Researchers built an AI camera model that spots diseased mulberry leaves in field photos and tells them apart from healthy ones. It matches or beats several leading detection systems without needing more computing power, which could help growers catch problems earlier.
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The modified YOLOv10 detector, using a diverse-branch module called C2fDBN, matched or exceeded baselines including YOLOv8, YOLOv9, YOLOv11, YOLOv26, RT-DETR and Faster R-CNN in mAP on both datasets.
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Structural reparameterization keeps inference efficiency comparable to the baseline models despite the richer feature extraction during training.
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Detection targets the whole leaf category rather than individual lesions, and was tested on an in-house field dataset plus an independent public dataset.
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