Abstract

The world of Big Data has been rapidly expanding into the domains of Engineering and Machine Learning. The biggest challenge in the Big Data landscape is the incompetence of processing vast amounts of data in a time-efficient manner. The agriculture domain has so long only relied on traditional method for yield prediction. This can be bettered by using novel Machine Learning techniques and innovative thinking. The study provides the review of most of the techniques already implemented in the ML, Big Data and Agriculture domain- traditional and modern- while focusing on highlighting the difference in accuracy between the traditional methods and the more advanced methods.

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