سال انتشار:

2021

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عنوان انگلیسی مقاله:

Computer-vision classification of corn seed varieties using deep convolutional neural network

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ترجمه فارسی عنوان مقاله:

طبقه بندی بینایی ماشین انواع بذر ذرت با استفاده از شبکه عصبی پیچیده عمیق

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منبع:

Sciencedirect - Elsevier - Journal of Stored Products Research, 92 (2021) 101800: doi:10:1016/j:jspr:2021:101800

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نویسنده:

Shima Javanmardi

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چکیده انگلیسی:

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.© 2021 Elsevier Ltd. All rights reserved.
Keywords: Machine vision | Deep learning | Feature extraction | Non-handcrafted features | Texture descriptors

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قیمت: رایگان

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توضیحات اضافی:

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