Detection of Pine Wilt Disease Using AAV Remote Sensing With an Improved YOLO Model

Abstract

Pine wilt disease (PWD) is a severe and highly contagious forest disease that poses a significant challenge to sustainable development. This study utilizes autonomous aerial vehicle (AAV) remote sensing and RGB images while combining deep learning techniques to address the challenge of detecting infected trees. The proposed You Only Look Once (YOLO)-PWD model integrates the squeeze-and-excitation network and convolutional block attention module to improve feature extraction capabilities. In addition, the integration of a bidirectional feature pyramid network enhances the capture of global scene information and adapts to complex environmental variations. We further enhance the detection accuracy by incorporating a dynamic convolutional kernel, ensuring the system's suitability for deployment on edge devices such as AAVs. Compared with the original YOLOv5s model, YOLO-PWD demonstrates a significant improvement in recall rate, with an increase of 14.2%, and achieves an impressive average precision (AP) of 95.2% for detecting discolored pine trees. The precision, recall, and AP for dead pine trees are also enhanced, with precision increasing by 9.7% and AP by 6.7%. Despite a mere 0.9MB increase in model size, the F1-score for discolored pine trees was improved by 4.1%, and the F1-score for dead pine trees increased by 3.3%. Experimental results suggest that the YOLO-PWD model can better meet the requirements of PWD detection by AAV remote sensing in large epidemic areas. This advancement has significant implications for the protection of pine forest resources and contributes to environmentally sustainable development.