A comprehensive analysis on IoT based smart farming solutions using machine learning algorithms

Ahamed Ali Samsu Aliar, Justindhas Yesudhasan, Manjunathan Alagarsamy, Karthikram Anbalagan, Jeevitha Sakkarai, Kannadhasan Suriyan


Agriculture and farming are the most important and basic industries that are very important to humanity and generate a considerable portion of any nation's GDP. For good agricultural and farming management, technological advancements and support are required. Smart agriculture (or) farming is a set of approaches that uses a variety of current information and communication technology to improve the production and quality of agricultural products with minimum human involvement and at a lower cost. Smart farming is mostly based on IoT technology, since there is a need to continually monitor numerous aspects in the agricultural field, such as water level, light, soil characteristics, plant development, and so on. Machine learning algorithms are used in smart farming to increase production and reduce the risk of crop damage. Data analytics has been shown through extensive study to improve the accuracy and predictability of smart agricultural systems. Data analytics is utilised in agricultural fields to make decisions and recommend acceptable crops for production. This study provides a comprehensive overview of the different methods and structures utilised in smart farming. It also provides a thorough analysis of different designs and recommends appropriate answers to today's smart farming problems.


Data analytics; IoT; Machine learning; Smart farming

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DOI: https://doi.org/10.11591/eei.v11i3.3310


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