000			02811nam a22003135i 4500
001			239173
003			ES-VaUE
005			20240306114305.0
007			cr nn 008mamaa
008			211004s2021 sz | o |||| 0|eng d
020			_a9783030774851
024	7		_a10.1007/978-3-030-77485-1 _2doi
040			_aES-VaU _bspa _cES-VaU _dES-VaU
050		4	_aT57.6-.97 _b2021 EB
100	1		_aVuppalapati, Chandrasekar. _eautor. _4aut _4http://id.loc.gov/vocabulary/relators/aut
245	0	0	_aMachine Learning and Artificial Intelligence for Agricultural Economics : _bPrognostic Data Analytics to Serve Small Scale Farmers Worldwide _cby Chandrasekar Vuppalapati
250			_a1st ed. 2021.
264		1	_aCham _c2021 _bSpringer International Publishing
300			_a1 recurso en línea
336			_atexto _btxt _2rdacontent
337			_aelectrónico _bc _2rdamedia
338			_arecurso electrónico _bcr _2rdacarrier
490	0		_aInternational Series in Operations Research & Management Science _x2214-7934 _v314
505	0		_a1. 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.
520			_aThis 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.
856	4	0	_uhttps://go.openathens.net/redirector/universidadeuropea.es?url=https://doi.org/10.1007/978-3-030-77485-1 _zAcceso a este recurso digital (usuarios Universidad Europea de Valencia)
942			_2lcc _cLE
988			_aSpringer_Business_2021
999			_c239173 _d239173