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

Wen, Sheng; Zhang, Quanyong; Deng, Jizhong; Lan, Yubin; Yin, Xuanchun; Jian, Shan

doi:10.3390/app8122482

Cited by 47 publications

(24 citation statements)

References 20 publications

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“…As a new type of plant protection in China in recent years, the low-altitude and low-volume spraying technology by using plant protection unmanned aerial vehicles (UAV) has advantages over traditional spraying methods since it is efficient and allows for a quick response to sudden pest outbreaks [5]. Labor costs for operation are also low, and crops and the physical structure of soil are not damaged by equipment [6]. Furthermore, aerial spraying can reduce pesticide application by 15-20% by using a low or ultra-low volume of spray, and it can be used as an important technical support for a reduction program for chemical fertilizers and pesticides [7,8].…”

Section: Introductionmentioning

confidence: 99%

Effect of Droplet Size Parameters on Droplet Deposition and Drift of Aerial Spraying by Using Plant Protection UAV

et al. 2020

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In the field of pesticide spraying, droplet size is one of the most important factors affecting droplet deposition and drift. In order to study the effect of different droplet size parameters on droplet deposition distribution and drift of aerial spraying by using plant protection UAV, an aerial spraying test with the same spraying rate and different size droplets in rice canopy was carried out by using multi-rotor unmanned aerial vehicles (UAV) and four TEEJET nozzles with different orifice sizes (these droplets with a volume median diameter (VMD) of 95.21, 121.43, 147.28, and 185.09 μm, respectively), and the deposition distribution and penetration of droplets in the target area and the drift distribution of droplets in the non-target area were compared and analyzed. The results showed that the deposition distribution and penetration of droplets in the target area and the drift distribution of droplets in the non-target area were influenced by the droplet size. The droplet deposition rate in the upper and lower rice canopies were increased in the target area with the increase of droplet size. The penetration results of droplets also increased with the increase of droplet size, and that of droplets with a VMD of 185.09 μm was the best, reaching 38.13%. The average values of the cumulative drift rate of droplets in the rice canopy in the four tests were 73.87%, 50.26%, 35.91%, and 23.06%, respectively, and the cumulative drift rate and the drift distance of droplets decreased with the increase of droplet size, which indicated that the increase of droplet size can effectively reduce droplet drift. It demonstrated that the droplet size is one of the most important factors affecting droplet deposition and drift for pesticide spraying by plant protection UAV, and for the application of plant protection UAV with extra-low volume spraying, the use of droplets with VMD less than 160 μm should be avoided and a more than 10 m buffer zone should be considered downwind of the spraying field to avoid drug damage caused by pesticide drift. The results have fully revealed the effect of droplet size parameters on droplet deposition and drift of aerial spraying. Moreover, the influence of the wind field below the rotors on the distribution of droplet deposition was surmised and analyzed from the perspective of plant protection UAV. It is important for optimizing the droplet parameters of aerial spraying, increasing the spraying efficiency, and realizing precision agricultural aviation spray.

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Section: Introductionmentioning

confidence: 99%

Effect of Droplet Size Parameters on Droplet Deposition and Drift of Aerial Spraying by Using Plant Protection UAV

et al. 2020

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“…PDR_ON is connected with VCC high level through R5. When PDR_ON is high power, it acts as internal power monitor [22,23]. The STM32 minimum system is shown in Figure 7.…”

Section: Stm32 Minimum System Designmentioning

confidence: 99%

Renovation of Automation System Based on Industrial Internet of Things: A Case Study of a Sewage Treatment Plant

Zhu

Wang

Zhang

2020

Sensors

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The Industrial Internet of Things (IIoT) is of great significance to the improvement of industrial efficiency and quality, and to reduce industrial costs and resources. However, there are few openly-reported practical project applications based on the IIoT up to now. For legacy automation devices in traditional industry, it is especially challenging to realize the upgrading of industrial automation adopting the IIoT technology with less investment. Based on the practical engineering experience, this paper introduces the automation renovation of a sewage treatment plant. The legacy automation devices are upgraded by the central controller of a STM32 processor (Produced by STMicroelectronics company, located in Geneva, Switzerland), and the WeChatApplet (Developed by Tencent company, located in Shenzhen, China) is used as the extended host computer. A set of remote monitoring and control systems of sewage treatment based on the IIoT is built to realize the wide-area monitoring and control of sewage treatment. The paper describes the field hardware system, wide-area monitoring and control application program, management cloud platform and security technologies in detail. The actual operation results show that the monitoring system has the requirements of high accuracy, good real-time performance, reliable operation and low cost.

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“…Among them, the relation between the flow rate of the micro-diaphragm pump and the duty ratio of the PWM square wave was fitted using a cubic polynomial. The relational can be expressed as [50]:D=false(20.776v3−21.452v2+8.242v−0.431false)×100% where v is the flow rate of single nozzle in L/min, and D is the duty ratio of PWM square wave in %.…”

Section: Design Of Variable Spray Systemmentioning

confidence: 99%

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

Wen

Zhang

Yin

et al. 2019

Sensors

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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.

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

Cited by 47 publications

References 20 publications

Effect of Droplet Size Parameters on Droplet Deposition and Drift of Aerial Spraying by Using Plant Protection UAV

Effect of Droplet Size Parameters on Droplet Deposition and Drift of Aerial Spraying by Using Plant Protection UAV

Renovation of Automation System Based on Industrial Internet of Things: A Case Study of a Sewage Treatment Plant

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

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