YOLOv8-RD: High-Robust Pine Wilt Disease Detection Method Based on Residual Fuzzy YOLOv8

Yuan, Junchao ORCID: https://orcid.org/0009-0009-9368-6606, Wang, Lina ORCID: https://orcid.org/0009-0009-7601-2917, Wang, Tingting ORCID: https://orcid.org/0000-0002-2666-7159, Bashir, Ali Kashif ORCID: https://orcid.org/0000-0003-2601-9327, Al Dabel, Maryam M, Wang, Jiaxing ORCID: https://orcid.org/0000-0002-7672-6911, Feng, Hailin ORCID: https://orcid.org/0000-0003-2734-480X, Fang, Kai ORCID: https://orcid.org/0000-0003-0419-1468 and Wang, Wei ORCID: https://orcid.org/0000-0002-1717-5785 (2025) YOLOv8-RD: High-Robust Pine Wilt Disease Detection Method Based on Residual Fuzzy YOLOv8. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 18. pp. 385-397. ISSN 2151-1535

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Abstract

Pine Wilt Disease (PWD) poses a severe threat to the health of pine trees and has resulted in substantial losses to global pine forest resources. Due to the minute size of the pathogens and the concealed symptoms of PWD, early detection through remote sensing image technology is essential. However, in practical applications, remote sensing images are easily affected by factors such as cloud cover and changes in illumination, resulting in significant noise and blurriness in the images. These interference factors significantly reduce the accuracy of existing object detection models. Therefore, this paper presents a novel and highly robust methodology for detecting PWD, termed YOLOv8-RD. We synthesized the benefits of residual learning and Fuzzy Deep Neural Networks (FDNN) to develop a Residual Fuzzy module (ResFuzzy), which adeptly filters image noise and refines background features with enhanced smoothness. Simultaneously, we integrated a Detail Processing Module (DPM) into the ResFuzzy module to enhance the low-frequency detail features transmitted in residual learning. Furthermore, by incorporating the Dynamic upSampling operator (DySample), our model can dynamically adjust the sampling step size based on the variations in the input feature map during the upsampling process, thereby effectively recovering detail from the feature map. Our model exhibited exceptional robustness to severe noise. When evaluated on a PWD dataset with 100% interference samples at an intensity of 0.07, our model achieved an average precision improvement of 4.9%, 6.3%, 7.3%, and 3.0% compared to four most representative models, making it well-suited for PWD detection in interfering environments.