Detection of citrus canker and Huanglongbing using fluorescence imaging spectroscopy and support vector machine technique

Applied Optics

Volumn 55, Issue 2, 2016, Pages 400-407

  Wetterich, Caio Bruno ^a   De Oliveira Neves, Ruan Felipe ^a   Belasque, José ^a   Marcassa, Luis Gustavo ^a  

^a UNIVERSITY OF SÃO PAULO   (Brazil)

Author keywords

[No Author keywords available]

Indexed keywords

ARTIFICIAL INTELLIGENCE; FLUORESCENCE; LOSSES; ZINC;

CITRUS PRODUCTION; CLASSIFICATION RESULTS; FLUORESCENCE IMAGING; HIGHLY ACCURATE; HUANGLONGBING(HLB); MACHINE LEARNING TECHNIQUES; SUPPORT VECTOR MACHINE TECHNIQUES; ZINC DEFICIENCY;

LEARNING SYSTEMS;

ZINC;

CITRUS; DEFICIENCY; FLUORESCENCE IMAGING; MICROBIOLOGY; PLANT DISEASE; PROCEDURES; SPECTROFLUOROMETRY; SUPPORT VECTOR MACHINE;

CITRUS; OPTICAL IMAGING; PLANT DISEASES; SPECTROMETRY, FLUORESCENCE; SUPPORT VECTOR MACHINE; ZINC;

EID: 84962577356     PISSN: 1559128X     EISSN: 21553165     Source Type: Journal    
DOI: 10.1364/AO.55.000400     Document Type: Article

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