MSCF-LUNet: A Lightweight Three-Stage Pine Wilt Disease Segmentation Model with Multi-Scale Context Fusion Mechanism

Dejing Zhou^1,2Junxian Chen¹Wenxi Cai¹Jie Lin¹Tiantian Meng¹Yuanhang Li¹Baihan Liu^1,3Mengting Luo¹Yubin Lan^1,3Wanjie Xiong^1,3*Tianyi Liu^4*Zhao Jing^1,3*

• ¹College of Electronic Engineering, south china agriculture university, Guangzhou, China
• ²Department of Computer and Information science, University of Macau, Taipa, Macao, SAR China
• ³The National Center for international Collaboration Research on Precision Agricultural Aviation Pesticides Spraying Technology, Guangzhou, China
• ⁴College of forestry and landscape architecture, south china agriculture university, Guangzhou, China

Pine wilt disease (PWD) is a highly destructive infectious disease that severely damages pine forests worldwide. Because symptoms emerge first in the tree crown, detection from unmanned aerial vehicles (UAVs) is efficient. However, most methods perform only binary classification and lack pixel-level staging, which leads to missed initial symptoms and confusion with other species. We propose MSCF-LUNet, a lightweight three-stage semantic segmentation model based on multi-scale context fusion. The model uses an improved multi-scale patch embedding guided by attention with relative position encoding (AWRP) to adapt the sampling field of view and to fuse local details with global context. Under contextual attention, the network learns fine-grained features and location cues. In complex environments, MSCF-LUNet achieves 89.56% precision, 92.13% recall, 88.92% intersection over union (IoU), and 96.54% pixel accuracy (PA), balancing performance and computational cost. The model effectively segments PWD-infected regions and determines disease stages from remote-sensing imagery.