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A UAV-based semi-supervised segmentation framework for pine wilt disease via vegetation Index-RGB synergy
Tao Liu1, Wentao Peng1, Huaiqing Zhang2
1Central South University of Forestry and Technology, Changsha, Hunan, 410004, China.
Plant Phenomics (Washington, D.C.)
|August 9, 2026
Summary
Accurate pine wilt disease detection using drone imagery is improved with a new multimodal segmentation framework. This method enhances early-stage detection and reduces misidentification in cluttered forest backgrounds.
Area of Science:
- Forestry
- Remote Sensing
- Plant Pathology
Background:
- Pine wilt disease (PWD), caused by Bursaphelenchus xylophilus, poses a significant threat to forest ecosystems.
- Unmanned aerial vehicle (UAV) remote sensing offers potential for large-scale PWD screening, but accurate canopy segmentation is challenging.
- Existing methods struggle with varying disease stages, costly annotations, and visually similar forest backgrounds.
Purpose of the Study:
- To develop a robust and lightweight multimodal segmentation framework for accurate PWD detection using UAV imagery.
- To address the challenges of stage-specific appearance changes, semi-supervised learning limitations, and background clutter.
- To improve the practical feasibility of UAV-based forest disease monitoring.
Main Methods:
- A standardized UAV canopy dataset for PWD was created.
- A multimodal segmentation framework integrating Nested-Tempo Memory Consolidation (NTMC), Drift-Compensated Consistency Regularization (DCCR), and Vegetation-index-conditioned Cross-Modal Attention (VCCA) was developed.
- NTMC enhances stage-aware learning, DCCR improves semi-supervised learning with reliability calibration, and VCCA utilizes vegetation indices (NDVI, EVI) to refine visual features.
Main Results:
- The proposed framework demonstrated consistent improvements in mean intersection over union (mIoU), F1-score, and Matthews correlation coefficient (MCC) across multiple datasets.
- The framework improved mIoU from 0.6015 to 0.6829 compared to the baseline.
- The method produced cleaner disease boundaries and reduced false alarms in cluttered forest backgrounds.
Conclusions:
- The developed lightweight multimodal segmentation framework effectively addresses key challenges in UAV-based PWD detection.
- The integration of stage-aware memory, drift-compensated regularization, and cross-modal attention significantly enhances segmentation accuracy and reliability.
- The framework shows strong potential for practical application in large-scale, real-time forest disease monitoring systems.
