AgriMine: A Deep Learning integrated Spatio-temporal analytics framework for diagnosing nationwide agricultural issues using farmers’ helpline data

• Published in: Computers and electronics in agriculture 2022-10, Vol.201, p.107308, Article 107308
• Main Authors: Godara, Samarth, Toshniwal, Durga, Parsad, Rajender, Bana, Ram Swaroop, Singh, Deepak, Bedi, Jatin, Jhajhria, Abimanyu, Dabas, Jai Prakash Singh, Marwaha, Sudeep
• Format: Article
• Language: English
• Subjects: Artificial intelligence in agriculture; Big Data; Data analytics in agriculture; Decision making; Deep Learning; Helpline center data; Spatio-temporal analysis
• ISSN: 0168-1699; 1872-7107
• DOI: 10.1016/j.compag.2022.107308

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. •Spatial patterns regarding the Indian farmers’ questions concerning mushroom crop.•Temporal insights regarding the farmers’ telephonic queries concerning the rice crop.•Topic-wise planning of agricultural-extension activities based on past years’ data.•TCN-based time-series forecasting of farmers’ demand for telephonic help.•Performance comparison of Deep learning-based forecasting models’.