Raw Lemon Leaf Image Dataset for Disease Detection: Canker and Healthy Classification Under Diverse Conditions
Description
This dataset consists of high-resolution images of lemon leaves categorized into two classes: ''Canker'' (580 images of diseased leaves) and ''Healthy'' (369 images of healthy leaves). The images were captured in diverse environmental conditions, including varying lighting, angles, and leaf orientations, to ensure the dataset’s robustness for real-world applications. The dataset aims to support research in automated lemon leaf disease detection, particularly for canker, using machine learning and deep learning models. While it is unannotated, future versions will provide labels and bounding boxes for object detection tasks. Researchers can use this dataset for image classification, disease localization, and to explore strategies for addressing class imbalance, such as data augmentation. This collection serves as a valuable resource for developing and testing models for plant disease detection in real-world conditions.
Files
Steps to reproduce
The dataset was collected from nursery gardens, where both healthy and diseased lemon leaves were photographed under varying environmental conditions, including different lighting, weather, and leaf orientations, to simulate real-world scenarios. The images were captured using a Realme 7 Pro mobile phone, ensuring high resolution suitable for disease detection tasks. After capturing the images, they were manually organized into two folders: Canker (580 images of diseased leaves) and Healthy (369 images of healthy leaves).