A Real-World Hibiscus and Tea Leaf Image Dataset for Classification
Description
The Combined Hibiscus and Tea Leaf Image Dataset is a comprehensive and well-balanced collection comprising 1,413 original high-quality leaf images captured under natural outdoor conditions across various regions of Bangladesh using a SONY α7 II DSLR camera and a OnePlus 7T smartphone. The dataset includes two major plant species—Hibiscus and Tea—and aims to facilitate research in plant disease detection, agricultural image analysis, and computer vision–based crop health monitoring. The Hibiscus subset consists of 1,165 images categorized into eight distinct classes: Healthy (473 images), Mild Edge Damage (226 images), Citruspot (150 images), Slightly Diseased (109 images), Early Mild Spotting (83 images), Wrinkled (56 images), Senescent (40 images), and Fungal Infected (28 images). The Tea subset contains 248 images divided into five disease categories: Algal Leaf Spot (54 images), Brown Blight (48 images), Grey Blight (53 images), Healthy (49 images), and Red Leaf Spot (44 images). These class distributions capture a diverse range of leaf conditions, disease severities, and environmental variations, providing a realistic foundation for machine learning and deep learning applications. To overcome class imbalance and enrich the dataset, extensive image augmentation was performed using both PIL and OpenCV techniques, including brightness and contrast adjustment, color enhancement, rotation, flipping, scaling, cropping, shifting, zooming, and Gaussian noise addition. Through this process, each class was expanded to 1,000 images, resulting in a total of 13,000 augmented images evenly distributed across the 13 classes. All images are stored in .JPG format and organized into separate folders per class, maintaining a consistent structure and naming convention. Overall, this dataset offers a rich and diverse resource for developing robust models for leaf disease classification, precision agriculture, and automated plant health monitoring, making it a valuable contribution to the fields of computer vision and agricultural research.