Computer-vision classification of corn seed varieties using deep convolutional neural network. (May 2021) - Javanmardi, Shima; Miraei Ashtiani, Seyed-Hassan; Verbeek, Fons J.; Martynenko, Alex
Abstract: Automated classification of seed varieties is of paramount importance for seed producers to maintain the purity of a variety and crop yield. Traditional approaches based on computer vision and simple feature extraction could not guarantee high accuracy classification. This paper presents a new approach using a deep convolutional neural network (CNN) as a generic feature extractor. The extracted features were classified with artificial neural network (ANN), cubic support vector machine (SVM), quadratic SVM, weighted k-nearest-neighbor (kNN), boosted tree, bagged tree, and linear discriminant analysis (LDA). Models trained with CNN-extracted features demonstrated better classification accuracy of corn seed varieties than models based on only simple features. The CNN-ANN classifier showed the best performance, classifying 2250 test instances in 26.8 s with classification accuracy 98.1%, precision 98.2%, recall 98.1%, and F1-score 98.1%. This study demonstrates that the CNN-ANN classifier is an efficient tool for the intelligent classification of different corn seed varieties. Highlights: The performance of different algorithms for corn seeds classification was evaluated. The accuracy of classification models increased by combining CNN with handcrafted features. CNN-ANN classification algorithm performed better than other models.