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

ISBN-13: 9783030774875 / Angielski / Miękka / 2022

Chandrasekar Vuppalapati
Machine Learning and Artificial Intelligence for Agricultural Economics: Prognostic Data Analytics to Serve Small Scale Farmers Worldwide Vuppalapati, Chandrasekar 9783030774875 Springer International Publishing - książkaWidoczna okładka, to zdjęcie poglądowe, a rzeczywista szata graficzna może różnić się od prezentowanej.

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

ISBN-13: 9783030774875 / Angielski / Miękka / 2022

Chandrasekar Vuppalapati
cena 645,58 zł
(netto: 614,84 VAT:  5%)

Najniższa cena z 30 dni: 616,85 zł
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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.

Kategorie:
Nauka, Ekonomia i biznes
Kategorie BISAC:
Business & Economics > Operations Research
Business & Economics > Industries - Agribusiness
Computers > Artificial Intelligence - General
Wydawca:
Springer International Publishing
Seria wydawnicza:
International Series in Operations Research & Management Sci
Język:
Angielski
ISBN-13:
9783030774875
Rok wydania:
2022
Waga:
0.94 kg
Wymiary:
23.5 x 15.5
Oprawa:
Miękka
Dodatkowe informacje:
Wydanie ilustrowane

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.

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.

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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