GDL

Journal from gdlhub / 2017-11-07 14:13:21
Oleh : RuJing Wang, Jie Zhang, Wei Dong, Jian Yu, ChengJun Xie, Rui Li, TianJiao Chen, HongBo Chen, Telkomnika
Dibuat : 2017-11-07, dengan 1 file

Keyword : crop pests image classification, deep learning, convolutional neural network
Url : http://journal.uad.ac.id/index.php/TELKOMNIKA/article/view/5382
Sumber pengambilan dokumen : WEB

Conventional pests image classification methods may not be accurate due to the complex farmland background, sunlight and pest gestures. To raise the accuracy, the deep convolutional neural network (DCNN), a concept from Deep Learning, was used in this study to classify crop pests image. On the ground of our experiments, in which LeNet-5 and AlexNet were used to classify pests image, we have analyzed the effects of both convolution kernel and the number of layers on the network, and redesigned the structure of convolutional neural network for crop pests. Further more, 82 common pest types have been classified, with the accuracy reaching 91%. The comparison to conventional classification methods proves that our method is not only feasible but preeminent

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