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

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Title AgriMine: A deep learning integrated Spatio-temporal analytics framework for diagnosing nationwide agricultural issues using farmers helpline data
Not Available
 
Creator Samarth Godara
Durga Toshniwal
Rajender Parsad
Ram Swaroop Bana
Deepak Singh
Jatin Bedi
Abimanyu Jhajhria
Jai Prakash Singh Dabas
Sudeep Marwaha
 
Subject Artificial intelligence in agriculture
Data analytics in agriculture
Big DataDecision making
Deep Learning
Helpline center data
Spatio-temporal analysis
 
Description Not Available
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.
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Date 2022-09-02T07:01:36Z
2022-09-02T07:01:36Z
2022-08-31
 
Type Research Paper
 
Identifier Samarth Godara, Durga Toshniwal, Rajender Parsad, Ram Swaroop Bana, Deepak Singh, Jatin Bedi, Abimanyu Jhajhria, Jai Prakash Singh Dabas and Sudeep Marwaha (2022). AgriMine: A deep learning integrated Spatio-temporal analytics framework for diagnosing nationwide agricultural issues using farmers’ helpline data. Computers and Electronics in Agriculture, 201, 107308. https://doi.org/10.1016/j.compag.2022.107308
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http://krishi.icar.gov.in/jspui/handle/123456789/73866
 
Language English
 
Relation Not Available;
 
Publisher Computers and Electronics in Agriculture, Elseveir