Machine Learning Approach for an Automatic Irrigation System in Southern Jordan Valley

  • A. H. Blasi Department of Computer Information Systems, Mutah University, Jordan
  • M. A. Abbadi Department of Computer Science, Mutah University, Jordan
  • R. Al-Huweimel Department of Computer Science, Mutah University, Jordan
Volume: 11 | Issue: 1 | Pages: 6609-6613 | February 2021 | https://doi.org/10.48084/etasr.3944

Abstract

The agriculture sector is the most water-consuming sector. Due to the critical situation of available water resources in Jordan, attention should be paid to the issues of water demand and appropriate irrigation in order to spread the right management ways of modern irrigation to the farmers. The objectives of this paper are to improve the irrigation process and provide irrigation water to the highest possible extent through the use of artificial intelligence to construct a smart irrigation system that controls the irrigation mechanism using the necessary tools for sensing soil moisture and temperature, giving alerts of any change in the parameters entered as the baseline values for comparison, and installing system sensors buried at a depth of 3-5 inches below the roots to measure the moisture content in the soil. The sensors measure the humidity and temperature in the soil every ten minutes. They prevent the automatic irrigation process if the humidity is high, and permit it if the humidity is low. The smart automatic irrigation system model was built using the Decision Tree (DT) algorithm, which is a machine learning algorithm that trains the system on a part of the collected data to build the model that will be used to examine and predict the remaining data. The system had a prediction accuracy of 97.86%, which means that it may be successfully used in providing irrigation water for the agricultural sector.

Keywords: irrigation, agriculture, artificial intelligence, sensor, machine learning, decision tree algorithm

Downloads

Download data is not yet available.

References

"ArabiaWeather," ArabiaWeather. https://www.arabiaweather.com/ (accessed Dec. 16, 2020).

"Annual Report 2015," Ministry of Water & Irrigation, Water Authority- Jordan Valley Authority, 2015.

"Soil Survey Project -Gour Al-Safi/Al-Karak," The National Center for Agricultural Research and Extension.

R. R. Weil and N. C. Brady, The Nature and Properties of Soils, 15th edition. Columbus, OH, USA: Pearson, 2016.

S. Jadhav and S. Hambarde, "Android based Automated Irrigation System using Raspberry Pi," International Journal of Science and Research, vol. 5, no. 6, pp. 2345-2351, Jun. 2016. DOI: https://doi.org/10.21275/v5i6.NOV164836 DOI: https://doi.org/10.21275/v5i6.NOV164836

A. Norazizan, M. Muzni, M. Ramsaid, M. Ismail, and M. Mazalan, "Design and Development of the Irrigation System for Planting (Part 1)," Mechanical System Design, May 2016.

V. Dharmaraj and C. Vijayanand, "Artificial Intelligence (AI) in Agriculture," International Journal of Current Microbiology and Applied Sciences, vol. 7, no. 12, pp. 2122-2128, 2018. DOI: https://doi.org/10.20546/ijcmas.2018.712.241 DOI: https://doi.org/10.20546/ijcmas.2018.712.241

W. R. Mendes, F. M. U. Araújo, and S. Er-Raki, "Integrating Remote Sensing Data into Fuzzy Control System for Variable Rate Irrigation Estimates," Irrigation - Water Productivity and Operation, Sustainability and Climate Change, Jun. 2019.

V. M. Wagh, D. B. Panaskar, A. A. Muley, S. V. Mukate, Y. P. Lolage, and M. L. Aamalawar, "Prediction of groundwater suitability for irrigation using artificial neural network model: a case study of Nanded tehsil, Maharashtra, India," Modeling Earth Systems and Environment, vol. 2, no. 4, pp. 1-10, Dec. 2016. DOI: https://doi.org/10.1007/s40808-016-0250-3 DOI: https://doi.org/10.1007/s40808-016-0250-3

S. A. Hamoodi, A. N. Hamoodi, and G. M. Haydar, "Automated irrigation system based on soil moisture using arduino board," Bulletin of Electrical Engineering and Informatics, vol. 9, no. 3, pp. 870-876, Jun. 2020. DOI: https://doi.org/10.11591/eei.v9i3.1736 DOI: https://doi.org/10.11591/eei.v9i3.1736

I. Prasojo, P. T. Nguyen, O. Tanane, and N. Shahu, "Design of Ultrasonic Sensor and Ultraviolet Sensor Implemented on a Fire Fighter Robot Using AT89S52," Journal of Robotics and Control (JRC), vol. 1, no. 2, pp. 55-58, Jan. 2020. DOI: https://doi.org/10.18196/jrc.1212 DOI: https://doi.org/10.18196/jrc.1212

C. A. Bolu, J. Azeta, F. Alele, E. O. Daranijo, P. Onyeubani, and A. A. Abioye, "Solar Powered Microcontroller-based Automated Irrigation System with Moisture Sensors," Journal of Physics: Conference Series, vol. 1378, Dec. 2019, Art. no. 032003. DOI: https://doi.org/10.1088/1742-6596/1378/3/032003 DOI: https://doi.org/10.1088/1742-6596/1378/3/032003

"What is Knowledge Discovery in Databases (KDD)? - Definition from Techopedia," Techopedia. http://www.techopedia.com/definition/25827/knowledge-discovery-in-databases-kdd (accessed Dec. 16, 2020).

F. Li, Y. Y. Li, and C. Wand, "Uncertain data decision tree classification algorithm," Journal of Computer Applications, vol. 29, no. 11, pp. 3092-3095, Dec. 2009. DOI: https://doi.org/10.3724/SP.J.1087.2009.03092 DOI: https://doi.org/10.3724/SP.J.1087.2009.03092

https://informatic-ar.com/id3_algorithm/ (accessed Dec. 16, 2020)

"scikit-learn: machine learning in Python - scikit-learn 0.23.2 documentation." https://scikit-learn.org/stable/ (accessed Dec. 16, 2020).

M. Hasnain, M. F. Pasha, I. Ghani, M. Imran, M. Y. Alzahrani, and R. Budiarto, "Evaluating Trust Prediction and Confusion Matrix Measures for Web Services Ranking," IEEE Access, vol. 8, pp. 90847-90861, 2020. DOI: https://doi.org/10.1109/ACCESS.2020.2994222 DOI: https://doi.org/10.1109/ACCESS.2020.2994222

M. Hossin and M. N. Sulaiman, "A Review on Evaluation Metrics for Data Classification Evaluations," International Journal of Data Mining & Knowledge Management Process, vol. 5, no. 2, pp. 1-11, Nov. 2019. DOI: https://doi.org/10.5121/ijdkp.2015.5201 DOI: https://doi.org/10.5121/ijdkp.2015.5201

A. Blasi, "Performance Increment of High School Students using ANN model and SA algorithm," Journal of Theoretical and Applied Information Technology, vol. 95, no. 11, pp. 2417-2425, Jan. 2017.

R. A. Aroud, A. H. Blasi, and M. A. Alsuwaiket, "Intelligent Risk Alarm for Asthma Patients using Artificial Neural Networks," International Journal of Advanced Computer Science and Applications, vol. 11, no. 6, 58/30 2020. DOI: https://doi.org/10.14569/IJACSA.2020.0110612 DOI: https://doi.org/10.14569/IJACSA.2020.0110612

A. Blasi, "Scheduling Food Industry System using Fuzzy Logic," Journal of Theoretical and Applied Information Technology, vol. 96, no. 19, pp. 6463-6473, Oct. 2018.

M. A. A. Lababede, A. H. Blasi, and M. A. Alsuwaiket, "Mosques Smart Domes System using Machine Learning Algorithms," International Journal of Advanced Computer Science and Applications, vol. 11, no. 3, 40/30 2020. DOI: https://doi.org/10.14569/IJACSA.2020.0110347 DOI: https://doi.org/10.14569/IJACSA.2020.0110347

M. Alsuwaiket, A. H. Blasi, and K. Altarawneh, "Refining Student Marks based on Enrolled Modules' Assessment Methods using Data Mining Techniques," Engineering, Technology & Applied Science Research, vol. 10, no. 1, pp. 5205-5210, Feb. 2020. DOI: https://doi.org/10.48084/etasr.3284 DOI: https://doi.org/10.48084/etasr.3284

M. Alsuwaiket, A. H. Blasi, and R. A. Al-Msie'deen, "Formulating Module Assessment for Improved Academic Performance Predictability in Higher Education," Engineering, Technology & Applied Science Research, vol. 9, no. 3, pp. 4287-4291, Jun. 2019. DOI: https://doi.org/10.48084/etasr.2794 DOI: https://doi.org/10.48084/etasr.2794

Metrics

Abstract Views: 118
PDF Downloads: 78

Metrics Information
Bookmark and Share

Most read articles by the same author(s)