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Big Data Analytics-Based Agro Advisory System for Crop Recommendation Using Spark Platform

Big Data Analytics-Based Agro Advisory System for Crop Recommendation Using Spark Platform

Madhuri J., Indiramma M.
Copyright: © 2023 |Pages: 21
ISBN13: 9781668471050|ISBN10: 1668471051|ISBN13 Softcover: 9781668471067|EISBN13: 9781668471074
DOI: 10.4018/978-1-6684-7105-0.ch012
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MLA

Madhuri J., and Indiramma M. "Big Data Analytics-Based Agro Advisory System for Crop Recommendation Using Spark Platform." Handbook of Research on AI and Machine Learning Applications in Customer Support and Analytics, edited by Md Shamim Hossain, et al., IGI Global, 2023, pp. 227-247. https://doi.org/10.4018/978-1-6684-7105-0.ch012

APA

Madhuri J. & Indiramma M. (2023). Big Data Analytics-Based Agro Advisory System for Crop Recommendation Using Spark Platform. In M. Hossain, R. Ho, & G. Trajkovski (Eds.), Handbook of Research on AI and Machine Learning Applications in Customer Support and Analytics (pp. 227-247). IGI Global. https://doi.org/10.4018/978-1-6684-7105-0.ch012

Chicago

Madhuri J., and Indiramma M. "Big Data Analytics-Based Agro Advisory System for Crop Recommendation Using Spark Platform." In Handbook of Research on AI and Machine Learning Applications in Customer Support and Analytics, edited by Md Shamim Hossain, Ree Chan Ho, and Goran Trajkovski, 227-247. Hershey, PA: IGI Global, 2023. https://doi.org/10.4018/978-1-6684-7105-0.ch012

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Abstract

The advancements in science and technology have led to the generation of colossal data in the agricultural sector as a result of which has entered the world of big data. Big data analytics is the solution to store and analyze such large amounts of data to improve productivity in agricultural practices. Hence, the purpose of this research work is to develop a big data recommendation framework that enables farmers to choose the right crops considering the location-specific parameters. The location-specific weather parameters, soil parameters crop characteristics, and demand for the agricultural product in the previous years are considered in the work. The proposed recommendation model is based on the Spark framework that accepts the soil data in real-time analyses along with weather and pricing data by applying artificial neural networks and suggesting a suitable crop for the field conditions. The chapter prioritizes developing an application useful for farmers, agriculture officers, and researchers to provide efficient crop recommendations.

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