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- W3136993381 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-neighbour (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%, recall98.1%, 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." @default.
- W3136993381 created "2021-03-29" @default.
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- W3136993381 date "2021-05-01" @default.
- W3136993381 modified "2023-10-11" @default.
- W3136993381 title "Computer-vision classification of corn seed varieties using deep convolutional neural network" @default.
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- W3136993381 doi "https://doi.org/10.1016/j.jspr.2021.101800" @default.
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