Crop Suggestion Using Machine Learning Based on Soil Conditions

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M.S. Roobini, R. SivaSangari, L. Sujihelen, T. Ananthi, G. Nagarajan

Abstract

Crop growth and production depend on multiple circumstances such as soil conditions, climatic changes and more importantly the usage of artificial fertilizers which used excessively can cause permanent damage to the farmland. In order to reuse these excess fertilizers. There are several types of soil and they differ based on how soil is nourished the climate they type of soil and due to that different crops are being grown in different states and different countries. In order to determine the yield rate, we need to know the soil conditions. Machine learning is a technology which can predict an outcome based on previous dataset and the idle dataset. The larger the data gathered the accurate the data is.  To suggest suitable crop there are various machine learning algorithms such as k – nearest neighbour (KNN), Gaussian support vector machine (SVM), bragged tree.  These are good algorithms present compared to others.

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