Variable Spraying System Research Articles

Design and Testing of a Fruit Tree Variable Spray System Based on ExG-AABB

This paper addresses the issue of pesticide waste and low utilization rates resulting from traditional plant protection via spraying operations, which apply equal dosages to different targets or to different parts of the same target. To tackle this problem, we designed a variable fruit tree spraying system based on the ExG-AABB (excess green and axis-aligned bounding box) algorithm. We used a Kinect depth camera to capture information about the fruit tree canopy and constructed a spray flow model using pulse width modulation and variable spray control technology. Variable multi-nozzle spraying was guided by combining this canopy data. We evaluated the accuracy of each model in calculating canopy volume by comparing the coefficient of determination (R2) and root mean square error (RMSE) of the ExG-AABB with the slice convex hull method, voxel method, three-dimensional alpha-shape method, and QuickHull method. The ExG-AABB algorithm had the highest R2 value (0.9334) and the lowest RMSE value (0.0353 m3) among the five models, indicating that it most accurately reflects the true volume of the fruit tree canopy. This validates the effectiveness of the ExG-AABB algorithm in calculating canopy volume. We established a correlation model between canopy volume and spray volume, designed a canopy-adaptive layering method based on point cloud processing, and achieved precise calculation of nozzle flow. Comparative field experiments were conducted to analyze the spray coverage rate and observed flow, thereby evaluating the spraying effect of this variable spraying system. The experimental results showed that compared to conventional continuous spraying, this variable spraying system not only achieves more uniform spray coverage but also significantly reduces pesticide usage by 48.1%. Furthermore, through system optimization, the average coverage rate of the middle layer of the canopy decreased by 17.53%, effectively reducing the phenomenon of overlapping spraying from multiple nozzles and improving spraying efficiency.

基于PTO协议的变量喷雾系统设计与试验

The aim of this research is to address the issues of low precision in variable spraying within the existing farmland application system, whereby nozzles cannot be controlled independently and low pesticide utilisation is observed. A variable spraying system based on PTO protocol has been designed. The system comprises an STM32 microcontroller as the control core, including the host computer, multi-channel controller, electronic switch, and electromagnetic spray nozzle. The master control unit receives the spray amount set by the user and calculates the duty cycle of the corresponding nozzle, which is then sent to the multiplex controller in real time through CAN communication. The multiplex controller adjusts the on-off frequency and duty cycle of each electromagnetic nozzle in real time according to the duty cycle of the corresponding nozzle it receives, thus enabling the nozzles to be controlled independently. This study offers theoretical and technical support for the independent control of spray nozzles and the optimisation of pesticide utilisation for variable spray systems based on PTO protocols.

Multi-species weed detection and variable spraying system for farmland based on W-YOLOv5

Weed infestations have the potential to cause significant economic losses for farmers as a result of diminished crop yields and escalated labor and input costs linked with weed management. Traditional weed control methods often entail indiscriminate application of herbicides across entire fields, irrespective of weed density or species composition. In this investigation, we propose a hierarchical detection algorithm for identifying multi-species weeds and have devised a variable spraying system in agricultural settings. The weed classification detection algorithm comprises two components: crop seedling target detection and weed detection. The enhanced YOLOv5 algorithm was initially merged with Vision Transformer (ViT) to introduce the W-YOLOv5 crop seedling target detection algorithm. Experimental validation results indicate that the mean average precision (mAP) of the proposed W-YOLOv5 stands at 87.6%, representing a 3.2% increase over the original YOLOv5. Compared with YOLOv7, this method demonstrates a 4.4% improvement in mAP, accompanied by an 80.14% reduction in floating point operations (FLOPs). These findings underscore the effectiveness of the proposed crop seedling target detection algorithm in ensuring detection accuracy while minimizing model FLOPs. Subsequently, following the detection of crop seedlings, the Hue, Saturation, and Value (HSV) color space filtering algorithm was employed to identify the locations of all weeds in the image, thereby facilitating the detection of weeds among wheat (Triticum aestivum L.), radish (Raphanus sativus L.), cucumber (Cucumis sativus L.), soybean (Glycine max (L.) Merr.), and corn (Zea mays L.) seedlings. Finally, the severity of weed infestation in farmland was classified into five levels. Leveraging these severity levels, we developed a variable spraying system and integrated the developed algorithm into the system. Field validation experiments demonstrate that at a speed of 4 km/h, the spraying accuracy of the system can reach 90.32%, effectively enabling precise variable spraying.

Design and Experimental Verification of Targeted and Variable Sprayer for the Potato

For potatoes, a crop with a specific plant spacing and a large row spacing, continuous spray has problems including low spraying accuracy and washability. In order to improve the utilisation of this crop, this manuscript designed a targeted and variable sprayer. To ensure that the spray function is achieved, the design and verification of the vehicle body and the targeting device of the sprayer were carried out. To guarantee that the automation and precision of spray are improved, the autonomous marching system based on the Ultra-wideband (UWB) module and the targeted and variable spray system based on the Open machine vision (Open MV) module, were built. The experiments showed that the sprayer could follow a preset route, correct its marching direction in time, and provide feedback on its position. The targeted and variable spray was influential on the surface and back of the leaf (about 66%) and more uniform than the general spray mode. Moreover, the sprayer’s targeted and variable spray mode reduced the amount of liquid applied by 37.9% compared to the continuous spray mode, significantly improving the liquid utilisation rate.

The Reduced-Order Model for Droplet Drift of Aerial Spraying under Random Lateral Wind

The droplet drift during aerial spraying process of oilseed rape, which is induced by complex flow field including random lateral wind, is difficult to predict and suppress. In this study, the high-fidelity computational fluid dynamics (CFD) technique is employed to simulate the two-phase flow of droplets in the rotor flow field, and the influence of main operation parameters on spraying effect is investigated numerically. Furthermore, the mechanism of droplet deposition in various operation conditions is discussed according to the analysis of unsteady flow field characteristics. However, the simulation via CFD technique is time-consuming, and it is not suitable for multidisciplinary work and optimization design. To address such issue, a filter white Gaussian noise signal is used to mimic the random lateral wind, and the droplet drift distance is obtained numerically. Based on the input and output dataset of CFD, the recursive algorithm including nonlinear autoregressive exogenous model and surrogate-based recurrence framework and the deep learning method for time-series prediction called long short-term memory neural network are used to build the efficient reduced-order model, respectively. Numerical simulations show that the droplet drift distance can be predicted by measurable lateral wind speed via the reduced-order model approach, which agreed well with the results obtained via the CFD method. In addition, the reduced-order model could decrease computation cost by 6 orders of magnitude with an acceptable accuracy, which indicates that the proposed method could be used for the design of off-line closed-loop controller of a variable spraying system.

Precise variable spraying system based on improved genetic proportional-integral-derivative control algorithm

Currently, the most efficient method of resolving the pollution problem of weed management is by using variable spraying technology. In this study, an improved genetic proportional-integral-derivative control algorithm (IGA-PID) was developed for this technology. It used a trimmed mean operator to optimize the selection operator for an improved searching rate and accuracy. An adaptive crossover operator and mutation operator were constructed for a rapid convergence speed. The weed density detection was performed through an image acquisition and processing subsystem which was capable of determining the spraying quantity. The variable spraying control sub-system completed variable spraying operation. The performance of the system was evaluated by simulations and field tests, and compared with conventional methods. The simulation results indicated that the parameters of the overshoot (1.25%), steady-state error (1.21%) and the adjustment time (0.157s) of IGA-PID were the lowest when compared with the standard algorithms. Furthermore, the field validation results showed that the system with the proposed algorithm achieved the optimal performance with spraying quantity error being 2.59% and the respond time being 3.84s. Overall, the variable spraying system based on an IGA-PID meets the real-time and accuracy requirements for field applications which could be helpful for weed management in precise agriculture.

Design of Variable Spray System for Plant Protection UAV Based on CFD Simulation and Regression Analysis.

Multi-rotor unmanned aerial vehicles (UAVs) for plant protection are widely used in China’s agricultural production. However, spray droplets often drift and distribute nonuniformly, thereby harming its utilization and the environment. A variable spray system is designed, discussed, and verified to solve this problem. The distribution characteristics of droplet deposition under different spray states (flight state, environment state, nozzle state) are obtained through computational fluid dynamics simulation. In the verification experiment, the wind velocity error of most sample points is less than 1 m/s, and the deposition ratio error is less than 10%, indicating that the simulation is reliable. A simulation data set is used to train support vector regression and back propagation neural network with multiple parameters. An optimal regression model with the root mean square error of 6.5% is selected. The UAV offset and nozzle flow of the variable spray system can be obtained in accordance with the current spray state by multi-sensor fusion and the predicted deposition distribution characteristics. The farmland experiment shows that the deposition volume error between the prediction and experiment is within 30%, thereby proving the effectiveness of the system. This article provides a reference for the improvement of UAV intelligent spray system.

An Intelligent Multi-Sensor Variable Spray System with Chaotic Optimization and Adaptive Fuzzy Control.

During the variable spray process, the micro-flow control is often held back by such problems as low initial sensitivity, large inertia, large hysteresis, nonlinearity as well as the inevitable difficulties in controlling the size of the variable spray droplets. In this paper, a novel intelligent double closed-loop control with chaotic optimization and adaptive fuzzy logic is developed for a multi-sensor based variable spray system, where a Bang-Bang relay controller is used to speed up the system operation, and adaptive fuzzy nonlinear PID is employed to improve the accuracy and stability of the system. With the chaotic optimization of controller parameters, the system is globally optimized in the whole solution space. By applying the proposed double closed-loop control, the variable pressure control system includes the pressure system as the inner closed-loop and the spray volume system as the outer closed-loop. Thus, the maximum amount of spray droplets deposited on the plant surface may be achieved with the minimum medicine usage for plants. Multiple sensors (for example: three pressure sensors and two flow rate sensors) are employed to measure the system states. Simulation results show that the chaotic optimized controller has a rise time of 0.9 s, along with an adjustment time of 1.5 s and a maximum overshoot of 2.67% (in comparison using PID, the rise time is 2.2 s, the adjustment time is 5 s, and the maximum overshoot is 6.0%). The optimized controller parameters are programmed into the hardware to control the established variable spray system. The experimental results show that the optimal spray pressure of the spray system is approximately 0.3 MPa, and the flow rate is approximately 0.08 m3/h. The effective droplet rate is 89.4%, in comparison to 81.3% using the conventional PID control. The proposed chaotically optimized composite controller significantly improved the dynamic performance of the control system, and satisfactory control results are achieved.

Design of Plant Protection UAV Variable Spray System Based on Neural Networks.

Recently, unmanned aerial vehicles (UAVs) have rapidly emerged as a new technology in the fields of plant protection and pest control in China. Based on existing variable spray research, a plant protection UAV variable spray system integrating neural network based decision making is designed. Using the existing data on plant protection UAV operations, combined with artificial neural network (ANN) technology, an error back propagation (BP) neural network model between the factors affecting droplet deposition is trained. The factors affecting droplet deposition include ambient temperature, ambient humidity, wind speed, flight speed, flight altitude, propeller pitch, nozzles pitch and prescription value. Subsequently, the BP neural network model is combined with variable rate spray control for plant protection UAVs, and real-time information is collected by multi-sensor. The deposition rate is determined by the neural network model, and the flow rate of the spray system is regulated according to the predicted deposition amount. The amount of droplet deposition can meet the prescription requirement. The results show that the training variance of the ANN is 0.003, and thus, the model is stable and reliable. The outdoor tests show that the error between the predicted droplet deposition and actual droplet deposition is less than 20%. The ratio of droplet deposition to prescription value in each unit is approximately equal, and a variable spray operation under different conditions is realized.

Design and Experiment of a Variable Spray System for Unmanned Aerial Vehicles Based on PID and PWM Control

Unmanned aerial vehicle (UAV) variable-rate spraying technology, as the development direction of aviation for plant protection in the future, has been developed rapidly in recent years. In the actual agricultural production, the severity of plant diseases and insect pests varies in different locations. In order to reduce the waste of pesticides, pesticides should be applied according to the severity of pests, insects and weeds. On the basis of explaining the plant diseases and insect pests map in the target area, a pulse width modulation variable spray system is designed. Moreover, the STMicroelectronics-32 (STM32) chip is invoked as the core of the control system. The system combines with sensor technology to get the prescription value through real-time interpretation of prescription diagram in operation. Then, a pulse square wave with variable duty cycles is generated to adjust the flow rate. A closed-loop Proportional-Integral-Derivative (PID) control algorithm is used to shorten the time of system reaching steady state. The results indicate that the deviation between volume and target traffic is stable, which is within 2.16%. When the duty cycle of the square wave is within the range of 40% to 100%, the flow range of the single nozzle varies from 0.16 L/min to 0.54 L/min. Variable spray operation under different spray requirements is achieved. The outdoor tests of variable spray system show that the variable spray system can adjust the flow rapidly according to the prescription value set in the prescription map. The proportion of actual droplet deposition and deposition density in the operation unit is consistent with the prescription value, which proves the effectiveness of the designed variable spray system.

Color Image Filtering Methods for Variable Spray Systems

Abstract The color images of the actual field were collected and processed in the variable spray system based on machine vision. The variable spray decision was made according to the identified results. The variable spraying was realized by the sprayer for the variable spray decision. However, there were many noises in the field images because of the complexity of the field image acquisition condition. The identification result could be affected by the noises. By analyzing the noise distribution of the color images that were acquisitioned in the actual field, it could be found that there were more noises in the images which were collected in sunny day and with shadows. Meanwhile, there were fewer noises in the images which were collected in cloudy and with the simple soil background. There was mainly random impulse noise in the actual field images. According to the noises characteristics in the color images, four color image filtering algorithms were tested. The test results showed that the filter based o...