Crop recommendation using machine learning algorithms
Prashant Kumar, Keshav Bhagat, Kusum Lata, Sushant Jhingran
Abstract
A large part of the population of India considers agriculture to be their Main occupation. Crop production is important for our economy. Poor quality crop production is often caused by selecting the wrong crops on the wrong soil or having less knowledge of the different crop’s growth capabilities. The proposed system in which ML is used for crop recommendation is based on previously recorded measurements of soil parameters. This technique lessens the possibility of soil degradation and aids in crop health maintenance. Many factors which include rainfall. Temperature, pH, and N, P, K, humidity are analyzed using machine learning algorithms such as random forest, Naive Bayes, KNN, decision tree, Logistic regression on which suggestions are made for growing a suitable crop.