Soybean Seed Quality Assessment Using AI and Machine Learning Models

Sajad Sabzi; Raziyeh Pourdarbani; Mahan Masoumzade; José Luis Hernandez-Hernandez

doi:10.2478/ata-2026-0021

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Soybean Seed Quality Assessment Using AI and Machine Learning Models

Acta Technologica Agriculturae

Volume 29 (2026): Issue 3 (September 2026)

By: Sajad Sabzi, Raziyeh Pourdarbani, Mahan Masoumzade and José Luis Hernandez-Hernandez

Open Access

|Sep 2026

Abstract

Soybean is a key crop due to its nutritional, industrial, and agricultural benefits. Its production performance faces challenges such as improper storage, seed damage, and moisture imbalance. This paper aims to classify soybean seeds into five categories – healthy, damaged, immature, broken, and stained – using image processing and deep learning. Four classifiers DenseNet, MobileNet, ResNet, and EfficientNet were used to classify seeds on a dataset of 5513 soybean seed images. The best performance among the models used was obtained from DenseNet with an accuracy of 96% and an F1-score of 94%. The results also showed that all models achieved an AUC value above 0.99, indicating high performance of the models. This research highlights the potential of machine learning and artificial intelligence in optimising soybean classifiers.

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Articles in this issue

DOI: https://doi.org/10.2478/ata-2026-0021 | Journal eISSN: 1338-5267 | Journal ISSN: 1335-2555

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Language: English

Page range: 179 - 187

Published on: Sep 5, 2026

Published by: Slovak University of Agriculture in Nitra

In partnership with: Paradigm Publishing Services

Keywords:

soybean,

deep learning,

convolutional neural network,

Related subjects:

Introductions and overviews,

Engineering, other

© 2026 Sajad Sabzi, Raziyeh Pourdarbani, Mahan Masoumzade, José Luis Hernandez-Hernandez, published by Slovak University of Agriculture in Nitra
This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.

Volume 29 (2026): Issue 3 (September 2026)