Application of conventional UAV-based high-throughput object detection to the early diagnosis of pine wilt disease by deep learning

Abstract

The early diagnosis of pine wilt disease (PWD) is crucial to its management. Substantial effort has been made to develop an accurate early diagnosis method. However, none of the existing methods are suitable for large-scale rapid screening in the field. In this study, an unmanned aerial vehicle (UAV) was used to collect a large number of images from a pine tree canopy in an early stage of infection for the generation of a training dataset. Owing to the inability to develop object detection models using nine regular machine learning classifiers, two advanced deep learning algorithms were employed, namely the Faster Region-based Convolutional Network (Faster R-CNN) and You Only Look Once version 3 (YOLOv3). Model performances were compared based on the precision (mAP), size, and processing speed. All four models possessed similar precision (0.602–0.64), but the YOLO-based models had a smaller size and faster processing speed than the Faster R-CNN-adapted models. The population of infected trees (in the early or late stage of infection) was predicted under different treatments (retaining or removing the dead trees from the forest) using these four models to explore their application. The results indicate that retaining dead trees after chopping them down results in fewer dead trees but more early infected trees the following year. We propose a cost-effective and high-throughput method for the early diagnosis of PWD in the field using UAV-based image processing and object detection (uses YOLOv3) based on deep learning.