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

TL;DR

FID-Net is a novel deep learning model that accurately detects pest-affected trees in UAV imagery and analyzes infestation patterns using spatial metrics, aiding efficient forest pest management.

Contribution

The paper introduces FID-Net, a lightweight, feature-enhanced deep learning model with novel modules for improved pest detection and infestation analysis from UAV imagery.

Findings

FID-Net achieves high detection accuracy with 86.10% precision.

It outperforms mainstream YOLO models in detection metrics.

The framework effectively identifies infestation hotspots and risk zones.

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.