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

Authors

Sheng Wen, South China Agricultural UniversityFollow
Quanyong Zhang, National Center for International Collaboration Research on Precision Agriculture Aviation Pesticides Spraying TechnologyFollow
Xuanchun Yin, National Center for International Collaboration Research on Precision Agriculture Aviation Pesticides Spraying TechnologyFollow
Yubin Lan, National Center for International Collaboration Research on Precision Agriculture Aviation Pesticides Spraying TechnologyFollow
Jiantao Zhang, National Center for International Collaboration Research on Precision Agriculture Aviation Pesticides Spraying TechnologyFollow
Yufeng Ge, University of Nebraska-LincolnFollow

ORCID IDs

0000-0003-0488-0642

Document Type

Article

Date of this Version

2019

Citation

Sensors 2019, 19, 1112

Comments

© 2019 by the authors.

Open access

doi:10.3390/s19051112

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.