Semi-supervised learning techniques for detection of dead pine trees with UAV imagery for pine wilt disease control

Pine wilt disease poses a serious threat to pine forests worldwide. It causes rapid pine tree mortality, profoundly impacting ecosystems and economic assets. Using unmanned aerial vehicle (UAV) imagery to identify, locate, and promptly remove dead pine trees in forest areas is an effective method to control the spread of pine wilt disease. However, dataset annotation and validation remains challenging. To address the high cost and time-consuming nature of manual annotation in fully supervised learning for identifying dead pine trees, this study introduces semi-supervised learning framework for detecting dead pine trees. Unlike traditional fully supervised learning methods, our approach reduces the need for a large quantity of precisely annotated data. By incorporating attention-enhanced pyramid and adaptive thresholding mechanisms, the model can effectively learn from a small amount of labelled data and generate high-quality pseudo-labels from unlabelled data. Specifically, the model employs the channel prior convolutional attention mechanism in feature pyramid fusion to enhance sensitivity to key features of dead trees and improve consistency learning. Meanwhile, probabilistic clustering Gaussian Mixture Model is also used to adaptively adjust the threshold for pseudo-labels assignment, enhancing the accuracy of pseudo-labels during training. Experiments were conducted using self-constructed dead pine tree image datasets to validate the effectiveness of the proposed method. The results show that our method requires only 25% of the labelled data to achieve recognition results close to those of fully supervised method with a fully labelled. Additionally, its performance surpasses existing semi-supervised methods. The proposed algorithm provides an efficient and cost-effective approach for detecting dead pine trees for pine wilt disease control using UAV imagery.