Deep learning and evolutionary intelligence with fusion-based feature extraction for classification of wheat varieties

Abstract One of the most important aspects of producing quality wheat is obtaining pure wheat seed varieties. It is of great importance to obtain pure wheat seeds for high grain quality, efficiency, and durability of wheat varieties. For this purpose, collective wheat images of 5 different bread wheat seed varieties registered by computer vision system were taken. Then, 8354 bread wheat grain images were obtained using image processing techniques. The use of important features that affect the image classification is critical for high classification success. The features obtained from CNN models are fused and combined. The optimal feature subset was selected with the whale optimization algorithm (WOA), one of the meta-heuristic algorithms. Each resulting feature set is classified by machine learning algorithms. The best performance in classification results was obtained with the Support Vector Machine (SVM) classifier. The performance of the system was 95.2% with Fusion + SVM and WOA + SVM. The study also provides results of performance metrics such as sensitivity, precision, specificity and F1 score, Matthews correlation coefficient and kappa values. The contribution of the article is as follows the use of the proposed method allows this process to be carried out with fewer features, less time, and less cost, as well as high accuracy in the classification of bread wheat seed varieties.