Crop Yield Prediction Using Epsilon Density Based Prediction

Main Article Content

D. Esther Rani, Dr.N.Sathyanarayana, Dr. B. Vishnu Vardhan

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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How to Cite
D. Esther Rani, Dr.N.Sathyanarayana, Dr. B. Vishnu Vardhan. (2021). Crop Yield Prediction Using Epsilon Density Based Prediction. Turkish Journal of Computer and Mathematics Education (TURCOMAT), 11(3), 1249–1256. https://doi.org/10.17762/turcomat.v11i3.10467
Section
Research Articles