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Machine Learning and Artificial Intelligence for Agricultural Economics [electronic resource] : Prognostic Data Analytics to Serve Small Scale Farmers Worldwide / by Chandrasekar Vuppalapati

Author
Vuppalapati, Chandrasekar
Published
Cham : Springer International Publishing : Imprint: Springer, 2021.
Edition
1st ed. 2021.
Physical Description
XIX, 599 p. 317 illus., 286 illus. in color. online resource
Additional Creators
SpringerLink (Online service)
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Series
Contents
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.
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.
Subject(s)
ISBN
9783030774851
Digital File Characteristics
text file PDF
Part Of
Springer Nature eBook
View MARC record | catkey: 35582192