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

• Published in: Archives of computational methods in engineering 2022-11, Vol.29 (7), p.4557-4597
• Main Authors: Shaikh, Tawseef Ayoub, Mir, Waseem Ahmad, Rasool, Tabasum, Sofi, Shabir
• Format: Article
• Language: English
• Subjects: Agricultural management; Agriculture; Artificial intelligence; Crop yield; Digital data; Digitization; Engineering; Farming; Machine learning; Mathematical and Computational Engineering; Optimization; Review Article; Robotics
• ISSN: 1134-3060; 1886-1784
• DOI: 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.