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All Journal International Journal of Informatics and Communication Technology (IJ-ICT)
Ezekiel Mensah Martey
University of Mines and Technology
Computer Science & IT

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From data to decisions: ensemble deep learning framework for maize seed variety classification with explainable AI Emmanuel Asante; Obed Appiah; Ezekiel Mensah Martey; Emmanuel Effah; Eric Opoku; Ernest Kwame Ampomah
International Journal of Informatics and Communication Technology (IJ-ICT) Vol 15, No 4: December 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijict.v15i4.pp1648-1663

Abstract

Accurate and non-destructive identification of maize seed varieties is essential for maintaining seed purity, improving crop productivity, and supporting agricultural quality assurance. Although recent studies report high accuracies using advanced imaging and deep learning techniques, many rely on hyperspectral sensors or controlled acquisition environments, or offer limited interpretability, thereby restricting real-world adoption. This study investigates maize seed variety classification using low-cost RGB imaging and an ensemble deep learning framework, focusing on three widely cultivated Ghanaian varieties: Bhihilifa, Sanzal Sima, and Wang Dataa. In collaboration with Heritage Seeds Ghana, an expert-verified dataset of 17,724 maize seed images was curated under realistic acquisition conditions with an Apple iPhone 12. A baseline convolutional neural network (CNN) was developed for benchmarking, followed by transfer learning with DenseNet169, MobileNetV2, and Xception architectures. An ensemble framework integrating DenseNet169 and Xception was proposed to improve robustness and generalization. Across five-fold cross-validation, the ensemble achieved a mean accuracy of 98.89±0.17%, precision of 98.86±0.18%, recall of 98.80±0.17%, and F1-score of 98.83±0.17%. To enhance transparency, Grad-CAM and saliency maps were employed to visualize discriminative features influencing model decisions. Results demonstrate that accurate and interpretable classification of maize seed varieties is achievable using standard RGB imagery without specialized sensing equipment.
Co-Authors Emmanuel Asante Emmanuel Effah Eric Opoku Ernest Kwame Ampomah Obed Appiah