FID-Net: A Feature-Enhanced Deep Learning Network for Forest Infestation Detection

• URL: http://arxiv.org/abs/2512.13104v1
• Date: Mon, 15 Dec 2025 09:01:10 GMT
• Title: FID-Net: A Feature-Enhanced Deep Learning Network for Forest Infestation Detection
• Authors: Yan Zhang, Baoxin Li, Han Sun, Yuhang Gao, Mingtai Zhang, Pei Wang,
• Abstract summary: We propose FID-Net, a deep learning model that detects pest-affected trees from UAV visible-light imagery.<n>Experiments on UAV imagery from 32 forest plots in eastern Tianshan, China, show 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.
• Score: 18.263863060603615
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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.

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