Detection of Silybum marianum infection with Microbotryum silybum using VNIR field spectroscopy

Computers and Electronics in Agriculture

Volumn 137, Issue , 2017, Pages 130-137

  Pantazi, X E ^a   Tamouridou, A A ^a   Alexandridis, T K ^a   Lagopodi, A L ^a   Kontouris, G ^a   Moshou, D ^a  

^a ARISTOTLE UNIVERSITY OF THESSALONIKI   (Greece)

Author keywords

Artificial intelligence; Disease detection; Hierarchical Self Organizing Maps (SOMs); Intersystemic infection; Plant pathology

Indexed keywords

ARTIFICIAL INTELLIGENCE; CONFORMAL MAPPING; DECISION MAKING; DEEP NEURAL NETWORKS; DIAGNOSIS; INFRARED DEVICES; INFRARED SPECTROMETERS; NEURAL NETWORKS; PRINCIPAL COMPONENT ANALYSIS; SPECTROMETERS; WEED CONTROL;

DISEASE DETECTION; IDENTIFICATION RATES; INTERSYSTEMIC INFECTION; PLANT PATHOLOGY; PRINCIPAL COMPONENT EXTRACTION; SELF ORGANIZING MAPS(SOMS); SELF-ORGANIZING MODEL; VISIBLE AND NEAR INFRARED;

SELF ORGANIZING MAPS;

ANGIOSPERM; ARTIFICIAL NEURAL NETWORK; BIOCONTROL AGENT; BIOLOGICAL CONTROL; DECISION MAKING; DETECTION METHOD; DISEASE CONTROL; EXTRACTION METHOD; FUNGAL DISEASE; FUNGUS; INFECTIOUS DISEASE; NUMERICAL MODEL; PATHOLOGY; SPECTROSCOPY; WEED;

FUNGI; MICROBOTRYUM SILYBUM; SILYBUM MARIANUM;

EID: 85017161489     PISSN: 01681699     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.compag.2017.03.017     Document Type: Article

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