the anchor point coordinates of each plant object in each frame of the image to be identified, it can be determined whether there are two anchor point coordinates corresponding to the same plant object, and further determine whether each plant object is in a clear and identifiable stable identification state.

[0090] For example, if the anchor point spacing is less than the first distance threshold, it is the same plant object; if the anchor point spacing is further less than the second distance threshold, it is in a stable recognition state. The same plant object that appears repeatedly in two frames of images does not need to be recognized a second time, and plant objects that are not in a stable recognition state can avoid recognition errors.

[0091] In addition, the image of the plant object, such as the image of the object frame in which the plant object is located, can be used as input to the trained health detection model, and the health status of the plant object can be determined based on the output of the health detection model.

[0092] There are no specific restrictions on the algorithm used to implement the health detection model, but the specific method for determining the health status of the plant object based on the model output results is consistent with the specific algorithm implemented by the model.

[0093] For example, assuming that health status includes disease information, the original health detection model can be trained in a supervised manner using a set of sample images labeled with plant diseases. If the health detection model is a classification model, the presence and type of disease of the current plant object can be determined by the confidence level of each disease category in the output results.

[0094] If the health status includes a health score, the original health detection model can be trained in a supervised manner using a set of sample images labeled with plant health scores. In the case that the health detection model is a scoring model, the output result can give the health score of the current plant object.

[0095] Finally, after determining the target species to which the current plant object belongs and its health status, the maintenance information of the plant object can be obtained by combining the two.

[0096] The maintenance information includes various plant care-related information such as watering, fertilizing, repotting, and infectious disease prevention. There are multiple possible ways to obtain the maintenance information of the plant object. For example, maintenance information for different plant species under various health conditions can be pre-set, and then, based on the target species to which the current plant object belongs and its health status, the corresponding maintenance information for the plant object can be obtained through a pre-defined mapping relationship.

[0097] More preferably, based on the target species and the health status, a trained model can be used to perform corresponding analyses on different maintenance information such as watering, fertilization, repotting, and infectious disease prevention to obtain the maintenance information corresponding to the plant object.

[0098] Please refer to Figure 3 , Figure 3 The diagram shown is a flowchart illustrating a method for determining plant care information in an exemplary embodiment.

[0099] In one alternative implementation, the health status of each plant object includes the disease information of the plant object. In step 206, determining the maintenance information of the plant object may include the following specific steps:

[0100] Step 206A: If the plant object is found to have an infectious disease based on the disease information, the prevention and control task for the plant object is determined by combining the target species and the disease information; wherein the prevention and control task includes one or more of the following: protection task and treatment task.

[0101] Specifically, given the health status of each plant object, including its disease information, after obtaining the disease information of the current plant object, it can first determine whether the plant object has an infectious disease. If so, based on the target species to which the plant object belongs and the infectious disease that the plant object suffers from, one or more of the prevention and control tasks such as protection tasks and treatment tasks for the plant object can be determined. The specific process of determining the prevention and control tasks can also be completed using a trained model.

[0102] The protective task is used to distinguish between diseased plants and healthy plants, or to distinguish between diseased plants, susceptible plants and healthy plants, and to control the distance between diseased plants and healthy plants, thereby preventing healthy plants from being affected by diseased plants and becoming infected. The treatment task is used to provide the information needed to treat the diseased plant, such as specific treatment measures such as washing, spraying the corresponding pesticide, and applying the corresponding agent.

[0103] Please refer to Figure 4 , Figure 4 The diagram shown is a flowchart illustrating a method for determining plant care information in another exemplary embodiment.

[0104] In another alternative implementation, step 206, determining the maintenance information of the plant object, may include the following specific steps:

[0105] Step 206B: The maintenance status of the plant object is determined using a trained maintenance detection model. Then, the maintenance task of the plant object is determined by combining the target species, the health status, and the maintenance status.

[0106] Specifically, the image of the current plant object can be used as input to the trained maintenance detection model, and the maintenance status of the plant object can be determined based on the output of the maintenance detection model. Then, by combining the target species to which the plant object belongs, its health status and maintenance status, one or more maintenance tasks such as repotting, watering, fertilizing and changing water can be determined. The specific process of determining the maintenance tasks can also be completed using the trained model.

[0107] The specific algorithm used to implement the maintenance detection model is not limited. The method for determining the maintenance status of the plant object based on the model output is consistent with the specific algorithm used to implement the model. For example, assuming that the maintenance status includes soil information, the original maintenance detection model can be trained in a supervised manner using a set of sample images with labeled soil statuses. If the maintenance detection model is a classification model, the soil status with the highest confidence level among the soil status categories in the output results can be determined as the current soil status of the plant object.

[0108] When the plant is a soil-grown plant, the maintenance status includes one or more of the following: flowerpot information and soil information; and the maintenance task includes one or more of the following: repotting task, watering task, and fertilizing task.

[0109] The repotting task provides information such as the size and material of the target flowerpot to replace the current flowerpot; the watering task provides information such as the watering frequency and the amount of water to be applied per watering for the plant; and the fertilizing task provides information such as the fertilizing frequency and the amount of fertilizer to be applied per fertilizing for the plant.

[0110] Taking fertilization as an example, users can pre-set fertilizer information such as variety and concentration or pre-upload fertilizer packaging images for fertilizer information recognition. Then, in the process of determining the fertilization task, the fertilization frequency and amount per application can be determined by combining the species, health status and maintenance status of the current plant object and the fertilizer information.

[0111] When the plant is a hydroponic plant, the maintenance status includes one or more of the following: container information, water quality information, and water level information. The maintenance task includes one or more of the following: container replacement task, water change task, and nutrient solution application task.

[0112] The container replacement task provides information such as the size and material of the target container for replacing the current container; the water change task provides information such as the water change frequency and the amount of water changed per time for the plant object; and the nutrient solution application task provides information such as the nutrient solution change frequency and the amount of nutrient solution applied per time for the plant object.

[0113] Taking the nutrient solution application task as an example, users can pre-set nutrient solution information such as variety and concentration, or pre-upload images of nutrient solution packaging for nutrient solution information recognition. Then, in the process of determining the nutrient solution application task, the nutrient solution replacement frequency and the amount of nutrient solution applied at one time can be determined by combining the species, health status and maintenance status of the current plant object and the nutrient solution information.

[0114] To further enrich plant care functions and enhance user experience, after determining the species and health status of each plant object in step 206, in addition to obtaining information on plant prevention and control, maintenance, and other related information, information on plant migration, beautification, and pruning can also be obtained. The following is a supplementary explanation of the relevant content.

[0115] Please refer to Figure 5 , Figure 5 The diagram shown is a flowchart illustrating a method for determining plant migration tasks in an exemplary embodiment.

[0116] In one alternative implementation, the plant care method may further include:

[0117] Step 502: Use the trained environment detection model to determine the illumination distribution and airflow distribution in the target scene;

[0118] Step 504: For each plant object, based on the light distribution and airflow distribution, combined with the target species to which the plant object belongs and the health status of the plant object, determine the ideal coordinates of the plant object in the target scene, and then determine the migration task of the plant object by the anchor point coordinates and the ideal coordinates of the plant object.

[0119] Step 506: Through the visualization interface of the XR camera, respond to the user with the migration task of the plant objects based on the coordinate information in the migration task of each plant object.

[0120] Specifically, multiple frames of images to be identified in the target scene can be used as input parameters to a trained environment detection model. Based on the output of the environment detection model, the light distribution and airflow distribution in the target scene can be determined. Then, combined with the target species to which the plant object belongs and its health status, the ideal coordinates of the plant object in the target scene can be determined. The ideal coordinates are the position where the light and ventilation conditions of the plant object in the target scene are optimal. The determination process can also be completed using a trained model. Then, the migration task of the plant object is determined by the coordinates of the anchor point where the plant object is currently located and the ideal coordinates.

[0121] More preferably, the environmental detection model can also be used to determine the soil condition and flowerpot parameters such as size and material in the target scene. At the same time, it can comprehensively determine the ideal coordinates of each plant object by combining the light distribution and airflow distribution, soil condition, flowerpot parameters, target species, and health status, so that the ideal coordinates can be further optimized to achieve the best position of the plant object in the target scene by comprehensively considering multiple environmental conditions such as light, ventilation, soil, and flowerpot.

[0122] The specific algorithm used to implement the environmental detection model is not limited, but the method for determining the illumination and airflow distribution in the target scene based on the model output is consistent with the specific algorithm used to implement the model. For example, the original environmental detection model can be trained in a supervised manner using a set of sample images with labeled illumination and airflow distributions. If the environmental detection model is a scoring model, its output can provide the illumination and airflow data for each coordinate in the target scene.

[0123] After determining the migration task of the current plant object, the XR camera's visualization interface can respond to the user based on the coordinate information in the migration task, instructing the user to move the plant object from its current location to a location with better lighting and ventilation.

[0124] Please refer to Figure 6 , Figure 6 The diagram shown is a flowchart illustrating a method for determining a plant beautification task in an exemplary embodiment.

[0125] In another alternative implementation, the plant care method may further include:

[0126] Step 602: For the plant objects selected by the user, the beautification task of the plant objects is determined by using a trained layout recommendation model, taking into account one or more of the target species to which each plant object belongs and the health status of each plant object.

[0127] Step 604: Through the XR camera's visualization interface, based on the coordinate information in the beautification task, respond to the user with the beautification task of the several plant objects; wherein, the beautification task indicates the deployment of the beautified positions of the several plant objects in the target scene.

[0128] Specifically, after identifying and determining the various plant objects included in the target scene, in response to the user's selection operation for several plant objects, the images of the several plant objects currently selected by the user, the target species to which each of the several plant objects belongs, and their health status are used as input parameters to the trained layout recommendation model, and then the beautification task of the several plant objects is determined based on the output result of the layout recommendation model.

[0129] The algorithm used in the layout recommendation model is not specifically limited, but the specific method for determining the beautification task of several plant objects based on the model output is consistent with the specific algorithm implemented in the model. For example, the original layout recommendation model can be trained in a supervised manner using a sample image set of labeled beautified arrangement data of several plant objects. When the layout recommendation model is a recommendation model, the beautified position deployment of each of the several plant objects can be determined from the beautified arrangement data of several plant objects in the output results.

[0130] After determining the beautification task of several plant objects currently selected by the user, the XR camera's visualization interface can respond to the user based on the coordinate information in the beautification task, instructing the user to move the plant objects from their current positions to more visually appealing locations.

[0131] Optionally, users can also access various beautification schemes from third-party experts or other online users for the plants currently selected by the user.

[0132] Please refer to Figure 7 , Figure 7 The diagram shown is a flowchart illustrating a method for determining plant pruning tasks in an exemplary embodiment.

[0133] In another alternative implementation, the plant care method may further include:

[0134] Step 702: For each plant object, combine one or more of the target species to which the plant object belongs, the health status of the plant object, and the current season information, and use a trained pruning recommendation model to determine the pruning task of the plant object.

[0135] Step 704: Through the XR camera's visualization interface, based on the coordinate information in the cropping scheme, respond to the user with a cropping task for the plant object; wherein, the cropping task indicates the target part of the plant object to be cropped and the corresponding cropping method.

[0136] Specifically, the image of the current plant object, the target species to which the plant object belongs and its health status, and the current season information can be used as input parameters to the trained cropping recommendation model, and the cropping task of the plant object can be determined based on the output of the cropping recommendation model.

[0137] The specific algorithm used in the cropping recommendation model is not limited, but the specific method for determining the cropping task of the plant object based on the model output is consistent with the specific algorithm implemented in the model. For example, the original cropping recommendation model can be trained in a supervised manner using a sample image set in which the plant parts to be cropped are labeled. In the case that the cropping recommendation model is a region recognition model, the coordinate data of the target parts of the plant object to be cropped can be determined from the pixel coordinate data in the output results.

[0138] After determining the cropping task for the current plant object, the XR camera's visualization interface can respond to the user based on the coordinate information in the cropping task, instructing the user to crop the diseased parts of the plant object in the correct way or to crop the plant object into a more visually appealing shape.

[0139] Optionally, users can also access various pruning schemes for the current plant object from third-party experts or other online users for reference.

[0140] Step 208: Through the XR camera's visualization interface, based on the anchor point coordinates of each plant object, respond to the user with the target species to which the plant object belongs, the health status of the plant object, and the maintenance information of the plant object.

[0141] In this embodiment, after determining the species, health status, and maintenance information of each plant object, the target species, health status, and maintenance information of each plant object can be fed back to the user through the XR camera's visualization interface using text, images, animations, etc., based on the anchor point coordinates of each plant object determined in step 204. For example, the target species, health status, and maintenance information can be superimposed on the vicinity of the plant object in the real world constructed by the AR camera based on the anchor point coordinates, or displayed near the plant object in the virtual world constructed by the XR or MR camera based on the anchor point coordinates.

[0142] There are multiple ways to respond to users with information such as species, health, prevention, maintenance, migration, beautification, and pruning of various plant objects in the target scene through the visualization interface of the XR camera. The specific response method can be adapted to the specific content to be responded to. In order to enable those skilled in the art to understand step 208 more clearly, the following explanation is given.

[0143] Please refer to Figure 8 , Figure 8 The diagram shown is a flowchart illustrating a method for responding to plant care information in an exemplary embodiment.

[0144] In one alternative implementation, the maintenance information to be responded to is a prevention and control task for a plant object with an infectious disease. In step 208, responding to the user with the maintenance information of the plant object may include the following specific steps:

[0145] Step 208A1: Through the visualization interface of the XR camera, based on the anchor point coordinates of the plant object and the distance information in the protection task, the protection task responds to the user with the protection task of the plant object; wherein, the protection task uses different colors to distinguish between healthy plant objects and diseased plant objects, and uses a distance scale to indicate the safe distance between healthy plant objects and diseased plant objects.

[0146] Step 208A2: Through the visualization interface of the XR camera, based on the anchor point coordinates of the plant object, a rescue task for the plant object is responded to the user; wherein, the rescue task indicates the rescue measures corresponding to the plant object.

[0147] Specifically, for plant object A with an infectious disease, plant object E susceptible to the same infectious disease in the same target scene, and healthy plant objects B, C, and D, taking a protection task as an example, through the visualization interface of the XR camera, plant object A can be marked in red, plant object E in pink, and plant objects B, C, and D in green. After plant object A recovers from its infectious disease, plant objects A and E can be remarked in green. At the same time, a ruler is used to indicate the potential danger range of plant object A, prompting the user to place susceptible plant objects and healthy plant objects at a safe distance from plant object A.

[0148] Taking the rescue mission as an example, through the visualization interface of the XR camera, specific rescue measures such as washing, spraying pesticides, and applying medicines can be displayed near the plant object A to treat the infectious disease it suffers from.

[0149] Please refer to Figure 9 , Figure 9 The diagram shown is a flowchart illustrating a method for responding to plant care information, as illustrated in another exemplary embodiment.

[0150] In another alternative implementation, the maintenance information to be responded to is the maintenance task of the current plant object. In step 208, responding to the user with the maintenance information of the plant object may include the following specific steps:

[0151] Step 208B: Within the timeframe of each maintenance task, based on the execution frequency of the maintenance task, periodically push the content of the maintenance task to the user.

[0152] Specifically, for soil-grown plant A, taking the watering task as an example, assuming the time frame of the watering task is 30 days and the execution frequency of the watering task for soil-grown plant A is once every 3 days, then within 30 days from the date of this identification and detection, the watering task for soil-grown plant A can be pushed to the user every 3 days through the visualization interface of the XR camera, instructing the user to water soil-grown plant A on that day, with a single watering volume of 200 ml.

[0153] For hydroponic plant B, taking the nutrient solution application task as an example, assuming the time frame for the nutrient solution application task is 45 days and the execution frequency of the nutrient solution application task for hydroponic plant B is once every 7 days, then within 30 days from the date of this identification and detection, the nutrient solution application task for hydroponic plant B can be pushed to the user every 7 days through the visualization interface of the XR camera, instructing the user to change the nutrient solution for hydroponic plant B on that day, with a single nutrient solution application amount of 2 ml.

[0154] Please refer to Figure 10 , Figure 10 The diagram shown is a flowchart illustrating a method for responding to a plant information card, as presented in an exemplary embodiment.

[0155] In one alternative implementation, the plant care method may further include the following specific steps:

[0156] Step 1002: For each plant object, determine the information card of the plant object, and respond to the user with the information card of the plant object based on the anchor point coordinates of the plant object through the visualization interface of the XR camera; wherein, the information card displays one or more of the following in a thumbnail format: target species, health status, maintenance information, migration task, beautification task, and pruning task.

[0157] Step 1004: In response to the user's checkbox operation, a sub-information card corresponding to the checkbox operation is displayed to the user through the XR camera's visualization interface; wherein, the sub-information card displays the complete content of any one of the following: target species, health status, maintenance information, migration task, beautification task, or cropping task.

[0158] Specifically, taking plant object B as an example, through the visualization interface of the XR camera, information cards similar to those in Table 1 below can be displayed near plant object B, showing in a thumbnail the target species to which plant object B belongs, its health status, maintenance information, and tasks such as migration, beautification, and pruning.

[0159]

[0160] In response to a user's selection of any item in the information card for plant object B—target species, health status, maintenance information, migration task, beautification task, or pruning task—a corresponding sub-information card can be displayed near plant object B. For example, the maintenance information sub-information card can fully display the container material, container capacity, water level, water quality, and the specific details of the nutrient solution replacement task currently pending for plant object B. Similarly, the pruning task sub-information card can display an image of the target part of plant object B to be pruned, an animation of the correct pruning method, and video, image, or text suggestions from experts regarding pruning solutions for plant object B.

[0161] Please refer to Figure 11 , Figure 11 The diagram shown is a flowchart illustrating a method for responding to associated plant information in an exemplary embodiment.

[0162] In one alternative implementation, the plant care method may further include the following specific steps:

[0163] Step 1102: When responding to information through the visualization interface of the XR camera, display the related plant objects in a related manner.

[0164] Specifically, when responding to information regarding any of the aforementioned species, health status, prevention and control tasks, maintenance tasks, migration tasks, beautification tasks, and pruning tasks, information on several related plant objects can be displayed in association. These related plant objects include those belonging to the same species, those with conflicting habits, and those suffering from the same disease. The specific process of determining the association between the plant objects to be responded to can also be completed using a trained model before the response.

[0165] Taking plant objects belonging to the same species as an example, assuming that plant objects C and D are in the same growth stage and are both healthy and disease-free, the watering and fertilization tasks of plant objects C and D can be displayed uniformly. Within 30 days from the time of identification and detection, the watering tasks of plant objects C and D are pushed to plant objects D every 3 days, and the fertilization tasks of plant objects C and D are pushed to plant objects D every 10 days.

[0166] Similarly, control tasks for several plant species suffering from the same disease can also be displayed uniformly.

[0167] Taking plant objects with conflicting habits as an example, suppose plant object E is lily of the valley and plant object F is daffodil. Since both lily of the valley and daffodil produce fragrances that inhibit the growth of the other, when responding to information about plant objects E and F, an association prompt can be given to inform the user to keep plant objects E and F separately.

[0168] Please refer to Figure 12 , Figure 12 The diagram shown is a flowchart illustrating a method for responding to plant guidance information in an exemplary embodiment.

[0169] In one alternative implementation, the plant care method may further include the following specific steps:

[0170] Step 1202: When responding to information through the XR camera's visual interface, access guidance information from third-party experts or other online users for joint display.

[0171] Specifically, when responding to information related to any of the aforementioned prevention, maintenance, relocation, beautification, or pruning tasks, guidance information from third-party experts or other online users can be integrated and displayed alongside the existing information. Taking a plant object A suffering from an infectious disease as an example, in addition to displaying specific treatment measures for plant object A, the XR camera's visualization interface can also play videos of expert advice on treating the infectious disease near plant object A, or provide a window for online communication with experts or other users near plant object A.

[0172] As can be seen from the above description, after the present invention calls the XR camera to collect multiple frames of images of the target scene, it first combines image recognition technology and XR technology to determine several plant objects and their anchor point coordinates in the target scene. Then, it uses a trained model to obtain relevant data such as the species and health status of each plant object to comprehensively determine the maintenance information of each plant object. Subsequently, it responds to the user with the maintenance information required for maintaining each plant object in the target scene through the XR camera's visualization interface in a 3D interactive manner.

[0173] This solution combines XR, image recognition, and other technologies in plant care applications. On the one hand, it provides users with rich and diverse plant care information in an intelligent and efficient manner. On the other hand, it leverages XR technology to respond to relevant content in a 3D interactive way, making it more intuitive and convenient. Thus, while ensuring the efficiency and accuracy of the solution, it effectively improves the user experience.

[0174] Please refer to Figure 13 , Figure 13 The diagram shows a schematic representation of the structure of an electronic device containing a plant care device according to an exemplary embodiment of the present invention. At the hardware level, the electronic device includes a processor 1302, an internal bus 1304, a network interface 1306, a memory 1308, and a non-volatile memory 1410, and may also include other hardware required for various operations.

[0175] One or more embodiments of the present invention can be implemented in software, such as by the processor 1302 reading the corresponding computer program from the non-volatile memory 1410 into the memory 1308 and then running it. Of course, in addition to the software implementation, one or more embodiments of the present invention do not exclude other implementation methods, such as logic devices or a combination of hardware and software, etc. That is to say, the execution subject of the following processing flow is not limited to each logic unit, but can also be hardware or logic devices.

[0176] Please refer to Figure 14 , Figure 14 The image shows a plant care device provided by an exemplary embodiment of the present invention. The device can be applied to, for example... Figure 13 The electronic device shown implements the technical solution of the present invention. The plant maintenance device includes an image acquisition unit 1410, an anchor point determination unit 1420, an information determination unit 1430, and an information response unit 1440; wherein:

[0177] The image acquisition unit 1410 is used to call the XR camera to acquire multiple frames of images to be identified in the target scene;

[0178] The anchor point determination unit 1420 is used to determine, for each frame of image to be identified, several plant objects contained in the image to be identified using a trained plant recognition model, and then combine XR technology to determine the anchor point coordinates of each plant object.

[0179] The information determination unit 1430 is used to determine the target species to which the plant object belongs using a trained species identification model and to determine the health status of the plant object using a trained health detection model for each plant object, and then combine the target species and the health status to determine the maintenance information of the plant object.

[0180] The information response unit 1440 is used to respond to the user, based on the anchor point coordinates of each plant object, through the visualization interface of the XR camera, the target species to which the plant object belongs, the health status of the plant object, and the maintenance information of the plant object.

[0181] Optionally, the health status of each plant object includes the disease information of the plant object; the information determining unit 1430, when determining the maintenance information of the plant object, is specifically used for:

[0182] If the plant is found to have an infectious disease based on the disease information, the prevention and control tasks for the plant are determined by combining the target species with the disease information; wherein the prevention and control tasks include one or more of the following: protection tasks and treatment tasks.

[0183] Alternatively, when responding to the user with maintenance information for the plant object, the information response unit 1440 is specifically used for:

[0184] Through the XR camera's visualization interface, based on the anchor point coordinates of the plant object and the distance information in the protection task, the protection task responds to the user with the protection task of the plant object; wherein, the protection task uses different colors to distinguish between healthy plant objects and diseased plant objects, and uses a distance scale to indicate the safe distance between healthy plant objects and diseased plant objects.

[0185] And / or, through the visualization interface of the XR camera, based on the anchor point coordinates of the plant object, a rescue task for the plant object is responded to the user; wherein the rescue task indicates rescue measures corresponding to the plant object.

[0186] Alternatively, when determining the maintenance information of the plant object, the information determining unit 1430 is specifically used for:

[0187] The maintenance status of the plant object is determined by using a trained maintenance detection model, and then the maintenance task of the plant object is determined by combining the target species, the health status and the maintenance status.

[0188] In the case where the plant is a soil-grown plant, the maintenance status includes one or more of the following: flowerpot information and soil information; and the maintenance task includes one or more of the following: repotting task, watering task, and fertilizing task.

[0189] When the plant is a hydroponic plant, the maintenance status includes one or more of the following: container information, water quality information, and water level information. The maintenance task includes one or more of the following: container replacement task, water change task, and nutrient solution application task.

[0190] Alternatively, when responding to the user with maintenance information for the plant object, the information response unit 1440 is specifically used for:

[0191] Within the timeframe of each maintenance task, the content of the maintenance task is periodically pushed to the user based on the execution frequency of the maintenance task.

[0192] Alternatively, the information determination unit 1430 is further configured to use a trained environmental detection model to determine the illumination distribution and airflow distribution in the target scene;

[0193] For each plant object, based on the light distribution and airflow distribution, combined with the target species to which the plant object belongs and the health status of the plant object, the ideal coordinates of the plant object in the target scene are determined. Then, the migration task of the plant object is determined by the anchor point coordinates and the ideal coordinates of the plant object.

[0194] The information response unit 1440 is also used to respond to the user about the migration task of the plant objects through the visualization interface of the XR camera, based on the coordinate information in the migration task of each plant object.

[0195] Optionally, the information determination unit 1430 is further configured to determine the beautification task of the plant objects selected by the user by combining one or more of the target species to which each plant object belongs and the health status of each plant object.

[0196] The information response unit 1440 is also used to respond to the user with the beautification task of the plurality of plant objects through the visualization interface of the XR camera, based on the coordinate information in the beautification task; wherein the beautification task indicates the deployment of the beautified positions of the plurality of plant objects in the target scene.

[0197] Alternatively, the information determination unit 1430 is further configured to determine the cropping task of each plant object by combining one or more of the target species to which the plant object belongs, the health status of the plant object, and the current season information, using a trained cropping recommendation model.

[0198] The information response unit 1440 is further configured to respond to the user with a cropping task for the plant object based on the coordinate information in the cropping scheme through the visualization interface of the XR camera; wherein the cropping task indicates the target part of the plant object to be cropped and the corresponding cropping method.

[0199] Alternatively, the information response unit 1440 is further configured to:

[0200] For each plant object, an information card for the plant object is determined, and the information card for the plant object is responded to the user through the visualization interface of the XR camera based on the anchor point coordinates of the plant object; wherein, the information card displays one or more of the following in a thumbnail format: target species, health status, maintenance information, migration task, beautification task, and pruning task.

[0201] In response to the user's checkbox operation, the XR camera's visual interface displays a sub-information card corresponding to the checkbox operation; wherein the sub-information card displays the complete content of any one of the following: target species, health status, maintenance information, migration task, beautification task, or cropping task.

[0202] Alternatively, the information response unit 1440 is further configured to:

[0203] When responding to information through the visualization interface of the XR camera, several related plant objects are displayed together.

[0204] Alternatively, the information response unit 1440 is further configured to:

[0205] When responding to information through the XR camera's visual interface, guidance information from third-party experts or other online users is also displayed together.

[0206] The systems, devices, modules, or units described in the above embodiments can be implemented by computer chips or entities, or by products with certain functions. A typical implementation device is a computer, which can take the form of a personal computer, laptop computer, cellular phone, camera phone, smartphone, personal digital assistant, media player, navigation device, email sending and receiving device, game console, tablet computer, wearable device, or any combination of these devices.

[0207] In a typical configuration, a computer includes one or more processors (CPU), input / output interfaces, network interfaces, and memory.

[0208] Memory may include non-persistent storage in computer-readable media, such as random access memory (RAM) and / or non-volatile memory, such as read-only memory (ROM) or flash RAM. Memory is an example of computer-readable media.

[0209] Computer-readable media, including both permanent and non-permanent, removable and non-removable media, can store information using any method or technology. Information can be computer-readable instructions, data structures, modules of programs, or other data. Examples of computer storage media include, but are not limited to, phase-change memory (PRAM), static random access memory (SRAM), dynamic random access memory (DRAM), other types of random access memory (RAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), flash memory or other memory technologies, CD-ROM, digital versatile optical disc (DVD) or other optical storage, magnetic tape, disk storage, quantum memory, graphene-based storage media or other magnetic storage devices, or any other non-transferable medium that can be used to store information accessible by a computing device. As defined herein, computer-readable media does not include transient computer-readable media, such as modulated data signals and carrier waves.

[0210] It should also be noted that the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus. Without further limitation, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes said element.

[0211] The foregoing has described specific embodiments of the invention. Other embodiments are within the scope of the appended claims. In some cases, the actions or steps described in the claims may be performed in a different order than that shown in the embodiments and may still achieve the desired results. Furthermore, the processes depicted in the drawings do not necessarily require the specific or sequential order shown to achieve the desired results. In some embodiments, multitasking and parallel processing are also possible or may be advantageous.

[0212] The terminology used in one or more embodiments of the invention is for the purpose of describing particular embodiments only and is not intended to limit the invention. The singular forms “a,” “the,” and “the” used in one or more embodiments of the invention and in the appended claims are also intended to include the plural forms unless the context clearly indicates otherwise. It should also be understood that the term “and / or” as used herein refers to and includes any or all possible combinations of one or more associated listed items.

[0213] It should be understood that although the terms first, second, third, etc., may be used to describe various information in one or more embodiments of the present invention, such information should not be limited to these terms. These terms are only used to distinguish information of the same type from one another. For example, first information may also be referred to as second information without departing from the scope of one or more embodiments of the present invention, and similarly, second information may also be referred to as first information. Depending on the context, the word "if" as used herein may be interpreted as "when," "when," or "in response to a determination."

[0214] The above description is merely a preferred embodiment of one or more embodiments of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of one or more embodiments of the present invention should be included within the protection scope of one or more embodiments of the present invention.

Claims

1. A method for plant care, characterized in that, The method includes: Use an XR camera to acquire multiple frames of images to be identified in the target scene; For each frame of the image to be identified, a trained plant recognition model is used to determine several plant objects contained in the image, and then XR technology is combined to determine the anchor point coordinates of each plant object. For each plant object, a trained species identification model is used to determine the target species to which the plant object belongs, and a trained health detection model is used to determine the health status of the plant object. Then, by combining the target species and the health status, the maintenance information of the plant object is determined. Through the XR camera's visualization interface, based on the anchor point coordinates of each plant object, the system responds to the user with the target species to which the plant object belongs, the health status of the plant object, and the maintenance information of the plant object. The response to the user with the maintenance information of the plant object includes: through the XR camera's visualization interface, based on the anchor point coordinates of the plant object and the distance information in the protection task, the system responds to the user with the protection task of the plant object. The protection task uses different colors to distinguish between healthy and diseased plant objects and uses a distance scale to indicate the safe distance between healthy and diseased plant objects. A trained environmental detection model is used to determine the light and airflow distribution in the target scene. For each plant object, based on the light and airflow distribution, combined with the target species to which the plant object belongs and the health status of the plant object, the ideal coordinates of the plant object in the target scene are determined. Then, the migration task of the plant object is determined by the anchor coordinates and ideal coordinates of the plant object. Through the visualization interface of the XR camera, the migration task of the plant object is responded to the user based on the coordinate information in the migration task of each plant object.

2. The method of claim 1, wherein, The health status of each plant object includes the disease information of the plant object; The determination of the maintenance information of the plant object includes: If the plant is found to have an infectious disease based on the disease information, the prevention and control tasks for the plant are determined by combining the target species with the disease information; wherein the prevention and control tasks include one or more of the following: protection tasks and treatment tasks.

3. The method of claim 1, wherein, The determination of the maintenance information of the plant object includes: The maintenance status of the plant object is determined by using a trained maintenance detection model, and then the maintenance task of the plant object is determined by combining the target species, the health status and the maintenance status. In the case where the plant object is a soil-grown plant, the maintenance status includes one or more of the following: flowerpot information and soil information; and the maintenance task includes one or more of the following: repotting task, watering task, and fertilizing task. When the plant object is a hydroponic plant, the maintenance status includes one or more of the following: container information, water quality information, and water level information; and the maintenance task includes one or more of the following: container replacement task, water change task, and nutrient solution application task.

4. The method of claim 3, wherein, The step of responding to the user with maintenance information for the plant object includes: Within the timeframe of each maintenance task, the content of the maintenance task is periodically pushed to the user based on the execution frequency of the maintenance task.

5. The method according to claim 1, characterized in that, The method further includes: For the plant objects selected by the user, the beautification task of the plant objects is determined by combining one or more of the target species to which each plant object belongs and the health status of each plant object. Through the XR camera's visualization interface, based on the coordinate information in the beautification task, the beautification task of the several plant objects is responded to the user; wherein, the beautification task indicates the deployment of the beautified positions of the several plant objects in the target scene.

6. The method according to claim 1, characterized in that, The method further includes: For each plant object, the pruning task of the plant object is determined by combining one or more of the target species to which the plant object belongs, the health status of the plant object, and the current season information, using a trained pruning recommendation model. Through the XR camera's visualization interface, based on the coordinate information in the cropping task, the system responds to the user with the cropping task of the plant object; wherein, the cropping task indicates the target part of the plant object to be cropped and the corresponding cropping method.

7. The method according to any one of claims 1 to 6, characterized in that, The method further includes: For each plant object, an information card for the plant object is determined, and the information card for the plant object is responded to the user through the visualization interface of the XR camera based on the anchor point coordinates of the plant object; wherein, the information card displays one or more of the following in a thumbnail format: target species, health status, maintenance information, migration task, beautification task, and pruning task. In response to the user's checkbox operation, the XR camera's visual interface displays a sub-information card corresponding to the checkbox operation; wherein the sub-information card displays the complete content of any one of the following: target species, health status, maintenance information, migration task, beautification task, or cropping task.

8. The method according to any one of claims 1 to 6, characterized in that, The method further includes: When responding to information through the visualization interface of the XR camera, several related plant objects are displayed together.

9. The method according to any one of claims 1 to 6, characterized in that, The method further includes: When responding to information through the XR camera's visual interface, guidance information from third-party experts or other online users is also displayed together.

10. A plant maintenance device characterized by comprising: The device includes an image acquisition unit, an anchor point determination unit, an information determination unit, and an information response unit; wherein: The image acquisition unit is used to call the XR camera to acquire multiple frames of images to be identified in the target scene; The anchor point determination unit is used to determine several plant objects contained in each frame of the image to be identified using a trained plant recognition model, and then combine XR technology to determine the anchor point coordinates of each plant object. The information determination unit is used to determine the target species to which the plant object belongs using a trained species identification model and to determine the health status of the plant object using a trained health detection model for each plant object. Then, by combining the target species and the health status, the maintenance information of the plant object is determined. The information response unit is used to respond to the user, through the XR camera's visualization interface, based on the anchor point coordinates of each plant object, with information about the target species to which the plant object belongs, the health status of the plant object, and the maintenance information of the plant object. The response to the user with the maintenance information of the plant object includes: responding to the user with a protection task for the plant object through the XR camera's visualization interface, based on the anchor point coordinates of the plant object and the distance information in the protection task; wherein the protection task uses different colors to distinguish healthy and diseased plant objects, and uses a distance scale to indicate the safe distance between healthy and diseased plant objects; and using a trained environmental detection model to determine the light distribution and airflow distribution in the target scene; for each plant object, based on the light distribution and airflow distribution, combined with the target species to which the plant object belongs and the health status of the plant object, determining the ideal coordinates of the plant object in the target scene, and then determining the migration task of the plant object based on the anchor point coordinates and ideal coordinates; and responding to the user with the migration task of the plant object through the XR camera's visualization interface, based on the coordinate information in the migration task of each plant object.

11. An electronic device, comprising: processor; Memory used to store processor-executable instructions; The processor implements the steps of the method according to any one of claims 1-9 by running the executable instructions.

12. A computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the steps of the method according to any one of claims 1-9.

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