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Towards leveraging the role of machine learning and artificial intelligence in precision agriculture and smart farming

Ayoub Shaikh, T and Rasool, T and Rasheed Lone, F (2022) Towards leveraging the role of machine learning and artificial intelligence in precision agriculture and smart farming. In: Computers and Electronics in Agriculture, 198 .

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Official URL: https://doi.org/10.1016/j.compag.2022.107119


The digitalization of data has resulted in a data tsunami in practically every industry of data-driven enterprise. Furthermore, man-to-machine (M2M) digital data handling has dramatically amplified the information wave. There has been a significant development in digital agriculture management applications, which has impacted information and communication technology (ICT) to deliver benefits for both farmers and consumers, as well as pushed technological solutions into rural settings. This paper highlights the potential of ICT technologies in traditional agriculture, as well as the challenges that may arise when they are used in farming techniques. Robotics, Internet of things (IoT) devices, and machine learning issues, as well as the functions of machine learning, artificial intelligence, and sensors in agriculture, are all detailed. In addition, drones are being considered for crop observation as well as crop yield optimization management. When applicable, worldwide and cutting-edge IoT-based farming systems and platforms are also highlighted. We do a thorough review of the most recent literature in each area of expertise. We conclude the present and future trends in artificial intelligence (AI) and highlight existing and emerging research problems in AI in agriculture due to this comprehensive assessment. © 2022 Elsevier B.V.

Item Type: Journal Article
Publication: Computers and Electronics in Agriculture
Publisher: Elsevier B.V.
Additional Information: The copyright for this article belongs to the Elsevier B.V.
Keywords: Agricultural technology; Antennas; Crops; Data handling; Engineering education; Internet of things; Learning systems; Machine learning; Unmanned aerial vehicles (UAV), Aerial vehicle; Automated irrigation control; Data driven; Information and Communication Technologies; Irrigation controls; Machine-learning; Precision Agriculture; Smart farming; Unarmed aerial vehicle, Precision agriculture
Department/Centre: Division of Interdisciplinary Sciences > Interdisciplinary Centre for Water Research
Date Deposited: 06 Oct 2022 10:07
Last Modified: 06 Oct 2022 10:07
URI: https://eprints.iisc.ac.in/id/eprint/77208

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