Deep-learning-based spraying area recognition system for unmanned-aerial-vehicle-based sprayers

Abstract

Unmanned aerial vehicle (UAV)-based spraying system employing machine learning techniques is a recent advancement in precision agriculture for precise spraying, promoting saving chemicals (pesticide/herbicide), and enhancing their effectiveness. This study aims to develop an efficient deep learning system for UAV-based sprayers, which has the capability to accurately recognize spraying areas. A deep learning system is proposed and developed incorporating a faster region-based convolutional neural network (R-CNN) for the imagery collected. In order to develop a classifier for identifying spraying areas from nonspraying areas, four different agriculture croplands and orchards were considered. All the experiments were performed in agriculture fields through DJI Spark with an RGB camera. During experimentation, heights of 2.5 m and 6 m were attained for cropland and orchard image collection. The developed recognition system achieved 87.77% and 88.57% accuracy for recognizing spraying areas in crops and orchards, respectively, for a limited dataset and variable target sizes. The developed deep learning system on comparison outperformed other machine learning and deep learning systems in the literature. The developed system could be easily integrated into real-time UAV-based sprayers for precision agriculture.