AgriMine: A deep learning integrated Spatio-temporal analytics framework for diagnosing nationwide agricultural issues using farmers helpline data

Abstract

In the current scenario, exploring new means to gain accurate information regarding agriculture-related problems is the need of the hour. In this direction, we propose a multi-stage framework to perform spatial mapping and time series analysis on more than 26 million farmers’ helpline call-log records, made available by the Ministry of Agriculture & Farmers’ Welfare, Government of India. The proposed spatial analysis framework delivers hidden patterns regarding the crop-wise density of farmers calling for help from various regions of the country. Furthermore, the proposed step-plot concept gives insights into the time span of the problems in the agriculture sector. Additionally, the proposed framework explores the potential of high-end forecasting models, including five Deep Learning-based models to predict the topic-wise demand for help (number of query calls) by the producers of the target regions. To elaborate on the utility of the presented work, the article outlines two case studies corresponding to policy recommendations regarding agriculture extension and other related domains using AgriMine.