Rice Leaf Disease Detection is a rice leaf image dataset specifically designed for precision agriculture target detection tasks. It aims to improve the ability of computer vision models to detect, classify, and locate diseases in real agricultural scenarios. It is widely used in YOLO series model training, agricultural disease detection, edge vision deployment, and intelligent rice planting management. This dataset contains 8,665 images of rice leaves, covering nine categories, including healthy rice leaves and eight common diseases: bacterial leaf blight, brown spot, rice leaf roller damage, rice blast, leaf scorch, leaf smut, narrow brown spot, and neck blast. The dataset has been preprocessed and organized in standard YOLO format, divided into training and validation sets. Each image corresponds to a .txt annotation file containing the object's bounding box coordinates and category ID.

Dataset composition:

• Rice__NeckBlast: 453 images, 951 bounding boxes
• Rice__BacterialLeafBlight (bacterial leaf blight): 2,000 images, 2,353 bounding boxes
• Rice__BrownSpot: 2,000 images, 15,257 bounding boxes
• Rice__Healthy (Healthy Rice Leaves): 433 images, 821 bounding boxes
• Rice__Hispa (rice leaf beetle damage): 431 images, 933 bounding boxes
• Rice__LeafBlast (rice blast disease): 449 images, 763 bounding boxes
• Rice__LeafScald (Leaf Scald): 456 images, 564 bounding boxes
• Rice__LeafSmut (Leaf Smut): 2,000 images, 6,386 bounding boxes
• Rice__NarrowBrownLeafSpot: 443 images, 737 bounding boxes

  

Citations

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