Machine-Learning Methods to Identify Key Predictors of Site-Specific Vineyard Yield and Vine Size
نویسندگان
چکیده
Background and goals Lake Erie Concord growers have access to high-resolution spatial soil production data but lack protocols information on the optimum time collect these data. This study intends provide clearer regarding type timing of sensor support in-season management. Methods key findings A three-year in a 2.6 ha vineyard collected yield, pruning mass, canopy vigor data, including yield mass from previous year, at 321 sites. Stepwise linear regression random forest approaches were used model site-specific using historical multi-temporal The more complex elaboration process was best modelled with non-linear while simpler development by regression. Conclusions significance Canopy weeks preceding bloom most important predictor current season’s should be generate stratified sampling designs for crop estimation 30 days after bloom. In contrast, not well predicted vigor, even late-season which is widely advocated viticulture. year’s dominant season. To going forward, approach start measuring it. Further work still needed develop robust, local models operational decision-making concord vineyards.
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ژورنال
عنوان ژورنال: American Journal of Enology and Viticulture
سال: 2023
ISSN: ['0002-9254', '1943-7749']
DOI: https://doi.org/10.5344/ajev.2022.22050