• Meghana S
• Dharani N V

Soil is an important ingredient of agriculture. There are several kinds of soil. Each sort of soil can have different sorts of features and different sorts of crops grow on differing types of soils. We need to know the features and characteristics of varied soil types to understand which crops grow better in certain soil types. Machine learning techniques are often helpful during this case. In recent years, it's progressed tons. Machine learning remains an emerging and challenging research field in agricultural data analysis. In this paper, we've proposed a model which will predict soil series with land type and consistent with prediction, it can suggest suitable crops. Machine learning algorithms such as Random Forest and Support Vector Machines (SVM) are used for soil classification and Crop Yield Prediction. The main goal of our project is to create a one¬stop solution to various problems in the domain of agriculture. Nowadays Machine Learning is getting more popular as it is the technique of teaching machines to make decisions by the provided data. This stream of computer Science helps a lot in achieving our goals Predicting the Suitable Crop based on the condition of the cultivation land, Estimating the number of Fertilizers to be used for better yield, and predicting the yield based on the weather condition and various practices taken. This Project eliminates the manual and inaccurate approach practiced by the farmers and helps them to make the right decisions for a better Yield.

Keywords: Crop Prediction, Yield Prediction, Machine learning, Support Vector Machine(SVM), Random Forest.

"Crop and Yield Prediction with Fertilizer Estimation", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.8, Issue 7, page no.f428-f431, July-2021, Available :http://www.jetir.org/papers/JETIR2107685.pdf

"Crop and Yield Prediction with Fertilizer Estimation", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.8, Issue 7, page no. ppf428-f431, July-2021, Available at : http://www.jetir.org/papers/JETIR2107685.pdf