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Big Data Transformation in Agriculture: From Precision Agriculture Towards Smart Farming

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Big Data Transformation in Agriculture: From Precision Agriculture Towards Smart Farming

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dc.contributor.author Rodríguez-Sánchez, María De Los Ángeles es_ES
dc.contributor.author Cuenca, L. es_ES
dc.contributor.author Ortiz Bas, Ángel es_ES
dc.date.accessioned 2020-12-19T04:31:58Z
dc.date.available 2020-12-19T04:31:58Z
dc.date.issued 2019 es_ES
dc.identifier.issn 1868-4238 es_ES
dc.identifier.uri http://hdl.handle.net/10251/157502
dc.description.abstract [EN] Big data is a concept that has changed the way to analyse data and information in different environments such as industry and recently, in agriculture. It is used to describe a large volume of data (structured or unstructured data), which are difficult to obtain, process or parse using conventional technologies and tools like relational databases or conventional statistics, in a reasonable time for their insight. However, Big Data is applied differently in each area to take advantage of its potential and capabilities. Specially in agriculture that presents more demanding conditions due to its inherent uncertainty, so Big Data methods and models from other environments cannot be used straight away in this area. In this paper, we present a review/update of term Big Data and analyse the evolution and the role of Big Data in agriculture outlined the element of collaboration. es_ES
dc.description.sponsorship All authors acknowledge the partial support of Project 691249, RUC-APS: Enhancing and implementing Knowledge based ICT solutions within high Risk and Uncertain Conditions for Agriculture Production Systems, funded by the EU under its funding scheme H2020-MSCA-RISE-2015; and the project "Development of an integrated maturity model for agility, resilience and gender perspective in supply chains (MoMARGE). Application to the agricultural sector." Ref. GV/2017/025 funded by the Generalitat Valenciana. This first author was supported by the Aid Programme of Research and Development of Universitat Politecnica de Valencia [PAID-01-18]. es_ES
dc.language Inglés es_ES
dc.publisher Springer es_ES
dc.relation.ispartof IFIP Advances in Information and Communication Technology es_ES
dc.relation.ispartof Collaborative Networks and Digital Transformation es_ES
dc.rights Reserva de todos los derechos es_ES
dc.subject Big data es_ES
dc.subject Smart farming es_ES
dc.subject Precision agriculture es_ES
dc.subject.classification ORGANIZACION DE EMPRESAS es_ES
dc.title Big Data Transformation in Agriculture: From Precision Agriculture Towards Smart Farming es_ES
dc.type Artículo es_ES
dc.type Comunicación en congreso es_ES
dc.type Capítulo de libro es_ES
dc.identifier.doi 10.1007/978-3-030-28464-0_40 es_ES
dc.relation.projectID info:eu-repo/grantAgreement/EC/H2020/691249/EU/Enhancing and implementing Knowledge based ICT solutions within high Risk and Uncertain Conditions for Agriculture Production Systems/ es_ES
dc.relation.projectID info:eu-repo/grantAgreement/UPV//PAID-01-18/ es_ES
dc.relation.projectID info:eu-repo/grantAgreement/GVA//GV%2F2017%2F025/ es_ES
dc.rights.accessRights Abierto es_ES
dc.contributor.affiliation Universitat Politècnica de València. Departamento de Organización de Empresas - Departament d'Organització d'Empreses es_ES
dc.description.bibliographicCitation Rodríguez-Sánchez, MDLÁ.; Cuenca, L.; Ortiz Bas, Á. (2019). Big Data Transformation in Agriculture: From Precision Agriculture Towards Smart Farming. IFIP Advances in Information and Communication Technology. 568:467-474. https://doi.org/10.1007/978-3-030-28464-0_40 es_ES
dc.description.accrualMethod S es_ES
dc.relation.conferencename 20th IFIP Working Conference on Virtual Enterprises (PRO-VE 2019) es_ES
dc.relation.conferencedate Septiembre 23-25,2019 es_ES
dc.relation.conferenceplace Turin, Italy es_ES
dc.relation.publisherversion https://doi.org/10.1007/978-3-030-28464-0_40 es_ES
dc.description.upvformatpinicio 467 es_ES
dc.description.upvformatpfin 474 es_ES
dc.type.version info:eu-repo/semantics/publishedVersion es_ES
dc.description.volume 568 es_ES
dc.relation.pasarela S\410824 es_ES
dc.contributor.funder Generalitat Valenciana es_ES
dc.contributor.funder Universitat Politècnica de València es_ES
dc.description.references Cox, M., Ellsworth, D.: Application-controlled demand paging for out-of-core visualization. In: Proceedings of the 8th Conference on Visualization 1997, p. 235. IEEE Computer Society Press (1997) es_ES
dc.description.references Laney, D.: 3D data management: controlling data volume, velocity and variety. META Group Res. Note 6, 1 (2001) es_ES
dc.description.references Beyer, M.A., Laney, D.: The Importance of “Big Data”: A Definition. Gartner, Stamford (2012) es_ES
dc.description.references Kamilaris, A., et al.: A review on the practice of big data analysis in agriculture. Computers and Electronics in Agriculture 143(C), 23–37 (2017) es_ES
dc.description.references Marr, B.: How Much Data Do We Create Every Day? The Mind-Blowing Stats Everyone Should Read (2019). https://www.forbes.com/sites/bernardmarr/2018/05/21/how-much-data-do-we-create-every-day-the-mind-blowing-stats-everyone-should-read/#5671a61d60ba es_ES
dc.description.references NIST. The definition of Big Data. https://bigdatawg.nist.gov/home.php es_ES
dc.description.references IBM. The definition of Big Data. https://www.ibm.com/analytics/hadoop/big-data-analytics es_ES
dc.description.references Oracle. The definition of Big Data. https://www.oracle.com/big-data/guide/what-is-big-data.html es_ES
dc.description.references Shahbaz, M., Gao, Ch., Zhai, L., Shahzad, F., Hu, Y.: Investigating the adoption of big data analytics in healthcare: the moderating role of resistance to change. J. Big Data 6 (2019). https://doi.org/10.1186/s40537-019-0170-y es_ES
dc.description.references Trom, L., Cronje, J.: Analysis of data governance implications on big data. In: Arai, K., Bhatia, R. (eds.) FICC 2019. LNNS, vol. 69, pp. 645–654. Springer, Cham (2020). https://doi.org/10.1007/978-3-030-12388-8_45 es_ES
dc.description.references Tao, F., et al.: A field programmable gate array implemented fibre channel switch for big data communication towards smart manufacturing. Robotics and Computer Integrated Manufacturing 57, 166–181 (2019) es_ES
dc.description.references Lu, Y., Li, X., Zhong, J., Xiong, Y.: Research on the innovation of strategic business model in green agricultural products based on Internet of Things (IOT) - May 2010 (2010) es_ES
dc.description.references Zhao, L., Yin, S., Liu, L., Zhang, Z., Wei, S.: A crop monitoring system based on wireless sensor network - December 2011 (2011) es_ES
dc.description.references Chi, M., Plaza, A., Benediktsson, J.A., Sun, Z., Shen, J., Zhu, Y.: Big data for remote sensing: challenges and opportunities. Proc. IEEE 104(11), 2207–2219 (2016) https://doi.org/10.1109/jproc.2016.2598228 es_ES
dc.description.references Rodriguez, M.A., Cuenca, L., Bas, A.: FIWARE open source standard platform in smart farming - a review. In: Proceedings of the 19th IFIP WG 5.5 Working Conference on Virtual Enterprises, PRO-VE 2018, Cardiff, UK, 17–19 September 2018 (2018). https://doi.org/10.1007/978-3-319-99127-6_50 es_ES
dc.description.references Stafford, J., LeBars, J.: A GPS backpack system for mapping soil and crop parameters in agricultural fields. J. Navig. 49(1), 9–21 (1996) es_ES
dc.description.references Robert, P.C.: Precision agriculture: research needs and status in the USA. In: Stafford, J.V. (ed.) Proceedings of the 2nd European Conference on Precision Agriculture, Part 1, pp. 19–33. Academic Press, SCI/Sheffield (1999) es_ES
dc.description.references Long, D.S., Nielsen, G.A., Henry, M.P., Westcott, M.P.: Remote sensing for northern plains precision agriculture. In: Paper Presented at the Space 2000, pp. 208–214 (2000) es_ES
dc.description.references Ge, Y., Thomasson, J.A., Sui, R.: Remote sensing of soil properties in precision agriculture: a review. Front. Earth Sci. 5(3), 229–238 (2011) es_ES
dc.description.references Sundmaeker, H., Verdouw, C., Wolfert, S., Pérez L.: Internet of food and farm 2020. In: Paper presented at Digitising the Industry - Internet of Things Connecting Physical, Digital and Virtual Worlds, River Publishers, Gistrup/Delft, pp. 129–151 (2016) es_ES
dc.description.references Barmpounakis, S., et al.: Management and control applications in agriculture domain via a FI Business-to-Business platform. Inf. Process. Agric. 2(1), 51–63 (2015) es_ES
dc.description.references Musat, G., et al.: Advanced services for efficient management of smart farms. J. Parallel Distrib. Comput. 116, 3–17 (2018) es_ES
dc.description.references FIspace. https://www.fispace.eu/whatisfispace.html es_ES
dc.description.references Agricolus (2019). https://www.agricolus.com/ es_ES
dc.description.references Paton, N.W.: Automating data preparation: can we? Should we? Must we? In: CEUR Workshop Proceedings, p. 2324 (2019) es_ES
dc.description.references Kim, K.S., Yoo, B.H., Shelia, V., Porter, C.H., Hoogenboom, G.: START: a data preparation tool for crop simulation models using web-based soil databases. Comput. Electron. Agric. 154, 256–264 (2018). https://doi.org/10.1016/j.compag.2018.08.023 es_ES
dc.description.references IoF2020 (2019). https://www.iof2020.eu/ es_ES


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