Abstract
Forest pests threaten ecosystem stability, requiring efficient monitoring. To overcome the limitations of traditional methods in large-scale, fine-grained detection, this study focuses on accurately identifying infected trees and analyzing infestation patterns. We propose FID-Net, a deep learning model that detects pest-affected trees from UAV visible-light imagery and enables infestation analysis via three spatial metrics. Based on YOLOv8n, FID-Net introduces a lightweight Feature Enhancement Module (FEM) to extract disease-sensitive cues, an Adaptive Multi-scale Feature Fusion Module (AMFM) to align and fuse dual-branch features (RGB and FEM-enhanced), and an Efficient Channel Attention (ECA) mechanism to enhance discriminative information efficiently. From detection results, we construct a pest situation analysis framework using: (1) Kernel Density Estimation to locate infection hotspots; (2) neighborhood evaluation to assess healthy trees' infection risk; (3) DBSCAN clustering to identify high-density healthy clusters as priority protection zones. Experiments on UAV imagery from 32 forest plots in eastern Tianshan, China, show that FID-Net achieves 86.10% precision, 75.44% recall, 82.29% mAP@0.5, and 64.30% mAP@0.5:0.95, outperforming mainstream YOLO models. Analysis confirms infected trees exhibit clear clustering, supporting targeted forest protection. FID-Net enables accurate tree health discrimination and, combined with spatial metrics, provides reliable data for intelligent pest monitoring, early warning, and precise management.
Abstract (translated)
森林害虫威胁着生态系统的稳定性，需要高效的监测方法。为了克服传统方法在大规模、精细化检测上的局限性，本研究专注于准确识别受害树木并分析其感染模式。我们提出了FID-Net，这是一种基于无人机可见光影像的深度学习模型，能够检测受病虫侵害的树木，并通过三种空间度量进行虫害分析。 FID-Net是基于YOLOv8n构建的，加入了轻量级特征增强模块（Feature Enhancement Module, FEM），用于提取疾病敏感线索；同时引入自适应多尺度特性融合模块（Adaptive Multi-scale Feature Fusion Module, AMFM）来对双通道特性进行对齐和融合（RGB和FEM增强后的图像）；最后是高效的信道注意机制(Efficient Channel Attention, ECA)，用来高效地提升判别信息。 基于检测结果，我们构建了一个害虫情况分析框架：(1) 使用核密度估计来定位感染热点；(2) 通过邻居评估来评估健康树木的感染风险；以及(3) 利用DBSCAN聚类算法识别高密度健康树群作为优先保护区域。 在中国天山东部地区的无人机影像实验中，FID-Net在32个森林地块上的检测性能表现突出：准确率为86.10%，召回率为75.44%，mAP@0.5为82.29%，mAP@0.5:0.95为64.30%。这些结果优于主流的YOLO模型。 数据分析表明，受感染的树木表现出明显的群聚现象，支持了有针对性的森林保护措施。FID-Net能够准确区分树种健康状况，并结合空间度量提供可靠数据用于智能虫害监控、早期预警及精确管理。
URL
https://arxiv.org/abs/2512.13104
