Machine Learning convergence for weather based crop selection
Sonal Jain, Dharavath Ramesh
Abstract
Agriculture plays a vital role in Indian economy. It contributes 18% of total India's GDP. In India, most of the crops are solely dependent upon weather conditions. Hence, more yield of crops can be achieved by analysing agro-climate data using machine learning techniques. This paper proposes a crop selection method to maximize crop yield based on weather and soil parameters. It also suggests the proper sowing time for suitable crops using seasonal weather forecasting. Machine learning algorithms such as Recurrent neural network is used for weather prediction, and Random forest classification algorithm is used to select suitable crops. The result of proposed weather forecasting technique is compared with conventional Artificial neural network, which shows better performance results for each selected weather parameters.