Performance Of Soil Prediction Using Machine Learning For Data Clustering Methods

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M. Rajeshwari, N. Shunmuganathan, Dr. R. Sankara subramanian


Objective: Agricultural play a major role in human life. The crop yield prediction is the needed one, because the investment and work process consume high but the yield output going low in every year. Methods: Here introduces machine learning (ML), which can be a key differentiator for obtaining real, estimated predictions for yield issues. In ML, we choose the random forest algorithm for the yield predictions. The classifier model used here includes logistic regression, naive Bayes, and random forests, of which extended random forests provide maximum accuracy Findings: Based on the dataset provided, we got the yields prediction by RF. The crop yield is different by the crops and usage of fertilizer. The fertilizer also depends upon the soil of the place. Novelty: The clustering method considers data-related environmental factors, soil factors and weather, soil fertility, and production over the past year, and recommends profitable plants that remain mature in the expected atmospheric conditions.

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