The Development of Precision Agriculture Design by Using a Smart Sensor for Time Series Forecasting Analysis on Relative Humidity

Zainur Rasyid Ridlo; Sudarti; Joko Waluyo; Dafik

doi:10.2991/978-94-6463-174-6_23

<Previous Article In Volume

The Development of Precision Agriculture Design by Using a Smart Sensor for Time Series Forecasting Analysis on Relative Humidity

Authors

Zainur Rasyid Ridlo¹^{, 2}^{, *}, Sudarti³, Joko Waluyo⁴, Dafik⁵

¹Department of Natural Science Education, University of Jember, Jember, Indonesia

²PUI-PT Combinatorics and Graph, CGANT, University of Jember, Jember, Indonesia

³Department of Physics Education, University of Jember, Jember, Indonesia

⁴Department of Biology Education, University of Jember, Jember, Indonesia

⁵Department of Mathematics Education Postgraduate, University of Jember, Jember, Indonesia

^*Corresponding author. Email: zainur.fkip@unej.ac.id

Corresponding Author

Zainur Rasyid Ridlo

Available Online 22 May 2023.

DOI: 10.2991/978-94-6463-174-6_23 How to use a DOI?
Keywords: DHT 11; NodemCu; Soil Moisture 2.0; Thinkspeak; Artificial Neural Networks
Abstract: This research aims to design IoT effective and efficient tools for precision agriculture using NodemCu Board for measuring Temperature and Rh (Relative Humidity). The sensor for measuring Temperature and Rh uses DHT 11, a type of sensor DHT 11 using NTC (Negative Temperature Coefficient) as resistance based to measure temperature and Relative Humidity. The data from the sensor is sent to the Thingspeak website and compared with data from standard sensors. as a calibration process. The Rh data from DHT 11 used for time series forecasting for Rh with ANN models namely Feedforwardnet, Fitnet, Patternnet, and Cascade Forwardnet, the architecture of ANN using 468, 579, and 723. The Best result from ANN is best model Cascade Forwardnet with architecture 723 times 1.165, MSE train 1.4249 x 10^–21, MSE test 8.5620 x 10^–22 with regression 1.
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.

Download article (PDF)

<Previous Article In Volume

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_23
ISSN: 1951-6851
DOI: 10.2991/978-94-6463-174-6_23 How to use a DOI?
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

ris enw bib

TY  - CONF
AU  - Zainur Rasyid Ridlo
AU  - Sudarti
AU  - Joko Waluyo
AU  - Dafik
PY  - 2023
DA  - 2023/05/22
TI  - The Development of Precision Agriculture Design by Using a Smart Sensor for Time Series Forecasting Analysis on Relative Humidity
BT  - Proceedings of the 1st International Conference on Neural Networks and Machine Learning 2022 (ICONNSMAL 2022)
PB  - Atlantis Press
SP  - 324
EP  - 335
SN  - 1951-6851
UR  - https://doi.org/10.2991/978-94-6463-174-6_23
DO  - 10.2991/978-94-6463-174-6_23
ID  - Ridlo2023
ER  -

download .riscopy to clipboard