ArticlePlants (Basel, Switzerland)2021
Automated Grapevine Cultivar Identification via Leaf Imaging and Deep Convolutional Neural Networks: A Proof-of-Concept Study Employing Primary Iranian Varieties.
Article in Plants (Basel, Switzerland), 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 17 papers.
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17 citing papers in PubMed, 67 citations in OpenAlex.
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- Artificial Intelligence Techniques in Grapevine Research: A Comparative Study with an Extensive Review of Datasets, Diseases, and Techniques Evaluation.Sensors (Basel, Switzerland) · 2024Review
- MultiFuseYOLO: Redefining Wine Grape Variety Recognition through Multisource Information Fusion.Sensors (Basel, Switzerland) · 2024Article
- Classification of field wheat varieties based on a lightweight G-PPW-VGG11 model.Frontiers in plant science · 2024Article
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- Leaf Count Aided Novel Framework for Rice (Plants (Basel, Switzerland) · 2022Article
- A Hyperspectral Data 3D Convolutional Neural Network Classification Model for Diagnosis of Gray Mold Disease in Strawberry Leaves.Frontiers in plant science · 2022Article
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- Hyperspectral Monitoring of Powdery Mildew Disease Severity in Wheat Based on Machine Learning.Frontiers in plant science · 2022Article
- Investigating Explanatory Factors of Machine Learning Models for Plant Classification.Plants (Basel, Switzerland) · 2021Article
- Meta-Learning for Few-Shot Plant Disease Detection.Foods (Basel, Switzerland) · 2021Article
- Detection Method of Citrus Psyllids With Field High-Definition Camera Based on Improved Cascade Region-Based Convolution Neural Networks.Frontiers in plant science · 2021Article
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Authors and funding
5 authors at 5 institutions in 5 countries.
Funding
No grant is acknowledged in the PubMed record.
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.
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