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001445335 001__ 1445335
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001445335 020__ $$a9783030841485$$q(electronic bk.)
001445335 020__ $$a3030841480$$q(electronic bk.)
001445335 020__ $$z9783030841478$$q(print)
001445335 020__ $$z3030841472
001445335 0247_ $$a10.1007/978-3-030-84148-5$$2doi
001445335 035__ $$aSP(OCoLC)1305027807
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001445335 050_4 $$aS494.5.D3
001445335 08204 $$a630.2085$$223
001445335 24500 $$aInformation and communication technologies for agriculture.$$nTheme II,$$pData /$$cDionysis D. Bochtis, Dimitrios E. Moshou, Giorgos Vasileiadis, Athanasios Balafoutis, Panos M. Pardalos, editors.
001445335 24630 $$aData
001445335 264_1 $$aCham, Switzerland :$$bSpringer,$$c2022.
001445335 300__ $$a1 online resource (xiv, 288 pages) :$$billustrations (some color).
001445335 336__ $$atext$$btxt$$2rdacontent
001445335 337__ $$acomputer$$bc$$2rdamedia
001445335 338__ $$aonline resource$$bcr$$2rdacarrier
001445335 4901_ $$aSpringer optimization and its applications,$$x1931-6836 ;$$vvolume 183
001445335 5050_ $$aSection 1 Data Technologies: You Got Data.... Now What: Building the Right Solution for the Problem (Jackman) -- Data fusion and its applications in Agriculture (Moshou) -- Machine learning technology and its current implementation in agriculture (Anagnostis) -- Section 2 Applications: Application possibilities of IoT based management systems in agriculture (Tóth) -- Plant species detection using image processing and deep learning: A mobile-based application (Mangina) -- Computer vision-based detection and tracking in the olive sorting pipeline (Gogos) -- Integrating spatial with qualitative data to monitor land use intensity: evidence from arable land - animal husbandry systems (Vasilakos) -- Air drill seeder distributor head evaluation: a comparison between laboratory tests and Computational Fluid Dynamics simulations (R. Scola) -- Section 3 Value Chain: Data - based agricultural business continuity management policies (Podaras) -- Soybean price trend forecast using deep learning techniques based on prices and text sentiments (F. Silva) -- Use of unsupervised machine learning for agricultural supply chain data labeling (F. Silva).
001445335 506__ $$aAccess limited to authorized users.
001445335 520__ $$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.
001445335 588__ $$aOnline resource; title from PDF title page (SpringerLink, viewed March 23, 2022).
001445335 650_0 $$aAgricultural informatics$$vCongresses.
001445335 650_0 $$aAgricultural innovations$$vCongresses.
001445335 650_0 $$aInternet of things$$xIndustrial applications$$vCongresses.
001445335 650_6 $$aAgriculture$$xInformatique$$vCongrès.
001445335 650_6 $$aAgriculture$$xInnovations$$vCongrès.
001445335 650_6 $$aInternet des objets$$xApplications industrielles$$vCongrès.
001445335 655_7 $$aConference papers and proceedings.$$2fast$$0(OCoLC)fst01423772
001445335 655_0 $$aElectronic books.
001445335 7001_ $$aBochtis, Dionysis D.$$eeditor.
001445335 7001_ $$aMoshou, Dimitrios,$$eeditor.
001445335 7001_ $$aVasileiadis, Giorgos,$$eeditor.
001445335 7001_ $$aBalafoutis, Athanasios,$$eeditor.
001445335 7001_ $$aPardalos, P. M.$$q(Panos M.),$$d1954-$$eeditor.$$1https://orcid.org/0000-0003-2824-101X
001445335 7112_ $$aEFITA Congress$$n(12th)
001445335 77608 $$iPrint version:$$z3030841472$$z9783030841478$$w(OCoLC)1260132927
001445335 830_0 $$aSpringer optimization and its applications ;$$vv. 183.$$x1931-6836
001445335 852__ $$bebk
001445335 85640 $$3Springer Nature$$uhttps://univsouthin.idm.oclc.org/login?url=https://link.springer.com/10.1007/978-3-030-84148-5$$zOnline Access$$91397441.1
001445335 909CO $$ooai:library.usi.edu:1445335$$pGLOBAL_SET
001445335 980__ $$aBIB
001445335 980__ $$aEBOOK
001445335 982__ $$aEbook
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001445335 994__ $$a92$$bISE