Portable crop water stress index automatic detection device and method

Abstract

The invention discloses a portable crop water stress index automatic detection device and method, and relates to the field of crop water stress index detection, and the device comprises an image collection module which is used for collecting image data of a crop canopy and image data of a wet reference plane; the temperature acquisition module is used for acquiring ambient air temperature of crops; the main control module is respectively connected with the image acquisition module and the temperature acquisition module, and is used for determining image data of a crop canopy area and image data of a wet reference surface area by adopting a built-in instance segmentation model based on the image data of the crop canopy and the image data of the wet reference surface; and according to the image data of the crop canopy region, the image data of the wet reference plane region and the ambient air temperature, calculating to obtain a crop water stress index. According to the invention, the real-time performance and effectiveness of crop water stress index detection can be improved.

Description

Technical Field

[0001] The present application relates to the field of crop water stress index detection, and in particular to a portable crop water stress index automatic detection device and method. Background Art

[0002] Against the backdrop of global warming and increasing water shortages, agricultural production faces enormous challenges. Crop water stress directly affects crop growth, yield and quality, so the need for real-time detection and management of crop water stress is becoming increasingly urgent.

[0003] Traditional crop water stress detection equipment has the following problems: some equipment collects a single type of data, relying on a single temperature data, and cannot fully reflect the degree of water stress of crops. The data processing and analysis functions are relatively backward, lacking automated data upload and intelligent analysis support, making it difficult to achieve real-time and efficient data sharing and decision-making guidance. Summary of the invention

[0004] The purpose of the present application is to provide a portable crop water stress index automatic detection device and method, which can improve the real-time and effectiveness of crop water stress index detection.

[0005] To achieve the above objectives, this application provides the following solutions: In a first aspect, the present application provides a portable crop water stress index automatic detection device, comprising: An image acquisition module, used for acquiring image data of the crop canopy and image data of the wet reference surface; Temperature collection module, used to collect the ambient air temperature of crops; The main control module is connected to the image acquisition module and the temperature acquisition module respectively, and is used to obtain image data of the crop canopy area and image data of the wet reference surface area by segmenting based on the image data of the crop canopy and the image data of the wet reference surface using a built-in instance segmentation model, and calculate the crop water stress index according to the image data of the crop canopy area, the image data of the wet reference surface area and the ambient air temperature; wherein the instance segmentation model is a trained YOLOv5-seg model.

[0006] In a second aspect, the present application provides a method for automatically detecting a crop water stress index, comprising: Collecting image data of the ambient air temperature of the crop, the crop canopy and the wet reference surface; the image data includes a thermal infrared image and a visible light image, the visible light image includes a left visible light image and a right visible light image, and the thermal infrared image includes a grayscale image and a temperature matrix; Based on the image data of the crop canopy and the image data of the wet reference surface, an instance segmentation model is used to detect and obtain image data of the crop canopy area and image data of the wet reference surface area, and the crop water stress index is calculated according to the image data of the crop canopy area, the image data of the wet reference surface area and the ambient air temperature; wherein the instance segmentation model is a trained YOLOv5-seg model.

[0007] According to the specific embodiments provided in this application, this application discloses the following technical effects: The present application provides a portable crop water stress index automatic detection device and method, which can simultaneously obtain image data of the crop canopy and the wet reference surface and the ambient air temperature through an image acquisition module and a temperature acquisition module, segment the crop canopy area and the wet reference surface area through an instance segmentation model set by a main control module, and calculate the crop water stress index in real time, thereby improving the real-time and effectiveness of the crop water stress index. BRIEF DESCRIPTION OF THE DRAWINGS

[0008] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the drawings required for use in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0009] Figure 1 This is a schematic diagram of the overall structure of a portable automatic detection device for crop water stress index in one embodiment of the present application; Figure 2 This is a rear view of the overall structure of a portable crop water stress index automatic detection device in one embodiment of the present application; Figure 3 A schematic diagram of a mask image of a crop canopy region provided in an embodiment of the present application; Figure 4 A schematic diagram of a mask image of a wet reference surface area provided in an embodiment of the present application; Figure 5 A schematic diagram of the registration effect of the left visible light image and the right visible light image provided in an embodiment of the present application; Figure 6 A flow chart of a method for automatically detecting crop water stress index provided in one embodiment of the present application. DETAILED DESCRIPTION

[0010] The following will be combined with the drawings in the embodiments of the present application to clearly and completely describe the technical solutions in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of this application.

[0011] In order to make the above-mentioned objects, features and advantages of the present application more obvious and easy to understand, the present application is further described in detail below with reference to the accompanying drawings and specific implementation methods.

[0012] The embodiment of the present application provides a portable crop water stress index automatic detection device, which includes: an image acquisition module, a temperature acquisition module and a main control module.

[0013] The image acquisition module is used to collect image data of crop canopy and wet reference surface, such as Figure 1 As shown, the image acquisition module includes a thermal infrared camera 1 and binocular visible light cameras 2 arranged on the left and right sides of the thermal infrared camera 1. The binocular visible light cameras 2 can acquire high-resolution visible light images of the crop canopy and the wet reference surface. The binocular visible light cameras 2 are respectively a left visible light camera and a right visible light camera. Therefore, the acquired image data includes thermal infrared images and visible light images, the visible light images include left visible light images and right visible light images, and the thermal infrared images include grayscale images and temperature matrices.

[0014] Specifically, the thermal infrared camera 1 is connected to the main control module via a USB3.0 interface, and is used to capture thermal infrared images of the crop canopy and the wet reference surface, capture the temperature distribution information on the leaf surface to obtain the temperature matrix of the crop canopy and the wet reference surface, and transmit the collected grayscale image and the corresponding temperature matrix to the main control module for analysis and processing; the binocular visible light camera 2 is symmetrically arranged on the left and right sides of the thermal infrared camera 1, and is connected to the main control module via a USB2.0 interface, and is used to capture visible light images of the crop canopy and the wet reference surface, and the visible light images include left visible light images and right visible light images.

[0015] In an exemplary embodiment, the wet reference surface is a simulation device placed next to the crop to simulate the temperature when the stomata of the crop leaves are fully open and in a full transpiration state.

[0016] The simulation device consists of a plastic box, a polystyrene foam board, a water-absorbent non-woven fabric, a viscose blended fabric, and a polyester non-woven fabric. The plastic box contains water, and the polystyrene foam board is covered on top of the plastic box. The surface of the polystyrene foam board is wrapped with a layer of water-absorbent non-woven fabric and a viscose blended fabric, and the outermost layer is covered with a polyester non-woven fabric. This structure enables the material on the polystyrene foam board to continuously absorb water, effectively reducing water evaporation, thereby maintaining a low temperature, simulating the temperature of crop leaves when the stomata are fully open and in a full transpiration state, that is, the wet reference surface temperature, in degrees Celsius (℃).

[0017] The design of the binocular camera in the binocular visible light camera 2 improves the image depth perception capability, facilitates the acquisition of three-dimensional structure information, and helps in canopy segmentation and crop water stress index calculation to enhance the comprehensive analysis of crop stress conditions.

[0018] In an exemplary embodiment, before using the thermal infrared camera 1 and the binocular visible light camera 2 to collect image data, they need to be calibrated.

[0019] Specifically, a thermal infrared camera 1 and a binocular visible light camera 2 are used to simultaneously capture a set of black and white checkerboard images including a thermal infrared image, a left visible light image, and a right visible light image, and the three images are calibrated using the Zhang Zhengyou calibration method.

[0020] Zhang Zhengyou's calibration method takes multiple black and white checkerboard images at different angles and uses the least squares method to calculate the intrinsic and extrinsic parameters of the binocular visible light camera. The intrinsic and extrinsic parameters only depend on the optical characteristics and relative position of the camera. Therefore, the process of calculating the intrinsic and extrinsic parameters will not affect the processing time of the registration of thermal infrared images and visible light images. The calibration uses a special aluminum oxide black and white checkerboard calibration plate. The front panel is a black checkerboard made of aluminum oxide, and the back panel is a glass substrate. When the back of the calibration plate is evenly heated, the two materials have different thermal conductivity and reflectivity, so that the calibration plate will form a black and white checkerboard image on both the thermal infrared image and the visible light image.

[0021] The calibration is divided into two groups: one group is the calibration of the binocular visible light camera, which obtains the intrinsic and extrinsic parameters of the left visible light camera and the right visible light camera; the other group is the calibration between the left visible light camera and the thermal infrared camera in the binocular visible light camera, which obtains the intrinsic and extrinsic parameters of the left visible light camera and the thermal infrared camera. The internal parameters include focal length, principal point and distortion coefficient, and the external parameters include rotation matrix and translation vector.

[0022] The camera's intrinsic parameters mainly represent the mapping from the camera coordinate system to the image coordinate system, and the camera's extrinsic parameters describe the transformation from the world coordinate system to the camera coordinate system. The rotation matrix describes the direction of the coordinate axes of the world coordinate system relative to the camera coordinate axes, and the translation matrix describes the position of the space origin in the camera coordinate system.

[0023] The temperature acquisition module is used to collect the ambient air temperature of the crop. The temperature acquisition module consists of a temperature sensor 3, which is connected to the main control module through an RS-485 interface. It is used to collect the ambient air temperature in real time and provide necessary data support for the subsequent calculation of the Crop Water Stress Index (CWSI).

[0024] The main control module is connected to the image acquisition module and the temperature acquisition module respectively. The main control module uses the MODBUS RTU (Remote Terminal Unit) protocol to communicate with the temperature acquisition module to realize real-time reading of temperature data and command sending, so as to facilitate the subsequent calculation of crop water stress index. Based on the image data of the crop canopy and the image data of the wet reference surface, the main control module uses the built-in instance segmentation model to determine the image data of the crop canopy area and the image data of the wet reference surface area, and calculates the crop water stress index based on the image data of the crop canopy area, the image data of the wet reference surface area and the ambient air temperature. Specifically, the instance segmentation model is a trained YOLOv5-seg model.

[0025] In an exemplary embodiment, the thermal infrared camera 1 and the binocular visible light camera 2 are aimed at the crop and photographed from the upper side of the crop to obtain image data of the complete crop canopy and image data of the wet reference surface of the same crop. The visible light images of the crop canopy and the wet reference surface are first scaled and cropped, and the resolution of the visible light image is kept consistent with that of the thermal infrared image through appropriate scale transformation. On this basis, the visible light images of the crop canopy and the wet reference surface are annotated to obtain a training set and trained to obtain a trained YOLOv5-seg model. The specific steps are as follows: S3.1. Data labeling: Use the Segment Anything Model (SAM) to automatically label the collected visible light images of the crop canopy and wet reference surface. The labeled labels are the crop canopy area and the wet reference surface area.

[0026] The SAM model is a highly versatile large segmentation model that uses an image encoder to extract features from the input image and generate a one-time image embedding. The image encoder divides the image into multiple small blocks and converts the color value of each small block into a vector representation as the input sequence of the Transformer. Based on the prompts provided by the user (such as points, boxes, text, etc.), the prompt encoder is used to generate prompt embeddings. The prompt encoder can convert different types of prompts into a unified vector representation for subsequent processing. Combining the outputs of the image encoder and the prompt encoder, the mask decoder is used to predict the segmentation mask. The segmentation mask represents the probability that each pixel in the image belongs to the foreground or background, thereby achieving accurate segmentation of the target in the image.

[0027] By introducing the SAM model, the crop canopy area and the wet reference surface area in the collected visible light images of the crop canopy and the wet reference surface can be automatically annotated quickly and accurately, and standard data with high precision and consistency can be generated, providing a reliable data basis for the training of the YOLOv5-seg model.

[0028] S3.2, Model training: The labeled visible light images of crop canopies and wet reference surfaces are divided into training set, test set and validation set in a ratio of 7:2:1. The training set is used to train the YOLOv5-seg model to optimize the parameters of the YOLOv5-seg model, the test set is used to preliminarily verify the performance of the YOLOv5-seg model, and the validation set is used to evaluate the final effect of the YOLOv5-seg model. After multiple rounds of iterative optimization, the YOLOv5-seg model can accurately segment the crop canopy area and the wet reference surface area in the visible light images of the crop canopy and the wet reference surface.

[0029] S3.3, Model conversion: After training is completed, the trained YOLOv5-seg model is converted to RKNN (RockchipNeural Network) format for adaptation to embedded devices.

[0030] Specifically, the trained YOLOv5-seg model is saved in .pt format and needs to be converted to ONNX (OpenNeural Network Exchange) format, which is an open neural network exchange format that can be used for model conversion and sharing between different deep learning frameworks. In order to deploy the trained YOLOv5-seg model on the main control module, the ONNX format needs to be further converted to RKNN format. RKNN is a neural network inference engine designed specifically for Rockchip series chips, which provides efficient neural network inference capabilities for adapting embedded devices.

[0031] S3.4, Model Embedding: The trained YOLOv5-seg model is embedded in the main control module as an instance segmentation model, so that it can automatically segment the crop canopy area and the wet reference surface area of ​​the real-time collected image data in the field environment, and obtain the mask images of the crop canopy area and the wet reference surface area, respectively. Figure 3 and Figure 4 As shown, the image preprocessing technology based on corrosion operation is used to optimize the boundary of the mask image, remove boundary noise, and ensure the stability of the generated points.

[0032] The image data of the crop canopy region and the image data of the wet reference surface region are determined by using the optimized mask image of the crop canopy region and the mask image of the wet reference surface region in combination with the image data of the crop canopy and the image data of the wet reference surface.

[0033] In an exemplary embodiment, the main control module specifically includes a data acquisition unit, an instance segmentation unit, a feature registration unit, a position confirmation unit, a first calculation unit, a second calculation unit and an index calculation module.

[0034] The data acquisition unit is used to acquire image data of ambient air temperature, crop canopy and wet reference surface; the image data includes thermal infrared image and visible light image, and the visible light image includes left visible light image and right visible light image.

[0035] The instance segmentation unit is connected to the data acquisition unit, and is used to determine the image data of the crop canopy area and the image data of the wet reference surface area by using an instance segmentation model based on the image data of the crop canopy and the image data of the wet reference surface.

[0036] Specifically, the visible light image in the image data of the crop canopy and the visible light image in the image data of the wet reference surface are segmented using the instance segmentation model in the instance segmentation unit to obtain a mask image of the crop canopy region and a mask image of the wet reference surface region. The image data of the crop canopy region and the image data of the wet reference surface region are determined based on the mask image of the crop canopy region, the mask image of the wet reference surface region, the image data of the crop canopy and the image data of the wet reference surface.

[0037] The feature registration unit is connected to the instance segmentation unit, and is used to register the left visible light image and the right visible light image in the image data of the crop canopy area to obtain feature point pairs.

[0038] Specifically, the feature registration unit includes an initial feature point confirmation subunit and a feature point pair determination subunit.

[0039] The initial feature point confirmation subunit is used to divide the left visible light image and the right visible light image of the crop canopy area into a plurality of regularly distributed grid points according to a preset step size, so as to obtain an initial feature point set of the left visible light image and the right visible light image; wherein the image feature at the location of each grid point is an initial feature point.

[0040] The feature point pair determination subunit is connected to the initial feature point confirmation subunit, and the feature point pair determination subunit is used to align the initial feature points of the left visible light image and the right visible light image of the crop canopy area according to the geometric relationship constraint conditions to obtain the feature point pair; wherein the geometric relationship constraint conditions are the coordinate differences of the initial feature points in the horizontal and vertical directions.

[0041] Specifically, through the geometric constraint matching algorithm, the coordinate differences in the horizontal and vertical directions are used as constraints, and the initial feature point pairs are matched one by one in the initial feature point sets of the left visible light image and the right visible light image of the crop canopy area to ensure that the matching initial feature point pairs satisfy the spatial geometric relationship. Finally, the initial feature point pairs that do not meet the geometric constraints are eliminated, the matching initial feature point pairs are retained, and the matching initial feature point pairs are used as matching point pairs to complete the registration, such as Figure 5 At the same time, all matching point pairs are visualized and the coordinates of the matching point pairs are obtained, and the matching point pairs are used as feature point pairs, and finally the feature point pairs of the regular grid of the left visible light image and the right visible light image of the crop canopy area are obtained.

[0042] The position confirmation unit is connected to the instance segmentation unit and the feature registration unit, and is used to determine the position of the feature point pair on the grayscale image of the crop canopy area according to the position of the feature point pair on the visible light image of the crop canopy area.

[0043] Specifically, the intrinsic parameters and extrinsic parameters of the left visible light camera and the right visible light camera are obtained by using the above calibration. The world coordinates of the feature point pairs can be obtained by combining the positions of the feature point pairs on the visible light image of the crop canopy area. The feature points in the feature point pairs can be back-projected onto the grayscale image of the crop canopy area through the world coordinates of the feature point pairs and the intrinsic parameters and extrinsic parameters of the left visible light camera and the thermal infrared camera obtained by calibration. The specific principle is as follows: Assume that the coordinate system of the left visible light camera in the binocular visible light camera coincides with the world coordinate system. The coordinate of a point in the world coordinate system is , whose coordinates in the left visible light camera coordinate system and the image coordinate system are and , whose coordinates in the right visible light camera coordinate system and the image coordinate system in the binocular visible light camera are and .

[0044] It is known that the relationship between the left visible light camera coordinate system, the right visible light camera coordinate system and the world coordinate system in the binocular visible light camera is formula (1) and formula (2) respectively: (1); (2); in, and is the depth factor.

[0045] (3); (4); (5); (6); in, is the focal length of the left camera in the left visible light camera of the binocular visible light camera; is the focal length of the right camera in the right visible light camera of the binocular visible light camera; is the principal point of the right camera in the right visible light camera of the binocular visible light camera; and are respectively the rotation matrix and translation matrix of the right camera of the right visible light camera in the binocular visible light camera relative to the left camera of the left visible light camera in the binocular visible light camera; and They are the intrinsic parameter matrix of the left visible light camera and the intrinsic parameter matrix of the right visible light camera respectively; to are the element values ​​of the intrinsic parameter matrix of the left visible light camera; to are all element values ​​of the intrinsic parameter matrix of the right visible light camera.

[0046] Substituting formula (3) and formula (4) into formula (1) and formula (2), we can get formula (7). If the coordinates of a pair of points in the image coordinate system of the left camera and the right camera in the binocular visible light camera are known, the coordinates of a point in the world coordinate system can be solved by formula (7).

[0047] (7); The world coordinates are obtained through the above calculations , through the external parameters of the left visible light camera and the thermal infrared camera (rotation matrix and translation vector ), transforming the point from the world coordinate system to the thermal infrared camera coordinate system , using the internal parameter matrix of the thermal infrared camera The point in the thermal infrared camera coordinate system Projected to the grayscale image coordinate system, the transformation relationship is shown in formula (8): (8); (9); in, is the depth factor; is the internal parameter matrix of the thermal infrared camera; is the focal length of the thermal infrared camera; It is the main point of the thermal infrared camera.

[0048] By using formula (8) and formula (9), we can get the grayscale image coordinates: , that is, the pixel position of the feature point on the thermal grayscale image. Similarly, the pixel position of the centroid position of the wet reference surface area on the grayscale image can also be obtained.

[0049] The first calculation unit is connected to the position confirmation unit and the instance segmentation unit, and is used to calculate the average temperature of the crop canopy area according to the position of the feature point pair on the grayscale image of the crop canopy area and the temperature matrix of the crop canopy area.

[0050] Specifically, according to the position of the feature point on the grayscale image of the crop canopy area, the corresponding temperature value in the temperature matrix of the crop canopy area is located, the temperature matrix of the crop canopy area is traversed, and all temperature values ​​at the position corresponding to the feature point are averaged to obtain the average temperature of the crop canopy area, providing data support for CWSI calculation.

[0051] The second computing unit is connected to the instance segmentation unit, and is used to determine the centroid position of the grayscale image of the wet reference surface area based on the visible light image in the image data of the wet reference surface area, and calculate the temperature of the wet reference surface area based on the centroid position and the temperature matrix of the wet reference surface area.

[0052] The index calculation module is connected to the data acquisition unit, the first calculation unit and the second calculation unit, and is used to calculate the crop water stress index according to the ambient air temperature, the average temperature of the crop canopy area and the temperature of the wet reference surface area.

[0053] Specifically, formula (10) is used to calculate the crop water stress index: (10); in, is the crop water stress index, is the average temperature of the crop canopy area, is the temperature of the wet reference surface area, is the temperature of the dry reference surface.

[0054] The temperature of the dry reference surface is the dry reference surface temperature when the crop stomata are completely closed and no transpiration occurs. , To obtain the ambient air temperature.

[0055] Calculating the crop water stress index by using the portable crop water stress index automatic detection device can improve the real-time and effectiveness of crop water stress index detection.

[0056] In an exemplary embodiment, the portable crop water stress index automatic detection device also includes a wireless communication positioning module, a display module, a power module, a heat dissipation module, a grip handle and a trigger switch.

[0057] The wireless communication positioning module is connected to the main control module, and the wireless communication positioning module is connected to the main control module via a USB interface. Figure 1 As shown, the wireless communication positioning module uses a WiFi antenna 4, which is set on the top of the portable crop water stress index automatic detection device. The wireless communication positioning module is used to obtain the geographical location information of the portable crop water stress index automatic detection device, that is, the global positioning system (GPS) information and the water stress index sent by the receiving main control module, and send the geographical location information and water stress index to the cloud server to realize the remote real-time transmission and positioning function of data. The wireless communication positioning module makes the crop water stress index correspond to the geographical location information one by one, which is convenient for data archiving, analysis and future retrieval.

[0058] The display module is connected to the main control module, such as Figure 2 As shown, the display module uses a 4.3-inch LCD touch screen 8, which is connected to the main control module through a MIPI interface, and is designed with a user-friendly QT Creator interface. The display module displays the visible light image and grayscale image sent by the main control module in real time, and can also display the crop water stress index. Based on this, the visualization of the visible light image, grayscale image and crop water stress index is realized.

[0059] The heat dissipation module is connected to the main control module via a USB interface. The heat dissipation module is set as a small fan. When the temperature inside the portable crop water stress index automatic detection device reaches a certain temperature, the fan will start automatically, effectively preventing the thermal infrared camera 1 from being abnormal due to overheating, and ensuring that the thermal infrared camera 1 can work stably in a high temperature environment.

[0060] The power module is connected to the main control module. The power module supplies power to the main control module through the power interface, and then provides power to the display module, temperature acquisition module, image acquisition module, wireless communication positioning module, and heat dissipation module. Figure 1As shown, the power module is arranged at the bottom of the portable crop water stress index automatic detection device, and adopts a removable rechargeable battery 7 with a capacity of 5000mAh, which can provide a portable power source for the device. When replacement or replenishment of power is needed, the user can quickly replace the battery to ensure that the device can continue to work.

[0061] like Figure 1 As shown, the portable crop water stress index automatic detection device is equipped with a USB interface 4 on the side, which is convenient for connecting external devices and performing data transmission. Figure 1 As shown, a gripping handle 6 is provided. The gripping handle 6 is ergonomically designed, convenient for hand-held operation, and suitable for outdoor use in multiple scenarios. A trigger switch 5 is provided on the gripping handle 6. The user can realize synchronous collection of image data and ambient air temperature by pressing the trigger switch 5. Specifically, the trigger switch 5 adopts a GPIO (General Purpose Input Output) button to facilitate user operation.

[0062] The portable crop water stress index automatic detection device can be held by hand and is easy to move. It has good portability and independent power supply design and can adapt to complex application requirements in field environments.

[0063] The portable crop water stress index automatic detection device is used to calculate the crop water stress index. The crop water stress index can be used to evaluate the current water status of the crop, quantify the water stress level of the crop, and provide a scientific basis for precise irrigation. The present invention is suitable for real-time crop water stress monitoring in a field environment and has guiding significance for field crop water irrigation.

[0064] Based on the same inventive concept, the embodiment of the present application also provides a method for automatically detecting a crop water stress index. The implementation scheme for solving the problem provided by the method is similar to the implementation scheme recorded in the above-mentioned device, so the specific limitations of one or more embodiments of the method for automatically detecting a crop water stress index provided below can refer to the limitations of the method for automatically detecting a crop water stress index above, and will not be repeated here.

[0065] In an exemplary embodiment, Figure 6 As shown, a method for automatically detecting a crop water stress index is provided, comprising: Step 101 , collecting image data of the crop's ambient air temperature, crop canopy, and wet reference surface.

[0066] Step 102: Based on the image data of the crop canopy and the image data of the wet reference surface, an instance segmentation model is used to perform segmentation to obtain image data of the crop canopy area and image data of the wet reference surface area, and the crop water stress index is calculated according to the image data of the crop canopy area, the image data of the wet reference surface area and the ambient air temperature; wherein the instance segmentation model is a trained YOLOv5-seg model.

[0067] In an exemplary embodiment, step 102 includes steps 201 to 207: Step 201, acquiring image data of ambient air temperature, crop canopy and wet reference surface; wherein the image data includes thermal infrared image and visible light image, the visible light image includes left visible light image and right visible light image, and the thermal infrared image includes grayscale image and temperature matrix.

[0068] Step 202: Based on the image data of the crop canopy and the image data of the wet reference surface, an instance segmentation model is used to determine the image data of the crop canopy region and the image data of the wet reference surface region.

[0069] Step 203 , registering the left visible light image and the right visible light image in the image data of the crop canopy region to obtain feature point pairs.

[0070] Step 204 : determining the positions of the feature point pairs on the grayscale image of the crop canopy region according to the positions of the feature point pairs on the visible light image of the crop canopy region.

[0071] Step 205 , calculating the average temperature of the crop canopy region according to the positions of the feature point pairs on the grayscale image of the crop canopy and the temperature matrix of the crop canopy region.

[0072] Step 206 , determining the centroid position of the grayscale image of the wet reference surface area according to the visible light image in the image data of the wet reference surface area, and calculating the temperature of the wet reference surface area according to the centroid position and the temperature matrix of the wet reference surface area.

[0073] Step 207: Calculate the crop water stress index according to the ambient air temperature, the average temperature of the crop canopy area, and the temperature of the wet reference surface area.

[0074] In an exemplary embodiment, step 203 specifically includes steps 301 and 302: Step 301, dividing the left visible light image and the right visible light image of the crop canopy area into a plurality of regularly distributed grid points according to a preset step size, and obtaining an initial feature point set of the left visible light image and the right visible light image of the crop canopy area; wherein the image feature at the location of each grid point is an initial feature point.

[0075] Step 302, aligning the initial feature points of the left visible light image and the right visible light image of the crop canopy region according to geometric relationship constraints to obtain feature point pairs; wherein the geometric relationship constraints are the coordinate differences of the initial feature points in the horizontal and vertical directions.

[0076] It should be noted that the user information (including but not limited to user device information, user personal information, etc.) and data (including but not limited to data used for analysis, stored data, displayed data, etc.) involved in this application are all information and data authorized by the user or fully authorized by all parties, and the collection, use and processing of relevant data must comply with relevant regulations.

[0077] Those skilled in the art can understand that all or part of the processes in the above-mentioned embodiment methods can be completed by instructing the relevant hardware through a computer program, and the computer program can be stored in a non-volatile computer-readable storage medium. When the computer program is executed, it can include the processes of the embodiments of the above-mentioned methods. Among them, any reference to the memory, database or other medium used in the embodiments provided in this application can include at least one of non-volatile and volatile memory. Non-volatile memory can include read-only memory (ROM), magnetic tape, floppy disk, flash memory, optical memory, high-density embedded non-volatile memory, resistive random access memory (ReRAM), magnetoresistive random access memory (MRAM), ferroelectric random access memory (FRAM), phase change memory (PCM), graphene memory, etc. Volatile memory can include random access memory (RAM) or external cache memory, etc. By way of illustration and not limitation, RAM may be in various forms, such as Static Random Access Memory (SRAM) or Dynamic Random Access Memory (DRAM).

[0078] The database involved in each embodiment provided in this application may include at least one of a relational database and a non-relational database. The non-relational database may include a distributed database based on blockchain, etc., but is not limited thereto. The processor involved in each embodiment provided in this application may be a general-purpose processor, a central processing unit, a graphics processor, a digital signal processor, a programmable logic device, a data processing logic device based on quantum computing, etc., but is not limited thereto.

[0079] The technical features of the above embodiments may be combined arbitrarily. To make the description concise, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0080] This article uses specific examples to illustrate the principles and implementation methods of this application. The description of the above embodiments is only used to help understand the method and core ideas of this application. At the same time, for those skilled in the art, according to the ideas of this application, there will be changes in the specific implementation methods and application scope. In summary, the content of this specification should not be understood as limiting this application.

Claims

1. A portable crop water stress index automatic detection device, characterized in that: The portable crop water stress index automatic detection device comprises: An image acquisition module, used for acquiring image data of the crop canopy and image data of the wet reference surface; Temperature collection module, used to collect the ambient air temperature of crops; The main control module is connected to the image acquisition module and the temperature acquisition module respectively, and is used to obtain image data of the crop canopy area and image data of the wet reference surface area by segmenting based on the image data of the crop canopy and the image data of the wet reference surface using a built-in instance segmentation model, and calculate the crop water stress index according to the image data of the crop canopy area, the image data of the wet reference surface area and the ambient air temperature; wherein the instance segmentation model is a trained YOLOv5-seg model.

2. The portable crop water stress index automatic detection device according to claim 1, characterized in that: The main control module specifically includes: A data acquisition unit, used to acquire image data of ambient air temperature, crop canopy and wet reference surface; wherein the image data includes thermal infrared image and visible light image, the visible light image includes left visible light image and right visible light image, and the thermal infrared image includes grayscale image and temperature matrix; An instance segmentation unit connected to the data acquisition unit, and configured to determine the image data of the crop canopy region and the image data of the wet reference surface region by using an instance segmentation model based on the image data of the crop canopy and the image data of the wet reference surface; a feature registration unit connected to the instance segmentation unit, and used to register the left visible light image and the right visible light image in the image data of the crop canopy region to obtain a feature point pair; a position confirmation unit connected to the instance segmentation unit and the feature registration unit, and configured to determine the position of the feature point pair on the grayscale image of the crop canopy region according to the position of the feature point pair on the visible light image of the crop canopy region; a first calculation unit connected to the position confirmation unit and the instance segmentation unit, and configured to calculate an average temperature of the crop canopy region according to the position of the feature point pair on the grayscale image of the crop canopy region and a temperature matrix of the crop canopy region; a second calculation unit, connected to the instance segmentation unit, for determining a centroid position of a grayscale image of the wet reference surface region according to a visible light image in the image data of the wet reference surface region, and calculating a temperature of the wet reference surface region according to the centroid position and a temperature matrix of the wet reference surface region; An index calculation module is connected to the data acquisition unit, the first calculation unit, and the second calculation unit, and is used to calculate the crop water stress index according to the ambient air temperature, the average temperature of the crop canopy area, and the temperature of the wet reference surface area.

3. The portable crop water stress index automatic detection device according to claim 2, characterized in that: The portable crop water stress index automatic detection device also includes: The wireless communication positioning module is connected to the main control module, and is used to obtain the geographical location information of the portable crop water stress index automatic detection device and receive the crop water stress index sent by the main control module, and send the geographical location information and the crop water stress index to the cloud server.

4. The portable crop water stress index automatic detection device according to claim 3, characterized in that: The portable crop water stress index automatic detection device also includes: The display module is connected to the main control module and is used to receive the visible light image and grayscale image sent by the main control module and display them in real time.

5. The portable crop water stress index automatic detection device according to claim 4, characterized in that: The portable crop water stress index automatic detection device also includes: The power module is connected to the main control module and is used to provide power to the main control module, the display module, the temperature acquisition module, the wireless communication positioning module, and the image acquisition module.

6. The portable crop water stress index automatic detection device according to claim 1, characterized in that: The image acquisition module includes a thermal infrared camera and binocular visible light cameras arranged on the left and right sides of the thermal infrared camera.

7. A method for automatically detecting crop water stress index, applied to the portable automatic detection device for crop water stress index according to any one of claims 1 to 6, characterized in that: The crop water stress index automatic detection method comprises: Collect image data of crop ambient air temperature, crop canopy and wet reference surface; Based on the image data of the crop canopy and the image data of the wet reference surface, an instance segmentation model is used to segment the image data of the crop canopy area and the image data of the wet reference surface area, and the crop water stress index is calculated according to the image data of the crop canopy area, the image data of the wet reference surface area and the ambient air temperature; wherein the instance segmentation model is a trained YOLOv5-seg model.

8. The method for automatically detecting crop water stress index according to claim 7, characterized in that: The crop water stress index is calculated based on the image data of the crop canopy area, the image data of the wet reference surface area and the ambient air temperature, specifically including: Acquire image data of ambient air temperature, crop canopy and wet reference surface; wherein the image data includes thermal infrared image and visible light image, the visible light image includes left visible light image and right visible light image, and the thermal infrared image includes grayscale image and temperature matrix; Based on the image data of the crop canopy and the image data of the wet reference surface, an instance segmentation model is used to determine the image data of the crop canopy region and the image data of the wet reference surface region; Registering a left visible light image and a right visible light image in the image data of the crop canopy region to obtain a pair of feature points; Determining the position of the feature point pair on the grayscale image of the crop canopy region according to the position of the feature point pair on the visible light image of the crop canopy region; Calculating the average temperature of the crop canopy region according to the positions of the feature point pairs on the grayscale image of the crop canopy region and the temperature matrix of the crop canopy region; Determine the centroid position of the grayscale image of the wet reference surface area according to the visible light image in the image data of the wet reference surface area, and calculate the temperature of the wet reference surface area according to the centroid position and the temperature matrix of the wet reference surface area; The crop water stress index is calculated based on the ambient air temperature, the average temperature of the crop canopy area and the temperature of the wet reference surface area.

9. The method for automatically detecting crop water stress index according to claim 8, characterized in that: The left visible light image and the right visible light image of the crop canopy region are registered to obtain feature point pairs of the left and right visible light images, specifically including: Dividing the left visible light image and the right visible light image of the crop canopy region into a plurality of regularly distributed grid points according to a preset step length, and obtaining an initial feature point set of the left visible light image and the right visible light image of the crop canopy region; wherein the image feature at the position of each grid point is an initial feature point; The initial feature points of the left visible light image and the right visible light image of the crop canopy area are aligned according to geometric relationship constraints to obtain feature point pairs; wherein the geometric relationship constraints are the coordinate differences of the initial feature points in the horizontal and vertical directions.

10. The method for automatically detecting crop water stress index according to claim 7, characterized in that: The calculation formula of crop water stress index is: ； in, is the crop water stress index, is the average temperature of the crop canopy area, is the temperature of the wet reference surface area, is the temperature of the dry reference surface.

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