Crop Management System Using Machine Learning


Authors : Sai Anand P; Sandeep S; V Pranav Srinivas; Rajeshwari G.L

Volume/Issue : Volume 6 - 2021, Issue 7 - July

Google Scholar : http://bitly.ws/9nMw

Scribd : https://bit.ly/3kt4Zua

Abstract : In India, Agriculture is very important and practised broadly across the country. This industry plays a major economic role in the country's development. So, three main criteria are needed to be considered while growing crops. Choosing fertilizers, selecting proper crops according to the region’s climatic condition and Knowledge of crop price are the criteria that benefit the farmers and also helps the economic growth of the country. This paper proposes three different models for each of the above-mentioned criteria. The first model is the Fertilizer predictor, which predicts the suitable fertilizer that can be used for the given soil constituents. The second model is the crop predictor, which predicts the three most likely growable crops based on the given climatic conditions. The third model is the future crop price predictor, which predicts the crop price in future based on past trends and prices.

Keywords : Crop Management, Machine Learning, Data Visualization, KNN, ARIMA, Random Forest, Logistic Regression.

In India, Agriculture is very important and practised broadly across the country. This industry plays a major economic role in the country's development. So, three main criteria are needed to be considered while growing crops. Choosing fertilizers, selecting proper crops according to the region’s climatic condition and Knowledge of crop price are the criteria that benefit the farmers and also helps the economic growth of the country. This paper proposes three different models for each of the above-mentioned criteria. The first model is the Fertilizer predictor, which predicts the suitable fertilizer that can be used for the given soil constituents. The second model is the crop predictor, which predicts the three most likely growable crops based on the given climatic conditions. The third model is the future crop price predictor, which predicts the crop price in future based on past trends and prices.

Keywords : Crop Management, Machine Learning, Data Visualization, KNN, ARIMA, Random Forest, Logistic Regression.

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