Deep Learning-based query-count forecasting using farmers' helpline data

Forecasting the nationwide demand for agriculture-related help plays a crucial role in supporting the decision making activities of the agri-sector. In this direction, the article presents an artificial intelligence-based framework for predicting the agriculture-related query-calls count in the nation's farmers-helpline network. The present work utilizes advanced data mining techniques to operate on the available dataset. The dataset utilized for the study includes over 1.3 million query-call logs accumulated from the "Kisan Call Center", a farmers' helpline network administered by the Ministry of Agriculture, Government of India. Moreover, to validate the proposed framework, we process data corresponding to the top-five rice-producing states of India. In addition, the study compares the forecasting performance of four Machine Learning and Deep Learning-based models, i.e., Support Vector Regression, Multi-layer Perceptron, Long Short-Term Memory Networks, and Gated Recurrent Units. The experimental results convey that the proposed framework is useful for predicting trends in farmers' problems. Furthermore, the framework is valuable in developing fully automated AI-based systems connected with the data servers of the Kisan Call Centers and providing the forecast in a mechanized manner.