Vineyard water status estimation using multispectral imagery from an UAV platform and machine learning algorithms for irrigation scheduling management

Computers and Electronics in Agriculture

Volumn 147, Issue , 2018, Pages 109-117

  Romero, Maria ^a,b   Luo, Yuchen ^c   Su, Baofeng ^d   Fuentes, Sigfredo ^e  

^a UNIV MONTPELLIER   (France)

^b UNIVERSITY OF TURIN   (Italy)

^c HOCHSCHULE GEISENHEIM UNIVERSITY   (Germany)

^d NORTHWEST A F UNIVERSITY   (China)

^e UNIVERSITY OF MELBOURNE   (Australia)

Author keywords

Artificial neural networks; Remote sensing; Stem water potential; Unmanned aerial vehicle; Water stress

Indexed keywords

ANTENNAS; ARTIFICIAL INTELLIGENCE; FORESTRY; IRRIGATION; LEARNING SYSTEMS; NEURAL NETWORKS; PATTERN RECOGNITION; REMOTE SENSING; SCHEDULING; SCHEDULING ALGORITHMS; UNMANNED AERIAL VEHICLES (UAV); VEGETATION;

COEFFICIENT OF DETERMINATION; EXPERIMENTAL TRIALS; IRRIGATION MANAGEMENT; IRRIGATION SCHEDULING; MULTI-SPECTRAL IMAGERY; STATISTICAL SIGNIFICANCE; STEM WATER POTENTIALS; WATER STRESS;

LEARNING ALGORITHMS;

ALGORITHM; ARTIFICIAL NEURAL NETWORK; ESTIMATION METHOD; IRRIGATION SYSTEM; MACHINE LEARNING; MULTISPECTRAL IMAGE; PATTERN RECOGNITION; REMOTE SENSING; UNMANNED VEHICLE; VINEYARD; WATER STRESS;

CHINA; SHANGRI-LA; YUNNAN;

EID: 85042859007     PISSN: 01681699     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.compag.2018.02.013     Document Type: Article

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