ification tasks and future agricultural research applications . Data source location National Botanical Garden, Mirpur-2, Dhaka – 1216 Latitude: 23.8121° N Longitude: 90.3531° E Zone: Dhaka Country: Bangladesh Data accessibility Repository name: Mendeley Data Data identification number: 10.17632/zz7r5y4dc6.1 Direct URL to data: https://data.mendeley.com/datasets/zz7r5y4dc6/1 The dataset is published under the Creative Commons Attribution 4.0 International (CC BY 4.0) license. Related research article None 1. Value of the Data • This is a unique and complete dataset of images from different categories, including healthy, bacterial spot, shot hole, powdery mildew, and yellow leaf. This dataset
Open resource ↗Mendeley Data · 10.17632/zz7r5y4dc6.1 · lines:1-49Paper record
AI-MedLeafX: a large-scale computer vision dataset for medicinal plant diagnosis.
Data in brief · 5 Aug 2025 · 10.1016/j.dib.2025.111945
Abstract
This study presents a large, meticulously curated and manually validated dataset aimed at classifying leaf quality into five critical categories: Healthy, Bacterial Spot, Shot Hole, Yellow, and Powdery Mildew. The dataset encompasses four distinct plant species-Cinnamomum Camphora (Camphor), Terminalia Chebula (Haritaki), Moringa Oleifera (Sojina), and Azadirachta Indica (Neem)-each represented across three or four disease categories, depending on observed symptoms and final number of classes is thirteen (13 classes). Data collection was conducted between November 1, 2024, and January 5, 2025, utilizing four different mobile cameras to ensure diversity in image resolution, lighting, and environmental conditions. The original dataset comprised 10,858 high-resolution images, which were subsequently expanded to 65,148 through the application of six comprehensive data augmentation techniques, including rotations (45°, 60°, and 90°), horizontal flipping, zooming and brightness adjustment. All images were standardized to 512×512 pixels to ensure uniformity and seamless compatibility with machine learning and computer vision models. This enriched dataset serves as a crucial resource for the development of automated plant disease detection systems and supports advancements in precision agriculture. It not only addresses the pressing need for scalable, high-quality data in agricultural research but also establishes a solid foundation for benchmarking novel deep learning architectures. By enabling more accurate and efficient leaf disease classification, the dataset contributes significantly to enhancing tree health monitoring, improving crop yield, and promoting sustainable agricultural practices.
Code and data availability