Computer Science ›› 2018, Vol. 45 ›› Issue (11A): 226-229.

• Pattem Recognition & Image Processing • Previous Articles     Next Articles

Agricultural Insect Pest Detection Method Based on Regional Convolutional Neural Network

WEI Yang, BI Xiu-li, XIAO Bin   

  1. Chongqing Key Laboratory of Computational Intelligence,Chongqing University of Posts and Telecommunications,Chongqing 400065,China
  • Online:2019-02-26 Published:2019-02-26

Abstract: In the current integrated agricultural pest control,agricultural insect pests are detected primarily by professionals’ sample collection and sorting manually,such manual classification method is both expensive and time consuming.Existing computer-aided automatic detection of agricultural pests has a high requirement on the background environment of pests and cannot locate agricultural pests.To solve these problems,this paper proposed a new method for automatic detection of agricultural pests based on the idea of the deep learning.It contains the region proposal network and the Fast R-CNN network.Region proposal network extracts feature in one or more regions of arbitrary size and complicated background images,then gets preliminary position of the candidate regions of agricultural pests.Preliminary position of the candidate regions of agricultural pests is taken as an input to Fast R-CNN.Fast R-CNN finally learns the classification of target in the preliminary location candidate area and calculates exact coordinates by studying the intraspecific differences and interspecies similarity of agricultural pests.Meanwhile,this paper also established a labeled actual scene tag agricultural pests database,and the proposed method was tested on this database,with theaverage precision up to 82.13%.The experimental results show that the proposed method can effectively enhance the accuracy of agricultural pests detection,and get accurate positions,and is superior to the previous automated agricultural pest detection methods.

Key words: Agricultural pest database, Pest Classification, Precise positioning

CLC Number: 

  • TP301
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