Efficient Convolutional Neural Network for Pest Recognition-ExquisiteNet

Shi Yao Zhou, Chung Yen Su

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

Nowadays, due to the rapid population expansion, food shortage has become a critical issue. To stabilize food production, preventing crops from being attacked by pests is very important. In general, farmers use pesticides, however, the improper use also kills insects such as bees that are beneficial to crops. If the number of bees is too few, the supplement of food in the world will be short. Besides, excessive pesticides seriously pollute the environment. Accordingly, farmers need a machine that automatically recognizes the pests. Recently, deep learning is popular because of its effectiveness in the field of image classification. In this paper, we propose an efficient model called ExquisiteNet to recognize the pests. ExquisiteNet mainly consists of two blocks. One is double fusion with squeeze-and-excitation-bottleneck block (DFSEB block), and the other is max feature expansion block (ME block). ExquisiteNet only has 0.98 M parameters and its computing speed is fast almost the same as SqueezeNet. To evaluate our model's performance, the model is testedd on a benchmark pest dataset called IP102. The model achieves a higher accuracy of 52.32% on the test set of IP102 without any data augmentation than that of many state-of-the-art models such as ResNet101, ShuffleNetV2, MobileNetV3-large, EfficientNet, and so on.

Original languageEnglish
Title of host publication2nd IEEE Eurasia Conference on IOT, Communication and Engineering 2020, ECICE 2020
EditorsTeen-Hang Meen
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages216-219
Number of pages4
ISBN (Electronic)9781728180601
DOIs
Publication statusPublished - 2020 Oct 23
Event2nd IEEE Eurasia Conference on IOT, Communication and Engineering, ECICE 2020 - Yunlin, Taiwan
Duration: 2020 Oct 232020 Oct 25

Publication series

Name2nd IEEE Eurasia Conference on IOT, Communication and Engineering 2020, ECICE 2020

Conference

Conference2nd IEEE Eurasia Conference on IOT, Communication and Engineering, ECICE 2020
Country/TerritoryTaiwan
CityYunlin
Period2020/10/232020/10/25

Keywords

  • deep learning
  • efficient convolutional neural network
  • image classification
  • insect classification
  • IP102
  • pest classification

ASJC Scopus subject areas

  • Artificial Intelligence
  • Computer Networks and Communications
  • Computer Science Applications
  • Signal Processing
  • Biomedical Engineering
  • Electrical and Electronic Engineering
  • Control and Optimization
  • Instrumentation

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