Proceedings of the International Seminar of Science and Applied Technology (ISSAT 2020)

Fertigation Management System Model Using Supervised Machine Learning and Time-Duration Method on Agricultural Industrial Land

Authors
Rida Hudaya, Dodi Budiman Margana, R. Wahyu Tri Hartono, Alli Nur Magribi
Corresponding Author
Rida Hudaya
Available Online 22 December 2020.
DOI
https://doi.org/10.2991/aer.k.201221.007How to use a DOI?
Keywords
Fertigation management system, supervised machine learning, time-duration method
Abstract
This paper contains an explanation of the fertigation management system model to deal with the water crisis and the efficient use of plant nutrients in the case of agricultural industrial land in Lembang, Indonesia. The method developed uses a time-duration method equipped with supervised machine learning. Machine throughout the year learns the schedule of agricultural expert operators in providing water and nutrition. At the same time, the machine records the microclimate of humidity, rain, and sunny around the agricultural land. The time-duration and microclimate relationships were plotted with a linear approach. Obtained three time-equation TON and three duration-equation DUR over 24 hours with an average of R2 0.5654, around 04:58 with duration 2 hours and 23 minutes, around 09:44 with duration 2 hours 43 minutes, and around 14:44 with duration 5 hours 19 minutes.
Open Access
This is an open access article distributed under the CC BY-NC license.

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Volume Title
Proceedings of the International Seminar of Science and Applied Technology (ISSAT 2020)
Series
Advances in Engineering Research
Publication Date
22 December 2020
ISBN
978-94-6239-307-3
ISSN
2352-5401
DOI
https://doi.org/10.2991/aer.k.201221.007How to use a DOI?
Open Access
This is an open access article distributed under the CC BY-NC license.

Cite this article

TY  - CONF
AU  - Rida Hudaya
AU  - Dodi Budiman Margana
AU  - R. Wahyu Tri Hartono
AU  - Alli Nur Magribi
PY  - 2020
DA  - 2020/12/22
TI  - Fertigation Management System Model Using Supervised Machine Learning and Time-Duration Method on Agricultural Industrial Land
BT  - Proceedings of the International Seminar of Science and Applied Technology (ISSAT 2020)
PB  - Atlantis Press
SP  - 34
EP  - 38
SN  - 2352-5401
UR  - https://doi.org/10.2991/aer.k.201221.007
DO  - https://doi.org/10.2991/aer.k.201221.007
ID  - Hudaya2020
ER  -