A comparative study of bread wheat varieties identification on feature extraction, feature selection and machine learning algorithms

Wheat is unquestionably the primary source of sustenance in human dietary intake. The cultivation areas and production capacity of wheat worldwide have been observed to increase in parallel with the growth of the global population. Wheat grains from different varieties, when mixed with durum wheat, result in a reduction in the protein content. Various types of wheat grains also exhibit the same characteristic. In this particular scenario, the significance of accurately categorizing wheat becomes more pronounced. In recent years, there has been a proliferation of studies aimed at categorizing agricultural products through the application of deep learning and machine learning methodologies. In the present study, a novel approach was introduced to simultaneously analyze deep features and image patches. This was achieved by utilizing a dataset consisting of a comprehensive collection of 8354 images, encompassing various bread wheat varieties. The classification of wheat types was carried out by employing feature extraction using three distinct methods. MobileNetV2, EfficientNetV2B0, GLCM, and Color-Space algorithms were employed to extract features from the images. Lastly, the Support Vector Machine (SVM), Random Subspace ensemble with k-Nearest Neighbors (RSeslibKnn), Artificial Neural Network (ANN), and Random Forest algorithms were employed to develop models for the classification of bread wheat images. The evaluation of the experimental performances was conducted based on the criteria of accuracy, precision, recall, F-score, and mean absolute error (MAE). In general, the obtained accuracies ranged from 91.50 to 98.65%, which demonstrates the models' proficiency in accurately classifying the samples. When examining different algorithms, Support Vector Machines (SVM) consistently demonstrate robust performance by achieving high levels of accuracy, precision, recall, and F-scores across various feature combinations.