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