Computer vision artificial intelligence to establish variety and quality of soybean grains
Keywords:
grain quality, computer vision, artificial intelligence, machine learning, deep learningAbstract
This work presents an artificial intelligence system based on computer vision techniques to assess soybean grain quality, offering a fast and cost-effective alternative to manual methods by automating the evaluation of attributes such as grain size, shape, color, and the presence of insects and foreign materials. To this end, samples of healthy and damaged soybean grains were collected, including those contaminated with foreign materials and insects. Various image classification strategies were applied, including Machine Learning models with manual feature extraction and Deep Learning models that do not require this step. The results show that Deep Learning models outperform Machine Learning models in the task of classifying soybean grain quality.
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Copyright (c) 2025 Rodrigo Maranzana, Cristian Santander, Federico Marinzalda, Leandro Buffarini, Claudio Grasso, Clara Ibarzabal, Matias Ordoñez, Hernán Ordiales, Camila Correa, S. Verrastro
This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License.
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