Pitaya growth monitoring method, device and equipment and storage medium

Abstract

The invention relates to the field of fruit monitoring, and discloses a pitaya growth monitoring method, device and equipment and a storage medium, and the method is used for monitoring the growth state of pitaya. The method comprises the following steps: acquiring position information and basic information of a pitaya tree, and establishing an identity card and a pitaya file of the pitaya tree according to the position information and the basic information of the pitaya tree; planning a patrol route, and controlling the patrol robot to collect real-time images of the pitaya trees according to the patrol route; position information and fruit bearing quantity of the pitaya trees are identified according to the collected real-time images, and the fruit maturity of the pitaya trees is detected; the identity card of the pitaya tree is confirmed according to the recognized position information of the pitaya tree, and the pitaya file corresponding to the identity card of the pitaya tree is updated according to the collected real-time image, the recognized hanging amount of the pitaya tree and the detected fruit maturity of the pitaya tree.

Description

Technical Field

[0001] The present invention relates to the technical field of fruit monitoring, and in particular to a pitaya growth monitoring method, device, equipment and storage medium. Background Art

[0002] In recent years, the area of ​​pitaya cultivation in my country has grown rapidly. With the expansion of the planting scale, the realization of automated and intelligent management of orchards is an important part of large-scale production. Maturity identification in intelligent orchard management can help orchard managers more accurately grasp the growth and maturity of fruits, optimize fruit picking time, and thus improve orchard production efficiency. In orchard management, stems with more fruit need to be thinned to increase the sweetness of pitaya fruits. Unripe fruits are bagged to avoid damage to the fruits during growth, which is conducive to more uniform coloring of the fruit skin. Pitaya needs to be picked in time after maturity. If the best picking time is missed, it may lead to nutrient loss inside the fruit and affect the taste of the fruit. Since pitaya can produce up to 14 batches of fruit each year, the current monitoring of the growth status of pitaya is mainly manual and lacks systematic management.

[0003] Therefore, the existing technology still needs to be improved and developed. Summary of the invention

[0004] The invention provides a pitaya growth monitoring method, device, equipment and storage medium, which are used for monitoring the growth status of pitaya.

[0005] The first aspect of the present invention provides a pitaya growth monitoring method, which comprises: obtaining location information and basic information of a pitaya tree, establishing an identity card and a pitaya file of the pitaya tree according to the location information and basic information of the pitaya tree; planning a patrol route, and controlling a patrol robot to collect real-time images of the pitaya tree according to the patrol route; identifying the location information and the amount of fruit hanging on the pitaya tree according to the collected real-time images, and detecting the fruit maturity of the pitaya tree; confirming the identity card of the pitaya tree according to the identified location information of the pitaya tree, and updating the pitaya file corresponding to the identity card of the pitaya tree according to the collected real-time images, the identified amount of fruit hanging on the pitaya tree, and the detected fruit maturity of the pitaya tree.

[0006] Preferably, the method of obtaining the location information and basic information of the pitaya tree, and establishing an identity card and a pitaya file of the pitaya tree according to the location information and basic information of the pitaya tree, includes: using an unmanned aerial vehicle or ground measurement equipment to measure and record the location information of the pitaya tree, wherein the location information includes longitude and latitude; obtaining basic information of the pitaya tree, wherein the basic information includes age, crown size, and health status, and integrating the location information and basic information of the pitaya tree to establish an identity card of the pitaya tree; and establishing a pitaya file corresponding to the identity card of the pitaya tree in a preset pitaya growth database.

[0007] Preferably, the planning of the patrol route and controlling the patrol robot to collect real-time images of the pitaya trees along the patrol route include: using a laser radar to collect point cloud data in the area between the two ends of each row of pitaya trees, and using an RTK-GNSS device to collect spatial position data in the area at the two ends of each row of pitaya trees; constructing a three-dimensional point cloud map as a priori map based on the point cloud data; estimating the initial pose of the laser radar, and optimizing the initial pose of the laser radar based on the priori map to obtain the optimized pose of the laser radar; using a loosely coupled method to fuse the spatial position data collected by the RTK-GNSS device with the optimized pose of the laser radar to obtain a fused pose, and mapping the feature points at the corresponding time to the priori map according to the fused pose to construct an environmental map; obtaining a patrol starting point and a patrol end point, using a path planning algorithm to plan a patrol route in the environmental map, and controlling the patrol robot to collect real-time images of the pitaya trees along the patrol route.

[0008] Preferably, the constructing of a three-dimensional point cloud map as a prior map based on point cloud data includes: distinguishing the point cloud data in vertical and horizontal directions according to the scanning mode of the laser radar, and extracting edge points and plane points from the distinguished point cloud data as global feature points; performing motion deformation compensation on the point cloud data collected at different times in the same frame according to the global feature points to obtain single-frame point cloud data without deformation; aligning the single-frame point cloud data without deformation to the global coordinate system and performing voxel thinning to obtain a three-dimensional point cloud map, and using the three-dimensional point cloud map as the prior map.

[0009] Preferably, the estimating the initial pose of the laser radar and optimizing the initial pose of the laser radar based on the prior map to obtain the optimized pose of the laser radar include: loading the prior map, dividing the grid array in the prior map, and storing the global feature points in the corresponding grids; obtaining the IMU data of the laser radar, and determining the initial motion value of the laser radar according to the IMU data; obtaining the point cloud data of the current frame of the laser radar, extracting the current frame feature points from the point cloud data of the current frame, and determining the initial pose of the laser radar in the prior map according to the initial motion value of the laser radar and the current frame feature points; selecting the global feature points in the grid around the initial pose to form a local subgraph; performing feature matching between the current frame feature points and the global feature points in the local subgraph, and optimizing the initial pose of the laser radar according to the matching results to obtain the optimized pose of the laser radar.

[0010] Preferably, the identifying the position information and the amount of fruits on the pitaya tree according to the collected real-time image, and detecting the fruit maturity of the pitaya tree, includes: preprocessing the collected real-time image to obtain a preprocessed image; marking the position of the pitaya tree in the preprocessed image using a target detection algorithm to obtain a marked image; converting the position information of the pitaya tree in the marked image into actual position information according to preset camera parameters; counting the number of fruits of the pitaya tree in the marked image to obtain the amount of fruits on the pitaya tree; performing maturity detection on each fruit in the marked image, and outputting the maturity information of each fruit and the overall maturity distribution.

[0011] Preferably, the maturity detection is performed on each fruit in the marked image, and the maturity information of each fruit and the overall maturity distribution are output, including: using an image segmentation-based algorithm to extract the fruit area of ​​the marked image to obtain a plurality of fruit images; respectively extracting color features of the plurality of fruit images, and inputting the extracted color features into a pre-trained SVM classifier to obtain classification results output by the SVM classifier, the classification results including immature fruit, semi-mature fruit and ripe fruit; classifying the plurality of fruit images according to the classification results output by the SVM classifier, and counting the number and proportion of fruit images in each category.

[0012] The second aspect of the present invention provides a pitaya growth monitoring device, comprising: an establishment module, used to obtain location information and basic information of the pitaya tree, and establish an identity card and a pitaya file of the pitaya tree according to the location information and basic information of the pitaya tree; a planning module, used to plan a patrol route, and control a patrol robot to collect real-time images of the pitaya tree according to the patrol route; an identification and detection module, used to identify the location information and fruit amount of the pitaya tree according to the collected real-time image, and detect the fruit maturity of the pitaya tree; an updating module, confirming the identity card of the pitaya tree according to the identified location information of the pitaya tree, and updating the pitaya file corresponding to the identity card of the pitaya tree according to the collected real-time image, the identified fruit amount of the pitaya tree, and the detected fruit maturity of the pitaya tree.

[0013] The third aspect of the present invention provides a pitaya growth monitoring device, comprising: a memory and at least one processor, wherein the memory stores computer-readable instructions, and the memory and the at least one processor are interconnected through lines; the at least one processor calls the computer-readable instructions in the memory so that the pitaya growth monitoring device executes the various steps of the pitaya growth monitoring method as described above.

[0014] A fourth aspect of the present invention provides a computer-readable storage medium, in which computer-readable instructions are stored. When the computer-readable storage medium is run on a computer, the computer executes the various steps of the pitaya growth monitoring method described above.

[0015] In the technical solution provided by the present invention, the location information and basic information of the pitaya tree are collected, an identity card and a file are established, a patrol robot is used to collect real-time images, and the location and fruit quantity of the pitaya tree in the collected real-time images are identified by image recognition technology, and the maturity of the fruit is detected; finally, the pitaya file is updated according to the recognition result and the detection result, thereby realizing rapid and comprehensive monitoring of the growth status of the pitaya tree, greatly improving the monitoring efficiency, reducing manual intervention, and reducing management costs. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] Figure 1 A flow chart of a method for monitoring pitaya growth provided by an embodiment of the present invention;

[0017] Figure 2 A schematic diagram of the structure of a pitaya growth monitoring device provided by an embodiment of the present invention;

[0018] Figure 3 A schematic diagram of the structure of a pitaya growth monitoring device provided in an embodiment of the present invention. DETAILED DESCRIPTION

[0019] The terms "first", "second", "third", "fourth", etc. (if any) in the specification and claims of the present invention and the above-mentioned drawings are used to distinguish similar objects, and are not necessarily used to describe a specific order or sequence. It should be understood that the data used in this way can be interchanged where appropriate, so that the embodiments described herein can be implemented in an order other than that illustrated or described herein. In addition, the terms "including" or "having" and any variations thereof are intended to cover non-exclusive inclusions, for example, a process, method, system, product or device that includes a series of steps or units is not necessarily limited to those steps or units that are clearly listed, but may include other steps or units that are not clearly listed or inherent to these processes, methods, products or devices.

[0020] For ease of understanding, the specific process of the embodiment of the present invention is described below. Figure 1 A first embodiment of a method for monitoring growth of pitaya in an embodiment of the present invention includes:

[0021] S101, obtaining the location information and basic information of the pitaya tree, and establishing an identity card and a pitaya file of the pitaya tree according to the location information and basic information of the pitaya tree.

[0022] S102, planning a patrol route, and controlling the patrol robot to collect real-time images of the pitaya tree along the patrol route.

[0023] S103, identifying the location information and the amount of fruit on the pitaya tree according to the collected real-time image, and detecting the maturity of the fruit of the pitaya tree.

[0024] S104, confirming the identity card of the dragon fruit tree according to the position information of the identified dragon fruit tree, and updating the dragon fruit file corresponding to the identity card of the dragon fruit tree according to the collected real-time image, the identified hanging amount of the dragon fruit tree and the detected fruit maturity of the dragon fruit tree.

[0025] It is understandable that the execution subject of the present invention may be a pitaya growth monitoring device, or may be a terminal or a server, which is not limited here. The embodiment of the present invention is described by taking a server as the execution subject as an example.

[0026] In this embodiment, in step S101, the location information and basic information of the pitaya tree are obtained, an identity card of the pitaya tree is established according to the location information of the pitaya tree, and a pitaya file is established for the pitaya tree according to the identity card, specifically including: using an unmanned aerial vehicle or ground measurement equipment to measure and record the location information of the pitaya tree, the location information including longitude and latitude; obtaining the basic information of the pitaya tree, the basic information including the age, crown size and health status, and integrating the location information and basic information of the pitaya tree to establish an identity card of the pitaya tree; and establishing a pitaya file corresponding to the identity card of the pitaya tree in a preset pitaya growth database.

[0027] In this embodiment, a high-precision GPS system or ground measurement equipment (such as an RTK GPS receiver) is used to accurately locate each dragon fruit tree in the dragon fruit orchard. The longitude and latitude coordinates of each tree are recorded to ensure the accuracy and reliability of the data, providing a basis for the subsequent establishment of identity cards.

[0028] In this embodiment, basic information of the pitaya tree can be collected in advance through on-site investigation and recording, including tree age (determined by annual ring observation or planting records), crown size (using measuring tools such as tape measure or laser rangefinder to estimate the diameter or area of ​​the crown), and health status (observation of leaf color, pest and disease conditions, etc.). The basic information is then uploaded to the system, so that the basic information of the pitaya tree can be obtained in the system.

[0029] The location information of the pitaya tree is integrated with the basic information to form an identity card that contains key information such as the tree name, location information (latitude and longitude), tree age, crown size and health status.

[0030] In the preset pitaya growth database, a corresponding file is created for each pitaya tree. The information in the ID card is entered into the file to ensure the integrity and traceability of the data. The pitaya growth database supports data update and query functions so that the growth status and yield information of the pitaya tree can be updated in real time as it grows.

[0031] In this embodiment, the pitaya file can also be updated and maintained regularly to ensure the timeliness and accuracy of the data.

[0032] In practical applications, the information in the database can be used for data analysis and mining to discover problems and patterns in the growth process of pitaya, and based on the analysis results, formulate targeted management measures and optimization plans, such as adjusting the amount of fertilizer, pruning the crown, and preventing and controlling pests and diseases.

[0033] In this embodiment, the location information of the pitaya tree is accurately obtained by drones or ground measurement equipment, and an identity card is established in combination with the basic information, and then an exclusive file is created for each pitaya tree in the growth database, thereby realizing the intelligent and refined management of pitaya planting, and facilitating the tracking, analysis and optimization of the growth status of pitaya.

[0034] In this embodiment, in step S102, a patrol route is planned, and the patrol robot is controlled to collect real-time images of pitaya trees along the patrol route, including: using a laser radar to collect point cloud data in the area between the two ends of each row of pitaya trees, and using an RTK-GNSS device to collect spatial position data in the area at the two ends of each row of pitaya trees; constructing a three-dimensional point cloud map as a priori map based on the point cloud data; estimating the initial pose of the laser radar, and optimizing the initial pose of the laser radar based on the prior map to obtain the optimized pose of the laser radar; using a loosely coupled method to fuse the spatial position data collected by the RTK-GNSS device with the optimized pose of the laser radar to obtain a fused pose, and mapping the feature points at the corresponding time to the priori map according to the fused pose to construct an environmental map; obtaining a patrol starting point and a patrol end point, using a path planning algorithm to plan a patrol route in the environmental map, and controlling the patrol robot to collect real-time images of the pitaya trees along the patrol route.

[0035] In this embodiment, a three-dimensional point cloud map is constructed as a prior map based on point cloud data, including: according to the scanning mode of the laser radar, the point cloud data is distinguished in the vertical and horizontal directions, and edge points and plane points are extracted from the distinguished point cloud data as global feature points; according to the global feature points, motion deformation compensation is performed on the point cloud data collected at different times in the same frame to obtain single-frame point cloud data without deformation; the single-frame point cloud data without deformation is aligned to the global coordinate system and voxel thinning is performed to obtain a three-dimensional point cloud map, and the three-dimensional point cloud map is used as the prior map.

[0036] In this embodiment, the laser radar is installed on a mobile platform (such as a vehicle, robot or drone) in the pitaya orchard, and ensures that it can stably obtain point cloud data. Data collection is performed in the pitaya orchard according to a predetermined trajectory to ensure coverage of the entire pitaya orchard area, and special attention is paid to the location and distribution of the pitaya trees.

[0037] In this embodiment, the scanning mode of the laser radar generally includes scanning in the vertical direction (pitch angle) and the horizontal direction (yaw angle). By parsing the data format of the laser radar, the pitch angle and yaw angle information of each point cloud can be obtained. Based on this information, the point cloud data is distinguished according to the vertical direction and the horizontal direction to prepare for the subsequent feature point extraction.

[0038] In this embodiment, edge points are usually located on the contour or edge of an object and are more sensitive to rotation and shape changes. Plane points are located on a flat surface and are more sensitive to translation and surface changes. The feature point extraction method in the LOAM algorithm is used to distinguish edge points from plane points based on the curvature or other geometric characteristics of the point cloud.

[0039] In this embodiment, during the laser radar scanning process, due to the movement of the mobile platform, the point cloud data collected at different times in the same frame may be deformed by motion. A feature point matching and pose estimation method (such as the ICP algorithm or its improved version) is used to estimate the motion trajectory and pose change of the mobile platform based on the extracted edge points and plane points, and then motion compensation is performed on the point cloud data collected at different times in the same frame based on the estimated pose change to eliminate motion deformation.

[0040] In this embodiment, the ICP algorithm or its improved version is used to register the deformation-free single-frame point cloud data with the global map. During the registration process, the position and posture of the single-frame point cloud data are continuously adjusted until the matching degree between the single-frame point cloud data and the global map is optimal.

[0041] Furthermore, in order to reduce the redundancy of point cloud data and improve the sparsity of the map, the voxel thinning method is used to downsample the points in each voxel, and only the representative points in each voxel (such as center point, centroid, etc.) are retained to construct a sparse three-dimensional point cloud map.

[0042] In this embodiment, the initial pose of the laser radar is estimated, and based on the prior map, the initial pose of the laser radar is optimized to obtain the optimized pose of the laser radar, including: loading the prior map, dividing the grid array in the prior map, and storing the global feature points in the corresponding grids; obtaining the IMU data of the laser radar, and determining the initial motion value of the laser radar according to the IMU data; obtaining the point cloud data of the current frame of the laser radar, extracting the current frame feature points from the point cloud data of the current frame, and determining the initial pose of the laser radar in the prior map according to the initial motion value and the current frame feature points; selecting the global feature points in the grid around the initial pose to form a local sub-graph; performing feature matching between the current frame feature points and the global feature points in the local sub-graph, and optimizing the initial pose according to the matching results to obtain the optimized pose.

[0043] In this embodiment, a loose coupling method is adopted. Before the spatial position data collected by the RTK-GNSS device is fused with the optimized posture of the lidar, the RTK-GNSS data and the lidar data are time synchronized to ensure that they correspond to the same time.

[0044] In this embodiment, a fusion algorithm such as weighted average and Kalman filter is selected to fuse the data of RTK-GNSS and LiDAR to obtain a fused pose. Then, according to the fused pose, the feature points at the corresponding time are mapped to the prior map, and the above steps are repeated to map the new feature points to the prior map, and gradually build a complete environment map.

[0045] In this embodiment, A*, Dijkstra or other path planning algorithms can be used to plan the optimal path from the patrol starting point to the end point in the environment map as the patrol route.

[0046] In this embodiment, when controlling the patrol robot to collect real-time images of pitaya trees along the patrol route, a repositioning algorithm based on a priori maps is designed based on a three-dimensional SLAM algorithm to improve the accuracy and stability of positioning, and the estimated value of the 3D SLAM algorithm is corrected by GNSS, so that the global positioning accuracy of the patrol robot meets the requirements of autonomous navigation in the orchard.

[0047] In other embodiments, the patrol robot can also be turned on in remote control mode, and control data can be sent to the robot driver according to instructions, thereby realizing patrolling and taking pictures of pitaya.

[0048] In this embodiment, in step S103, the position information and the amount of fruit on the pitaya tree are identified according to the collected real-time image, and the fruit maturity of the pitaya tree is detected, which specifically includes: preprocessing the collected real-time image to obtain a preprocessed image; marking the position of the pitaya tree in the preprocessed image using a target detection algorithm to obtain a marked image; according to preset camera parameters, converting the position information of the pitaya tree in the marked image into actual position information; counting the number of fruits of the pitaya tree in the marked image to obtain the amount of fruit on the pitaya tree; performing maturity detection on each fruit in the marked image, and outputting the maturity information of each fruit and the overall maturity distribution.

[0049] In this implementation, the collected real-time images are preprocessed to improve image quality, reduce noise and interference, and provide clear input for subsequent target detection algorithms. Preprocessing includes image denoising, contrast enhancement, and resizing.

[0050] In this implementation, deep learning or computer vision techniques (such as object detection algorithms such as YOLO and Faster R-CNN) are used to identify dragon fruit trees in images. The algorithm outputs the location (usually in the form of a bounding box) and confidence level of each detected dragon fruit tree. This location information will be used for subsequent labeling and transformation.

[0051] In this implementation, through the camera's intrinsic parameters (such as focal length, optical center position, etc.) and extrinsic parameters (the camera's posture and position relative to the world coordinate system), the pixel coordinates in the marked image can be converted into actual three-dimensional space coordinates, thereby obtaining the exact position of the pitaya tree in the orchard.

[0052] In this implementation, the fruit quantity of each tree can be counted by counting the number of fruits within the bounding box or using other segmentation techniques.

[0053] In this implementation, maturity detection is performed on each fruit in the marked image, and the maturity information of each fruit and the maturity distribution of the pitaya tree as a whole are output, specifically including: using an image segmentation-based algorithm to extract the fruit area of ​​the marked image to obtain multiple fruit images; extracting color features of the multiple fruit images respectively, and inputting the extracted color features into a pre-trained SVM classifier to obtain classification results output by the SVM classifier, the classification results including immature fruit, semi-ripe fruit and ripe fruit; classifying the multiple fruit images according to the classification results output by the SVM classifier, and counting the number and proportion of each category.

[0054] In this embodiment, an algorithm based on image segmentation is applied to extract the fruit region, and the algorithm based on image segmentation can distinguish the fruit from the background, branches and leaves, etc., so as to obtain multiple independent fruit images. The algorithm based on image segmentation includes threshold segmentation, region growing, edge detection, level set method or deep learning method (such as semantic segmentation network).

[0055] After segmentation, some post-processing steps can be performed, such as morphological operations (dilation, erosion, opening, closing) to remove noise, fill small holes or segment stuck fruits.

[0056] Finally, the fruit region is extracted from the segmented image, and a unique number is assigned to each fruit image for subsequent processing.

[0057] In this embodiment, since the color of pitaya changes from green (unripe) to red (ripe), color features can be extracted from each fruit image based on the HSV color space, which may specifically include average color value, color histogram, color moment, etc.

[0058] In this embodiment, the pre-trained SVM classifier is trained based on samples including immature fruit, semi-mature fruit and mature fruit. The SVM classifier outputs a classification result for each fruit image according to the input color features, that is, whether the fruit is immature fruit, semi-mature fruit or mature fruit.

[0059] In this implementation, multiple fruit images are classified into corresponding categories (immature fruit, semi-mature fruit, and mature fruit) according to the output of the SVM classifier. Then, the number of fruits in each category is calculated, and based on the number of fruits in each category, their proportion in the total number of fruits is calculated to obtain the overall maturity distribution of the pitaya tree.

[0060] In this embodiment, in step S104, the identity card of the pitaya tree is accurately matched according to the identified location information. The collected real-time image, the identified fruit quantity and the detected fruit maturity information are integrated and updated to the pitaya file corresponding to the identity card. At the same time, the timestamp of the update is recorded for subsequent tracking and analysis.

[0061] For example, the location information of the pitaya tree is longitude 113.3°, latitude 23.1°, and the basic information includes that the tree name is pitaya tree A, the age is 5 years, the variety is red crystal, and the planting date is March 15, 2018.

[0062] The dragon fruit tree identity card is as follows:

[0063] Tree name: Pitaya tree A

[0064] Location: Longitude 113.3°, Latitude 23.1°

[0065] Age: 5 years

[0066] Type: Red crystal

[0067] Planting date: March 15, 2018.

[0068] The corresponding pitaya file initially contains only basic information, such as annual production records, pest and disease conditions, etc. Now, based on the latest data collected by the patrol robot, the file of this pitaya tree is updated.

[0069] The updated dragon fruit file is as follows:

[0070] Tree name: Pitaya tree A

[0071] Location: Longitude 113.3°, Latitude 23.1°

[0072] Age: 5 years

[0073] Type: Red crystal

[0074] Planting date: March 15, 2018

[0075] Growth monitoring records (new / updated)

[0076] Date: September 10, 2024

[0077] Real-time image: Attached is a high-definition picture of pitaya tree A taken by the patrol robot today. The picture clearly shows the overall growth condition of the fruit tree and the distribution of the fruit.

[0078] Fruit quantity: The current fruit quantity is 120.

[0079] Fruit maturity test:

[0080] Ripe fruits: 30, bright red in color, with full texture, expected to be picked this week.

[0081] Semi-ripe fruit: 60 pieces, color changes from green to red, some areas are still green, and it is expected to reach the best picking period next week.

[0082] Immature fruits: 30, mainly green in color, need to continue growing.

[0083] Note: Today's observations found that the fruit trees have lush leaves, no obvious signs of disease or insect pests were found, and the overall growth condition is good.

[0084] It is understandable that the location information may also be recorded using the orchard zone 3, row 5, column 2, and the like.

[0085] The present embodiment provides a pitaya growth monitoring method, which collects location information and basic information of pitaya trees, establishes identity cards and files, uses patrol robots to collect real-time images, and uses image recognition technology to identify the location and fruit quantity of pitaya trees in the collected real-time images, as well as the maturity of the fruits; finally, the pitaya files are updated according to the recognition results and the detection results, thereby realizing rapid and comprehensive monitoring of the growth status of pitaya trees, greatly improving monitoring efficiency, reducing manual intervention, and reducing management costs.

[0086] The above describes the method for monitoring the growth of pitaya in the embodiment of the present invention. The following describes the device in the embodiment of the present invention. Figure 2 , the implementation of the pitaya growth monitoring device in the embodiment of the present invention includes:

[0087] Establishing module 201, for obtaining the location information and basic information of the pitaya tree, and establishing the identity card and pitaya file of the pitaya tree according to the location information and basic information of the pitaya tree;

[0088] A planning module 202 is used to plan a patrol route and control the patrol robot to collect real-time images of the pitaya tree according to the patrol route;

[0089] Identification and detection module 203, used for identifying the location information and the amount of fruit on the pitaya tree according to the collected real-time image, and detecting the maturity of the fruit of the pitaya tree;

[0090] The updating module 204 confirms the identity card of the dragon fruit tree according to the location information of the identified dragon fruit tree, and updates the dragon fruit file corresponding to the identity card of the dragon fruit tree according to the collected real-time image, the identified hanging amount of the dragon fruit tree and the detected fruit maturity of the dragon fruit tree.

[0091] In this embodiment, by collecting the location information and basic information of the pitaya tree, establishing an identity card and a file, using a patrol robot to collect real-time images, and using image recognition technology to identify the location of the pitaya tree in the collected real-time image, the amount of fruit, and the maturity of the fruit are detected; finally, the pitaya file is updated according to the recognition results and the detection results, thereby achieving rapid and comprehensive monitoring of the growth status of the pitaya tree, greatly improving the monitoring efficiency, reducing manual intervention, and reducing management costs.

[0092] Figure 2 The structure of the pitaya growth monitoring device shown does not constitute a limitation of the pitaya growth monitoring device, and can implement the steps of the pitaya growth monitoring method provided in the above-mentioned method embodiments.

[0093] above Figure 2 The pitaya growth monitoring device in the embodiment of the present invention is described in detail from the perspective of modular functional entities, and the pitaya growth monitoring device in the embodiment of the present invention is described in detail from the perspective of hardware processing.

[0094] Figure 3 3 is a schematic diagram of the structure of a pitaya growth monitoring device provided by an embodiment of the present invention. The device 300 may have relatively large differences due to different configurations or performances, and may include one or more processors (central processing units, CPU) 310 (for example, one or more processors) and a memory 320, and one or more storage media 330 (for example, one or more mass storage devices) storing application programs 333 or data 332. Among them, the memory 320 and the storage medium 330 can be short-term storage or permanent storage. The program stored in the storage medium 330 may include one or more modules (not shown), and each module may include a series of instruction operations in the device 300. Furthermore, the processor 310 may be configured to communicate with the storage medium 330 to execute a series of instruction operations in the storage medium on the device 300.

[0095] The device 300 may also include one or more power supplies 340, one or more wired or wireless network interfaces 350, one or more input and output interfaces 360, and / or one or more operating systems 331, such as Windows Serve, Mac OS X, Unix, Linux, FreeBSD, etc.

[0096] An embodiment of the present invention also provides a computer-readable storage medium, which may be a non-volatile computer-readable storage medium or a volatile computer-readable storage medium. Instructions are stored in the computer-readable storage medium. When the instructions are executed on a computer, the computer executes the steps of the dragon fruit growth monitoring method.

[0097] Those skilled in the art can clearly understand that, for the convenience and brevity of description, the specific working process of the above-described system, device, or unit can refer to the corresponding process in the aforementioned method embodiment and will not be repeated here.

[0098] If the integrated unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention is essentially or the part that contributes to the prior art or all or part of the technical solution can be embodied in the form of a software product. The computer software product is stored in a storage medium, including a number of instructions to enable a computer device (which can be a personal computer, a server, or a network device, etc.) to perform all or part of the steps of the method described in each embodiment of the present invention. The aforementioned storage medium includes: U disk, mobile hard disk, read-only memory (ROM), random access memory (RAM), disk or optical disk and other media that can store program codes.

[0099] As described above, the above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit the same. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that the technical solutions described in the aforementioned embodiments may still be modified, or some of the technical features thereof may be replaced by equivalents. However, these modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A pitaya growth monitoring method, characterized in that, The pitaya growth monitoring method comprises: Obtaining the location information and basic information of the pitaya tree, and establishing an identity card and a pitaya file of the pitaya tree according to the location information and basic information of the pitaya tree; Plan patrol routes and control patrol robots to collect real-time images of dragon fruit trees along the patrol routes; Identify the location information and fruit quantity of the pitaya tree based on the collected real-time images, and detect the maturity of the fruit of the pitaya tree; The identity card of the pitaya tree is confirmed according to the identified location information of the pitaya tree, and the pitaya file corresponding to the identity card of the pitaya tree is updated according to the collected real-time image, the identified hanging amount of the pitaya tree and the detected fruit maturity of the pitaya tree.

2. The method for monitoring growth of pitaya according to claim 1, wherein The method of obtaining the location information and basic information of the pitaya tree and establishing an identity card and a pitaya file of the pitaya tree according to the location information and basic information of the pitaya tree comprises: Use a drone or ground measurement equipment to measure and record the location information of the pitaya tree, wherein the location information includes longitude and latitude; Obtain basic information of the pitaya tree, including tree age, crown size, and health status, and integrate the location information and basic information of the pitaya tree to create an identity card for the pitaya tree; A pitaya file corresponding to the identity card of the pitaya tree is established in a preset pitaya growth database.

3. The method for monitoring growth of pitaya according to claim 1, wherein: The planning of the patrol route and controlling the patrol robot to collect real-time images of the pitaya tree along the patrol route include: In the area between the two ends of each row of pitaya trees, the laser radar is used to collect point cloud data, and in the area at the two ends of each row of pitaya trees, the RTK-GNSS equipment is used to collect spatial position data; Construct a three-dimensional point cloud map as a priori map based on the point cloud data; estimating an initial pose of the laser radar, and optimizing the initial pose of the laser radar based on the prior map to obtain an optimized pose of the laser radar; In a loosely coupled manner, the spatial position data collected by the RTK-GNSS device is fused with the optimized posture of the laser radar to obtain a fused posture, and the feature points at the corresponding time are mapped to the prior map according to the fused posture to construct an environmental map; A patrol starting point and a patrol end point are obtained, a patrol route is planned in the environmental map using a path planning algorithm, and a patrol robot is controlled to collect real-time images of the pitaya tree along the patrol route.

4. The method for monitoring growth of pitaya according to claim 3, wherein: The step of constructing a three-dimensional point cloud map as a priori map according to the point cloud data includes: According to the scanning mode of the laser radar, the point cloud data is distinguished in the vertical direction and the horizontal direction, and edge points and plane points are extracted from the distinguished point cloud data as global feature points; According to the global feature points, motion deformation compensation is performed on the point cloud data collected at different times in the same frame to obtain single-frame point cloud data without deformation; The deformation-free single-frame point cloud data is registered to the global coordinate system and voxel thinning is performed to obtain a three-dimensional point cloud map, which is used as a priori map.

5. The method for monitoring pitaya growth according to claim 4, wherein: The estimating the initial pose of the laser radar and optimizing the initial pose of the laser radar based on the prior map to obtain the optimized pose of the laser radar includes: Loading the priori map, dividing the grid array in the priori map, and storing the global feature points in corresponding grids; Acquire IMU data of the laser radar, and determine the initial motion value of the laser radar according to the IMU data; Acquire point cloud data of the current frame of the laser radar, extract feature points of the current frame from the point cloud data of the current frame, and determine the initial position and posture of the laser radar in the priori map according to the initial motion value of the laser radar and the feature points of the current frame; Select global feature points in the grid around the initial pose to form a local sub-graph; The feature points of the current frame are used to perform feature matching with the global feature points in the local sub-image, and the initial posture of the laser radar is optimized according to the matching result to obtain the optimized posture of the laser radar.

6. The method for monitoring growth of pitaya according to claim 1, wherein: The method of identifying the location information and the amount of fruits on the pitaya tree according to the collected real-time image, and detecting the maturity of the fruits of the pitaya tree, comprises: Preprocessing the collected real-time image to obtain a preprocessed image; Using a target detection algorithm to mark the position of the pitaya tree in the preprocessed image to obtain a marked image; According to preset camera parameters, the position information of the pitaya tree in the marked image is converted into actual position information; Counting the number of fruits of the pitaya tree in the marked image to obtain the fruit quantity of the pitaya tree; For each fruit in the marked image, maturity detection is performed, and maturity information of each fruit and overall maturity distribution are output.

7. The method for monitoring pitaya growth according to claim 6, wherein: The step of performing maturity detection on each fruit in the marked image and outputting maturity information of each fruit and overall maturity distribution includes: Extracting the fruit region of the marked image using an algorithm based on image segmentation to obtain a plurality of fruit images; Extracting color features of the plurality of fruit images respectively, and inputting the extracted color features into a pre-trained SVM classifier to obtain classification results output by the SVM classifier, wherein the classification results include immature fruit, semi-mature fruit and mature fruit; The multiple fruit images are classified according to the classification results output by the SVM classifier, and the number and proportion of fruit images in each category are counted.

8. A pitaya growth monitoring device, characterized in that: include: Establish a module for obtaining the location information and basic information of the pitaya tree, and establish an identity card and a pitaya file of the pitaya tree according to the location information and basic information of the pitaya tree; A planning module is used to plan patrol routes and control patrol robots to collect real-time images of dragon fruit trees along the patrol routes; An identification and detection module is used to identify the location information and fruit quantity of the pitaya tree according to the collected real-time image, and detect the fruit maturity of the pitaya tree; The update module confirms the identity card of the dragon fruit tree according to the identified location information of the dragon fruit tree, and updates the dragon fruit file corresponding to the identity card of the dragon fruit tree according to the collected real-time image, the identified hanging amount of the dragon fruit tree and the detected fruit maturity of the dragon fruit tree.

9. A pitaya growth monitoring device, characterized in that: comprising a memory and at least one processor, wherein the memory has computer-readable instructions stored therein; The at least one processor calls the computer-readable instructions in the memory to execute the various steps of the pitaya growth monitoring method as described in any one of claims 1-7.

10. A computer-readable storage medium having computer-readable instructions stored thereon, characterized in that: When the computer-readable instructions are executed by a processor, the various steps of the pitaya growth monitoring method as described in any one of claims 1 to 7 are implemented.

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