Detecting rottenness caused by Penicillium genus fungi in citrus fruits using machine learning techniques

Expert Systems with Applications

Volumn 39, Issue 1, 2012, Pages 780-785

  Gómez Sanchis, Juan ^a   Martín Guerrero, José D ^a   Soria Olivas, Emilio ^a   Martínez Sober, Marcelino ^a   Magdalena Benedito, Rafael ^a   Blasco, José ^b  

^a UNIVERSITY OF VALENCIA   (Spain)

^b INSTITUTO VALENCIANO DE INVESTIGACIONES AGRARIAS IVIA   (Spain)

Author keywords

Agriculture; CART; Citrus fruits; Hyperspectral imagery; Neural networks; Rottenness

Indexed keywords

BAND SELECTION; CART; EARLY DETECTION; ECONOMIC LOSS; FRUIT PRODUCTION; GEOMETRIC CORRECTION; HYPERSPECTRAL IMAGERY; MACHINE LEARNING TECHNIQUES; PHYSICAL DEVICES; ROTTENNESS; ULTRA-VIOLET LIGHT;

AGRICULTURE; DATA HANDLING; DECISION TREES; DEFECTS; FUNGI; LOSSES; NEURAL NETWORKS; REMOTE SENSING; ULTRAVIOLET RADIATION;

CITRUS FRUITS;

EID: 81855167012     PISSN: 09574174     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.eswa.2011.07.073     Document Type: Article

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