BanglaRiceLeaf: A Benchmark Dataset for Automated Rice Leaf Disease Detection and Health Classification in Bangladesh

Accurate and timely detection of rice leaf diseases is essential for maintaining crop health and improving yield, yet large-scale field monitoring remains challenging due to rapid disease progression and varying environmental and lighting conditions. Deep learning based automated detection systems offer an effective solution, but their performance depends heavily on the availability of diverse and well annotated datasets, while existing rice leaf datasets often lack sufficient variation in disease stages, rice varieties, image quality, and real world conditions. To address these limitations, we introduce BanglaRiceLeaf, a comprehensive dataset containing 4,152 high resolution images of rice leaves collected from July 2023 to July 2024 in the experimental fields of the Bangladesh Rice Research Institute (BRRI), Gazipur. Images were captured using the rear cameras of seven smartphones, including five iPhone 11 Pro Max and two iPhone 11 devices, under diverse lighting and weather conditions such as low and high light. All images are stored in JPG, JPEG, and PNG formats with a resolution of 1440 × 2560 pixels. The dataset is organized into five classes, representing four rice leaf diseases, Bacterial Leaf Blight, Bacterial Leaf Streak, Sheath Blight, and Leaf Blast, along with Healthy Leaf samples, and is carefully annotated and split into training, validation, and test sets, providing a reliable resource for developing and evaluating deep learning models for precision agriculture.