Precision Agriculture with Crop Yield Prediction

Farming is the foundation of any country's monetary design and has a basic impact on the Indian economy. Farmers can utilize innovation to help them in figuring out what to cultivate and how to cultivate. It helps farmers to choose the correct crop and increase productivity. Farmers can make better marketing decisions if they have an accurate and high. confidence forecast of their annual production, allowing them to sell their products at the best possible price. Hence in this paper, we are building a model for the prediction of crop yield using machine learning algorithms. Crop yield prediction depends on different factors like genotype, natural factors, etc., due to which it is a challenging task. Our application deals with regression analysis to forecast the value of the dependent variable. The dataset we used consists of four sets of data like yield performance, management data, weather data, and soil data. We have used four types of regressors namely Multilinear Regression, Logistic Regression, Support Vector Regressor, and Artificial Neural Network Regressor to predict the yield. We have considered the Mean Absolute Error as an evaluation metric for our project and compared all the models with respect to it. The best result is obtained by the ANN Regressor algorithm.