A UAV-based semi-supervised segmentation framework for pine wilt disease via vegetation Index-RGB synergy.

Pine wilt disease (PWD), caused by the pine wood nematode (Bursaphelenchus xylophilus), continues to threaten forest ecological security. Unmanned aerial vehicle (UAV) remote sensing makes large-scale screening feasible, but accurate canopy segmentation remains difficult in practice. The main obstacles are threefold: (i) canopy appearance changes noticeably from early to late infection stages, which can cause model representations to drift toward later-stage symptoms and weaken subtle early-stage cues; (ii) dense pixel-wise annotation is costly, making semi-supervised learning dependent on imperfect pseudo-labels; and (iii) forest backgrounds are cluttered and often visually similar to diseased regions, limiting the discriminative ability of RGB appearance alone. To address these three practical difficulties, we build a standardized UAV canopy dataset for PWD and develop a lightweight multimodal segmentation framework. The method combines three components. First, Nested-Tempo Memory Consolidation (NTMC) is designed as a stage-aware extension of EMA-based teacher-student learning. It maintains nested fast/medium/slow temporal trajectories to retain stage-specific knowledge while improving cross-stage stability during Early-Middle-Late sequential training. Second, Drift-Compensated Consistency Regularization (DCCR) integrates reliability calibration into semi-supervised consistency learning, so that unlabeled samples contribute training signals mainly when they are sufficiently reliable, reducing error accumulation from noisy pseudo-supervision. Third, Vegetation-index-conditioned Cross-Modal Attention (VCCA) uses vegetation indices-Normalized Difference Vegetation Index (NDVI) and Enhanced Vegetation Index (EVI)-as physiological cues to modulate visual features, thereby reducing the dependence on RGB appearance and improving feature discrimination under texture-similar forest backgrounds. Experiments on one in-house dataset and three external datasets show consistent improvements in mean intersection over union (mIoU), F1-score, and Matthews correlation coefficient (MCC). With all components enabled, the framework improves mIoU from 0.6015 to 0.6829 over the baseline and produces cleaner disease boundaries with fewer background false alarms, demonstrating its potential for practical UAV-based forest disease monitoring.