Hugging Face Trending Papers June 4, 2026

Comparison of Deep Learning Frameworks For Rice Disease Mapping From UAV Multispectral Imaging

Read the original on Hugging Face Trending Papers →

In this study, UAV multispectral imagery is used to segment the severity of bacterial leaf blight (BLB) in rice using convolutional neural networks (CNNs) and transformer-based models. The evaluated architectures include U-Net with a ResNet- 101 encoder, U-Net++ with EfficientNet-B3 and EfficientNetB7, DeepLabV3+, and SegFormer, all trained under a common pipeline with three input configurations (multispectral only, multispectral+NDVI, and multispectral+NDRE).

Summary generated by The Flow from the publisher's feed. The full article lives at Hugging Face Trending Papers.

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