FieldMaizeLeaf-8: A Real-Field Early Vegetative Maize Leaf Stress Image Dataset from Bangladesh

Abstract

FieldMaizeLeaf-8 consists of 3,975 early vegetative maize leaf images collected as a research resource for studies in maize stress classification, computer vision, deep learning, and explainable artificial intelligence. The images were collected from real field conditions in Daudkandi, Cumilla, Bangladesh, and represent approximately 1–2 month-old maize plants, capturing the natural variations encountered during field-based maize leaf imaging. The dataset contains images representing eight maize leaf classes: Fall Armyworm Damage, Healthy Leaf, Leaf Spot, Magnesium Deficiency, Nitrogen Deficiency, Northern Leaf Blight, Phosphorus Deficiency, and Potassium Deficiency. These classes cover both biotic stresses, including diseases and insect damage, and abiotic stresses associated with nutrient deficiencies, along with healthy leaves. The images were collected from natural field environments and selected based on their suitability for analysis. As a real-field dataset, it contains natural variations in illumination, background, leaf orientation, image quality, viewing angle, symptom severity, and leaf appearance. These variations make the dataset suitable for developing and evaluating robust deep learning models under realistic agricultural conditions. FieldMaizeLeaf-8 is intended for research in areas including maize stress classification, plant disease recognition, nutrient deficiency detection, computer vision, deep learning, transfer learning, image-based agricultural diagnosis, and explainable artificial intelligence (XAI). The dataset can also support the development of practical AI-based tools for early maize stress identification and precision agriculture applications.