Akhil Wilson, Raji Sukumar, N. Hemalath. Machine learning model for rice yield prediction using KNN regression.. 2021. agriXiv.2021.00070
Machine learning model for rice yield prediction using KNN regression.
DOI: 10.31220/agriRxiv.2021.00070
Statement: This article is a preprint and has not been peer-reviewed. It reports new research that has yet to be evaluated and so should not be used to guide clinical practice.
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Abstract: Abstract The prediction of agriculture yield is the one of the challenging problem in smart farming, we have predicted the yield of rice in the state of Kerala, India with the help of Machine Learning by considering the soil properties, micro climatic condition and area of the rice. Here we have used Decision Tree Regression, Random Forest Regression, Linear Regression, K Nearest Neighbour Regression, Xgboost Regression and Support Vector Regression algorithms in order to predict the rice yield. From the experiments we got KNN regression to be the best with 98.77% accuracy.
Key words: regression; knn; linear regression; decision tree regression; accuracy; xgboost regressioSubmit time: 1 January 2021
Copyright: The copyright holder for this preprint is the author/funder, who has granted agriXiv a license to display the preprint in perpetuity. -
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