CROP YEILD PREDICTION USING ML

Abstract

In India's economy, agriculture is by far the most significant industry, and it has the greatest impact on the country's gross domestic product (GDP). An estimated 50 percent of the country's workforce is employed in the industry, which accounts for around 18 percent of the country's Gross Domestic Product (GDP). People in India have been engaged in agriculture for a long time, but the results have
never been satisfactory owing to a variety of variables that influence crop productivity at different times of the year in different regions. A high agricultural production is required to meet the demands of the world's approximately 1.2 billion people in order to ensure that they are met. All of the variables that influence crop output are directly related to soil type, precipitation, seed quality, and the existence
or lack of technical infrastructure, to name a few. To meet the increased demand, new technologies are required, and farmers must use their resources effectively by embracing new technology rather than relying on inefficient farming practises. The purpose of this project is to demonstrate how to develop a crop production forecast system using Data Mining methods. The dataset pertaining to agriculture
was the topic of the investigation. Several classifiers, including the J48, LWL, LAD Tree, and IBK are used to forecast it. The performance of each classifier is evaluated by comparing its performance to the others using the WEKA tools for enhancing Python with machine learning performance (python with machine learning). In order to evaluate total performance, it is necessary to include Accuracy factors such as linear regression, as well as the accuracy of Random forest and KNN classifiers, were employed in this study, and one of them was the accuracy of linear regression. The overall performance of the classifiers is then assessed by comparing their Root Mean Squared Error (RMSE), Mean Absolute Error (MAE), and Relative Absolute Error (RAE) values to the values of Root Mean Squared Error (RMSE) obtained from the training data (RAE). As a result, the technique will perform more correctly as the number of errors lowers. Classifiers are evaluated based on how well they perform in classification by making comparisons with one another