Fruit and vegetable monitoring and spraying system based on intelligent atomizing vehicle

Abstract

This invention discloses a fruit and vegetable monitoring and spraying system based on an intelligent atomizing vehicle, comprising a vegetation image acquisition unit, a vegetation image processing unit, and a spraying control unit. The vegetation image acquisition unit collects RGB images of vegetation areas containing fruit and vegetable vegetation. A region acquisition module performs vegetation region segmentation processing on the acquired RGB images to obtain vegetation outline regions and acquires vegetation density values ​​based on these outline regions. A leaf particle acquisition module performs vegetation outline localization processing on the acquired RGB images to obtain vegetation leaf particles. A servo motor control module controls the servo motor of the intelligent atomizing vehicle to rotate the nozzle to the vegetation leaf particles based on the vegetation leaf particles. A water pump control module controls the water pump output of the intelligent atomizing vehicle based on the vegetation density values ​​and the vegetation leaf particles. This system solves the current problems of using manual spraying and drone spraying for vegetation spraying, which are often inefficient, costly, and have low pesticide utilization rates. Drone spraying is the preferred solution in the market, but it can only be applied to large-scale site operations and has problems such as high operational difficulty and high risk.

Description

Technical Field

[0001] This invention relates to the field of pesticide spraying technology for small-scale planting areas such as orchards, greenhouses, and farmland, and in particular to a fruit and vegetable monitoring and spraying system based on an intelligent atomizing vehicle. Background Technology

[0002] Fruits and vegetables are essential renewable resources for humankind. They not only provide basic necessities but also improve soil fertility, control soil erosion, and enhance the human living environment. However, during their growth and development, fruits and vegetables are frequently affected by various adverse environmental biotic and abiotic factors, leading to diseases. The direct losses caused by fruit and vegetable diseases directly impact yields. Furthermore, the excessive use of pesticides and herbicides not only increases production costs and wastes funds but also directly harms human and animal health, pollutes agricultural products, and contaminates the environment and water quality.

[0003] Currently, the most common methods for spraying pesticides on vegetation are manual spraying and drone spraying. However, traditional manual spraying is inefficient, costly, and has a low pesticide utilization rate. Drone spraying is the preferred solution in the market, but it can only be applied to large-scale operations and has problems with high operational difficulty and risk. Therefore, there is an urgent need for an automated control spraying solution that can achieve precise top-point spraying based on fruit and vegetable vegetation, reduce costs, and minimize environmental pollution. Summary of the Invention

[0004] This invention provides a fruit and vegetable monitoring and spraying system based on an intelligent atomizing vehicle to overcome the above-mentioned technical problems.

[0005] To achieve the above objectives, the technical solution of the present invention is as follows:

[0006] A fruit and vegetable monitoring and spraying system based on an intelligent atomizing vehicle includes a vegetation image acquisition unit, a vegetation image processing unit, and a spraying control unit.

[0007] The vegetation image acquisition unit is fixedly installed at one end of the intelligent atomizing vehicle and is used to acquire RGB images of vegetation areas containing fruit and vegetable vegetation and transmit them to the vegetation image processing unit.

[0008] The vegetation image processing unit includes a vegetation region acquisition module and a leaf surface particle acquisition module;

[0009] The region acquisition module is used to perform vegetation region segmentation processing on the acquired RGB image of the vegetation region, obtain the vegetation outline region, and obtain the vegetation density value based on the vegetation outline region.

[0010] The leaf particle acquisition module is used to perform vegetation contour localization processing on the acquired RGB image of the vegetation area to acquire the leaf particles of the vegetation area in the RGB image of the vegetation area.

[0011] The vegetation density value and the vegetation leaf surface particles are then transmitted to the spray control unit.

[0012] The spray control unit includes a servo motor control module and a water pump control module;

[0013] The servo control module is used to control the servo motor of the intelligent atomizing vehicle to rotate the nozzle to the vegetation leaf surface particles based on the vegetation leaf surface particles.

[0014] The water pump control module is used to control the amount of medicine dispensed by the water pump of the intelligent atomizing vehicle based on the leaf surface mass points of the vegetation and the vegetation density value.

[0015] Furthermore, the method for obtaining vegetation density values ​​through the vegetation area acquisition module specifically includes the following steps:

[0016] S11: The cvtColor function based on OpenCV is used to convert the RGB image of the vegetation area to a color space to obtain the HSV image of the vegetation area;

[0017] S12: The medianBlur function of OpenCV is used to filter and denoise the HSV image of the vegetation area to obtain a denoised image of the vegetation area;

[0018] S13: Set the H channel color threshold range for fruit and vegetable vegetation, and use the inRange function to perform color filtering on the denoised image of the vegetation area to obtain the vegetation outline area.

[0019] The specific method for color selection is as follows:

[0020] The pixel values ​​of each pixel in the denoised image of the vegetation area that meet the H channel color threshold range are updated to the first preset fixed pixel value; the pixel values ​​of each pixel in the denoised image of the vegetation area that do not meet the H channel color threshold range are updated to the second preset fixed pixel value.

[0021] S14: Based on the area ratio of the vegetation outline region to the RGB image of the vegetation region, the area ratio is used as the vegetation density value.

[0022] Furthermore, the method for obtaining vegetation leaf surface particles from the initial image through the leaf surface particle acquisition module specifically includes the following steps:

[0023] S21: Using the Laplacian edge detection algorithm based on OpenCV, extract the shape of the vegetation leaf area in the RGB image of the vegetation area;

[0024] S22: Use OpenCV's Canny function to draw the outline of the shape of the vegetation leaf area according to the edge;

[0025] S23: Preset the two-dimensional coordinate axis of the RGB image of the vegetation area, and define the x-axis as the length of the RGB image of the vegetation area and the y-axis as the width of the RGB image of the vegetation area.

[0026] Based on the findContours function, the edge contour points of the contour border are extracted and stored;

[0027] S24: Traverse the stored edge contour point coordinates to obtain the vegetation leaf surface particles of the RGB image of the vegetation area; and the calculation formula for the vegetation leaf surface particles is as follows:

[0028]

[0029] In the formula: α represents the abscissa of a particle on the leaf surface; β represents the ordinate of a particle on the leaf surface; x i The x-coordinate of the i-th edge contour point; y i The ordinate represents the ordinate of the i-th edge contour point; n represents the total number of edge contour points.

[0030] Furthermore, the water pump control module controls the amount of pesticide dispensed by the pump based on the vegetation density value. Specifically, this is achieved by controlling the PWM duty cycle of the intelligent atomizing vehicle, expressed as follows:

[0031]

[0032] In the formula: R represents the PWM duty cycle of the intelligent atomizing vehicle; D represents the vegetation density value; D min D represents the minimum threshold for vegetation density. max R represents the maximum threshold for vegetation density. max This indicates the maximum value of the PWM duty cycle of the intelligent atomizing vehicle; R min This represents the minimum PWM duty cycle of the intelligent atomizing vehicle.

[0033] Furthermore, it also includes a system control terminal, which includes an MQTT server, an ESP32-CAM unit, and an intelligent display unit;

[0034] The ESP32-CAM unit includes an image capture module, an image compression module, and an image transmission module;

[0035] The image capture module is used to set the image capture frame rate / resolution of the vegetation image acquisition unit;

[0036] The image compression module is used to compress the RGB image of the vegetation area containing fruit and vegetable vegetation acquired by the vegetation image acquisition unit to obtain a JPEG vegetation image.

[0037] The image transmission module is used to obtain MQTT messages based on JPEG vegetation images and transmit the MQTT messages to the MQTT server according to the preset MQTT protocol.

[0038] Furthermore, the MQTT message includes an MQTT message subject for identifying the type of vegetation image and an MQTT message carrier for transmitting JPEG vegetation images;

[0039] The MQTT server is used to receive MQTT messages and to set a task timer for receiving MQTT messages;

[0040] The task timer is used to send the JPEG vegetation image of the received MQTT message to the intelligent display unit via JavaScript when the task execution time of the task timer is reached.

[0041] The intelligent display unit is used to monitor and display JPEG vegetation images.

[0042] Furthermore, it also includes an integrated agricultural assistance platform built on Spring Boot + Vue + MySQL + WebSocket technology, which includes a front-end module and a back-end module; and the front-end module connects to the back-end module via the WebSocket protocol.

[0043] The front-end module is used to create several user interaction modules based on Vue components for publishing tasks performed by the smart atomizing vehicle, and to send JSON data containing atomizing vehicle information to the back-end module through the user interaction modules.

[0044] The information about the atomizing vehicle includes specifications, performance parameters, and model parameters;

[0045] The backend module includes a Controller layer, a Service layer, a Mapper layer, and a storage layer;

[0046] The Controller layer is used to convert the obtained JSON data to obtain Java object data, and then transmit the Java object data to the Service layer.

[0047] The Service layer is used to obtain permission verification tasks according to preset business logic modules, and to perform permission verification on the received Java object data based on the permission verification tasks.

[0048] If the permission verification is successful, the Service layer obtains the verification success signal and sends it back to the user interaction module, and the user interaction module obtains the first status code prompt box.

[0049] If the permission verification fails, the verification failure signal is obtained through the Service layer and fed back to the user interaction module, and the second status code prompt box is obtained through the user interaction module.

[0050] The system monitors JSON data containing atomizing vehicle information by using the first and second status code prompt boxes, and transmits Java object data that has passed permission verification to the Mapper layer.

[0051] The Mapper layer is used to obtain the SQL statement file based on the MyBatis persistence layer framework and the preset XML mapping file, according to the Java object data that has passed the permission verification output by the Service layer;

[0052] The storage layer includes a parsing module and a MySQL storage database;

[0053] The parsing module is used to parse the SQL statement file, obtain the parsed JSON data, and transmit it to the URL address of the corresponding API service in the MySQL storage database.

[0054] Beneficial Effects: This invention provides a fruit and vegetable monitoring and spraying system based on an intelligent atomizing vehicle. The system uses a region acquisition module to segment the acquired RGB images of vegetation areas, obtaining vegetation outline regions and then calculating vegetation density values ​​based on these outline regions. A leaf particle acquisition module performs vegetation outline localization processing on the acquired RGB images of vegetation areas, obtaining leaf particle locations within the RGB images. A servo control module controls the servo motor of the intelligent atomizing vehicle to rotate the nozzles to these leaf particle locations. A water pump control module controls the pesticide output of the intelligent atomizing vehicle based on the leaf particle locations and vegetation density values. By precisely controlling the amount of pesticide used and the spraying location based on the specific conditions and needs of the planting area, pesticide waste is reduced, environmental pollution is minimized, and the quality and safety of agricultural products are improved. Attached Figure Description

[0055] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0056] Figure 1 This is a schematic diagram of a fruit and vegetable monitoring and spraying system based on an intelligent atomizing vehicle according to the present invention;

[0057] Figure 2 This is a schematic diagram of the water pump control module controlling the water pump spraying in this embodiment;

[0058] Figure 3 This is a flowchart of the image processing unit for vegetation image processing in this embodiment;

[0059] Figure 4 This is a schematic diagram of the main screen interface of the system control terminal in this embodiment;

[0060] Figure 5 This is a schematic diagram of the secondary screen interface of the system control terminal in this embodiment;

[0061] Figure 6 This is a schematic diagram of the management interface of the integrated agricultural assistance platform's intelligent atomizing vehicle in this embodiment;

[0062] Figure 7 This is a schematic diagram of the system structure of the intelligent atomizing vehicle in this embodiment. Detailed Implementation

[0063] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0064] This embodiment provides a fruit and vegetable monitoring and spraying system based on an intelligent atomizing vehicle, such as... Figure 1 As shown, it includes a vegetation image acquisition unit, a vegetation image processing unit, and a spraying control unit;

[0065] The vegetation image acquisition unit is fixedly installed at one end of the intelligent atomizing vehicle and is used to acquire RGB images of vegetation areas containing fruit and vegetable vegetation and transmit them to the vegetation image processing unit.

[0066] Specifically, such as Figure 7 As shown, the system of the intelligent atomizing vehicle includes...

[0067] OV2640 camera: Installed at the front of the atomizing vehicle in the direction of travel, used to collect images of vegetated areas; the camera angle and distance can be adjusted according to the actual scene, and the size and resolution of the collected images are fixed during camera initialization, so the collection area is adjustable and the defined range is unlimited; however, the characteristics of the vegetated area may be affected by excessively strong or weak light intensity after the images are collected, so a light shield can be optionally installed above the OV2640 camera, and a supplementary light can be optionally installed below the camera;

[0068] Main controller: The ESP32-CAM is used as the main controller, which is responsible for the overall control, data processing and wireless communication of the vehicle;

[0069] Auxiliary Controller: The STC89C52 acts as an auxiliary controller to assist the main controller in completing preset execution tasks, such as line tracking, pesticide spraying, and image capture.

[0070] Ultrasonic obstacle avoidance module: The HC-SR04 module is used to detect obstacles ahead in real time to ensure the safety of the trolley operation.

[0071] Servo motor and nozzle: The SG90 servo motor is used to control the direction of the high-pressure nozzle for precise spraying;

[0072] Motor drive module: The L298N motor drive module is used to drive the movement of the trolley;

[0073] Power supply voltage regulator module: The MP1584 power supply voltage regulator module ensures the stable operation of all components of the vehicle;

[0074] Relay control: One 5V high-level relay is used to control the switching on and off of the water pump;

[0075] The mechanical structure of the intelligent atomizing vehicle described therein is known technology and is not the inventive point of this application, so it will not be described in detail here.

[0076] The vegetation image processing unit includes a vegetation region acquisition module and a leaf surface particle acquisition module;

[0077] The region acquisition module is used to perform vegetation region segmentation processing on the acquired RGB image of the vegetation region, obtain the vegetation outline region, and obtain the vegetation density value based on the vegetation outline region.

[0078] Specifically, such as Figures 2 to 3 As shown, the method for obtaining vegetation density values ​​through the vegetation area acquisition module includes the following steps:

[0079] S11: The cvtColor function based on OpenCV is used to convert the RGB image of the vegetation area to a color space to obtain the HSV image of the vegetation area;

[0080] S12: The medianBlur function of OpenCV is used to filter and denoise the HSV image of the vegetation area to obtain a denoised image of the vegetation area;

[0081] S13: Set the H channel color threshold range for fruit and vegetable vegetation, and use the inRange function to perform color filtering on the denoised image of the vegetation area to obtain the vegetation outline area.

[0082] The specific method for color selection is as follows:

[0083] The pixel values ​​of each pixel in the denoised image of the vegetation area that meet the H channel color threshold range are updated to the first preset fixed pixel value; the pixel values ​​of each pixel in the denoised image of the vegetation area that do not meet the H channel color threshold range are updated to the second preset fixed pixel value.

[0084] For example: Set the H channel color threshold range to [a, b];

[0085] The pixel value of the current pixel in the denoised image of the vegetation area is κ. If the pixel value κ of the current pixel satisfies the H channel color threshold range [a,b], then the pixel value of the current pixel is set to κ equal to the first preset fixed pixel value τ1; if the pixel value κ of the current pixel does not satisfy the H channel color threshold range [a,b], then the pixel value of the current pixel is set to κ equal to the second preset fixed pixel value τ2.

[0086] S14: Based on the area ratio of the vegetation outline region to the RGB image of the vegetation region, the area ratio is used as the vegetation density value;

[0087] The leaf particle acquisition module is used to perform vegetation contour localization processing on the acquired RGB image of the vegetation area to acquire the leaf particles of the vegetation area in the RGB image of the vegetation area.

[0088] Specifically, the method for acquiring vegetation leaf surface particles of the initial image through the leaf surface particle acquisition module includes the following steps:

[0089] S21: Using the Laplacian edge detection algorithm based on OpenCV, extract the shape of the vegetation leaf area in the RGB image of the vegetation area;

[0090] S22: Use OpenCV's Canny function to draw the outline of the shape of the vegetation leaf area according to the edge;

[0091] S23: Preset the two-dimensional coordinate axis of the RGB image of the vegetation area, and define the x-axis as the length of the RGB image of the vegetation area and the y-axis as the width of the RGB image of the vegetation area.

[0092] Based on the findContours function, the edge contour points of the contour border are extracted and stored;

[0093] S24: Traverse the stored edge contour point coordinates to obtain the vegetation leaf surface particles of the RGB image of the vegetation area; and the calculation formula for the vegetation leaf surface particles is as follows:

[0094]

[0095] In the formula: α represents the abscissa of a particle on the leaf surface; β represents the ordinate of a particle on the leaf surface; x i The x-coordinate of the i-th edge contour point; y i The ordinate represents the y-coordinate of the i-th edge contour point; n represents the total number of edge contour points.

[0096] The vegetation density value and the vegetation leaf surface particles are then transmitted to the spray control unit.

[0097] The spray control unit includes a servo motor control module and a water pump control module;

[0098] The servo control module is used to control the servo motor of the intelligent atomizing vehicle to rotate the nozzle to the vegetation leaf surface particles based on the vegetation leaf surface particles.

[0099] The water pump control module is used to control the amount of pesticide dispensed by the intelligent atomizing vehicle based on the vegetation density value and the leaf surface mass points. Specifically, the water pump control module controls the pesticide dispensed by the pump based on the vegetation density value by controlling the PWM duty cycle of the intelligent atomizing vehicle, the expression of which is:

[0100]

[0101] In the formula: R represents the PWM duty cycle of the intelligent atomizing vehicle; D represents the vegetation density value; D min D represents the minimum threshold for vegetation density. max R represents the maximum threshold for vegetation density. max This indicates the maximum value of the PWM duty cycle of the intelligent atomizing vehicle; R min This represents the minimum PWM duty cycle of the intelligent atomizing vehicle.

[0102] This embodiment uses a region acquisition module to segment the acquired RGB image of the vegetation area to obtain the vegetation outline region, and then obtains the vegetation density value based on the vegetation outline region. A leaf particle acquisition module performs vegetation outline localization processing on the acquired RGB image of the vegetation area to obtain the vegetation leaf particle points in the RGB image. A servo control module controls the servo motor of the intelligent atomizing vehicle to rotate the nozzle to the vegetation leaf particle points based on the vegetation leaf particle points. A water pump control module controls the amount of pesticide dispensed by the intelligent atomizing vehicle's water pump based on the vegetation density value and the vegetation leaf particle points. By precisely controlling the amount of pesticide used and the spraying location of the pesticide in the vegetation area according to the specific conditions and needs of the vegetation planting area, pesticide waste is reduced, environmental pollution is decreased, and the quality and safety of agricultural products are improved.

[0103] In a specific embodiment, such as Figures 4 to 5 As shown, it also includes a system control terminal, which includes an MQTT server, an ESP32-CAM unit, and an intelligent display unit;

[0104] The ESP32-CAM unit includes an image capture module, an image compression module, and an image transmission module;

[0105] The image capture module is used to set the image capture frame rate / resolution of the vegetation image acquisition unit;

[0106] The image compression module is used to compress the RGB image of the vegetation area containing fruit and vegetable vegetation acquired by the vegetation image acquisition unit to obtain a JPEG vegetation image.

[0107] The image transmission module is used to obtain MQTT messages based on JPEG vegetation images and transmit the MQTT messages to the MQTT server according to the preset MQTT protocol.

[0108] Furthermore, the MQTT message includes an MQTT message subject for identifying the type of vegetation image and an MQTT message carrier for transmitting JPEG vegetation images;

[0109] The MQTT server is used to receive MQTT messages and to set a task timer for receiving MQTT messages;

[0110] The task timer is used to send the JPEG vegetation image of the received MQTT message to the intelligent display unit via JavaScript when the task execution time of the task timer is reached.

[0111] The intelligent display unit is used to monitor and display JPEG vegetation images.

[0112] The system control terminal also includes a user device terminal, which can communicate with the system control terminal and the intelligent atomizing vehicle to realize remote control of the intelligent atomizing vehicle and real-time viewing of vegetation areas. The user device terminal includes mobile phones or computers, etc.

[0113] In specific embodiments, it also includes an integrated agricultural assistance platform built based on Spring Boot + Vue + MySQL + WebSocket technologies, such as... Figure 6 As shown, the integrated agricultural assistance platform includes a front-end module and a back-end module; and the front-end module connects to the back-end module via the WebSocket protocol.

[0114] The front-end module is used to create several user interaction modules based on Vue components for publishing tasks performed by the smart atomizing vehicle, and to send JSON data containing atomizing vehicle information to the back-end module through the user interaction modules.

[0115] The information about the atomizing vehicle includes specifications, performance parameters, and model parameters;

[0116] This also includes the user interaction module obtaining the interaction strategy with the backend module, that is, installing the axios library into the Vue component of the frontend module through npm, so as to obtain the HTTP requests of the user interaction module through axios; the HTTP requests include GET requests, PUT requests, POST requests and DELETE requests.

[0117] The HTTP request is then transmitted to the backend module via the WebSocket protocol.

[0118] If the HTTP request received by the front-end module is a GET request or a POST request, then the JSON data containing the atomizing vehicle information is sent to the back-end module for processing;

[0119] If the HTTP request received by the front-end module is a Put request or a Delete request, the corresponding JSON data file is retrieved from the MySQL storage database to modify or delete the preset task, and the modified or deleted JSON data file is sent to the back-end module as a new JSON data file for processing.

[0120] The backend module includes a Controller layer, a Service layer, a Mapper layer, and a storage layer;

[0121] The Controller layer is used to convert the obtained JSON data to obtain Java object data, and then transmit the Java object data to the Service layer.

[0122] The Service layer is used to obtain permission verification tasks according to preset business logic modules, and to perform permission verification on the received Java object data based on the permission verification tasks.

[0123] If the permission verification is successful, the Service layer will receive a successful verification signal and send it back to the user interaction module. The user interaction module will then receive the first status code prompt (e.g., a successful status code: 200).

[0124] If the permission verification fails, the Service layer obtains the verification failure signal and sends it back to the user interaction module, and the user interaction module obtains the second status code prompt box (e.g., failure status code: 400).

[0125] The system monitors JSON data containing atomizing vehicle information by using the first and second status code prompt boxes, and transmits Java object data that has passed permission verification to the Mapper layer.

[0126] The Mapper layer is used to obtain the SQL statement file based on the MyBatis persistence layer framework and the preset XML mapping file, according to the Java object data that has passed the permission verification output by the Service layer;

[0127] The method of obtaining the SQL statement file based on the MyBatis persistence layer and the preset XML mapping file is a well-known existing technology and will not be described in detail here.

[0128] The storage layer includes a parsing module and a MySQL storage database;

[0129] The parsing module is used to parse the SQL statement file, obtain the parsed JSON data, and transmit it to the URL address of the corresponding API service in the MySQL storage database.

[0130] In summary, the beneficial effects of this embodiment are as follows:

[0131] 1. Improved operational safety: The intelligent atomizing truck system controls the operation of the truck, eliminating the need for users to perform high-altitude operations or come into contact with harmful pesticides. This greatly reduces safety risks caused by improper operation or accidents. The atomizing truck can be remotely controlled, further reducing the need for farmers to enter potentially dangerous areas and protecting the lives of workers.

[0132] 2. Improved operational efficiency: Automated spraying vehicles can quickly and evenly cover the entire planting area, which is more efficient than manual spraying and reduces time costs. At the same time, users can monitor the intelligent atomizing vehicle in real time through an integrated platform.

[0133] 3. Reduce pesticide use: This system can precisely control the amount of pesticides used according to the specific conditions and needs of the planting area, reducing waste and environmental pollution; through precise spraying, pesticide residues are reduced, improving the quality and safety of agricultural products.

[0134] 4. Facilitates multi-terminal coordination: The system supports multi-terminal coordination, allowing farmers to view and control the spraying vehicle anytime, anywhere via mobile phones, computers, and other terminal devices, greatly improving operational convenience. The multi-terminal coordination function also enables farmers to collaborate remotely and jointly manage planting areas, improving work efficiency.

[0135] 5. High adaptability: The design of the spraying cart and integrated platform fully considers the actual conditions of small greenhouses, orchards, and other planting sites, exhibiting strong adaptability and flexibility. Whether it's a personal farm or a small planting base, suitable configuration schemes can be preset according to actual conditions, achieving intelligent management and storage.

[0136] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, and not to limit them; although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some or all of the technical features; and these modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the scope of the technical solutions of the embodiments of the present invention.

Claims

1. A fruit and vegetable monitoring and spraying system based on an intelligent atomization vehicle, characterized in that, The application relates to a vegetation image acquisition unit, a vegetation image processing unit and a spraying control unit. The vegetation image acquisition unit is fixedly installed at one end of the intelligent atomization vehicle, is used for collecting a vegetation region RGB image containing fruit and vegetable vegetation, and transmits the image to the vegetation image processing unit. The vegetation image processing unit comprises a vegetation region acquisition module and a leaf surface particle acquisition module. The region acquisition module is used for carrying out vegetation region segmentation processing on the acquired vegetation region RGB image, acquiring a vegetation contour region, and acquiring a vegetation density value according to the vegetation contour region. The leaf surface particle acquisition module is used for carrying out vegetation contour positioning processing on the acquired vegetation region RGB image, acquiring vegetation leaf surface particles of the vegetation region RGB image, and specifically comprises the following steps. S21: based on the Laplacian edge detection algorithm of opencv, the vegetation leaf surface region shape in the vegetation region RGB image is extracted; S22: the contour border of the vegetation leaf surface region shape is drawn by edge description by adopting the Canny function of opencv; S23: the two-dimensional coordinate axis of the vegetation region RGB image is preset, and the x-axis is defined as the length of the vegetation region RGB image, and the y-axis is defined as the width of the vegetation region RGB image; Based on the findContours function, the edge contour points of the contour border are extracted and stored; S24: the stored edge contour point coordinates are traversed to acquire the vegetation leaf surface particles of the vegetation region RGB image; and the calculation formula of the vegetation leaf surface particles is In the formula: The x-coordinate of a particle on the leaf surface of vegetation; The ordinate of a particle on the leaf surface of vegetation; Indicates the first The x-coordinates of the edge contour points; Indicates the first The ordinates of the edge contour points; Indicates the total number of edge contour points; The vegetation density value and the vegetation leaf surface particles are transmitted to the spraying control unit. The spraying control unit comprises a rudder control module and a water pump control module. The rudder control module is used for controlling the rudder of the intelligent atomization vehicle to drive the nozzle to rotate to the vegetation leaf surface particles according to the vegetation leaf surface particles. The water pump control module is used for controlling the water pump output of the intelligent atomization vehicle according to the vegetation density value based on the vegetation leaf surface particles. The water pump output is controlled by the water pump control module according to the vegetation density value, specifically by controlling the pwm duty cycle of the intelligent atomization vehicle, and the expression is R ( ) ） In the formula: R represents the pwm duty cycle of the intelligent atomization vehicle; represents the vegetation density value; represents the set minimum threshold of vegetation density represents the set maximum threshold of vegetation density; represents the maximum value of the pwm duty cycle of the intelligent atomization vehicle; represents the minimum value of the pwm duty cycle of the intelligent atomization vehicle.

2. The fruit and vegetable monitoring and spraying system based on the intelligent atomization vehicle according to claim 1, characterized in that, The method for acquiring the vegetation density value by the vegetation region acquisition module specifically comprises the following steps. S11: based on the cvtColor function of opencv, the vegetation region RGB image is subjected to color space conversion to acquire a vegetation region HSV image; S12: based on the medianBlur function of opencv, the vegetation region HSV image is subjected to filter denoising processing to acquire a vegetation region denoising image; S13: the H channel color threshold range of fruit and vegetable vegetation is set, and the inRange function is adopted to carry out color screening on the vegetation region denoising image to acquire a vegetation contour region; And the color screening method specifically comprises: the pixel values of each pixel point in the vegetation region denoising image satisfying the H channel color threshold range are updated into a first preset fixed pixel value; and the pixel values of each pixel point in the vegetation region denoising image not satisfying the H channel color threshold range are updated into a second preset fixed pixel value. S14: According to the area proportion of the vegetation contour region and the vegetation region RGB image, and taking the area proportion as the vegetation density value.

3. The fruit and vegetable monitoring and spraying system based on the intelligent atomization vehicle according to claim 1, characterized in that, The system control end is also included, and the system control end includes an MQTT server, an ESP32-CAM unit, and a smart display unit. The ESP32-CAM unit includes an image capture module, an image compression module, and an image transmission module. The image capture module is used to set the image capture frame rate / resolution of the vegetation image acquisition unit. The image compression module is used to compress the vegetation region RGB image containing fruit and vegetable vegetation collected by the vegetation image acquisition unit to obtain a JPEG vegetation picture. The image transmission module is used to obtain an MQTT message according to the JPEG vegetation picture, and transmit the MQTT message to the MQTT server according to a preset MQTT protocol. The MQTT message includes an MQTT message topic for identifying the type of vegetation picture and an MQTT message carrier for transmitting the JPEG vegetation picture. The MQTT server is used to receive the MQTT message and set a task timer for receiving the MQTT message. The task timer is used to send the received JPEG vegetation picture of the MQTT message to the smart display unit and the vegetation image acquisition unit through JavaScript when the task execution time of the task timer is reached. The smart display unit is used to monitor and display the JPEG vegetation picture.

4. The fruit and vegetable monitoring and spraying system based on the intelligent atomization vehicle according to claim 1, characterized in that, An integrated agricultural assistance platform based on SpringBoot+Vue+Mysql+WebSocket technology is also included, which includes a front-end module and a back-end module. The front-end module is used to create a plurality of user interaction modules for publishing smart fogging vehicle execution tasks according to Vue components, and send JSON data containing fogging vehicle information to the back-end module through the user interaction modules. The fogging vehicle information includes specification parameters, performance parameters, and model parameters. The back-end module includes a Controller layer, a Service layer, a Mapper layer, and a storage layer. The Controller layer is used to convert the obtained JSON data to Java object data and transmit the Java object data to the Service layer. The Service layer is used to obtain a permission verification task according to a preset business logic module, and perform permission verification on the received Java object data based on the permission verification task. If the permission verification is passed, a verification success signal is fed back to the user interaction module through the Service layer, and a first status code prompt box is obtained through the user interaction module. If the permission verification is not passed, a verification failure signal is fed back to the user interaction module through the Service layer, and a second status code prompt box is obtained through the user interaction module. The first status code prompt box and the second status code prompt box are used to monitor the JSON data containing atomization vehicle information, and the Java object data passing the permission verification is transmitted to a Mapper layer; The Mapper layer is used to acquire an SQL statement file based on a MyBatis persistence layer framework and a preset XML mapping file according to the Java object data passing the permission verification output by the Service layer; The storage layer comprises a parsing module and a Mysql storage database; The parsing module is used to perform data analysis on the SQL statement file, acquire analyzed JSON data, and transmit the JSON data to a corresponding URL address of an API service in the Mysql storage database.

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