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Toward Grapevine Digital Ampelometry Through Vision Deep Learning Models
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DOI：10.1109/JSEN.2023.3261544.png)
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摘要
En 中文
Several thousand grapevine varieties exist, with even more naming identifiers. Adequate specialized labor is not available for proper classification or identification of grapevines, making the value of commercial vines uncertain. Traditional methods, such as genetic analysis or ampelometry, are time-consuming, expensive, and often require expert skills that are even rarer. New vision-based systems benefit from advanced and innovative technology and can be used by nonexperts in ampelometry. To this end, deep learning (DL) and machine learning (ML) approaches have been successfully applied for classification purposes. This work extends the state of the art by applying digital ampelometry techniques to larger grapevine varieties. We benchmarked MobileNet v2, ResNet-34, and VGG-11-BN DL classifiers to assess their ability for digital ampelography. In our experiment, all the models could identify the vines' varieties through the leaf with a weighted F1 score higher than 92%.
Keyword:
Artificial neural networks (ANN)
computer vision
image processing
precision agriculture
vine species identification
期刊
IF:
4.5
论文数:
2.2W
被引数:
7.3W
机构
引用论文
A Modern Ampelography: A Genetic Basis for Leaf Shape and Venation Patterning in Grape.png)
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