Recognition of the duration and prediction of insect prevalence of stored rough rice infested by the red flour beetle (Tribolium castaneum Herbst) using an electronic nose

Sensors (Switzerland)

Volumn 17, Issue 4, 2017, Pages

  Xu, Sai ^a   Zhou, Zhiyan ^a,b   Li, Keliang ^a   Jamir, Sierra Mari ^c   Luo, Xiwen ^a  

^a SOUTH CHINA AGRICULTURAL UNIVERSITY   (China)

^b Southern Regional Collaborative Innovation Center for Grain and Oil Crops in China   (China)

^c Cornell University ^*  (United States)

Author keywords

Duration; Electronic nose; Insect prevalence; Red flour beetle; Rough rice; Storage

Indexed keywords

BACKPROPAGATION; DISCRIMINANT ANALYSIS; ELECTRONIC EQUIPMENT; ENERGY STORAGE; FORECASTING; GRAIN (AGRICULTURAL PRODUCT); NEURAL NETWORKS;

BACK PROPAGATION ARTIFICIAL NEURAL NETWORK (BPANN); DURATION; INSECT PREVALENCE; INTELLIGENT MONITORING; K-MEANS CLUSTERING ANALYSIS; LINEAR DISCRIMINANT ANALYSES (LDA); RED FLOUR BEETLES; ROUGH RICE;

ELECTRONIC NOSE;

ANIMAL; BEETLE; ELECTRONIC NOSE; INSECT CONTROL; ORYZA; PREVALENCE;

ANIMALS; COLEOPTERA; ELECTRONIC NOSE; INSECT CONTROL; ORYZA; PREVALENCE;

EID: 85018462708     PISSN: 14248220     EISSN: None     Source Type: Journal    
DOI: 10.3390/s17040688     Document Type: Article

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