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- Author: Chandrasekar Vuppalapati
- Publisher: Springer
- ISBN-10: 3030774872
- ISBN-13: 9783030774875
- Format: 15.6 x 23.4 x 3.2 cm, softcover
- Language: English
- SAVE -10% with code: EXTRA
Machine Learning and Artificial Intelligence for Agricultural Economics (e-book) (used book) | bookbook.eu
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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.EXTRA 10 % discount with code: EXTRA
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- Author: Chandrasekar Vuppalapati
- Publisher: Springer
- ISBN-10: 3030774872
- ISBN-13: 9783030774875
- Format: 15.6 x 23.4 x 3.2 cm, softcover
- Language: English English
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.
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