Proceedings of the 2nd International Conference on Smart and Innovative Agriculture (ICoSIA 2021)

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Early Warning System Using Change Point Analysis to Detect Microclimate Anomalies

Authors
Muhammad Salman Ibnu Chaer1, Andri Prima Nugroho1, *, Guyup Mahardhian Dwi Putra1, Ngadisih Ngadisih1, Lilik Sutiarso1, Takashi Okayasu2
1Smart Agricultural Research, Department of Agricultural and Biosystems Engineering, Faculty of Agricultural Technology, Universitas Gadjah Mada, Yogyakarta, INDONESIA
2Department of Agro-Environmental Sciences, Faculty of Agriculture, Kyushu University, 744 Motooka, Nishi-ku, Fukuoka 810-0395, JAPAN
*Corresponding author. Email: andrew@ugm.ac.id
Corresponding Author
Andri Prima Nugroho
Available Online 10 March 2022.
DOI
10.2991/absr.k.220305.021How to use a DOI?
Keywords
early warning system; change point analysis; evapotranspiration; change point score; microclimate anomaly
Abstract

The agricultural sector is required to provide food products for human needs. To increase agricultural yields, precision agriculture approaches are required by the utilization of information and technology to maximize agricultural productivity. Precision agriculture systems cannot be separated from monitoring and controlling the environment. This system is necessary to keep the surrounding environment or microclimate by plants requirements. However, during the monitoring and control process, some failures may occur due to technical and non-technical problems, and they will cause damage if not treated immediately. Therefore, to keep the microclimate under control according to plant growing requirements, an early warning system is necessary. The objective of this study was to develops an early warning system for microclimate anomalies using Change Point Analysis based on evapotranspiration calculations. This system works to detect changes in microclimate anomalies caused by malfunctions in the monitoring or control system. Microclimate time-series data obtained from monitoring in the growth chamber are used to calculate evapotranspiration. To represent the environmental condition inside the systems, reference evapotranspiration time-series data estimated from climate data using Penmann-Monteith 56 model, were analyzed using Singular Spectrum Transformation (SST) to obtain the change point score. As the result of the performance test and observation, microclimate anomalies inside the growth chamber could be detected by the change point detection representing by the change point score.

Copyright
© 2022 The Authors. Published by Atlantis Press International B.V.
Open Access
This is an open access article under the CC BY-NC license.

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Volume Title
Proceedings of the 2nd International Conference on Smart and Innovative Agriculture (ICoSIA 2021)
Series
Advances in Biological Sciences Research
Publication Date
10 March 2022
ISBN
978-94-6239-550-3
ISSN
2468-5747
DOI
10.2991/absr.k.220305.021How to use a DOI?
Copyright
© 2022 The Authors. Published by Atlantis Press International B.V.
Open Access
This is an open access article under the CC BY-NC license.

Cite this article

risenwbib
TY  - CONF
AU  - Muhammad Salman Ibnu Chaer
AU  - Andri Prima Nugroho
AU  - Guyup Mahardhian Dwi Putra
AU  - Ngadisih Ngadisih
AU  - Lilik Sutiarso
AU  - Takashi Okayasu
PY  - 2022
DA  - 2022/03/10
TI  - Early Warning System Using Change Point Analysis to Detect Microclimate Anomalies
BT  - Proceedings of the 2nd International Conference on Smart and Innovative Agriculture (ICoSIA 2021)
PB  - Atlantis Press
SP  - 144
EP  - 149
SN  - 2468-5747
UR  - https://doi.org/10.2991/absr.k.220305.021
DO  - 10.2991/absr.k.220305.021
ID  - Chaer2022
ER  -