Support Vector Machine-Based Energy Efficient Management of UAV Locations for Aerial Monitoring of Crops over Large Agriculture Lands

Mohammed Al-Naeem (), M M Hafizur Rahman, Anuradha Banerjee and Abu Sufian
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Mohammed Al-Naeem: Department of Computer Networks & Communications, CCSIT, King Faisal University, Al Ahsa 31982, Saudi Arabia
M M Hafizur Rahman: Department of Computer Networks & Communications, CCSIT, King Faisal University, Al Ahsa 31982, Saudi Arabia
Anuradha Banerjee: Department of Computer Applications, Kalyani Governement Engineering College, Maulana Abul Kalam Azad University of Technology, Kalyani 741235, India
Abu Sufian: Department of Computer Science, University of Gour Banga, English Bazar 732103, India

Sustainability, 2023, vol. 15, issue 8, 1-17

Abstract: Crop monitoring and smart spraying have become indispensable parts of precision agriculture where unmanned aerial vehicles (UAVs) play a lead role. In particular, in large agricultural fields, aerial monitoring is a sustainable solution provided it can be performed in an energy-efficient manner. The existing literature points out that the research on precision agriculture using UAVs is still very minimal. In this article, we propose a support vector machine (SVM)-based UAV location management technique where UAVs change position over various portions or regions of a large agricultural field so that crops are properly monitored in an energy-efficient manner. Whenever a processing request is generated from any sensor in a part of the field, the UAV investigates with an SVM to decide whether to move on to the center of that field based on various parameters or characteristics such as region-id, packet-id, time of day, waiting times of the packets, the average waiting time of others within a predefined time window, location of the UAV, residual energy of the UAV after processing the packet, and movement after processing the packet. We use 70% of our data for training and the other 30% for testing. In our simulation study, we use accuracy, precision, and recall to measure in both contexts to determine the efficiency of the model, and also the amount of energy preserved is computed corresponding to every move. We also compare our approach with current state-of-the-art energy-preserving UAV movement control techniques which are compatible with the present application scenario. The proposed technique produced 6.5%, 34.5%, and 61.5% better results in terms of percentage of successful detection (PSD), composite energy consumption (CEC), and average delay (ADL), respectively.

Keywords: crop monitoring; energy efficient UAV; precision agriculture; SVM; trajectory optimization; UAV application; UAV-IoT (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2023
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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