An Open-Source Multi-Format Maize Leaf Image Dataset for Disease Detection and Classification
Description
This dataset presents a comprehensive collection of maize (corn) leaf images captured from maize-growing regions in Bangladesh during June 2026. The dataset was developed to support research on automated plant disease diagnosis using machine learning (ML), deep learning (DL), and computer vision techniques. As maize has become an increasingly important crop in Bangladesh for food, livestock feed, and industrial applications, foliar diseases pose a significant threat to crop yield, grain quality, and farmers' income. Early and accurate disease identification is therefore essential for effective disease management and sustainable maize production. Existing maize disease datasets are often limited in terms of regional representation and data formats. Since disease occurrence and symptom characteristics vary according to climate, environmental conditions, and agricultural practices, a region-specific dataset is necessary for developing reliable diagnostic models. This dataset addresses these limitations by providing high-quality, well-annotated images collected under real field conditions in Bangladesh and made available in multiple formats to facilitate their use across different ML and DL frameworks. The dataset includes images belonging to ten disease categories and one healthy class: * Northern Corn Leaf Blight * Southern Corn Leaf Blight * Common Rust * Gray Leaf Spot * Leaf Blight * Eyespot * Curvularia Leaf Spot * Maize Streak Disease * Bacterial Leaf Streak * Healthy Leaves The diversity of disease classes and image conditions makes the dataset suitable for a wide range of applications, including image classification, object detection, disease severity assessment, transfer learning, and explainable artificial intelligence (XAI). It can also serve as a benchmark dataset for evaluating and comparing different machine learning and deep learning algorithms. The primary objective of this dataset is to assist researchers, data scientists, agricultural engineers, and plant pathologists in developing accurate and robust maize disease detection systems. In addition to supporting research in Bangladesh, the dataset may also be valuable for studies conducted in maize-growing regions with similar agroecological and climatic conditions. Ultimately, the use of this dataset can contribute to early disease diagnosis, improved crop management practices, reduced production losses, and enhanced maize productivity and profitability.