e-LivestockProv: An Architecture based on Provenance to Manage Traceability in Precision Livestock Farming
Resumo
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-LivestockProv architecture, focusing on data provenance.Referências
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: [link]. 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. Proceedings of the 15th 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.
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: [link]. 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. Proceedings of the 15th 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.
Publicado
28/09/2021
Como Citar
GOMES, Jonas S.; DAVID, José Maria N.; BRAGA, Regina; ARBEX, Wagner; BARBOSA, Bryan; GOMES, Wneiton Luiz; FONSECA, Leonardo M. Gravina.
e-LivestockProv: An Architecture based on Provenance to Manage Traceability in Precision Livestock Farming. In: WORKSHOP DE PRÁTICAS DE CIÊNCIA ABERTA PARA ENGENHARIA DE SOFTWARE (OPENSCIENSE), 1. , 2021, Joinville.
Anais [...].
Porto Alegre: Sociedade Brasileira de Computação,
2021
.
p. 43-48.
DOI: https://doi.org/10.5753/opensciense.2021.17145.
