Design of Machine Learning Based Smart Irrigation System for Precision Agriculture

被引:6
作者
Abuzanouneh, Khalil Ibrahim Mohammad [1 ]
Al-Wesabi, Fahd N. [2 ,3 ]
Albraikan, Amani Abdulrahman [4 ]
Al Duhayyim, Mesfer [5 ]
Al-Shabi, M. [6 ]
Hilal, Anwer Mustafa [7 ]
Hamza, Manar Ahmed [7 ]
Zamani, Abu Sarwar [7 ]
Muthulakshmi, K. [8 ]
机构
[1] Qassim Univ, Coll Comp, Dept Informat Technol, Al Bukairiyah 52571, Saudi Arabia
[2] King Khalid Univ, Dept Comp Sci, Muhayel Aseer 62529, Saudi Arabia
[3] Sanaa Univ, Fac Comp & IT, Sanaa 61101, Yemen
[4] Princess Nourah Bint Abdulrahman Univ, Coll Comp & Informat Sci, Dept Comp Sci, Riyadh 11564, Saudi Arabia
[5] Prince Sattam Bin Abdulaziz Univ, Coll Community Aflaj, Dept Nat & Appl Sci, Al Kharj 16278, Saudi Arabia
[6] Taibah Univ, Coll Business & Adm, Dept Management Informat Syst, Medina 42353, Saudi Arabia
[7] Prince Sattam Bin Abdulaziz Univ, Dept Comp & Self Dev, Preparatory Year Deanship, Al Kharj 16278, Saudi Arabia
[8] Dr NGP Inst Technol, Dept Elect & Commun Engn, Coimbatore 641048, Tamil Nadu, India
来源
CMC-COMPUTERS MATERIALS & CONTINUA | 2022年 / 72卷 / 01期
关键词
Automatic irrigation; precision agriculture; smart farming; machine learning; cloud computing; decision making; internet of things; IOT;
D O I
10.32604/cmc.2022.022648
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 ;
摘要
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.
引用
收藏
页码:109 / 124
页数:16
相关论文
共 22 条
[1]   A distributed system for supporting smart irrigation using Internet of Things technology [J].
Abdelmoamen Ahmed, Ahmed ;
Al Omari, Suhib ;
Awal, Ripendra ;
Fares, Ali ;
Chouikha, Mohamed .
ENGINEERING REPORTS, 2021, 3 (07)
[2]   IoT-based monitoring and data-driven modelling of drip irrigation system for mustard leaf cultivation experiment [J].
Abioye, Emmanuel Abiodun ;
Abidin, Mohammad Shukri Zainal ;
Mahmud, Mohd Saiful Azimi ;
Buyamin, Salinda ;
AbdRahman, Muhammad Khairie Idham ;
Otuoze, Abdulrahaman Okino ;
Ramli, Muhammad Shahrul Azwan ;
Ijike, Ona Denis .
INFORMATION PROCESSING IN AGRICULTURE, 2021, 8 (02) :270-283
[3]   A review on monitoring and advanced control strategies for precision irrigation [J].
Abioye, Emmanuel Abiodun ;
Abidin, Mohammad Shukri Zainal ;
Mahmud, Mohd Saiful Azimi ;
Buyamin, Salinda ;
Ishak, Mohamad Hafis Izran ;
Abd Rahman, Muhammad Khairie Idham ;
Otuoze, Abdulrahaman Okino ;
Onotu, Patrick ;
Ramli, Muhammad Shahrul Azwan .
COMPUTERS AND ELECTRONICS IN AGRICULTURE, 2020, 173
[4]   Machine Learning Approach for an Automatic Irrigation System in Southern Jordan Valley [J].
Blasi, Anas H. ;
Abbadi, Mohammad A. ;
Al-Huweimel, Rufaydah .
ENGINEERING TECHNOLOGY & APPLIED SCIENCE RESEARCH, 2021, 11 (01) :6609-6613
[5]  
Cardoso J., 2020, 2020 INT C DAT AN BU, P1, DOI [10.1109/ICDABI51230.2020.9325680, DOI 10.1109/ICDABI51230.2020.9325680]
[6]   Integration of LSSVM technique with PSO to determine asphaltene deposition [J].
Chamkalani, Ali ;
Zendehboudi, Sohrab ;
Bahadori, Alireza ;
Kharrat, Riaz ;
Chamkalani, Reza ;
James, Lesley ;
Chatzis, Ioannis .
JOURNAL OF PETROLEUM SCIENCE AND ENGINEERING, 2014, 124 :243-253
[7]   Sustainable Irrigation System for Farming Supported by Machine Learning and Real-Time Sensor Data [J].
Gloria, Andre ;
Cardoso, Joao ;
Sebastiao, Pedro .
SENSORS, 2021, 21 (09)
[8]   Internet of Things (IoT): A vision, architectural elements, and future directions [J].
Gubbi, Jayavardhana ;
Buyya, Rajkumar ;
Marusic, Slaven ;
Palaniswami, Marimuthu .
FUTURE GENERATION COMPUTER SYSTEMS-THE INTERNATIONAL JOURNAL OF ESCIENCE, 2013, 29 (07) :1645-1660
[9]  
Hamzi T. N., 2018, P 1 INT C COMP APPL, P1, DOI [10.1109/CAIS.2018.8441977.\n[35]R, DOI 10.1109/CAIS.2018.8441977]
[10]   The rise of "big data" on cloud computing: Review and open research issues [J].
Hashem, Ibrahim Abaker Targio ;
Yaqoob, Ibrar ;
Anuar, Nor Badrul ;
Mokhtar, Salimah ;
Gani, Abdullah ;
Khan, Samee Ullah .
INFORMATION SYSTEMS, 2015, 47 :98-115