A scientific software ecosystem architecture for the livestock domain

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...

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Bibliographic Details
Published in:	Information and software technology Vol. 160; p. 107240
Main Authors:	Gomes, Jonas, Esteves, Izaque, Valdemar Vicente Graciano Neto, David, José Maria N., Braga, Regina, Arbex, Wagner, Kassab, Mohamad, de Oliveira, Roberto Felício
Format:	Journal Article
Language:	English
Published:	Elsevier B.V 01-08-2023
Subjects:	Compost barn Dairy cattle Experiments Livestock Self-adaptive systems Sensors data Software ecosystem Software ecosystem Compost barn Sensors data Dairy cattle Self-adaptive systems Livestock Experiments
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Summary:	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. 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. 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 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. 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. •Agribusiness Production Systems (APS) demand software-intensive solutions.•A Software Ecosystem architecture can abstract and accommodate the solutions.•The solutions include IoT, self-adaptation and artificial intelligence.•APS with those solutions can be derived from the software ecosystem architecture.•The APS can then monitor, reason and perform automated actions.
ISSN:	0950-5849 1873-6025
DOI:	10.1016/j.infsof.2023.107240