Abstract

Maize (Zea mays L.) is a major staple crop vulnerable to post-harvest deterioration caused by fungal infection and mycotoxin contamination. Visual defects such as discoloration, breakage, insect damage, and mold growth are commonly associated with reduced grain quality and increased contamination risk. This article presents a publicly available RGB image dataset of white maize kernels deposited in Harvard Dataverse for the development of computer vision models for automated grain quality assessment. The dataset contains 5143 high-resolution RGB images acquired using Samsung Galaxy A12 and Samsung Galaxy A54 smartphone cameras under semi-controlled imaging conditions. Images contain either single or multiple kernels and were annotated at the instance level using the YOLO format, resulting in 13,533 labeled kernel instances. Annotations were assigned by experts experienced in mycotoxin-related grain quality inspection. Labels are based solely on visual surface characteristics and do not represent direct chemical measurements of aflatoxins, fumonisins, or other mycotoxins. The dataset provides a practical resource for developing and evaluating machine learning models for maize kernel defect detection, quality screening, and risk-oriented grain inspection applications.

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