A Machine Learning Approach For Predicting Crop Seasonal Yield and Cost For Smart Agriculture

Abstract

In our economy, agriculture plays a key component. Current scenario has plunged the agricultural sector into extensive losses leading to a lot of food shortages and cases of farmer suicides. This can be solved through the implementation of advanced scientific methods like Machine learning, Deep Learning, and Internet of Things. The Proposed framework Smart Agriculture using Seasonal Yield and Cost Prediction (SYCP) uses Machine Learning to predict the ideal crop or set of crops to be grown based on a particular season. The Model also analyzes the current market trends and predicts the approximate price of the crop for that season and geographic region based on which the farmer can decide accordingly. Random forest has been found to be suitable for both crop and price prediction. The framework has been experimented with an agricultural dataset and the results were found to be more efficient than the existing methods. This would be an efficient solution, where it offers a seasonal yield along with price prediction to improve the economy of farmers and to push them to grow.