期刊
COMPUTERS AND ELECTRONICS IN AGRICULTURE
卷 91, 期 -, 页码 75-86出版社
ELSEVIER SCI LTD
DOI: 10.1016/j.compag.2012.12.001
关键词
Frequency domain reflectometry; Irrigation scheduling; Principal components analysis; Artificial neural networks
资金
- Conselleria de Agricultura, Pesca y Alimentacion de la Generalitat Valenciana [2007TAHAVAL00018]
Stem water potential seems to be a sensitive measure of plant water status. Nonetheless, it is a labour-intensive measurement and is not suited for automatic irrigation scheduling or control. This study describes the application of artificial neural networks to estimate stem water potential from soil moisture at different depths and standard meteorological variables, considering a limited data set. The experiment was carried out with 'Navelina' citrus trees grafted on 'Cleopatra' mandarin. Principal components analysis and multiple linear regression were used preliminarily to assess the relationships among observations and to propose other models to allow a comparative analysis, respectively. Two principal components account for the systematic data variation. The optimum regression equation of stem water potential considered temperature, relative humidity, solar radiation and soil moisture at 50 cm as input variables, with a determination coefficient of 0.852. When compared with their corresponding regression models, ANNs presented considerably higher performance accuracy (with an optimum determination coefficient of 0.926) due to a higher input-output mapping ability. (C) 2012 Elsevier B.V. All rights reserved.
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