ALV-Leaf: A Multi-Class Aloe Vera Leaf Image Dataset for Disease and Health Classification

This dataset contains a curated collection of Aloe Vera leaf images developed for research in plant disease detection, computer vision, and deep learning applications. The images were collected from Rajbari, Dhaka, Bangladesh between January and February 2026 using a OnePlus Nord CE 4 Lite smartphone camera. During data acquisition, detached leaves were placed on a uniform background to ensure clear visibility of leaf morphology and disease characteristics. The dataset consists of two main components: a metadata file in CSV format and an image directory named “Aloe Vera Leaf Image Dataset”. The dataset includes four classes of leaf conditions, with 1000 images per class, resulting in a total of 4000 images. All images were resized to 512 × 512 pixels, converted to RGB format, and stored in JPG format.

During preprocessing, background removal techniques were applied to isolate the leaf region, and pixel values were normalized for consistency. Data augmentation techniques, including rotation, horizontal and vertical flipping, brightness and contrast adjustment, Gaussian noise addition, and image sharpening, were applied to enhance dataset diversity and improve model performance. The metadata CSV file provides structured information such as class labels and image distribution, supporting efficient dataset management and reproducible deep learning experiments.