Architecture for Decision Support in Precision Livestock Farming
Resumo
The use of sensors in the agricultural domain generates a massive volume of heterogeneous data that must be treated, stored, and processed for decision-making. These decisions must be taken considering the diversity of devices and contextual information, which is often not considered but is important to the decision-making process. This paper presents an architecture to integrate data from sensors related to precision livestock farms. The integration and processing of these data can support decision-making, lead to more accurate results and enhance agribusiness sustainability.
Palavras-chave:
Decision Support, Precision Livestock, Architecture
Referências
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Fote, F., Mahmoudi, S., Roukh, A. and Mahmoudi, S. (2020), “Big Data Storage and Analysis for Smart Farming”, 5th International Conference on Cloud Computing and Artificial Intelligence: Technologies and Applications (CloudTech), p. 1-8.
Jafarpour, H., Desai, R. and Guy, D. (2019) “KSQL: Streaming SQL Engine for Apache Kafka”, International Conference on Extending Database Technology (EDBT), p. 524-533.
Ram, S. and LIU, J. (2007), “Understanding the Semantics of Data Provenance to Support Active Conceptual Modeling”, Active Conceptual Modeling of Learning. Chen, P.P., Wong, L.Y. (eds.), p. 17-29.
Rittenbruch, M. (2002), “ATMOSPHERE: A Framework for Contextual Awareness”, International Journal of Human-Computer Interaction 14 (2), 2002, pp. 159-180.
Roukh A., Fote F., Mahmoudi S. A. and Mahmoudi S. (2020), “Big Data Processing Architecture for Smart Farming”, Procedia Computer Science, p. 78-85.
Sprague, R. (1980), “A framework for the development of decision support systems”, MIS Quarterly 4 (4th edition), p. 1–15.
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 Engineering, p. 60-84.
Wang, J., Yang, Y., Wang, T., Sherratt R. and Zhang J. (2020), “Big data service architecture: a survey”, Journal of Internet Technology, p.393-405.
Xuan, S. and Nhat, L. (2019), “A dynamic model for temperature prediction in glass greenhouse”, 6th NAFOSTED Conference on Information and Computer Science (NICS), p. 274-278.
Zhai Z., Martínez J., Beltran, V. and Martínez, N. (2020), “Decision support systems for agriculture 4.0: Survey and challenges”, Computers and Electronics in Agriculture.
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”, Computers and Electronics in Agriculture, p. 459-466.
Belciug, S. and Gorunescu, F. (2020) “Intelligent decision support systems–a journey to smarter healthcare”, Springer International Publishing.
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 International Conference on Database Theory, p. 4-6.
Carbone, P., Katsifodimos, A., Ewen S., Markl, V., Haridi, S. and Tzoumas, K. (2015), “Apache Flink: Stream and Batch Processing in a Single Engine”, Bulletin of the IEEE Computer Society Technical Committee on Data Engineering, p. 28-38.
Embrapa Gado de Leite, (2020) “Brasil tem a primeira instalação de compost barn destinada à pesquisa – 2020”. Available in: [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.
Fote, F., Mahmoudi, S., Roukh, A. and Mahmoudi, S. (2020), “Big Data Storage and Analysis for Smart Farming”, 5th International Conference on Cloud Computing and Artificial Intelligence: Technologies and Applications (CloudTech), p. 1-8.
Jafarpour, H., Desai, R. and Guy, D. (2019) “KSQL: Streaming SQL Engine for Apache Kafka”, International Conference on Extending Database Technology (EDBT), p. 524-533.
Ram, S. and LIU, J. (2007), “Understanding the Semantics of Data Provenance to Support Active Conceptual Modeling”, Active Conceptual Modeling of Learning. Chen, P.P., Wong, L.Y. (eds.), p. 17-29.
Rittenbruch, M. (2002), “ATMOSPHERE: A Framework for Contextual Awareness”, International Journal of Human-Computer Interaction 14 (2), 2002, pp. 159-180.
Roukh A., Fote F., Mahmoudi S. A. and Mahmoudi S. (2020), “Big Data Processing Architecture for Smart Farming”, Procedia Computer Science, p. 78-85.
Sprague, R. (1980), “A framework for the development of decision support systems”, MIS Quarterly 4 (4th edition), p. 1–15.
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 Engineering, p. 60-84.
Wang, J., Yang, Y., Wang, T., Sherratt R. and Zhang J. (2020), “Big data service architecture: a survey”, Journal of Internet Technology, p.393-405.
Xuan, S. and Nhat, L. (2019), “A dynamic model for temperature prediction in glass greenhouse”, 6th NAFOSTED Conference on Information and Computer Science (NICS), p. 274-278.
Zhai Z., Martínez J., Beltran, V. and Martínez, N. (2020), “Decision support systems for agriculture 4.0: Survey and challenges”, Computers and Electronics in Agriculture.
Publicado
18/07/2021
Como Citar
GOMES, Jonas S.; DAVID, José Maria N.; BRAGA, Regina; STRÖELE, Victor; ARBEX, Wagner; BARBOSA, Bryan; GOMES, Wneiton Luiz; FONSECA, Leonardo M. Gravina.
Architecture for Decision Support in Precision Livestock Farming. In: BRAZILIAN E-SCIENCE WORKSHOP (BRESCI), 15. , 2021, Evento Online.
Anais [...].
Porto Alegre: Sociedade Brasileira de Computação,
2021
.
p. 41-48.
ISSN 2763-8774.
DOI: https://doi.org/10.5753/bresci.2021.15787.
