A Combined Dataset of Bottle Gourd, Zucchini, and Papaya Leaf Diseases for Machine Learning and Deep Learning Applications
Description
This dataset is a comprehensive collection of Bottle Gourd, Zucchini, and Papaya leaf disease images, developed at Daffodil International University, Dhaka, Bangladesh, to support research in machine learning, deep learning, and computer vision–based plant disease detection. A total of 2,144 original leaf images were collected from local agricultural fields and university research plots under natural lighting conditions between January 13, 2024, and October 22, 2024. All images were captured using smartphone and DSLR cameras and standardized to a resolution of 512 × 512 pixels in RGB format. The dataset covers multiple disease categories across three crops: Bottle Gourd (Alternaria Leaf Blight, Angular Leaf Spot, Anthracnose, Downy Mildew, Early Alternaria Leaf Blight, Fungal Damage, Healthy, and Mosaic Virus), Zucchini (Angular Leaf Spot, Anthracnose, Downy Mildew, Dry Leaf, Healthy, Insect Damage, Iron Chlorosis Damage, Xanthomonas Leaf Spot, and Yellow Mosaic Virus), and Papaya (Bacterial Blight, Carica Insect Hole, Curled Yellow Spot, Healthy Leaf, Pathogen Symptoms, and Yellow Necrotic Spots). To improve class balance and model generalization, extensive data augmentation techniques—such as rotation, flipping, brightness and contrast adjustment, zooming, and cropping—were applied, expanding the dataset to 23,000 augmented images. Each image preserves distinct disease features, making it highly suitable for training and testing machine learning and deep learning models for plant disease classification, detection, and recognition. This dataset serves as a valuable resource for advancing research in precision agriculture, smart farming, and AI-based crop health monitoring, and is freely available for academic and research purposes with proper acknowledgment to the creators and Daffodil International University.