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Evaluation of neural network modeling to predict non-water-stressed leaf temperature in wine grape for calculation of crop water stress index NWISRL
King, B.A.; Shellie, Krista.
Precision irrigation management in wine grape production is hindered by the lack of a reliable method to easily quantify and monitor vine water status. Mild to moderate water stress is desirable in wine grape for controlling vine vigor and optimizing fruit yield and quality. A crop water stress index (CWSI) that effectively monitors plant water status has not been widely adopted in wine grape because of the need to measure well-watered and non-transpiring leaf temperature under identical environmental conditions. In this study, we calculated a daily CWSI for the wine grape cultivars Syrah and Malbec (Vitis vinifera L.) by estimating well-watered leaf temperature with an artificial neural network (NN) model and non-transpiring leaf temperature based on the...
Tipo: Article Palavras-chave: Water management.
Ano: 2016 URL: http://eprints.nwisrl.ars.usda.gov/1611/1/1568.pdf
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Evaluation of neural network modeing to calculate well-watered leaf temperature of wine grape NWISRL
King, B.A.; Shellie, Krista.
Mild to moderate water stress is desirable in wine grape for controlling vine vigor and optimizing fruit yield and quality, but precision irrigation management is hindered by the lack of a reliable method to easily quantify and monitor vine water status. The crop water stress index (CWSI) that effectively monitors plant water status has not been widely adopted in wine grape because of the need to measure well-watered and non-transpiring leaf temperature under identical environmental conditions. In this study, a daily CWSI for the wine grape cultivar Syrah was calculated by estimating well-watered leaf temperature with an artificial neural network (NN) model and non-transpiring leaf temperature based on the cumulative probability of the measured difference...
Tipo: Conference or Workshop Item Palavras-chave: Irrigation control; Water.
Ano: 2014 URL: http://eprints.nwisrl.ars.usda.gov/1582/1/1538.pdf
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