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 language | English |
|---|---|
| Title of host publication | 2018 IEEE Region 10 Symposium, Tensymp 2018 |
| Publisher | Institute of Electrical and Electronics Engineers Inc. |
| Pages | 298-299 |
| Number of pages | 2 |
| ISBN (Electronic) | 9781538669891 |
| DOIs | |
| Publication status | Published - 2019 Apr 15 |
| Externally published | Yes |
| Event | 2018 IEEE Region 10 Symposium, Tensymp 2018 - Sydney, Australia Duration: 2018 Jul 1 → 2018 Jul 6 |
Publication series
| Name | 2018 IEEE Region 10 Symposium, Tensymp 2018 |
|---|
Conference
| Conference | 2018 IEEE Region 10 Symposium, Tensymp 2018 |
|---|---|
| Country | Australia |
| City | Sydney |
| Period | 18/7/1 → 18/7/6 |
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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
Cite this
Potential of IoT System and Cloud Services for Predicting Agricultural Pests and Diseases. / Materne, Ntihemuka; Inoue, Masahiro.
2018 IEEE Region 10 Symposium, Tensymp 2018. Institute of Electrical and Electronics Engineers Inc., 2019. p. 298-299 8691951 (2018 IEEE Region 10 Symposium, Tensymp 2018).Research output: Chapter in Book/Report/Conference proceeding › Conference contribution
}
TY - GEN
T1 - Potential of IoT System and Cloud Services for Predicting Agricultural Pests and Diseases
AU - Materne, Ntihemuka
AU - Inoue, Masahiro
PY - 2019/4/15
Y1 - 2019/4/15
N2 - 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.
AB - 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.
KW - Agricultural pests and diseases
KW - Cloud services
KW - IoT
KW - Machine learning
UR - http://www.scopus.com/inward/record.url?scp=85065055826&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85065055826&partnerID=8YFLogxK
U2 - 10.1109/TENCONSpring.2018.8691951
DO - 10.1109/TENCONSpring.2018.8691951
M3 - Conference contribution
AN - SCOPUS:85065055826
T3 - 2018 IEEE Region 10 Symposium, Tensymp 2018
SP - 298
EP - 299
BT - 2018 IEEE Region 10 Symposium, Tensymp 2018
PB - Institute of Electrical and Electronics Engineers Inc.
ER -