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Machine Learning for Smart Agriculture and Precision Farming: Towards Making the Fields Talk

Shaikh, TA and Mir, WA and Rasool, T and Sofi, S (2022) Machine Learning for Smart Agriculture and Precision Farming: Towards Making the Fields Talk. In: Archives of Computational Methods in Engineering .

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Official URL: https://doi.org/10.1007/s11831-022-09761-4


In almost every sector, data-driven business, the digitization of the data has generated a data tsunami. In addition, man-to-machine digital data handling has magnified the information wave by a large magnitude. There has been a pronounced increase in digital applications in agricultural management, which has impinged on information and communication technology (ICT) to provide benefits for both producers and consumers as well as leading to technological solutions being pushed into a rural setting. This paper showcases the potential ICT technologies in traditional agriculture, as well as the issues to be encountered when they are applied to farming practices. The challenges of robotics, IoT devices, and machine learning, as well as the roles of machine learning, artificial intelligence, and sensors used in agriculture, are all described in detail. In addition, drones are under consideration for conducting crop surveillance as well as for managing crop yield optimization. Additionally, whenever appropriate, global and state-of-the-art IoT-based farming systems and platforms are mentioned. We perform a detailed study of the recent literature in each field of our work. From this extensive review, we conclude that the current and future trends of artificial intelligence (AI) and identify current and upcoming research challenges on AI in agriculture.

Item Type: Journal Article
Publication: Archives of Computational Methods in Engineering
Publisher: Springer Science and Business Media B.V.
Additional Information: The copyright for this article belongs to the Springer Science and Business Media B.V.
Keywords: Agricultural technology; Crops; Engineering education; Internet of things; Machine learning; Precision agriculture, 'current; Agricultural management; Data driven; Digital applications; Digital datas; Digitisation; Information and Communication Technologies; Machine-learning; Precision-farming; Smart agricultures, Data handling
Department/Centre: Division of Interdisciplinary Sciences > Interdisciplinary Centre for Water Research
Date Deposited: 20 Jul 2022 12:17
Last Modified: 20 Jul 2022 12:17
URI: https://eprints.iisc.ac.in/id/eprint/74952

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