CDNN Model for Insect Classification Based on Deep Neural Network Approach

  • Hiep Xuan Huynh
  • Duy Bao Lam
  • Tu Van HoEmail author
  • Diem Thi Le
  • Ly Minh Le
Conference paper
Part of the Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering book series (LNICST, volume 298)


The Mekong Delta has made great progress in rice production over the past ten years. Intensive cultivation with multi-cropping brings many benefits to farmers as well as the food export industry. However, this is also an opportunity for raising epidemic outbreak, Brown Plant-hoppers can directly damage by sucking the rice’s vitality, and they can cause the wilting and complete drying of rice plants, a noncontagious disease known as “Hopper-burn”. In this article, we propose the CDNN model for insect classification based on Neural Network and Deep Learning approach. First, insect images are collected and extracted features based on Dense Scale-Invariant Feature Transform. Then, Bag of Features is used for image representation as feature vectors. Lastly, these feature vectors are trained and classified using CDNN model based on Deep Neural Network. The approach is demonstrated with experiments, and measured by a large amount of Brown Plant-hoppers and Ladybugs samples.


Bag of Features Brown Plant-hoppers Classification Deep neural network Dense SIFT Insect Ladybugs 


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Copyright information

© ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering 2019

Authors and Affiliations

  1. 1.Can Tho UniversityCan ThoVietnam
  2. 2.Mekong UniversityLong HồVietnam

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