Machine learning and artificial intelligence for agricultural economics : prognostic data analytics to serve small scale farmers worldwide / Chandrasekar Vuppalapati
Material type: TextSeries: Publisher: Cham : Bloomsbury academic [2022]Description: xix,599pgs. illustrations (chiefly color) NillISBN: 9783030774851; 3030774856Subject(s): Artificial intelligence | AgricultureDDC classification: 630.2085/63 Online resources: Click here to access online | Click here to access online | Click here to access onlineItem type | Current library | Call number | Status | Date due | Barcode |
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Books | Gabriel Afolabi Ojo Central Library (Headquarters). | S494 .V87 2022 (Browse shelf(Opens below)) | Available | 0187602 | |
Books | Gabriel Afolabi Ojo Central Library (Headquarters). | S494 .V87 2022 (Browse shelf(Opens below)) | Available | 0187603 | |
Books | Gabriel Afolabi Ojo Central Library (Headquarters). | S494 .V87 2022 (Browse shelf(Opens below)) | Available | 0187604 |
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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
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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