2026-10-15T21:08:51Z https://data.mendeley.com/oai

oai:data.mendeley.com/hvz9xh7c9t.3 2026-10-12T16:20:23Z

Billah, Md Masum BG-8: A Dataset for Multi-Class Classification of Bottle Gourd Leaf Diseases Mendeley Data The Dataset for Classification of Bottle Gourd Leaf Diseases comprises 1,711 original high-quality JPEG images collected across eight distinct classes to support the identification and classification of common diseases affecting bottle gourd plants. The dataset includes images captured under natural lighting and real-world conditions, representing variations in leaf positions and backgrounds. The eight categories are Bottle Gourd Anthracnose (276 images), Bottle Gourd Mosaic Virus (315 images), Bottle Gourd Alternaria Leaf Blight (303 images), Bottle Gourd Downy Mildew (286 images), Bottle Gourd Healthy (260 images), Bottle Gourd Early Alternaria Leaf Blight (179 images), Bottle Gourd Angular Spot (53 images), and Bottle Gourd Fungal Damage Leaf (39 images). The original dataset was divided into training (70%), testing (15%), and validation (15%) subsets, containing 1,196, 252, and 263 images, respectively. To address class imbalance and enhance model learning, data augmentation was applied to the training data, generating 1,000 augmented images per class and yielding 8,000 augmented training images. This dataset captures diverse disease symptoms, including dark lesions, mosaic patterns, yellowing, and fungal damage, providing a valuable foundation for developing robust deep learning models for automated disease classification, early disease detection, and precision agriculture. Disease Machine Learning Precision Agriculture Convolutional Neural Network Deep Learning Bottle Gourd Sagor, Saifuddin Rahman, Md. Anisur Hossan, Md. Faysal Parvin, Sanzida Uddin, Mohammad Shorif Dataset doi:10.17632/hvz9xh7c9t.3 oai:data.mendeley.com/hvz9xh7c9t.3 Creative Commons Attribution 4.0 International http://creativecommons.org/licenses/by/4.0 https://data.mendeley.com/datasets/hvz9xh7c9t 2026-10-12T16:20:23Z