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Machine learning and artificial intelligence for agricultural economics : prognostic data analytics to serve small scale farmers worldwide / Chandrasekar Vuppalapati.

Vuppalapati, Chandrasekar, 1972- author.
2022
S494.5.D3 V86 2022
Available Online
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Title
Machine learning and artificial intelligence for agricultural economics : prognostic data analytics to serve small scale farmers worldwide / Chandrasekar Vuppalapati.
Author
Vuppalapati, Chandrasekar, 1972- author.
ISBN
9783030774851 (electronic bk.)
3030774856 (electronic bk.)
9783030774844
3030774848
DOI
https://doi.org/10.1007/978-3-030-77485-1
Published
Cham : Springer, [2022]
Copyright
©2022
Language
English
Description
1 online resource : illustrations (chiefly color).
Item Number
10.1007/978-3-030-77485-1 doi
Call Number
S494.5.D3 V86 2022
Dewey Decimal Classification
630.2085/63
Summary
This book discusses machine learning and artificial intelligence (AI) for agricultural economics. It is written with a view towards bringing the benefits of advanced analytics and prognostics capabilities to small scale farmers worldwide. This volume provides data science and software engineering teams with the skills and tools to fully utilize economic models to develop the software capabilities necessary for creating lifesaving applications. The book introduces essential agricultural economic concepts from the perspective of full-scale software development with the emphasis on creating niche blue ocean products. Chapters detail several agricultural economic and AI reference architectures with a focus on data integration, algorithm development, regression, prognostics model development and mathematical optimization. Upgrading traditional AI software development paradigms to function in dynamic agricultural and economic markets, this volume will be of great use to researchers and students in agricultural economics, data science, engineering, and machine learning as well as engineers and industry professionals in the public and private sectors.
Bibliography, etc. Note
Includes bibliographical references and index.
Access Note
Access limited to authorized users.
Source of Description
Online resource; title from PDF title page (SpringerLink, viewed October 12, 2021).
Series
International series in operations research & management science ; 314.
Available in Other Form
Print version: 9783030774844
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1. Introduction
2. Data Engineering and Exploratory Data Analysis Techniques
3. Agricultural Economy and ML Models
4. Commodity Markets
Machine Learning Techniques
5. Weather Patterns and Machine Learning
6. Agriculture Employment and the Role of AI in improving Productivity
7. Role of Government and the AI Readiness
8. Future.

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