An Investigation Into Machine Learning Regression Techniques for the Leaf Rust Disease Detection Using Hyperspectral Measurement

IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing

Volumn 9, Issue 9, 2016, Pages 4344-4351

  Ashourloo, Davoud ^a   Aghighi, Hossein ^a   Matkan, Ali Akbar ^a   Mobasheri, Mohammad Reza ^b   Rad, Amir Moeini ^b  

^a SHAHID BEHESHTI UNIVERSITY   (Iran)

^b K N TOOSI UNIVERSITY OF TECHNOLOGY   (Iran)

Author keywords

Disease severity; disease symptoms; Gaussian process regression (GPR); hyperspectral measurement; plant disease; PLSR; wheat leaf rust (WLR); support vector regression ( SVR)

Indexed keywords

ARTIFICIAL INTELLIGENCE; CHEMICAL DETECTION; GROUND PENETRATING RADAR SYSTEMS; LEARNING ALGORITHMS; LEAST SQUARES APPROXIMATIONS; MEAN SQUARE ERROR; PLANTS (BOTANY); REGRESSION ANALYSIS; SAMPLING; VEGETATION;

COEFFICIENT OF DETERMINATION; GAUSSIAN PROCESS REGRESSION; HYPERSPECTRAL MEASUREMENTS; MACHINE LEARNING TECHNIQUES; PARTIAL LEAST SQUARE REGRESSION; SPECTRAL CHARACTERISTICS; SPECTRAL VEGETATION INDICES; SUPPORT VECTOR REGRESSION (SVR);

LEARNING SYSTEMS;

LEAF AREA; MACHINE LEARNING; RADIOMETER; REGRESSION ANALYSIS; RUST DISEASE; SPECTRAL ANALYSIS; VEGETATION INDEX; WHEAT;

PUCCINIA TRITICINA;

EID: 84981314135     PISSN: 19391404     EISSN: 21511535     Source Type: Journal    
DOI: 10.1109/JSTARS.2016.2575360     Document Type: Article

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