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). Experiments are conducted using the publicly available BLB dataset with performance reported using mean IoU (mIoU), mean F1 (mF1), mean accuracy (mAcc), precision, and recall. U-Net++ with EfficientNet-B3 achieved the highest performance, with an mIoU of 97.62%. SegFormer obtained lower segmentation accuracy but comparable inference speed. Overall, the results indicate that lightweight CNN backbones remain more reliable for operational BLB monitoring while integration of vegetation indices provides small and consistent improvements. The study also highlights the value of standardised UAV datasets to compare disease mapping methods and encourages the use of CNN architectures for field implementation.

翻译：本研究利用无人机多光谱影像，采用卷积神经网络（CNN）与基于Transformer的模型对水稻细菌性叶枯病（BLB）严重程度进行分割。评估的架构包括采用ResNet-101编码器的U-Net、采用EfficientNet-B3与EfficientNet-B7的U-Net++、DeepLabV3+及SegFormer，所有模型均在统一框架下以三种输入配置（仅多光谱、多光谱+NDVI、多光谱+NDRE）进行训练。实验基于公开的BLB数据集展开，采用平均交并比（mIoU）、平均F1值（mF1）、平均精度（mAcc）、精确率与召回率报告性能。采用EfficientNet-B3的U-Net++取得了最高性能，mIoU达97.62%。SegFormer的分割精度较低，但推理速度相当。总体结果表明，在BLB业务化监测中，轻量级CNN骨干网络仍更可靠，而植被指数的引入可带来微小但一致的性能提升。研究同时强调了标准化无人机数据集对比较病害制图方法的价值，并推荐在实际部署中采用CNN架构。