# Aquasafe: A Remote Sensing, Web-Based Platform for the Support of Precision Fish Farming
## 4 Discussion  
The close collaboration among experts throughout the project's various stages ensured the practicality and reliability of the proposed methods and the results obtained. The project's multidisciplinary approach has resulted in the development of innovative tools that can be effectively utilized to enhance aquaculture operations. Aquasafe is a web-based platform designed for aquaculture management, emphasizing user-friendliness and ease of access to important information for farmers and managers who may not have experience with complicated web applications. It enables real-time monitoring of a wide range of parameters, including predictive modeling for fish growth, detection of harmful algal blooms, and alerts for changes in water quality parameters. The platform's strength lies in its ability to integrate geospatial information with _in-situ_ data for comprehensive monitoring of environmental parameters, using remote sensing data and machine learning algorithms to provide real-time information on environmental conditions and potential risks to aquaculture operations. The platform's strength is not in competition with existing systems such as OxyQuard and Bioceanor for parameter monitoring and Aquamanager for aquaculture management but rather aims to collaborate with these systems to achieve more sustainable aquaculture practices. By incorporating new, innovative technologies such as remote sensing and machine learning into the platform, Aquasafe ensures continuous and comprehensive monitoring, leading to more efficient and sustainable management of aquaculture facilities. Overall, the novelty and advantages of Aquasafe as a monitoring tool provide opportunities for the development of collaborative approaches and new practices in marine aquaculture. With respect to the growth model, applications of the Aquasafe platform were shown for three study aquaculture areas. The model was able to depict the seasonal patterns of growth for the considered species and yielded reasonably accurate predictions when compared with field measurements. Moreover, it captured regional differences between areas that are attributed to their respective environmental profiles, thus demonstrating the capacity of the models to simulate different environments. Similar outputs were shown for fish respiration. These features may provide a useful metric to the Aquasafe users when considering the oxygen requirements of the fish in their farms, which in turn may lead to timely adjustment of operations relating to feeding and population management. Lastly, the alert-producing feature of the platform was explored via the simulation of cages with low and high fish populations. It is clear that more work is required in refining the feature, and detailed oxygen modeling approaches already exist ([55; 56]); however, the alert output from the platform is promising and will hopefully provide useful insights to aquaculture operators and support risk-management decisions. The performance of the dissolved oxygen model was evaluated by the mean absolute error (MAE) and R-squared (R\({}^{2}\)), both of which showed promising results. The low value of MAE (0.33) indicates high precision and accuracy of the model, while R\({}^{2}\) was considered adequate at 0.67 (Table 2). Although R\({}^{2}\) is mainly used to examine the fit of the model rather than its performance, it is still a useful measure to determine how well the model fits the dependent variables. However, it does not provide information about the distribution of the residuals, nor is it representative of the magnitude and direction of the error. Nonetheless, both metrics are promising for our project. The time-series plot showed seasonal variations in both temperature and dissolved oxygen, highlighting the need for continuous monitoring to ensure optimal water quality conditions for fish, especially during the summer months when the temperature reached nearly 28 \({}^{\circ}\)C and dissolved oxygen dropped to 4.7 mg/L (Figure 16).