Version 1
: Received: 20 September 2024 / Approved: 23 September 2024 / Online: 23 September 2024 (07:36:50 CEST)
How to cite:
Badreldin, N.; Cheng, X.; Youssef, A. An Overview of Software Sensor Applications in Biosystem Monitoring and Control. Preprints2024, 2024091722. https://doi.org/10.20944/preprints202409.1722.v1
Badreldin, N.; Cheng, X.; Youssef, A. An Overview of Software Sensor Applications in Biosystem Monitoring and Control. Preprints 2024, 2024091722. https://doi.org/10.20944/preprints202409.1722.v1
Badreldin, N.; Cheng, X.; Youssef, A. An Overview of Software Sensor Applications in Biosystem Monitoring and Control. Preprints2024, 2024091722. https://doi.org/10.20944/preprints202409.1722.v1
APA Style
Badreldin, N., Cheng, X., & Youssef, A. (2024). An Overview of Software Sensor Applications in Biosystem Monitoring and Control. Preprints. https://doi.org/10.20944/preprints202409.1722.v1
Chicago/Turabian Style
Badreldin, N., Xiaodong Cheng and Ali Youssef. 2024 "An Overview of Software Sensor Applications in Biosystem Monitoring and Control" Preprints. https://doi.org/10.20944/preprints202409.1722.v1
Abstract
This review article explores the innovative application of software sensors for monitoring and controlling biosystems, emphasizing their advantage in managing the complexities of various biological processes. Biosystems—from cellular interactions to ecological dynamics—are characterized by intrinsic nonlinearity, temporal variability, and uncertainty, posing significant challenges for traditional monitoring approaches. A critical challenge highlighted is that what is typically measurable may not align with what needs to be monitored. Software sensors bridge this gap by combining hardware sensor data with advanced computational modelling techniques to indirectly infer hard-to-measure target variables, such as stress level, animal and human health indicators, and chemical soil properties. The article outlines advancements in sensor technologies and their integration into model-based monitoring and control systems, leveraging the capabilities of Internet of Things (IoT) devices, wearables, remote sensing, and smart sensors. It provides an overview of common methodologies for designing software sensors, focusing on the modelling process. The discussion contrasts hypothetico-deductive (mechanistic) models with inductive (data-driven) models, illustrating the trade-offs between model accuracy and interpretability. Specific case studies are presented, showcasing software sensor applications such as the use of Kalman filter in greenhouse control, remote detection of soil organic matter, and sound recognition algorithms for early detection of respiratory infections in animals. Key challenges in designing software sensors, including the complexity of biological systems, inherent temporal and individual variabilities, and the trade-offs between model simplicity and predictive performance, are also discussed. This article positions software sensor as remarkable tool for advancing biosystem management, driving forward the potential for sustainable practices in various sectors such as agriculture, healthcare, and environmental monitoring.
Keywords
Software sensors; Biosystems; Monitoring; Control; Machine-Learning; Digital Agriculture
Subject
Engineering, Bioengineering
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
