TY - JOUR
T1 - A scientific software ecosystem architecture for the livestock domain
AU - Gomes, Jonas
AU - Esteves, Izaque
AU - Valdemar Vicente Graciano Neto, Vicente Graciano Neto
AU - David, José Maria N.
AU - Braga, Regina
AU - Arbex, Wagner
AU - Kassab, Mohamad
AU - de Oliveira, Roberto Felício
N1 - Funding Information:
We thank the researchers who participated in the evaluation of this proposal, as well as the Brazilian Agricultural Research Corporation (Embrapa/Brazil). This work was partially funded by UFJF/Brazil , CAPES/Brazil , CNPq/Brazil (grant: 311595/2019-7 ), and FAPEMIG/Brazil (grant: APQ-02685-17 ), (grant: APQ-02194-18 ). We also thank FUNAPE-UFG/Brazil via grant 01/2022 for partially funding this research.
Funding Information:
We thank the researchers who participated in the evaluation of this proposal, as well as the Brazilian Agricultural Research Corporation (Embrapa/Brazil). This work was partially funded by UFJF/Brazil, CAPES/Brazil, CNPq/Brazil (grant: 311595/2019-7), and FAPEMIG/Brazil (grant: APQ-02685-17), (grant: APQ-02194-18). We also thank FUNAPE-UFG/Brazil via grant 01/2022 for partially funding this research.
Publisher Copyright:
© 2023 Elsevier B.V.
PY - 2023/8
Y1 - 2023/8
N2 - Context: In the livestock domain, technologies are developed to sustainably raise animal production. However, the domain is critical, since animals are very sensitive to variables such as temperature and humidity, which can cause diseases and consequent production losses and discomfort. Livestock production systems then demand monitoring, reasoning, and acting on the environment so that the levels of those variables are preserved in pre-established intervals and undesired conditions are predicted, avoided, and mitigated with automated actions. Objective: The main contribution of this article is presenting E-SECO, a software ecosystem platform, and its evolution that encapsulates a new self-adaptive component to tackle animal production decisions, named e-Livestock architecture. Method: Two case studies were conducted involving a real system derived from the E-SECO platform encompassing a Compost Barn production system, i.e., the environment and surrounding technology where bovine milk production takes place. Results: Results showed the effectiveness of E-SECO to (i) abstract disruptive technologies based on the Internet of Things (IoT) and Artificial Intelligence and accommodate them in a single architecture for that specific domain, (ii) support reuse and derivation of a self-adaptive architecture to support engineering a complex system for a livestock sub-domain (milk production), and (iii) support empirical studies in a real smart farm towards a future transfer of technology to industry. Conclusion: The results showed that the E-SECO platform, which encompasses e-livestock architecture, can support monitoring, reasoning, prediction, and automated actions in a milk production/Compost Barn environment.
AB - Context: In the livestock domain, technologies are developed to sustainably raise animal production. However, the domain is critical, since animals are very sensitive to variables such as temperature and humidity, which can cause diseases and consequent production losses and discomfort. Livestock production systems then demand monitoring, reasoning, and acting on the environment so that the levels of those variables are preserved in pre-established intervals and undesired conditions are predicted, avoided, and mitigated with automated actions. Objective: The main contribution of this article is presenting E-SECO, a software ecosystem platform, and its evolution that encapsulates a new self-adaptive component to tackle animal production decisions, named e-Livestock architecture. Method: Two case studies were conducted involving a real system derived from the E-SECO platform encompassing a Compost Barn production system, i.e., the environment and surrounding technology where bovine milk production takes place. Results: Results showed the effectiveness of E-SECO to (i) abstract disruptive technologies based on the Internet of Things (IoT) and Artificial Intelligence and accommodate them in a single architecture for that specific domain, (ii) support reuse and derivation of a self-adaptive architecture to support engineering a complex system for a livestock sub-domain (milk production), and (iii) support empirical studies in a real smart farm towards a future transfer of technology to industry. Conclusion: The results showed that the E-SECO platform, which encompasses e-livestock architecture, can support monitoring, reasoning, prediction, and automated actions in a milk production/Compost Barn environment.
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U2 - 10.1016/j.infsof.2023.107240
DO - 10.1016/j.infsof.2023.107240
M3 - Article
AN - SCOPUS:85154577589
SN - 0950-5849
VL - 160
JO - Information and Software Technology
JF - Information and Software Technology
M1 - 107240
ER -