Research on identification of agricultural insects based on depth residual network with multi-feature and multi-granularity

doi:10.14088/j.cnki.issn0439-8114.2020.16.035

Abstract

Abstract: In order to realize insect identification under complex farmland background, this paper proposed a multi-feature and multi-granularity insect identification method based on deep residual network. Compared with the traditional SVM and BP neural network, the accuracy of insect identification based on deep residual network was significantly improved in complex farmland background. Compared with the deep convolutional neural network such as AlexNet, the performance of our method was further improved after the depth residual learning optimization. And the accuracy of 98.67% was obtained on the classification of crop insect images under the background of 10 types of complex farmland. Therefore, this method has high practical application value and can be integrated into the actual agricultural insect control task with the currently used agricultural networking system.

Key words: depth residual, multi-feature multi-granularity, identification of agricultural insects, residual network

CLC Number:

TP391.4
S435

LI Yan-hong, FAN Tong-ke. Research on identification of agricultural insects based on depth residual network with multi-feature and multi-granularity[J]. HUBEI AGRICULTURAL SCIENCES, 2020, 59(16): 153-157.

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