Artificial neural network to predict vine water status spatial variability using multispectral information obtained from an unmanned aerial vehicle (UAV)

Sensors (Switzerland)

Volumn 17, Issue 11, 2017, Pages

  Poblete, Tomas ^a   Ortega Farías, Samuel ^a   Moreno, Miguel Angel ^b   Bardeen, Matthew ^a  

^a UNIVERSIDAD DE TALCA   (Chile)

^b UNIVERSITY OF CASTILLA LA MANCHA   (Spain)

Author keywords

Artificial neural network; Midday stem water potential; Multispectral image processing; UAV

Indexed keywords

ERRORS; FORECASTING; IMAGE PROCESSING; MEAN SQUARE ERROR; NEURAL NETWORKS; PLANTS (BOTANY); QUALITY CONTROL; YIELD STRESS;

ARTIFICIAL NEURAL NETWORK MODELS; COEFFICIENT OF DETERMINATION; MULTI-SPECTRAL CAMERAS; MULTISPECTRAL IMAGE PROCESSING; MULTISPECTRAL IMAGES; MULTISPECTRAL INDICES; ROOT MEAN SQUARE ERRORS; STEM WATER POTENTIALS;

UNMANNED AERIAL VEHICLES (UAV);

ARTICLE; ARTIFICIAL NEURAL NETWORK; CHILE; IMAGE PROCESSING; PLANT STEM; STEM WATER POTENTIAL; VINEYARD; WATER AVAILABILITY; WATER CONTENT;

EID: 85032742762     PISSN: 14248220     EISSN: None     Source Type: Journal    
DOI: 10.3390/s17112488     Document Type: Article

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