Research Article
CDNN Model for Insect Classification Based on Deep Neural Network Approach
@INPROCEEDINGS{10.1007/978-3-030-34365-1_10, author={Hiep Huynh and Duy Lam and Tu Ho and Diem Le and Ly Le}, title={CDNN Model for Insect Classification Based on Deep Neural Network Approach}, proceedings={Context-Aware Systems and Applications, and Nature of Computation and Communication. 8th EAI International Conference, ICCASA 2019, and 5th EAI International Conference, ICTCC 2019, My Tho City, Vietnam, November 28-29, 2019, Proceedings}, proceedings_a={ICCASA \& ICTCC}, year={2019}, month={12}, keywords={Bag of Features Brown Plant-hoppers Classification Deep neural network Dense SIFT Insect Ladybugs}, doi={10.1007/978-3-030-34365-1_10} }- Hiep Huynh
Duy Lam
Tu Ho
Diem Le
Ly Le
Year: 2019
CDNN Model for Insect Classification Based on Deep Neural Network Approach
ICCASA & ICTCC
Springer
DOI: 10.1007/978-3-030-34365-1_10
Abstract
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.