## 5 Discussion  
The results of our study show that the developed multi-sensor platform is based on a reliable and precise architecture that combines different types and numbers of sensors that collect heterogeneous data that are stored following strict rules. This is consistent with the findings of other studies, such as those of [4], which focused on urban air quality, and [8], which investigated indoor air quality monitoring. However, our study uniquely addresses the specific needs of the livestock environment, filling a gap in the existing literature. Unlike the work of [9], which mainly discusses the use of metal oxide (MOx) gas sensors for residential air quality monitoring, our system uses a diverse array of sensors tailored to the agricultural sector.  
Using multiple sensors to measure the same parameter allows for cross-referencing and validation of data. This redundancy improves the accuracy and reliability of measurements, addressing challenges such as sensor drift or calibration errors [34]. The result is a more robust and trustworthy dataset.  
Air quality can vary widely within a place in an animal barn due to local factors and microenvironments. A multi-sensor system offers the flexibility to tailor monitoring efforts by strategically placing sensors in different locations based on specific environmental concerns. This adaptability ensures that monitoring is reflective of the unique characteristics of each area. The wealth of data generated by multi-sensor systems enables data-driven decision making [35]. Farmers can use this information to formulate effective strategies for pollution control. Multi-sensor integration allows for the inclusion of new sensors to monitor emerging pollutants or changing environmental conditions, ensuring that air quality monitoring systems remain adaptive and forward looking.  
However, challenges arise in maintaining consistency across sensors, especially in ensuring the accuracy and reliability of sensor readings which requires regular calibration and maintenance. Additional challenges for PLF include the continued development of IoT technologies ([[https://livestocksense.eu/](https://livestocksense.eu/)]([https://livestocksense.eu/](https://livestocksense.eu/))) and sensors for efficient transmission, storage,  
Figure 8: Correlation heatmap. It provides a view of the relationships between the observed parameters. Each cell in the heatmap represents the Pearson correlation coefficient between two variables, with values ranging from \(-1\) (perfect negative correlation) to \(1\) (perfect positive correlation).  
and data security that can be integrated into PLF systems and enable them to achieve the goals of precision agriculture [36]. Factors such as air ionization, noise level, amount of carbon monoxide, and bacterial contamination should also be considered in future PLF multi-sensor systems. The development of complex algorithms to integrate and interpret data from multiple sensors is also among the challenges and ongoing work. Integrating Artificial Intelligence (AI) algorithms for data analysis can enhance the capabilities of multi-sensor systems. Machine learning can improve pattern recognition, leading to more accurate and predictive models. Utilizing edge computing technologies allows for data processing closer to the source, reducing latency and enabling more efficient real-time monitoring without overburdening centralized systems.  
In conclusion, the importance of multi-sensor integration in air quality monitoring cannot be overstated. It transforms monitoring efforts from singular and limited perspectives to a comprehensive and dynamic approach, providing a deeper understanding of the intricate interplay between pollutants and environmental factors ([[https://www.clearfarm.eu/the-project/](https://www.clearfarm.eu/the-project/)]([https://www.clearfarm.eu/the-project/](https://www.clearfarm.eu/the-project/)) accessed on 10 April 2024). This evolution is essential for fostering sustainable practices in PLF.