A Crop Pests Image Classification Algorithm Based on Deep Convolutional Neural Network

RuJing Wang, Jie Zhang, Wei Dong, Jian Yu, ChengJun Xie, Rui Li, TianJiao Chen, HongBo Chen


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.


crop pests image classification; deep learning; convolutional neural network;

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DOI: http://dx.doi.org/10.12928/telkomnika.v15i3.5382

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TELKOMNIKA Telecommunication, Computing, Electronics and Control
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