India is a country where agricultural and agriculture-related sectors provide the majority of the country`s income. Agriculture is the country`s main source of revenue. It is also one of the countries that has major natural disasters such as drought or flooding, which have caused crop devastation and repeated crop cultivation leads to soil degradations due to this farmers suffer significant financial losses as a result of this, leading to suicide. The goal is to build a machine learning model for crop and fertilizer recommendations system based on soil features which includes different types of parameters value such as PH, Organic Carbon, Nitrogen, phosphorus, potassium, sulphur, zinc, iron, temperature, rainfall. Naïve Bayes and LVQ algorithms are used for crop recommendations and KNN classifier are used for fertilizer recommendations. This system displays the results of a study on the machine learning approaches and compare with the neural networks to forecast the best crops recommendations. The Machine Learning algorithm gives more accurate results than CNN.

Crop and Fertilizer Recommendation, Naïve Bayes (NB), Machine Learning, Agriculture, Learning vector quantization (LVQ)

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