MI-YOLO model for detecting mildly discolored pine trees infected with pine wilt disease with multispectral UAV images

SHAO Xinxin, LIU Wenping, WANG Han, ZONG Shixiang, YUAN Bo

Journal of Nanjing Forestry University (Natural Sciences Edition） ›› 2026, Vol. 50 ›› Issue (2) : 19-28.

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南京林业大学学报（自然科学版）
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Journal of Nanjing Forestry University (Natural Sciences Edition） ›› 2026, Vol. 50 ›› Issue (2) : 19-28. DOI: 10.12302/j.issn.1000-2006.202501038

MI-YOLO model for detecting mildly discolored pine trees infected with pine wilt disease with multispectral UAV images

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Abstract

【Objective】This research aims to accurately and efficiently detect mildly discolored pine trees infected with pine wilt disease in real time using multispectral unmanned aerial vehicle (UAV) remote sensing images, a Multispectral Images YOLO (MI-YOLO) model is proposed based on YOLOv8n.【Method】First, multispectral images are rapidly aligned using the cross power spectrum in the frequency domain. Second, a tiny multi-branch auxiliary feature pyramid network is introduced as the neck network to enhance feature utilization while maintaining model lightweight. Finally, the original C2f feature fusion module in YOLOv8n is replaced with a lightweight C2f-Faster module to reduce redundant computation.【Result】The proposed MI-YOLO model achieves an average precision of 84.5% at an IoU threshold of 0.5 (AP50), with 2.1 MB parameters and 7.25 GB floating point operations (FLOPs). Compared with YOLOv8n, AP50 is improved by 10.5 percentage points, while the number of parameters and FLOPs are reduced by 30% and 13%, respectively.【Conclusion】The MI-YOLO object detection model exhibits high accuracy and a lightweight structure, enabling real-time detection of mildly discolored pine trees infected with pine wilt disease in multispectral images.

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multispectral images / unmanned aerial vehicle (UAV) / pine wilt disease / object detection

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SHAO Xinxin , LIU Wenping , WANG Han , et al . MI-YOLO model for detecting mildly discolored pine trees infected with pine wilt disease with multispectral UAV images[J]. Journal of Nanjing Forestry University (Natural Sciences Edition）. 2026, 50(2): 19-28 https://doi.org/10.12302/j.issn.1000-2006.202501038

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