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2020 | OriginalPaper | Chapter

Investigation of Common Big Data Analytics and Decision-Making Requirements Across Diverse Precision Agriculture and Livestock Farming Use Cases

Authors : Spiros Mouzakitis, Giannis Tsapelas, Sotiris Pelekis, Simos Ntanopoulos, Dimitris Askounis, Sjoukje Osinga, Ioannis N. Athanasiadis

Published in: Environmental Software Systems. Data Science in Action

Publisher: Springer International Publishing

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Abstract

The purpose of this paper is to present the investigation of common requirements and needs of users across a diverse set of precision agriculture and livestock farming use cases that was based on a series of interviews with experts and farmers. The requirements were based on nine interviews that were conducted in order to identify common requirements and challenges in terms of data collection and management, Big Data technologies, High Performance Computing infrastructure and decision making. The common requirements that derived from the interviews and user requirement analysis per use case can serve as basis for identifying functional and non-functional requirements of a technological solution of high re-usability, interoperability, adaptability and overall efficiency in terms of addressing common needs for precision agriculture and livestock farming.

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Literature
1.
go back to reference Schimmelpfennig, D.: Farm profits and adoption of precision agriculture (No. 1477-2016-121190) (2016) Schimmelpfennig, D.: Farm profits and adoption of precision agriculture (No. 1477-2016-121190) (2016)
2.
go back to reference Schimmelpfennig, D., Ebel, R.: Sequential adoption and cost savings from precision agriculture. J. Agric. Resour. Econ. 41(1835-2016-149552), 97-115 (2016) Schimmelpfennig, D., Ebel, R.: Sequential adoption and cost savings from precision agriculture. J. Agric. Resour. Econ. 41(1835-2016-149552), 97-115 (2016)
3.
go back to reference Janssen, S.J., et al.: Towards a new generation of agricultural system data, models and knowledge products: information and communication technology. Agric. Syst. 155, 200–212 (2017)CrossRef Janssen, S.J., et al.: Towards a new generation of agricultural system data, models and knowledge products: information and communication technology. Agric. Syst. 155, 200–212 (2017)CrossRef
4.
go back to reference Srbinovska, M., Gavrovski, C., Dimcev, V., Krkoleva, A., Borozan, V.: Environmental parameters monitoring in precision agriculture using wireless sensor networks. J. Clean. Prod. 88, 297–307 (2015)CrossRef Srbinovska, M., Gavrovski, C., Dimcev, V., Krkoleva, A., Borozan, V.: Environmental parameters monitoring in precision agriculture using wireless sensor networks. J. Clean. Prod. 88, 297–307 (2015)CrossRef
5.
go back to reference Mahlein, A.K.: Plant disease detection by imaging sensors–parallels and specific demands for precision agriculture and plant phenotyping. Plant Dis. 100(2), 241–251 (2016)CrossRef Mahlein, A.K.: Plant disease detection by imaging sensors–parallels and specific demands for precision agriculture and plant phenotyping. Plant Dis. 100(2), 241–251 (2016)CrossRef
6.
go back to reference Vuran, M.C., Salam, A., Wong, R., Irmak, S.: Internet of underground things in precision agriculture: architecture and technology aspects. Ad Hoc Netw. 81, 160–173 (2018)CrossRef Vuran, M.C., Salam, A., Wong, R., Irmak, S.: Internet of underground things in precision agriculture: architecture and technology aspects. Ad Hoc Netw. 81, 160–173 (2018)CrossRef
7.
go back to reference Bendre, M.R., Thool, R.C., Thool, V.R.: Big data in precision agriculture: weather forecasting for future farming. In: 2015 1st International Conference on Next Generation Computing Technologies (NGCT), pp. 744–750. IEEE, September 2015 Bendre, M.R., Thool, R.C., Thool, V.R.: Big data in precision agriculture: weather forecasting for future farming. In: 2015 1st International Conference on Next Generation Computing Technologies (NGCT), pp. 744–750. IEEE, September 2015
8.
go back to reference Keswani, B., et al.: Adapting weather conditions based IoT enabled smart irrigation technique in precision agriculture mechanisms. Neural Comput. Appl. 31(1), 277–292 (2019)MathSciNetCrossRef Keswani, B., et al.: Adapting weather conditions based IoT enabled smart irrigation technique in precision agriculture mechanisms. Neural Comput. Appl. 31(1), 277–292 (2019)MathSciNetCrossRef
9.
go back to reference Wolf, J., Bhandari, S., Raheja, A.: Unmanned aerial vehicles for precision agriculture in Orchard crops (2017) Wolf, J., Bhandari, S., Raheja, A.: Unmanned aerial vehicles for precision agriculture in Orchard crops (2017)
10.
go back to reference Gevaert, C.M., Suomalainen, J., Tang, J., Kooistra, L.: Generation of spectral–temporal response surfaces by combining multispectral satellite and hyperspectral UAV imagery for precision agriculture applications. IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens. 8(6), 3140–3146 (2015)CrossRef Gevaert, C.M., Suomalainen, J., Tang, J., Kooistra, L.: Generation of spectral–temporal response surfaces by combining multispectral satellite and hyperspectral UAV imagery for precision agriculture applications. IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens. 8(6), 3140–3146 (2015)CrossRef
11.
go back to reference Wolfert, S., Ge, L., Verdouw, C., Bogaardt, M.J.: Big data in smart farming–a review. Agric. Syst. 153, 69–80 (2017)CrossRef Wolfert, S., Ge, L., Verdouw, C., Bogaardt, M.J.: Big data in smart farming–a review. Agric. Syst. 153, 69–80 (2017)CrossRef
12.
go back to reference van der Heide, E.M.M., Veerkamp, R.F., van Pelt, M.L., Kamphuis, C., Athanasiadis, I., Ducro, B.J.: Comparing regression, naive Bayes, and random forest methods in the prediction of individual survival to second lactation in Holstein cattle. J. Dairy Sci. 102(10), 9409–9421 (2019). https://doi.org/10.3168/jds.2019-1629CrossRef van der Heide, E.M.M., Veerkamp, R.F., van Pelt, M.L., Kamphuis, C., Athanasiadis, I., Ducro, B.J.: Comparing regression, naive Bayes, and random forest methods in the prediction of individual survival to second lactation in Holstein cattle. J. Dairy Sci. 102(10), 9409–9421 (2019). https://​doi.​org/​10.​3168/​jds.​2019-1629CrossRef
Metadata
Title
Investigation of Common Big Data Analytics and Decision-Making Requirements Across Diverse Precision Agriculture and Livestock Farming Use Cases
Authors
Spiros Mouzakitis
Giannis Tsapelas
Sotiris Pelekis
Simos Ntanopoulos
Dimitris Askounis
Sjoukje Osinga
Ioannis N. Athanasiadis
Copyright Year
2020
DOI
https://doi.org/10.1007/978-3-030-39815-6_14

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