Published August 28, 2021 | Version v1
Journal article Open

e-LivestockProv: An Architecture based on Provenance to Manage Traceability in Precision Livestock Farming

Creators

  • 1. Universidade Federal de Juiz de Fora
  • 2. Embrapa Gado de Leite

Description

 

The use of sensors in the agricultural sector generates a large volume of heterogeneous data that must be processed, stored, and analyzed to support decisions. In addition, decisions taken in agriculture need to be traceable due to the diversity of data and devices present in different agricultural contexts. With provenance, we can trace and analyze data to improve future decisions and avoid the usefulness ones. This article presents the e-Livestock Prov architecture, focusing on data provenance.

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Additional details

References

  • Bahlo, C., Dahlhausac P., Thompsonac, H. and Trotterbc, M. (2019) "The role of interoperable data standards in precision livestock farming in extensive livestock systems: A review". Comp. and Elect. Agric., p. 459-466.
  • Banhazi, T.M., Lehr, H., Black, J.L., Crabtree, H., Schofield, P., Tscharke, M., Berckmans, D. (2012) "Precision livestock farming: an international review of scientific and commercial aspects". Int. J. Agri. Bio. Eng. 5 (3), p. 1–9.
  • Belhajjame, K., Cheney, J., Coppens, S., Cresswell, S., Gil, Y., Groth, P., Klyne, G., Lebo, T., McCusker, J., Miles, S., Myers J., Sahoo, S., Tilmes, C., Moreau, L., and Missier, P. (2013) "PROV-DM: The PROV Data Model", W3C Recommendation REC-prov-dm-20130430, World Wide Web Consortium.
  • Buneman, P., Khanna, S. and Tan, C. (2001) "Why and where: A characterization of data provenance", 8th Int. Conf. on Database Theory, p. 4-6.
  • Da Cruz, S. M. S., Ceddia, M. B., Miranda, R. C. T., Rizzo, G., Klinger, F., Cerceau, R., Cruz, P. V. (2018) "Data Provenance in Agriculture". Int. Provenance and Annotation Workshop. Springer, Cham, p. 257-261.
  • Da Cruz, S.M.S., Do Nascimento, J.A.P. (2019) "Towards integration of data-driven agronomic experiments with data provenance". Comp. and Elect. Agric., 161, p. 14- 28.
  • Embrapa Gado de Leite. Brasil tem a primeira instalação de compost barn destinada à pesquisa - 2020. Disponível em: <https://www.embrapa.br/busca-de-noticias/-/noticia/53360675/brasil-tem-a-primeira-instalacao-de-compost-barn-destinada-a-pesquisa>. Accessed in 03 mar 2021.
  • Gomes, J., David, J. M. N., Braga, R., Ströele, V., Arbex, W., Barbosa, B., Gomes, W., Fonseca, L. (2021) "Architecture for Decision Support in Precision Livestock Farming". Anais do XV Brazilian e-Science Workshop. SBC, p. 41-48.
  • Karthick, G. S., Sridhar, M., & Pankajavalli, P. B. (2020) "Internet of things in animal healthcare (IoTAH): review of recent advancements in architecture, sensing technologies and real-time monitoring". SN Computer Science, 1(5), p. 1-16.
  • Villa-Henriksen A., Edwards G., Pesonenc, L., Greenbd O. and Sørensena, C. (2020), "Internet of things in arable farming: Implementation, applications, challenges and potential". Biosystems Eng., p. 60-84.
  • Yin, R. (2013). Case study research: Design and methods Sage Publications, Inc; 5th ed.
  • Zhai, Z., Martínez, J.F., Beltran, V., Martínez, N.L. (2020) "Decision support systems for agriculture 4.0: Survey and challenges". Comp. and Elect. Agric., 170, p. 105256.