A Comprehensive Real-World Dataset of Jackfruit Leaf Diseases and Growth Stages for Intelligent Crop Health Monitoring

This dataset contains a high-quality and visually diverse collection of jackfruit (Artocarpus heterophyllus) leaf images captured from real agricultural fields in Savar and Rajbari, Bangladesh. It includes eight well-defined classes—Blight, Dried, Fungal_Spot, Healthy, Leaf_Miner, Pest_Damage, Senescence, and Young—covering major disease conditions and leaf growth stages.To increase data diversity and reduce class imbalance, various image augmentation techniques were applied consistently across all classes. These include horizontal flipping, rotation, zooming, random cropping, and brightness adjustment with random combinations per image.The dataset also provides a CSV metadata file with image names and labels details.It is suitable for machine learning and deep learning tasks such as disease detection, growth-stage classification, and intelligent crop health monitoring, supporting precision agriculture and sustainable jackfruit cultivation.

Dataset Classes and Image Counts:
Blight:1000 images
Dried:1000 images
Fungal_Spot:1000 images
Healthy:1000 images
Leaf_Miner:1000 images
Pest_Damage:1000 images
Senescence:1000 images
Young:1000 images
Total: 8,000 images

Image Details:
Original image resolution:3072 x 4096
Resized image resolution:512 x 512
Image format: JPG
Color mode: RGB
Collection device: Smartphone camera
Location: Savar and Rajbari districts, Bangladesh