Crop Yield Prediction Using Epsilon Density Based Prediction
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Abstract
Machine learning algorithms play a significant role in data analysis in many disciplines like
Agriculture, Food, Medicine, and Twitter Data. Yield prediction is a significant agricultural problem that
remains to be solved based on the available data. Earlier yield prediction is an exciting challenge, and this
prediction is performed by considering farmers' knowledge of a specific field and crop. Machine learning
techniques are used to increase the crop yield, where data is collected from different agricultural sectors. In
machine learning, clustering plays a vital role. In this paper, various clustering techniques such as k-Means,
Expectation-Maximization, Hierarchical Micro Clustering, Density-Based Clustering, Weight-based clustering
are briefed, and a new clustering approach, Epsilon Density-Based Prediction(EDBP), is proposed for obtaining
the best crop yield prediction.
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