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.

Outlines agricultural economic models for small farmers Creates AI software architectures and patterns to develop AI applications in agricultural economics Provides comprehensive AI algorithms and techniques for analyzing agricultural datasets Explains AI solution development from agricultural and constrained device paradigms

Autorentext
Chandrasekar 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 and program management. He has held engineering and product leadership positions at Microsoft, GE Healthcare, Cisco Systems, St. Jude Medical, and Lucent Technologies. Chandrasekar has an MS in software engineering from San Jose State University (USA) and an MBA from Santa Clara University (USA) and currently teaches software engineering, large-scale analytics, data science, mobile computing, cloud technologies, and web and data mining at San Jose State University (USA).

Inhalt

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.