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

2025-12-15

AI Summary

To address the low detection accuracy of individual diseased trees and the difficulty in quantifying pest spatial distribution patterns in UAV-based visible-light imagery, this paper proposes a lightweight YOLOv8n-based framework. It introduces a Feature Enhancement Module (FEM) and an Adaptive Multi-scale Dual-branch Fusion Mechanism (AMFM), both integrated with ECA attention. Additionally, we design a 3D pest-situation analysis framework combining kernel density estimation, neighborhood risk assessment, and DBSCAN clustering. Evaluated on field-collected data from 32 forest plots in the eastern Tianshan Mountains, the model achieves 86.10% precision, 75.44% recall, and 82.29% mAP@0.5—significantly outperforming mainstream YOLO variants. This work is the first to enable fine-grained, individual-tree-level detection and quantitative validation of spatial aggregation patterns of pests, thereby supporting targeted, data-driven pest control decisions.

Technology Category

• Data Mining & Knowledge Management: Anomaly/Outlier Detection
• Computer Vision: Object Detection & Categorization
• Intelligent Robots: State Estimation

Application Category

• Web Mining and Content Analysis: Large pretrained models with web data
• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs
• User Modeling, Personalization and Recommendation: Attacks and countermeasures in recommendation systems

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.

Problem

• Detects pest-infected trees from UAV imagery using deep learning.
• Analyzes infestation patterns via spatial metrics for forest monitoring.
• Enables accurate tree health discrimination to support pest management.

Innovation

• Lightweight Feature Enhancement Module extracts disease-sensitive cues
• Adaptive Multi-scale Feature Fusion aligns and merges dual-branch features
• Efficient Channel Attention mechanism enhances discriminative information efficiently