Phenotyping grapevine red blotch virus and grapevine leafroll-associated viruses before and after symptom expression through machine-learning analysis of hyperspectral images

Grapevine leafroll-associated viruses (GLRaVs) and grapevine red blotch virus (GRBV) cause substantial economic losses and concern to North America's grape and wine industries. Fast and accurate identification of these two groups of viruses is key to informing disease management strategies and...

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Vydáno v:	Frontiers in plant science Ročník 14; s. 1117869
Hlavní autoři:	Sawyer, Erica, Laroche-Pinel, Eve, Flasco, Madison, Cooper, Monica L., Corrales, Benjamin, Fuchs, Marc, Brillante, Luca
Médium:	Journal Article
Jazyk:	angličtina
Vydáno:	Switzerland Frontiers Media SA 10.03.2023 Frontiers Media S.A
Témata:	Accuracy Artificial neural networks Blotch Cameras Classification convolutional neural network Datasets Decision trees Deep learning Disease disease detection Economic impact Flowers & plants Grapevines Growing season Hyperspectral imaging Image segmentation Insects Learning algorithms Leaves Machine learning Neural networks phenomics Phenotyping Plant Science Polymerase chain reaction Remote sensing Ripening Signs and symptoms spectroscopy Vectors Vines Viral infections Viruses Vitis vinifera L Wavelengths Wineries & vineyards Wines United States > US California Vitis vinifera L disease detection deep-learning spectroscopy convolutional neural network random forest phenomics
ISSN:	1664-462X, 1664-462X
On-line přístup:	Získat plný text
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