FieldMaizeLeaf-8: A real-field early vegetative maize leaf stress image dataset from Bangladesh
- 1Data Science Lab, Department of Software Engineering, Daffodil International University, Daffodil Smart City, Savar, Dhaka, 1216, Bangladesh
- 2Department of Plant Pathology, Faculty of Agriculture, Sher-e-Bangla Agricultural University, Sher-e-Bangla Nagar, Dhaka, 1207, Bangladesh
Abstract
Maize (Zea mays L.) is a rapidly expanding cereal crop in Bangladesh, but its yield is threatened by biotic diseases, insect pests, and nutrient-deficiency disorders that are difficult to distinguish visually, particularly at the early vegetative stage. Most publicly available leaf-image datasets used to train computer-vision models for crop diagnosis are captured under controlled laboratory conditions with plain backgrounds, which limits the field-readiness of models trained on them. FieldMaizeLeaf-8 addresses this gap by providing 3975 RGB photographs of maize leaves aged approximately 1–2 months, captured directly in commercial maize fields in Daudkandi, Cumilla, Bangladesh, using a consumer smartphone under natural daylight, background, and viewing-angle conditions, with no laboratory staging. Images are organised into eight classes: one healthy-leaf class and seven stress classes, comprising three biotic conditions (Fall Armyworm damage, Leaf Spot, and Northern Corn Leaf Blight) and four abiotic nutrient-deficiency conditions (Nitrogen, Phosphorus, Potassium, and Magnesium deficiency). Class labels assigned in the field were subsequently reviewed and validated by a plant-pathology expert. The dataset retains natural variability in illumination, leaf orientation, and symptom severity, and is intended to serve as a real-field benchmark for developing and evaluating deep-learning models for maize stress recognition, transfer learning, and explainable artificial intelligence (XAI) applications in precision agriculture.
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BibTeX
@article{akter2026fieldmaizeleaf8,
title = {{FieldMaizeLeaf-8: A real-field early vegetative maize leaf stress image dataset from Bangladesh}},
author = {Laboni Akter and Md Shohel Arman and Sakibul Hasan Chowdhury and Sayed Mohammad Mohsin},
journal = {Data in Brief},
year = {2026},
month = oct,
doi = {10.1016/j.dib.2026.113337},
url = {https://doi.org/10.1016/j.dib.2026.113337},
note = {CC BY 4.0},
}
RIS
TY - JOUR
TI - FieldMaizeLeaf-8: A real-field early vegetative maize leaf stress image dataset from Bangladesh
AU - Laboni Akter
AU - Md Shohel Arman
AU - Sakibul Hasan Chowdhury
AU - Sayed Mohammad Mohsin
PY - 2026
DA - 2026/10/09
JO - Data in Brief
DO - 10.1016/j.dib.2026.113337
UR - https://doi.org/10.1016/j.dib.2026.113337
AB - Maize (Zea mays L.) is a rapidly expanding cereal crop in Bangladesh, but its yield is threatened by biotic diseases, insect pests, and nutrient-deficiency disorders that are difficult to distinguish visually, particularly at the early vegetative stage. Most publicly available leaf-image datasets used to train computer-vision models for crop diagnosis are captured under controlled laboratory conditions with plain backgrounds, which limits the field-readiness of models trained on them. FieldMaizeLeaf-8 addresses this gap by providing 3975 RGB photographs of maize leaves aged approximately 1–2 months, captured directly in commercial maize fields in Daudkandi, Cumilla, Bangladesh, using a consumer smartphone under natural daylight, background, and viewing-angle conditions, with no laboratory staging. Images are organised into eight classes: one healthy-leaf class and seven stress classes, comprising three biotic conditions (Fall Armyworm damage, Leaf Spot, and Northern Corn Leaf Blight) and four abiotic nutrient-deficiency conditions (Nitrogen, Phosphorus, Potassium, and Magnesium deficiency). Class labels assigned in the field were subsequently reviewed and validated by a plant-pathology expert. The dataset retains natural variability in illumination, leaf orientation, and symptom severity, and is intended to serve as a real-field benchmark for developing and evaluating deep-learning models for maize stress recognition, transfer learning, and explainable artificial intelligence (XAI) applications in precision agriculture.
SN - 2352-3409
ER -