Crop Recommendation and Price Prediction using Machine Learning

Abstract— In rural places, agriculture is the main source of income. The vast majority of farmers in India rely on their instincts to choose which crop to grow during a certain season. The most frequent issue that a farmers facing is that they don't choose the right crop based on the needs of their soil and climate condition in that area. Therefore, productivity is affected. The KNN algorithm and linear regression are used in this paper to propose a machine learning technique for crop recommendation and cost prediction. The goal is to accurately inform farmers about the best crop to plant in their area and calculate the selling price of crop. The suggested method makes use of PH of the soil, nitrogen in the soil, phosphorous in soil, potassium in soil and weather data to train the machine learning models to predict the crop suitable for cultivation in a specific area and uses the sales dataset of a e-commerce website for predicting the selling price of a cro