Proceedings of the 1st International Conference on Neural Networks and Machine Learning 2022 (ICONNSMAL 2022)

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On Time Series Forecasting Analysis of Soil Moisture by Using Artificial Neural Networks Based - on Rainbow Antimagic Coloring for Autonomous Irrigation System on Horizontal Farming

Authors
Dini Mufidati1, Zainur Rasyid Ridlo2, 3, Slamin3, 4, Ika Nur Maylisa1, Dafik1, 3, *
1Department of Mathematics Education Postgraduate, University of Jember, Jember, Indonesia
2Department of Science Education, University of Jember, Jember, Indonesia
3PUI-PT Combinatorics and Graph, CGANT, University of Jember, Jember, Indonesia
4Department of Computer Science, University of Jember, Jember, Indonesia
*Corresponding author. Email: d.dafik@unej.ac.id
Corresponding Author
Dafik
Available Online 22 May 2023.
DOI
10.2991/978-94-6463-174-6_18How to use a DOI?
Keywords
Artificial neural network; rainbow antimagic coloring; time series forecasting analysis; soil moisture
Abstract

Precision agriculture is one of the fields that play an important role in improving the social economy, it is due to that many people depend on the agricultural products. One of the supporting factors in agriculture is advancement the precision agriculture under the development of autonomous irrigation. Irrigation is an effort made by humans to irrigate agricultural land. Prediction the soil moisture is one way to help farmers to do watering. We will use an Artificial Neural Networks (ANN) together with the concept of rainbow antimagic coloring to forecast soil moisture for autonomous irrigation system on horizontal farming. The result shows that the best architecture for is obtained by model ANN- cascade forwardnet 566 with an MSE of 4.5127 × 10 - 19 .

Copyright
© 2023 The Author(s)
Open Access
Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

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Volume Title
Proceedings of the 1st International Conference on Neural Networks and Machine Learning 2022 (ICONNSMAL 2022)
Series
Advances in Intelligent Systems Research
Publication Date
22 May 2023
ISBN
10.2991/978-94-6463-174-6_18
ISSN
1951-6851
DOI
10.2991/978-94-6463-174-6_18How to use a DOI?
Copyright
© 2023 The Author(s)
Open Access
Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

Cite this article

risenwbib
TY  - CONF
AU  - Dini Mufidati
AU  - Zainur Rasyid Ridlo
AU  - Slamin
AU  - Ika Nur Maylisa
AU  - Dafik
PY  - 2023
DA  - 2023/05/22
TI  - On Time Series Forecasting Analysis of Soil Moisture by Using Artificial Neural Networks Based - on Rainbow Antimagic Coloring for Autonomous Irrigation System on Horizontal Farming
BT  - Proceedings of the 1st International Conference on Neural Networks and Machine Learning 2022 (ICONNSMAL 2022)
PB  - Atlantis Press
SP  - 234
EP  - 256
SN  - 1951-6851
UR  - https://doi.org/10.2991/978-94-6463-174-6_18
DO  - 10.2991/978-94-6463-174-6_18
ID  - Mufidati2023
ER  -