Article
Design of Plant Protection UAV Variable Spray
System Based on Neural Networks
Sheng Wen
1,2
, Quanyong Zhang
2,3
, Xuanchun Yin
2,3,
* , Yubin Lan
2,3
, Jiantao Zhang
2,4
and
Yufeng Ge
5
1
South China Agricultural University Engineering Foundation Teaching and Training Center,
Guangzhou 510642, China; vincen@scau.edu.cn
2
National Center for International Collaboration Research on Precision Agriculture Aviation Pesticides
Spraying Technology, Guangzhou 510642, China; qy_zhang@stu.scau.edu.cn (Q.Z.); ylan@scau.edu.cn (Y.L.);
zhangjiantao@yeah.net (J.Z.)
3
Engineering College of South China Agricultural University, Guangzhou 510642, China
4
Mathematics and Informatics College of South China Agricultural University, Guangzhou 510642, China
5
Biological Systems Engineering college of University of Nebraska-Lincolin, Lincoln, NE 68583, USA;
yge2@unl.edu
* Correspondence: xc_yin@scau.edu.cn
Received: 26 January 2019; Accepted: 2 March 2019; Published: 5 March 2019
Abstract:
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.
Keywords: UAV; BP neural network; droplet deposition; variable spray
1. Introduction
Crop diseases and weeds are important factors that affect crop yield and quality, and are mainly
controlled through chemical pesticides. The plant-protection flight operation has also been changed
from traditional artificial spraying to mechanical spraying [
1
]. Since the 1920s, the manned aircrafts
have been used for agricultural production in the United States, which created a history of agricultural
aviation [
2
]. The agricultural application of UAVs as a new application in the field of agricultural plant
protection has been widely researched and applied [
3
,
4
]. In 2014, China’s “Central Document No. 1”
proposed to promote the development of eco-friendly agriculture, and especially pointed out that the
construction of agricultural aviation should be strengthened. In order to implement document No. 1
Sensors 2019, 19, 1112; doi:10.3390/s19051112 www.mdpi.com/journal/sensors
