A robust deep-learning-based detector for real-time tomato plant diseases and pests recognition

Sensors (Switzerland)

Volumn 17, Issue 9, 2017, Pages

  Fuentes, Alvaro ^a   Yoon, Sook ^b   Kim, Sang Cheol ^c   Park, Dong Sun ^a  

^a Chonbuk National University   (South Korea)

^b Mokpo National University   (South Korea)

^c National Institute of Animal Science   (South Korea)

Author keywords

Deep convolutional neural networks; Detection; Pest; Plant disease; Real time processing

Indexed keywords

CONVOLUTION; DEEP NEURAL NETWORKS; ERROR DETECTION; FRUITS; LOSSES; NETWORK ARCHITECTURE; NEURAL NETWORKS; OBJECT DETECTION; OBJECT RECOGNITION; STATISTICAL TESTS;

CONVOLUTIONAL NETWORKS; CONVOLUTIONAL NEURAL NETWORK; LEARNING ARCHITECTURES; LEARNING-BASED APPROACH; OBJECT DETECTION AND RECOGNITION; PEST; PLANT DISEASE; REALTIME PROCESSING;

DEEP LEARNING;

ARTIFICIAL NEURAL NETWORK; IMAGE PROCESSING; PLANT DISEASE; TOMATO;

IMAGE PROCESSING, COMPUTER-ASSISTED; LYCOPERSICON ESCULENTUM; NEURAL NETWORKS (COMPUTER); PLANT DISEASES;

EID: 85028909579     PISSN: 14248220     EISSN: None     Source Type: Journal    
DOI: 10.3390/s17092022     Document Type: Article

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