Automatic pine wilt disease detection based on improved YOLOv8 UAV multispectral imagery

Abstract

The pine wilt disease (PWD) can cause destructive death to pine trees in a short period. Utilizing unmanned aerial vehicle (UAV) remote sensing technology to promptly identify PWD-infected trees has become an effective and feasible method for precise PWD monitoring. In this study, UAV multispectral imagery was used to analyze the sensitive spectral bands and different vegetation indices for PWD discriminability. A dataset of optimal spectral combinations from visible light and multispectral images was constructed, along with an improved YOLOv8 deep learning model for rapid and accurate identification of PWD-infected trees. The improved YOLOv8 model used omni-dimensional dynamic convolution (ODConv) to enhance the performance of convolutional networks, designed a dynamic head (DyHead) module to capture PWD features more accurately, and applied MPDioU to improve the regression accuracy and model runtime efficiency. Experimental results showed that the [email protected] of the improved YOLOv8 model increased to 89.1 %, with a user accuracy of 90 % and a recall rate of 93.1 %. This achieved rapid and accurate detection of PWD-infected trees, providing effective technical support for automatic identification of PWD epidemic areas and control of PWD outbreaks based on UAV multispectral imagery.