| Summary: | Accurate tree health monitoring in orchards is essential for optimal orchard production. This study investigates the efficacy of a deep learning-based object detection single-step method for detecting tree health on multispectral UAV imagery. A modified Mask R-CNN framework is employed with four different backbones—ResNet-50, ResNet-101, ResNeXt-101, and Swin Transformer—on three image combinations: (1) RGB images, (2) 5-band multispectral images comprising RGB, Red-Edge, and Near-Infrared (NIR) bands, and (3) three principal components (3PCs) computed from the reflectance of the five spectral bands and twelve associated vegetation index images. The Mask R-CNN, having a ResNeXt-101 backbone, and applied to the 5-band multispectral images, consistently outperforms other configurations, with an F1-score of 85.68% and a mean Intersection over Union (mIoU) of 92.85%. To address the class imbalance, class weighting and focal loss were integrated into the model, yielding improvements in the detection of the minority class, i.e., the unhealthy trees. The tested method has the advantage of allowing the detection of unhealthy trees over UAV images using a single-step approach.
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