Anna Rini, N. Hemalatha, Raji Sukuma. Decision tree classification of digital soil, weather, crop mapping and yield prediction using linear regression with region influences.. 2021. agriXiv.2021.00072
Decision tree classification of digital soil, weather, crop mapping and yield prediction using linear regression with region influences.
DOI: 10.31220/agriRxiv.2021.00072
Statement: This article is a preprint and has not been peer-reviewed.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 This project deals with the study of soil properties, crop and the regional influences along with their dependencies which would be further used for a digital map. Both classification and regression algorithms were carried out and a decision tree as well as a decision regressor tree was plotted to finalise the results. Out of the 6 classification algorithms applied decision tree gave the highest accuracy of 95.24% and linear regression gave the best accurate results of 100% among the 3 regression algorithms.
Key words: linear regression; decision tree; the results; algorithms; accurate; plotteSubmit 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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ID Submit time Number Download 1 2021-01-01 10.31220/agriRxiv.2021.00072V1
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