An artificial neural network approach to the estimation of stem water potential from frequency domain reflectometry soil moisture measurements and meteorological data

Computers and Electronics in Agriculture

Volumn 91, Issue , 2013, Pages 75-86

  Martí, Pau ^a   Gasque, María ^a   González Altozano, Pablo ^a  

^a UNIVERSITAT POLITÈCNICA DE VALÈNCIA   (Spain)

Author keywords

Artificial neural networks; Frequency domain reflectometry; Irrigation scheduling; Principal components analysis

Indexed keywords

FREQUENCY DOMAIN ANALYSIS; IRRIGATION; LINEAR REGRESSION; METEOROLOGY; NEURAL NETWORKS; REFLECTION; REFLECTOMETERS; SCHEDULING; SOIL MOISTURE; SUN;

ARTIFICIAL NEURAL NETWORK APPROACH; AUTOMATIC IRRIGATION; CITRUS TREE; COMPARATIVE ANALYSIS; DETERMINATION COEFFICIENTS; FREQUENCY DOMAIN REFLECTOMETRY; INPUT VARIABLES; INPUT-OUTPUT MAPPING; IRRIGATION SCHEDULING; LABOUR-INTENSIVE; LIMITED DATA; METEOROLOGICAL DATA; MULTIPLE LINEAR REGRESSIONS; PLANT WATER; PRINCIPAL COMPONENTS; PRINCIPAL COMPONENTS ANALYSIS; REGRESSION EQUATION; REGRESSION MODEL; SOIL MOISTURE MEASUREMENT; STANDARD METEOROLOGICAL VARIABLES; STEM WATER POTENTIALS;

PRINCIPAL COMPONENT ANALYSIS;

ARTIFICIAL NEURAL NETWORK; DATA PROCESSING; DATA SET; ESTIMATION METHOD; METEOROLOGY; NUMERICAL MODEL; PERFORMANCE ASSESSMENT; REGRESSION; SOIL MOISTURE;

CITRUS; CLEOPATRA MANDARIN;

EID: 84872125421     PISSN: 01681699     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.compag.2012.12.001     Document Type: Article

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