Smart Agriculture Cloud Using AI Based Techniques

Muhammad Junaid, Asadullah Shaikh, Mahmood Ul Hassan, Abdullah Alghamdi, Khairan Rajab, Mana Saleh Al Reshan and Monagi Alkinani
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Muhammad Junaid: Department of Information Technology, The University of Haripur, Haripur 22620, KPK, Pakistan
Asadullah Shaikh: College of Computer Science and Information Systems, Najran University, Najran 61441, Saudi Arabia
Mahmood Ul Hassan: College of Computer Science and Information Systems, Najran University, Najran 61441, Saudi Arabia
Abdullah Alghamdi: College of Computer Science and Information Systems, Najran University, Najran 61441, Saudi Arabia
Khairan Rajab: College of Computer Science and Information Systems, Najran University, Najran 61441, Saudi Arabia
Mana Saleh Al Reshan: College of Computer Science and Information Systems, Najran University, Najran 61441, Saudi Arabia
Monagi Alkinani: Department of Computer Science and Artificial Intelligence, College of Computer Science and Engineering, University of Jeddah, Jeddah 21442, Saudi Arabia

Energies, 2021, vol. 14, issue 16, 1-15

Abstract: This research proposes a generic smart cloud-based system in order to accommodate multiple scenarios where agriculture farms using Internet of Things (IoTs) need to be monitored remotely. The real-time and stored data are analyzed by specialists and farmers. The cloud acts as a central digital data store where information is collected from diverse sources in huge volumes and variety, such as audio, video, image, text, and digital maps. Artificial Intelligence (AI) based machine learning models such as Support Vector Machine (SVM), which is one of many classification types, are used to accurately classify the data. The classified data are assigned to the virtual machines where these data are processed and finally available to the end-users via underlying datacenters. This processed form of digital information is then used by the farmers to improve their farming skills and to update them as pre-disaster recovery for smart agri-food. Furthermore, it will provide general and specific information about international markets relating to their crops. This proposed system discovers the feasibility of the developed digital agri-farm using IoT-based cloud and provides solutions to problems. Overall, the approach works well and achieved performance efficiency in terms of execution time by 14%, throughput time by 5%, overhead time by 9%, and energy efficiency by 13.2% in the presence of competing smart farming baselines.

Keywords: smart farming; AI-based agri-food; energy efficiency; digital transformation; environment; cloud based IoTs (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2021
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (4)

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