2023
DOI: 10.1016/j.atech.2023.100192
|View full text |Cite
|
Sign up to set email alerts
|

Drought tolerance classification of grapevine rootstock by machine learning for the São Francisco Valley

Help me understand this report

Search citation statements

Order By: Relevance

Paper Sections

Select...
1
1

Citation Types

0
2
0

Year Published

2023
2023
2024
2024

Publication Types

Select...
1
1
1
1

Relationship

0
4

Authors

Journals

citations
Cited by 4 publications
(2 citation statements)
references
References 17 publications
0
2
0
Order By: Relevance
“…The Gamaret plants studied here were grafted onto rootstock 3309C reported to have low drought tolerance (Verslype et al, 2023). After our results, this cultivar appears to be less sensitive to drought than expected, particularly in 2018, the driest year of the monitoring, and more generally in the Valais region.…”
Section: Discussionmentioning
confidence: 52%
“…The Gamaret plants studied here were grafted onto rootstock 3309C reported to have low drought tolerance (Verslype et al, 2023). After our results, this cultivar appears to be less sensitive to drought than expected, particularly in 2018, the driest year of the monitoring, and more generally in the Valais region.…”
Section: Discussionmentioning
confidence: 52%
“…Consequently, some studies have highlighted the effectiveness and higher accuracy of a classifier for specific applications compared to other model types. Verslype et al [ 50 ] evaluated and compared the effectiveness of XGBoost, SVM, RF, DT, KNN, and LDA in predicting the drought tolerance classes of grapevine rootstocks. The study revealed that RF emerged as the top-performing classifier, followed by XGBoost.…”
Section: Introductionmentioning
confidence: 99%