The accurate detection and classification of diseased pine trees with different levels of
severity is important in terms of monitoring the growth of these trees and for preventing and
controlling disease within pine forests. Our method combines a DDYOLOv5 with a ResNet50
network for detecting and classifying levels of pine tree disease from remote sensing UAV
images. In this approach, images are preprocessed to increase the background diversity of
the training samples, and efficient channel attention (ECA) and hybrid dilated convolution …