000			02979nam a22003135i 4500
001			92670
005			20240205142358.0
010			_a978-3-030-84148-5 _dcompra
090			_a92670
100			_a20231023d2022 k||y0pory50 ba
101	0		_aeng
102			_aCH
200	1		_aInformation and communication technologies for agriculture-theme II _bDocumento eletrónico _edata _gedited by Dionysis D. Bochtis ... [et al.]
210			_aCham _cSpringer _d2022
215			_aXIV, 288 p. _cil.
225	2		_aSpringer optimization and its applications _v183
303			_aThis volume is the second (II) of four under the main themes of Digitizing Agriculture and Information and Communication Technologies (ICT). The four volumes cover rapidly developing processes including Sensors (I), Data (II), Decision (III), and Actions (IV). Volumes are related to 'digital transformation" within agricultural production and provision systems, and in the context of Smart Farming Technology and Knowledge-based Agriculture. Content spans broadly from data mining and visualization to big data analytics and decision making, alongside with the sustainability aspects stemming from the digital transformation of farming. The four volumes comprise the outcome of the 12th EFITA Congress, also incorporating chapters that originated from select presentations of the Congress. The first part of this book (II) focuses on data technologies in relation to agriculture and presents three key points in data management, namely, data collection, data fusion, and their uses in machine learning and artificial intelligent technologies. Part 2 is devoted to the integration of these technologies in agricultural production processes by presenting specific applications in the domain. Part 3 examines the added value of data management within agricultural products value chain. The book provides an exceptional reference for those researching and working in or adjacent to agricultural production, including engineers in machine learning and AI, operations management, decision analysis, information analysis, to name just a few. Specific advances covered in the volume: Big data management from heterogenous sources Data mining within large data sets Data fusion and visualization IoT based management systems Data Knowledge Management for converting data into valuable information Metadata and data standards for expanding knowledge through different data platforms AI - based image processing for agricultural systems Data - based agricultural business Machine learning application in agricultural products value chain.
606			_aOperations research
606			_aManagement science
606			_aEnvironmental sciences _xMathematics
606			_aBig data
680			_aT57.6-57.97
680			_aT55.4-60.8
702			_971491 _aBochtis _bDionysis D. _4340
801		0	_aPT _gRPC
856	4		_uhttps://doi.org/10.1007/978-3-030-84148-5
942			_2lcc _cF _n0