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Design of Machine Learning Based Smart Irrigation System for Precision Agriculture

Khalil Ibrahim Mohammad Abuzanouneh1, Fahd N. Al-Wesabi2, Amani Abdulrahman Albraikan3, Mesfer Al Duhayyim4, M. Al-Shabi5, Anwer Mustafa Hilal6, Manar Ahmed Hamza6,*, Abu Sarwar Zamani6, K. Muthulakshmi7
1 Department of Information Technology, College of Computer, Qassim University, Al-Bukairiyah, 52571, Saudi Arabia
2 Department of Computer Science, King Khalid University, Muhayel Aseer, 62529, Saudi Arabia & Faculty of Computer and IT, Sana'a University, Sana'a, 61101, Yemen
3 Department of Computer Science, College of Computer and Information Sciences, Princess Nourah Bint Abdulrahman University, Riyadh, 11564, Saudi Arabia
4 Department of Natural and Applied Sciences, College of Community - Aflaj, Prince Sattam bin Abdulaziz University, Al-Kharj, 16278, Saudi Arabia
5 Department of Management Information System, College of Business and Administrative, Taibah University, Medina, 42353, Saudi Arabia
6 Department of Computer and Self Development, Preparatory Year Deanship, Prince Sattam bin Abdulaziz University, Al-Kharj, 16278, Saudi Arabia
7 Department of Electronics and Communication Engineering, Dr. N. G. P Institute of Technology, Coimbatore, 641048, India
* Corresponding Author: Manar Ahmed Hamza. Email:

Computers, Materials & Continua 2022, 72(1), 109-124.

Received 14 August 2021; Accepted 29 September 2021; Issue published 24 February 2022


Agriculture 4.0, as the future of farming technology, comprises numerous key enabling technologies towards sustainable agriculture. The use of state-of-the-art technologies, such as the Internet of Things, transform traditional cultivation practices, like irrigation, to modern solutions of precision agriculture. To achieve effective water resource usage and automated irrigation in precision agriculture, recent technologies like machine learning (ML) can be employed. With this motivation, this paper design an IoT and ML enabled smart irrigation system (IoTML-SIS) for precision agriculture. The proposed IoTML-SIS technique allows to sense the parameters of the farmland and make appropriate decisions for irrigation. The proposed IoTML-SIS model involves different IoT based sensors for soil moisture, humidity, temperature sensor, and light. Besides, the sensed data are transmitted to the cloud server for processing and decision making. Moreover, artificial algae algorithm (AAA) with least squares-support vector machine (LS-SVM) model is employed for the classification process to determine the need for irrigation. Furthermore, the AAA is applied to optimally tune the parameters involved in the LS-SVM model, and thereby the classification efficiency is significantly increased. The performance validation of the proposed IoTML-SIS technique ensured better performance over the compared methods with the maximum accuracy of 0.975.


Automatic irrigation; precision agriculture; smart farming; machine learning; cloud computing; decision making; internet of things

Cite This Article

K. Ibrahim Mohammad Abuzanouneh, F. N. Al-Wesabi, A. Abdulrahman Albraikan, M. Al Duhayyim, M. Al-Shabi et al., "Design of machine learning based smart irrigation system for precision agriculture," Computers, Materials & Continua, vol. 72, no.1, pp. 109–124, 2022.

This work is licensed under a Creative Commons Attribution 4.0 International License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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