Improving the Approval Process for Durum Wheat Grain Quality in Algeria Using Computer Vision and Machine Learning

Abstract

This research aimed to develop computer vision and machine learning models to improve durum wheat quality control in Algeria. Durum wheat is a vital cereal crop in Algeria used for many staple foods. Currently, quality control relies on manual evaluation which is too lengthy and tedious. To address this, models utilizing image processing and 200 image descriptors, including 102 texture features, 8 morphological features, and 90 colour features, were developed to automate classification of durum wheat species, varieties, and impurities. An optimized Support Vector Machine (SVM) model was implemented hyperparameters tuning. The models achieved exceptional performance, classifying durum wheat species with 99% accuracy, varieties with 95% accuracy, and impurities with 94% accuracy. This illustrates the significant potential of tailored computer vision and machine learning techniques to enable automated quality control. The models could be integrated into crop certification workflows, increasing productivity.