Crop Recommendation System using KNN and Random Forest considering Indian Data set
Tapas Kumar Mishra, Sambit Kumar Mishra, Kanaparthi Jeevan Sai, Bachu Sai Alekhya, Athukuri Rama Nishith
Abstract
The agriculture plays crucial role in the growth of the country's economy. In comparison to other countries, India has the highest production rate in agriculture. Agriculture when combined with technology can bring the finest results. Crop prediction is a highly complex trait determined by multiple factors such as Contents of Nitrogen, Phosphorous, Potassium, Rainfall, Temperature, Humidity, Ph level. Predicting the crop in advance would help the policymakers and farmers for taking appropriate measures for farming, marketing and storage. Thus, in this paper we propose crop selection using machine learning techniques such as K-Nearest Neighbour (KNN) and Random Forest. Both of the models are simulated comprehensively on Indian Data set and an analytical report has been presented. This model will help the farmers to know the type of the crop before cultivating onto the agricultural field and thus help them to make appropriate decisions.