用无人机影像和深度学习，精准识别水稻细菌性条斑病严重程度。
Comparison of Deep Learning Frameworks For Rice Disease Mapping From UAV Multispectral Imaging
- 对比多种模型在多光谱图像上分割病害，采用统一训练流程。
- U-Net+++EfficientNet-B3表现最佳，mIoU达97.62%。
- 轻量CNN更适于田间部署，植被指数提升有限但稳定。
本研究利用无人机多光谱影像，通过卷积神经网络（CNN）与基于Transformer的模型对水稻细菌性条斑病（BLB）的严重程度进行分割。评估模型包括：以ResNet-101为编码器的U-Net、以EfficientNet-B3和EfficientNet-B7为编码器的U-Net++、DeepLabV3+与SegFormer。所有模型均在三种输入配置下训练：仅多光谱、多光谱+NDVI、多光谱+NDRE。实验使用公开的BLB数据集，性能指标包括平均交并比（mIoU）、平均F1分数（mF1）、平均准确率（mAcc）、精确率与召回率。结果表明，采用EfficientNet-B3的U-Net++达到最高性能，mIoU为97.62%；SegFormer虽分割精度较低，但推理速度相近。总体显示，轻量级CNN骨干网络在实际监测中仍更可靠，而融合植被指数带来小幅且稳定的性能提升。研究强调标准化无人机数据集对方法比较的重要性，并建议在田间应用中优先采用CNN架构。
原文摘要 · Abstract (English)
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.
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