Aerial pesticide spraying drift measurement and control system and method of use

Abstract

This invention provides a spray drift monitoring and control system for pesticide sprayers and its usage method. The system is installed on the pesticide sprayer and includes: an information acquisition module for collecting environmental and droplet information; a central control system for receiving the environmental and droplet information in real time and controlling the opening and closing of a solenoid valve after analyzing the information; and an actuator for spraying according to the opening and closing of the solenoid valve. This invention is applicable to plant protection operations in agricultural work, reducing pesticide drift and production costs, while also protecting crops from damage caused by excessive pesticide application.

Description

Technical Field

[0001] This invention relates to the field of pesticide spraying technology, and in particular to a spray drift measurement and control system for pesticide spraying machines and its usage method. Background Technology

[0002] In agriculture, spraying is a fundamental task for improving soil fertility, crop quality, and productivity. However, spraying is always accompanied by drift. Spray drift refers to the movement of sprayed pesticide droplets beyond the target area, which can be caused by a variety of factors, including the influence of wind. Pesticide droplet drift in the spray can volatilize from plant and soil surfaces years after application, creating hazardous areas outside the target zone. Therefore, non-target areas may be acutely exposed and adversely affected immediately after spraying, leading to residues in crop products, water pollution, and adverse human exposure. Because of these life-threatening substances, reducing spray drift has always been a complex and critical issue in the agricultural industry. Therefore, accurate measurement and management of drift are essential in agriculture.

[0003] Traditional spray drift measurement methods can be categorized into three types: water-sensitive paper analysis, spray visualization, and fixed lidar-based methods. Existing spray drift measurement methods cannot analyze spray drift in real time and only monitor it within a limited range. Furthermore, they struggle to accurately predict the likelihood of spray drift because it is not only related to the nozzle but also significantly influenced by external environmental factors such as tree growth and spacing. Summary of the Invention

[0004] In order to overcome the shortcomings of the prior art, the purpose of this invention is to provide a spray drift measurement and control system for pesticide sprayers and its usage method.

[0005] To achieve the above objectives, the present invention provides the following solution:

[0006] A spray drift monitoring and control system for a pesticide sprayer, installed on the pesticide sprayer, includes:

[0007] The information acquisition module collects environmental information and droplet information from the sprayer.

[0008] The central control system is used to receive the environmental information and droplet information in real time, and control the opening and closing degree of the solenoid valve after analyzing the information;

[0009] An actuator is used to spray according to the opening degree of the solenoid valve.

[0010] Preferably, the information acquisition module includes: two RGB-D cameras and one millimeter-wave radar; the RGB-D cameras are used to acquire the environmental information; and the millimeter-wave radar is used to acquire the fog droplet information.

[0011] Preferably, the RGB-D cameras are respectively arranged on the upper sides of the rear of the sprayer, and the millimeter-wave radar is arranged in the middle of the upper rear side of the sprayer.

[0012] Preferably, the central control system is a Raspberry Pi microcomputer.

[0013] Preferably, the Raspberry Pi microcomputer embeds pre-trained deep learning models SegNet and PointNet++; SegNet is used to analyze the environmental information and divide the environment into target and non-target regions; PointNet++ is used to calculate the number of fog droplet point clouds in the environment.

[0014] Preferably, the actuator is provided in 4 groups, each group including 1 PWM solenoid valve and 3 nozzles.

[0015] A method of using the above-mentioned spray drift measurement and control system for a pesticide sprayer, characterized in that it includes:

[0016] The system boots up and initializes.

[0017] Set the upper and lower thresholds for the number of fog droplets in the target area, and set the upper threshold for the number of fog droplets in the non-target area;

[0018] Two RGB-D cameras continuously collect environmental information and transmit it to a Raspberry Pi microcomputer; a millimeter-wave radar continuously collects fog droplet information and transmits it to a Raspberry Pi microcomputer.

[0019] The Raspberry Pi microcomputer analyzes environmental information and divides it into target regions and sub-target regions based on a pre-trained SegNet model;

[0020] The Raspberry Pi microcomputer analyzes fog droplet information and identifies the number of fog droplets based on a pre-trained PointNet++ model;

[0021] If the number of droplets in a non-target area exceeds the upper threshold, the Raspberry Pi microcomputer controls the PWM solenoid valve of the corresponding area to close; if the number of droplets in the target area exceeds the upper threshold, the Raspberry Pi microcomputer controls the PWM solenoid valve of the corresponding area to close; if the number of droplets in the target area is lower than the lower threshold, the Raspberry Pi microcomputer controls the PWM solenoid valve of the corresponding area to open.

[0022] The nozzle spray volume is controlled based on the opening and closing degree of the PWM solenoid valve.

[0023] Preferably, when the PWM solenoid valve receives both open and close commands simultaneously, the open command is executed.

[0024] According to specific embodiments provided by the present invention, the present invention discloses the following technical effects:

[0025] This invention provides a spray drift monitoring and control system for pesticide sprayers and its usage method. The system is installed on the pesticide sprayer and includes: an information acquisition module for collecting environmental and droplet information; a central control system for receiving the environmental and droplet information in real time and controlling the opening and closing of a solenoid valve after analyzing the information; and an actuator for spraying according to the opening and closing of the solenoid valve. This invention is applicable to plant protection operations in agricultural work, reducing pesticide drift and production costs, while also protecting crops from damage caused by excessive pesticide application. Attached Figure Description

[0026] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0027] Figure 1 This is a block diagram of a pharmaceutical spray drift measurement and control system provided in an embodiment of the present invention;

[0028] Figure 2 A flowchart provided for an embodiment of the present invention;

[0029] Figure 3 This is a front view of the structure of the spray drift measurement and control system for a pesticide sprayer provided in an embodiment of the present invention;

[0030] Figure 4 This is a side view of the structure of the spray drift measurement and control system for a pesticide sprayer provided in an embodiment of the present invention.

[0031] Explanation of reference numerals in the attached figures:

[0032] 1-RGB-D Camera I; 2-Nozzle IA; 3-Millimeter-wave radar; 4-Nozzle IB; 5-Nozzle IC; 6-PWM solenoid valve I; 7-PWM solenoid valve II; 8-Nozzle IIA; 9-Nozzle IIB; 10-Nozzle IIC; 11-Nozzle IVC; 12-Nozzle IVB; 13-Nozzle IVA; 14-Solenoid valve IV; 15-Solenoid valve III; 16-Nozzle IIIC; 17-Nozzle IIIB; 18-Nozzle IIIA; 19-RGB-D Camera II. Detailed Implementation

[0033] 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, and 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.

[0034] To make the above-mentioned objects, features and advantages of the present invention more apparent and understandable, the present invention will be further described in detail below with reference to the accompanying drawings and specific embodiments.

[0035] like Figures 1 to 4 As shown, a spray drift measurement and control system for a pesticide sprayer is installed on the pesticide sprayer, comprising:

[0036] The information acquisition module collects environmental information and droplet information from the sprayer.

[0037] The central control system is used to receive the environmental information and droplet information in real time, and control the opening and closing degree of the solenoid valve after analyzing the information;

[0038] An actuator is used to spray according to the opening degree of the solenoid valve.

[0039] Preferably, the information acquisition module includes: two RGB-D cameras and one millimeter-wave radar; the RGB-D cameras are used to acquire the environmental information; and the millimeter-wave radar is used to acquire the fog droplet information.

[0040] Preferably, the RGB-D cameras (RGB-D camera I1 and RGB-D camera II19) are respectively arranged on the upper sides of the rear of the sprayer, and the millimeter-wave radar 3 is arranged in the middle of the upper rear side of the sprayer.

[0041] Preferably, the central control system is a Raspberry Pi microcomputer.

[0042] Preferably, the Raspberry Pi microcomputer embeds pre-trained deep learning models SegNet and PointNet++; SegNet is used to analyze the environmental information and divide the environment into target and non-target regions; PointNet++ is used to calculate the number of fog droplet point clouds in the environment.

[0043] Preferably, the actuator is provided in four groups, each group including one PWM solenoid valve (PWM solenoid valve I6, PWM solenoid valve II7, solenoid valve IV14 and solenoid valve III15) and three nozzles (nozzle IA2, nozzle IB4, nozzle IC5, nozzle IIA8, nozzle IIB9, nozzle IIC10, nozzle IVC11, nozzle IVB12, nozzle IVA13, nozzle IIIC16, nozzle IIIB17 and nozzle IIIA18). Each group is controlled independently and does not interfere with each other.

[0044] Specifically, a spray drift monitoring and control system for a pesticide sprayer mainly comprises three parts: an information acquisition module, a central control system, and an actuator. The information acquisition module includes two RGB-D cameras and a millimeter-wave radar. The central control system includes a Raspberry Pi microcomputer and its peripheral circuitry. The actuator includes a PWM solenoid valve and a nozzle.

[0045] The information acquisition module includes two RGB-D cameras positioned on either side of the rear of the sprayer, and a millimeter-wave radar positioned in the upper center of the rear of the sprayer. The RGB-D cameras are used to collect environmental information, while the millimeter-wave radar is used to collect spray point cloud data (including the number and size of droplets).

[0046] The Raspberry Pi microcomputer in the central control system serves two main purposes: First, it uses the deep learning model SegNet to segment environmental information into trees (including leaves, branches, and trunks), fruit, ground, and sky (note that trees and fruit are separated because fruit may not always be on the tree depending on the season). Then, it defines trees and fruit as target areas requiring spraying, while the ground and sky are non-target areas that do not require spraying. It sets upper and lower thresholds for the number of droplets in the target areas. When the number of droplets in the target areas falls below the lower threshold, the central control system sends a command to begin spraying. When the number of droplets in the target areas exceeds the upper threshold, spraying stops.

[0047] The second function of the Raspberry Pi microcomputer in the central control system is to analyze the droplet point cloud information collected by the millimeter-wave radar using the pre-trained 3D deep learning model PointNet++. It sets an upper limit threshold for the number of droplets in non-target areas; when the number of droplets in non-target areas exceeds the upper limit, spraying is stopped.

[0048] The actuator comprises four groups, arranged at the four corners of the sprayer's rear. Each group contains one PWM solenoid valve and three nozzles. The opening and closing degree of the PWM solenoid valve is controlled by a Raspberry Pi microcomputer, which in turn controls the spray volume of the nozzles.

[0049] Furthermore, this embodiment also provides a method for using the above-mentioned spray drift measurement and control system for pesticide sprayers, characterized in that it includes:

[0050] The system boots up and initializes.

[0051] Set the upper and lower thresholds for the number of fog droplets in the target area, and set the upper threshold for the number of fog droplets in the non-target area;

[0052] Two RGB-D cameras continuously collect environmental information and transmit it to a Raspberry Pi microcomputer; a millimeter-wave radar continuously collects fog droplet information and transmits it to a Raspberry Pi microcomputer.

[0053] The Raspberry Pi microcomputer analyzes environmental information and divides it into target regions and sub-target regions based on a pre-trained SegNet model;

[0054] The Raspberry Pi microcomputer analyzes fog droplet information and identifies the number of fog droplets based on a pre-trained PointNet++ model;

[0055] If the number of droplets in a non-target area exceeds the upper threshold, the Raspberry Pi microcomputer controls the PWM solenoid valve of the corresponding area to close; if the number of droplets in the target area exceeds the upper threshold, the Raspberry Pi microcomputer controls the PWM solenoid valve of the corresponding area to close; if the number of droplets in the target area is lower than the lower threshold, the Raspberry Pi microcomputer controls the PWM solenoid valve of the corresponding area to open.

[0056] The nozzle spray volume is controlled based on the opening and closing degree of the PWM solenoid valve.

[0057] Preferably, when the PWM solenoid valve receives both open and close commands simultaneously, the open command is executed.

[0058] The various embodiments in this specification are described in a progressive manner, with each embodiment focusing on its differences from other embodiments. Similar or identical parts between embodiments can be referred to interchangeably. The usage methods disclosed in the embodiments are described simply because they correspond to the systems disclosed in the embodiments; relevant details can be found in the usage method section.

[0059] This document uses specific examples to illustrate the principles and implementation methods of the present invention. The descriptions of the above embodiments are only for the purpose of helping to understand the method and core ideas of the present invention. Furthermore, those skilled in the art will recognize that, based on the ideas of the present invention, there will be changes in the specific implementation methods and application scope. Therefore, the content of this specification should not be construed as a limitation of the present invention.

Claims

1. A spray drift measurement and control system for a pesticide sprayer, characterized in that, The equipment installed on the pesticide sprayer includes: The information acquisition module collects environmental information and droplet information from the sprayer. The central control system is used to receive the environmental information and droplet information in real time, and control the opening and closing degree of the solenoid valve after analyzing the information; An actuator is used to spray according to the opening degree of the solenoid valve; The information acquisition module includes two RGB-D cameras and one millimeter-wave radar. The RGB-D cameras are used to acquire environmental information, and the millimeter-wave radar is used to acquire fog droplet information. The RGB-D cameras are respectively arranged on the upper sides of the rear of the sprayer, and the millimeter-wave radar is arranged in the middle of the upper rear side of the sprayer. The central control system is a Raspberry Pi microcomputer. The Raspberry Pi microcomputer embeds the trained deep learning models SegNet and PointNet++. SegNet is used to analyze the environmental information and divide the environment into target areas and non-target areas. PointNet++ is used to calculate the number of fog droplet point clouds in the environment. A threshold is set for the number of fog droplets in non-target areas. If the number of fog droplets in non-target areas exceeds the threshold, the Raspberry Pi microcomputer controls the solenoid valve of the corresponding area to close.

2. The spray drift measurement and control system for a pesticide sprayer according to claim 1, characterized in that, The actuator is provided in 4 groups, each group including 1 PWM solenoid valve and 3 nozzles.

3. A method of using the spray drift measurement and control system for a pesticide sprayer as described in claim 1, characterized in that, include: The system boots up and initializes. Set the upper and lower thresholds for the number of fog droplets in the target area, and set the upper threshold for the number of fog droplets in the non-target area; Two RGB-D cameras continuously collect environmental information and transmit this information to the Raspberry Pi microcomputer; The millimeter-wave radar continuously collects information about the fog droplets and transmits this information to the Raspberry Pi microcomputer. The Raspberry Pi microcomputer analyzes environmental information and divides it into target regions and sub-target regions based on a pre-trained SegNet model; The Raspberry Pi microcomputer analyzes fog droplet information and identifies the number of fog droplets based on a pre-trained PointNet++ model; If the number of droplets in a non-target area exceeds the upper threshold, the Raspberry Pi microcomputer controls the PWM solenoid valve of the corresponding area to close; if the number of droplets in the target area exceeds the upper threshold, the Raspberry Pi microcomputer controls the PWM solenoid valve of the corresponding area to close; if the number of droplets in the target area is lower than the lower threshold, the Raspberry Pi microcomputer controls the PWM solenoid valve of the corresponding area to open. The nozzle spray volume is controlled based on the opening and closing degree of the PWM solenoid valve.

4. The method of using the spray drift measurement and control system for pesticide sprayers according to claim 3, characterized in that, When the PWM solenoid valve receives both open and close commands simultaneously, it executes the open command.

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