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Automated Grapevine Cultivar Identification via Leaf Imaging and Deep Convolutional Neural Networks: A Proof-of-Concept Study Employing Primary Iranian Varieties

Authors :
Amin Nasiri
Amin Taheri-Garavand
Dimitrios Fanourakis
Yu-Dong Zhang
Nikolaos Nikoloudakis
Source :
Plants, Vol 10, Iss 8, p 1628 (2021)
Publication Year :
2021
Publisher :
MDPI AG, 2021.

Abstract

Extending over millennia, grapevine cultivation encompasses several thousand cultivars. Cultivar (cultivated variety) identification is traditionally dealt by ampelography, requiring repeated observations by experts along the growth cycle of fruiting plants. For on-time evaluations, molecular genetics have been successfully performed, though in many instances, they are limited by the lack of referable data or the cost element. This paper presents a convolutional neural network (CNN) framework for automatic identification of grapevine cultivar by using leaf images in the visible spectrum (400–700 nm). The VGG16 architecture was modified by a global average pooling layer, dense layers, a batch normalization layer, and a dropout layer. Distinguishing the intricate visual features of diverse grapevine varieties, and recognizing them according to these features was conceivable by the obtained model. A five-fold cross-validation was performed to evaluate the uncertainty and predictive efficiency of the CNN model. The modified deep learning model was able to recognize different grapevine varieties with an average classification accuracy of over 99%. The obtained model offers a rapid, low-cost and high-throughput grapevine cultivar identification. The ambition of the obtained tool is not to substitute but complement ampelography and quantitative genetics, and in this way, assist cultivar identification services.

Details

Language :
English
ISSN :
22237747
Volume :
10
Issue :
8
Database :
Directory of Open Access Journals
Journal :
Plants
Publication Type :
Academic Journal
Accession number :
edsdoj.bb0fd5d7cc9144149c5ef8bdeaec002b
Document Type :
article
Full Text :
https://doi.org/10.3390/plants10081628