Agricultural pests damage detection using deep learning

Ching Ju Chen, Jian Shiun Wu, Chuan Yu Chang, Yueh Min Huang

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

1 Citation (Scopus)

Abstract

In this study, a plurality of camera sensors distributed in the agricultural land was integrated into the Raspberry Pi, and photos were taken to observe whether the foliage of the crop was harmful or not. The image data were transmitted to the Alexnet, VGG-16 and VGG-19 convolutional nerves through deep learning methods. The network architecture extracts image features to detect the presence of pests and identifies the types of pests. Compared by the classification accuracy, training model and prediction time with a classifier based on a neural network, and a Support Vector Machine, the identified pest results will be immediately displayed on the farming management app as a timely epidemic prevention management of the farming.

Original languageEnglish
Title of host publicationAdvances in Networked-based Information Systems - The 22nd International Conference on Network-Based Information Systems, NBiS 2019
EditorsLeonard Barolli, Hiroaki Nishino, Tomoya Enokido, Makoto Takizawa
PublisherSpringer Verlag
Pages545-554
Number of pages10
ISBN (Print)9783030290283
DOIs
Publication statusPublished - 2020
Event22nd International Conference on Network-Based Information Systems, NBiS 2019 - Oita, Japan
Duration: 2019 Sep 52019 Sep 7

Publication series

NameAdvances in Intelligent Systems and Computing
Volume1036
ISSN (Print)2194-5357
ISSN (Electronic)2194-5365

Conference

Conference22nd International Conference on Network-Based Information Systems, NBiS 2019
CountryJapan
CityOita
Period19-09-0519-09-07

All Science Journal Classification (ASJC) codes

  • Control and Systems Engineering
  • Computer Science(all)

Fingerprint Dive into the research topics of 'Agricultural pests damage detection using deep learning'. Together they form a unique fingerprint.

Cite this