• Nikita S.Sapike
• Prof.S.S.Sambare

An important issue for the purposes of agricultural planning is a reliable yield estimate for the many crops involved in the planning. Machine learning is an approach to provide prac- tical and efficient solutions to this problem. Many comparisons of ML methods for yield prediction have been made for the most accurate technique. Generally, the number of evaluated crops and techniques is too low and does not provide proper information for agricultural planning purposes. This paper compares the predictive accuracy of ML algorithm for crop yield prediction. People of india are practicing agriculture for years but the results are never satisfying due to various factors that affect the crop yield. To fulfill the needs of around 1.2 billion people, it is very important to have a good yield of crops. Due to factors like soil type, precipitation, region, seed quality, season, lack of technical facilities etc. The crop yield is directly influenced. Hence, new technologies are necessary for satisfying the growing need and farmers must work smartly by opting new technologies rather than going for trivial methods.In this paper, an Association Rule Mining technique integrating features of the Eclat algorithm and Genetic algorithm into the proposed method. The idea is to use the eclat technique of association rule mining to create rules and to use genetic algorithms to further refine those rules.

Apriori algorithm, classification , Association Rule Mining technique, Machine Learning

"Crop yield Prediction Using Machine Learning Algorithm.", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.7, Issue 8, page no.1565-1571, August-2020, Available :http://www.jetir.org/papers/JETIR2008203.pdf

"Crop yield Prediction Using Machine Learning Algorithm.", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.7, Issue 8, page no. pp1565-1571, August-2020, Available at : http://www.jetir.org/papers/JETIR2008203.pdf