Deep learning-based seed variety classification: a case study in maize

Distinguishing among maize seed varieties is often challenging because of their morphological similarity and genetic diversity. Traditional seed identification methods frequently fail to balance accuracy, speed, and practicality, creating the need for innovative solutions. Recent advances in artificial intelligence, particularly in deep learning, provide promising alternatives for the precise classification of crop varieties. This study aimed to classify five maize seed genotypes (KSC201, KSC260, KSC400, KSC703, and KSC705) using only RGB digital images acquired under standardized lighting conditions. Image analysis and model training were performed in Python (Colab environment). The proposed convolutional neural network (CNN) model demonstrated excellent performance across all classes, achieving near-perfect accuracy, recall, and specificity for KSC260 and KSC703. The precision–recall analysis showed that KSC705 exhibited almost perfect classification with an average precision of 0.99, while KSC201 achieved 0.96. The overall model performance reached an accuracy of 96.67%, sensitivity of 96.67%, precision of 96.74%, and specificity of 99.18%. Results from the confusion matrix confirmed that the CNN effectively distinguished among all five maize genotypes, and the high area under the curve (AUC) values indicated almost perfect prediction capability. These findings demonstrate that CNN-based image analysis provides a reliable and accurate approach for seed variety identification, comparable to the performance of advanced architectures such as ConvNeXt_Tiny in other agricultural applications.