A Crop Pests Image Classification Algorithm Based on Deep Convolutional Neural Network
Abstract: 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.
Author: RuJing Wang
Journal Code: jptkomputergg170179
