Hasan Afif, Layes Tanvir Almas, Afridi Arafat Sahin, Rifat Shakhawath Hossain, Nur Fernaz Narin, Moon Nazmun Nessa
Department of Computer Science and Engineering, Daffodil International University, Dhaka, Bangladesh.
Department of Computer Science and Engineering, Military Institute of Science and Technology, Dhaka, Bangladesh.
Data Brief. 2025 Aug 13;62:111977. doi: 10.1016/j.dib.2025.111977. eCollection 2025 Oct.
This manuscript presents a comprehensive, expert-annotated dataset comprising 19,000 rice leaf images, including 2,753 original images and 16,247 augmented images, sourced from the Bangladesh Rice Research Institute (BRRI). The dataset includes seven disease classes: Healthy (603 original images), Rice Blast (696 original images), Scald (421 original images), Leaf-folder Injury (247 original images), Insect Infestation (281 original images), Rice Stripes (266 original images), and Tungro Disease (239 original images). These images, captured under varying environmental conditions using smartphone cameras, accurately reflect real-world conditions. The images have been meticulously annotated by agronomy experts for reliable disease labeling. To enhance dataset diversity, data augmentation methods such as rotation, scaling, brightness adjustment, and horizontal flipping were systematically applied, expanding the dataset by creating additional variants from the original images. The dataset serves as a rich resource for developing machine learning models for the automatic detection of rice diseases. This initiative aims to enable early disease detection, promote sustainable farming practices, and improve food security, particularly in rice-dependent developing countries.
本手稿展示了一个全面的、由专家注释的数据集，该数据集包含19000张水稻叶片图像，其中包括2753张原始图像和16247张增强图像，这些图像源自孟加拉国水稻研究所（BRRI）。该数据集包括七个病害类别：健康（603张原始图像）、稻瘟病（696张原始图像）、胡麻叶斑病（421张原始图像）、稻纵卷叶螟危害（247张原始图像）、虫害（281张原始图像）、水稻条纹病（266张原始图像）和东格鲁病（239张原始图像）。这些图像是使用智能手机相机在不同环境条件下拍摄的，准确反映了实际情况。图像已由农学专家进行了细致注释，以实现可靠的病害标注。为了提高数据集的多样性，系统地应用了旋转、缩放、亮度调整和水平翻转等数据增强方法，通过从原始图像创建额外的变体来扩展数据集。该数据集是开发用于自动检测水稻病害的机器学习模型的丰富资源。这一举措旨在实现病害的早期检测，促进可持续农业实践，并改善粮食安全，特别是在依赖水稻的发展中国家。
