A Machine Learning Influenced Recommendation System for Predicting the Rainfall and Price for Crops in Badulla District

Abstract

Every day, agriculture becomes more vital to the global economy. Daily population expansion necessitates substantial crop output for human existence. But as the population has increased, human activity has also altered the environment. Therefore, it has resulted in challenges with weather forecasting, which is crucial for crop planting in the agricultural sector. Thus, the globe needs a method to forecast agrarian weather. In addition, it is highly advantageous for farmers to understand the production rate they can achieve and the price range they may expect for their efforts. As a result, Machine learning technologies have become unique and fashionable in the agricultural industry due to their ability to provide accurate farming predictions. Selecting suitable plants for planting has evolved into a necessity. This study focuses on the application of machine learning to estimate the optimal crop for a given period. In this work, the author addresses the beginning part of the study: precipitation prediction under the weather forecast and pricing forecast. The authors have employed six distinct machine-learning models to forecast rainfall and crop prices.