A Dataset of Fresh and Disease Leaves

Published: 16 April 2024| Version 1 | DOI: 10.17632/m5h9gz6t5m.1
udit karnwal


This comprehensive dataset comprises a diverse collection of fresh and diseased leaves, encompassing 19 distinct classes derived from 5 types of leaves. The dataset includes both authentic samples and synthetically generated data to enhance its breadth and utility. Key Features: Fresh Leaves: Authentic samples representing the healthy state of each leaf type. Diseased Leaves: Real-world instances showcasing various types of leaf diseases across different plant species. Synthetic Generation: Augmented data generated using generative adversarial networks (GANs) to expand the dataset's size and diversity. Class Diversity: Encompasses 19 distinct classes derived from 5 different types of leaves, providing a rich variety for classification tasks. Annotated Labels: Each sample is meticulously annotated with its corresponding class label, facilitating supervised learning tasks. This dataset serves as a valuable resource for researchers, educators, and practitioners engaged in machine learning, computer vision, and agricultural studies, enabling the development and evaluation of algorithms for leaf classification, disease detection, and image synthesis.



MIET Engineering College Department of Computer Science and Engineering


Agricultural Plant, Leaf Studies