Potential of IoT System and Cloud Services for Predicting Agricultural Pests and Diseases

Ntihemuka Materne, Masahiro Inoue

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

Abstract

Controlling the outbreaks of pests and diseases in agricultural environment, it is still a big challenge to the farmers due to the changing climatic conditions. In this paper we are proposing the alternative method of predicting occurrences of pest and diseases in the plantation, by combining the advantage of IoT farmland monitoring system and Amazon Machine Learning cloud-based services to find hidden patterns into data. Logistic regression algorithm used to train our IoT collected dataset and classify the data with acceptable model quality score, to estimate the diseases forecasting based on sensing technology.

Original languageEnglish
Title of host publication2018 IEEE Region 10 Symposium, Tensymp 2018
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages298-299
Number of pages2
ISBN (Electronic)9781538669891
DOIs
Publication statusPublished - 2019 Apr 15
Externally publishedYes
Event2018 IEEE Region 10 Symposium, Tensymp 2018 - Sydney, Australia
Duration: 2018 Jul 12018 Jul 6

Publication series

Name2018 IEEE Region 10 Symposium, Tensymp 2018

Conference

Conference2018 IEEE Region 10 Symposium, Tensymp 2018
CountryAustralia
CitySydney
Period18/7/118/7/6

Keywords

  • Agricultural pests and diseases
  • Cloud services
  • IoT
  • Machine learning

ASJC Scopus subject areas

  • Computer Science Applications
  • Artificial Intelligence
  • Computer Networks and Communications
  • Computer Vision and Pattern Recognition
  • Energy Engineering and Power Technology
  • Waste Management and Disposal
  • Health Informatics

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  • Cite this

    Materne, N., & Inoue, M. (2019). Potential of IoT System and Cloud Services for Predicting Agricultural Pests and Diseases. In 2018 IEEE Region 10 Symposium, Tensymp 2018 (pp. 298-299). [8691951] (2018 IEEE Region 10 Symposium, Tensymp 2018). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/TENCONSpring.2018.8691951