Multispectral UAV-Based Detection of Phytophthora in Citrus Orchards Using RF-DETR with Spectral Index Fusion

Phytophthora root and crown rot causes irreversible canopy decline in citrus before ground-level symptoms appear, yet UAV-based detection still relies almost exclusively on RGB imagery and, in citrus, on leaf-level classification rather than field-scale localisation. This work addresses that gap with a crown-level object-detection pipeline for Phytophthora in orange orchards, in three contributions. First, a mean-initialised patch embedding expansion that adapts a pretrained detection transformer to N-channel input while preserving its DINOv2 representations and activation magnitude, applicable to any ViT-based detector. Second, a two-stage protocol that screens seven vegetation indices (GNDVI, SAVI, EVI, GRVI, ExG, CARI, MCARI) as fourth channels over three seeds; the screening does not resolve them, and GNDVI is retained because both bands of its ratio respond to root dysfunction-induced chlorophyll degradation and both come from a single sensor. Third, a matched-modality comparison isolating the contribution of the architecture from that of the spectral channel. On 1147 georeferenced RGB–multispectral pairs with 5560 expert-annotated instances, RF-DETR + GNDVI attains a test mAP50:95 of 0.590±0.008 and mAP50 of 0.873±0.005 over three seeds, exceeding a YOLO26n baseline on identical four-channel input by 9.6 and 8.4 percentage points at half the resolution, the architecture proving the decisive component and supporting georeferenced crown-level alerts.