Detecting the early stage of phaeosphaeria leaf spot infestations in maize crop using in situ hyperspectral data and guided regularized random forest algorithm

Journal of Spectroscopy

Volumn 2017, Issue , 2017, Pages

  Adam, Elhadi ^a   Deng, Houtao ^b   Odindi, John ^c   Abdel Rahman, Elfatih M ^c,d,e   Mutanga, Onisimo ^c  

^a UNIVERSITY OF THE WITWATERSRAND   (South Africa)

^b Intuit Inc   (United States)

^c UNIVERSITY OF KWAZULU NATAL   (South Africa)

^d INTERNATIONAL CENTRE OF INSECT PHYSIOLOGY AND ECOLOGY ICIPE   (Kenya)

^e UNIVERSITY OF KHARTOUM   (Sudan)

Author keywords

[No Author keywords available]

Indexed keywords

DECISION TREES; FEATURE EXTRACTION; STATISTICAL TESTS; TROPICS;

FEATURE SELECTION AND CLASSIFICATION; HIGH DIMENSIONALITY; HYPERSPECTRAL DATA; PHAEOSPHAERIA LEAF SPOTS; RANDOM FOREST CLASSIFIER; REGULARIZED RANDOM FORESTS; SPECTRO-RADIOMETERS; STATISTICALLY SIGNIFICANT DIFFERENCE;

CLASSIFICATION (OF INFORMATION);

ALGORITHM; ARTICLE; BIOCHEMICAL ANALYSIS; CONCENTRATION (PARAMETERS); CONTROLLED STUDY; CROP; FUNGAL PLANT DISEASE; LEAF SPOT; MAIZE; MODEL; NONHUMAN; PHAEOSPHAERIA; PHAEOSPHAERIA LEAF SPOT; RANDOM FOREST;

EID: 85025100391     PISSN: 23144920     EISSN: 23144939     Source Type: Journal    
DOI: 10.1155/2017/6961387     Document Type: Article

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