A real-world leaf image dataset of seven Bangladeshi medicinal plant species

This dataset contains a curated collection of leaf images from seven commonly used medicinal plant species native to Bangladesh, developed to support research in plant identification and classification using computer vision and machine learning techniques. The central research hypothesis is that leaf morphology provides sufficient visual information to discriminate medicinal plant species under real-world outdoor conditions.

The dataset comprises a total of 13,095 leaf images, including 6,700 raw images and 6,395 pre-processed images. All images are resized to 640 × 640 pixels. However, the images represent seven medicinal plant species widely used in traditional and modern healthcare practices in Bangladesh. The data show consistent intra-class visual characteristics such as leaf shape, venation patterns, texture, margin structure, and colour distribution while exhibiting clear inter-species differences that support reliable species-level classification and comparative analysis.

All images were manually collected from natural outdoor environments, including agricultural fields, nurseries, and home gardens located in the Akran Bazar, Ashulia, and Savar regions of Dhaka, Bangladesh. This real-world acquisition approach preserves natural variations in illumination, background complexity, leaf orientation, scale, and growth stages, making the dataset suitable for developing and evaluating robust recognition models under practical conditions.

The dataset is organized into two main folders. The Raw Images folder contains original, unaltered images without background removal or enhancement, preserving authentic acquisition conditions. The Pre-processed Images folder includes processed versions of the raw images to facilitate standardized experimentation; preprocessing steps may include resizing, normalization, and noise reduction, as documented. Images in both folders are arranged in class-wise subdirectories corresponding to each plant species.

All images are provided in commonly used formats, allowing researchers to apply custom preprocessing, feature extraction, and modelling strategies. The dataset can serve as a benchmark resource for evaluating classification robustness and generalization performance. In addition, it enables comparative studies across different algorithms, supports dataset augmentation research, and facilitates reproducible experimentation. The dataset is suitable for academic research, educational purposes, and applied studies in medicinal plant identification, biodiversity conservation, agricultural technology, and healthcare-oriented artificial intelligence research, particularly in regions where publicly available medicinal plant datasets remain limited.