Agricultural pests damage detection using deep learning

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

研究成果: Conference contribution

摘要

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.

原文English
主出版物標題Advances in Networked-based Information Systems - The 22nd International Conference on Network-Based Information Systems, NBiS 2019
編輯Leonard Barolli, Hiroaki Nishino, Tomoya Enokido, Makoto Takizawa
發行者Springer Verlag
頁面545-554
頁數10
ISBN(列印)9783030290283
DOIs
出版狀態Published - 2020 一月 1
事件22nd International Conference on Network-Based Information Systems, NBiS 2019 - Oita, Japan
持續時間: 2019 九月 52019 九月 7

出版系列

名字Advances in Intelligent Systems and Computing
1036
ISSN(列印)2194-5357
ISSN(電子)2194-5365

Conference

Conference22nd International Conference on Network-Based Information Systems, NBiS 2019
國家Japan
城市Oita
期間19-09-0519-09-07

    指紋

All Science Journal Classification (ASJC) codes

  • Control and Systems Engineering
  • Computer Science(all)

引用此

Chen, C. J., Wu, J. S., Chang, C. Y., & Huang, Y. M. (2020). Agricultural pests damage detection using deep learning. 於 L. Barolli, H. Nishino, T. Enokido, & M. Takizawa (編輯), Advances in Networked-based Information Systems - The 22nd International Conference on Network-Based Information Systems, NBiS 2019 (頁 545-554). (Advances in Intelligent Systems and Computing; 卷 1036). Springer Verlag. https://doi.org/10.1007/978-3-030-29029-0_53