@inproceedings{2063a0876673428c9fe24836e5678322,
title = "Potential of IoT System and Cloud Services for Predicting Agricultural Pests and Diseases",
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.",
keywords = "Agricultural pests and diseases, Cloud services, IoT, Machine learning",
author = "Ntihemuka Materne and Masahiro Inoue",
year = "2019",
month = apr,
day = "15",
doi = "10.1109/TENCONSpring.2018.8691951",
language = "English",
series = "2018 IEEE Region 10 Symposium, Tensymp 2018",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "298--299",
booktitle = "2018 IEEE Region 10 Symposium, Tensymp 2018",
address = "United States",
note = "2018 IEEE Region 10 Symposium, Tensymp 2018 ; Conference date: 01-07-2018 Through 06-07-2018",
}