识别特定葡萄园产量和葡萄大小关键预测因子的机器学习方法

IF 2.2 3区农林科学 Q3 BIOTECHNOLOGY & APPLIED MICROBIOLOGY

American Journal of Enology and Viticulture Pub Date : 2023-01-17 DOI:10.5344/ajev.2022.22050

J. Taylor, T. Bates, Rhiann Jakubowski, Hazaël Jones

引用次数: 1

摘要

非线性随机森林回归而修剪质量的简单发展是最好的

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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Machine-Learning Methods for the Identification of Key Predictors of Site-Specific Vineyard Yield and Vine Size

non-linear random forest regression while the simpler development of pruning mass was best

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来源期刊

American Journal of Enology and Viticulture 农林科学-生物工程与应用微生物

CiteScore

3.80

自引率

10.50%

发文量

审稿时长

12-24 weeks

期刊介绍： The American Journal of Enology and Viticulture (AJEV), published quarterly, is an official journal of the American Society for Enology and Viticulture (ASEV) and is the premier journal in the English language dedicated to scientific research on winemaking and grapegrowing. AJEV publishes full-length research papers, literature reviews, research notes, and technical briefs on various aspects of enology and viticulture, including wine chemistry, sensory science, process engineering, wine quality assessments, microbiology, methods development, plant pathogenesis, diseases and pests of grape, rootstock and clonal evaluation, effect of field practices, and grape genetics and breeding. All papers are peer reviewed, and authorship of papers is not limited to members of ASEV. The science editor, along with the viticulture, enology, and associate editors, are drawn from academic and research institutions worldwide and guide the content of the Journal.