An Approach from Software Engineering to an IoT and Machine Learning Technological Solution that Allows Monitoring and Controlling Environmental Variables in a Coffee Crop

Context: Software engineering allows us to approach software design and development from the practical application of scientific knowledge. In the case of this IoT solution and the machine learning approach to the monitoring and control of environmental variables in a coffee crop, it allows us to visualize certain artifacts of the system in their interaction with users and their behavior with other artifacts or devices that constitute a technological solution. Method: For this work, the application of software engineering from a conceptual approach and the behavior of the system is proposed. To meet these objectives, we decided to use the Unified Modeling Language (UML) in such a way that the most important components of the technological solution could be represented from a static perspective through the use case diagrams, as well as from a dynamic viewpoint through the sequence diagrams. Results: Through the application of the UML, it was possible to develop the conceptual and behavioral modeling of certain artifacts and components. This knowledge allowed identifying the interaction between physical components and devices (machine to machine) and human-machine interaction, that is, the relationship between users and the processes that make up the technological solution. Conclusions: Through software engineering, and more specifically the UML, we were able to establish the importance of knowing the different software artifacts that make up a system or application from a different technical and functional approach, while being able to collect valuable information about the behavior of certain system artifacts, as well as the interaction between users and processes.

• Autores: Ruiz-Martínez, William; González-Gómez, Arnaldo Andrés
• Tipo de recurso: Article of investigation
• Fecha de publicación: 2021
• Institución: Universidad de Cundinamarca
• Repositorio: Repositorio UdeC
• Idioma: eng
• OAI Identifier: oai:repositorio.cun.edu.co:cun/4173
• Acceso en línea: https://repositorio.cun.edu.co/handle/cun/4173
• Palabra clave: Ingeniería y operaciones afines; Internet of Things; Machine learning; Computer application; Static views; Dynamic views; Conceptual modeling; Behavioral modeling; UML
• Rights: closedAccess
• License: http://purl.org/coar/access_right/c_14cb