<b>Bacterial Leaf Blight (BLB) UAV Dataset and U-Net with ResNet-101 Code for BLB Detection</b>

The UAV dataset of paddy rice affected by Bacterial Leaf Blight (BLB) disease, with available code for a U-Net architecture using a ResNet-101 backbone for model training and analysis.The dataset can be accessed through the following Google Drive link: https://drive.google.com/drive/folders/17mCuj35euNjwNEIEqNqM_bJqNdHLuXwL?usp=sharingIf you use this code or dataset for your research, please consider citing:<i>@article{logavitool2025field,</i><br><i>title={Field-scale detection of Bacterial Leaf Blight in rice based on UAV multispectral imaging and deep learning frameworks},</i><br><i>author={Logavitool, Guntaga and Horanont, Teerayut and Thapa, Aakash and Intarat, Kritchayan and Wuttiwong, Kanok-on},</i><br><i>journal={PloS one},</i><br><i>volume={20},</i><br><i>number={1},</i><br><i>pages={e0314535},</i><br><i>year={2025},</i><br><i>publisher={Public Library of Science}</i><br><i>}</i><br><br>Logavitool G, Horanont T, Thapa A, Intarat K, Wuttiwong K-o (2025) Field-scale detection of Bacterial Leaf Blight in rice based on UAV multispectral imaging and deep learning frameworks. PLoS ONE 20(1): e0314535. https://doi.org/10.1371/journal.pone.0314535

本数据集为受白叶枯病（Bacterial Leaf Blight, BLB）侵染的稻田水稻无人机（Unmanned Aerial Vehicle, UAV）数据集，附带基于ResNet-101作为骨干网络的U-Net架构模型训练与分析代码。该数据集可通过以下Google Drive链接获取：https://drive.google.com/drive/folders/17mCuj35euNjwNEIEqNqM_bJqNdHLuXwL?usp=sharing。若将本代码或数据集用于科研工作，请引用如下文献：<i>@article{logavitool2025field,</i><br><i>title={基于无人机多光谱成像与深度学习框架的水稻白叶枯病田间尺度检测},</i><br><i>author={Logavitool, Guntaga and Horanont, Teerayut and Thapa, Aakash and Intarat, Kritchayan and Wuttiwong, Kanok-on},</i><br><i>journal={PLOS ONE},</i><br><i>volume={20},</i><br><i>number={1},</i><br><i>pages={e0314535},</i><br><i>year={2025},</i><br><i>publisher={Public Library of Science}</i><br><i>}</i><br><br>Logavitool G, Horanont T, Thapa A, Intarat K, Wuttiwong K-o (2025) 基于无人机多光谱成像与深度学习框架的水稻白叶枯病田间尺度检测. PLOS ONE 20(1): e0314535. https://doi.org/10.1371/journal.pone.0314535

搜集汇总

背景与挑战

背景概述

该数据集包含无人机采集的水稻细菌性叶枯病(BLB)图像数据，以及用于病害检测的U-Net与ResNet-101结合的深度学习代码。数据集可用于农业病害检测和深度学习模型训练研究。