Machine Learning and Artificial Intelligence for Agricultural Economics: Prognostic Data Analytics to Serve Small Scale Farmers Worldwide

by Chandrasekar Vuppalapati

Overview

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.

Product Details

ISBN-13:	9783030774875
Publisher:	Springer International Publishing
Publication date:	10/06/2021
Series:	International Series in Operations Research & Management Science , #314
Edition description:	1st ed. 2021
Pages:	599
Product dimensions:	6.10(w) x 9.25(h) x (d)

About the Author

Chandra Vuppalapati is a seasoned Software IT Executive with diverse experience in software technologies, enterprise software architectures, cloud computing, big data business analytics, internet of things (IoT), and software product & program management. Chandra has held engineering and product leadership roles at GE Healthcare, Cisco Systems, St. Jude Medical, and Lucent Technologies. Chandra has an MS in software engineering form San Jose State University (US) and an MBA from Santa Clara University (US) and teaches software engineering, mobile computing, cloud technologies, and web & data mining at San Jose State University.

Table of 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.