# Smart Low-Cost Control System for Fish Farm Facilities
## 6 Conclusions  
The proposed control system for aquaculture facilities outperforms the existing systems for aquaculture control. It is more complex in terms of included parameters, information sources, and actuators. In addition, the used architecture, communication technology, and network protocols ensure its scalability to large fish farms with dozens of cages or tanks. The use of low-cost devices, such as the nodes and sensors, as well as LoRa and WiFi as communication technologies, makes possible the system's low cost. The presented smart control algorithm ensures continuous machine learning, which will provide accurate decisions. Finally, the fault tolerance mechanisms, which include backup communication technologies with the DB, an off-line operation mode thanks to the predefined control algorithms, and data processing methods to detect false values, endow the system with a high level of robustness. All these characteristics, together with the versatility and adaptability of the system to different fish farm scenarios, as presented in Example 1 and Example 2, make this system extremely useful for the fish farm industry.  
It is acknowledged that the maritime climate is a harsh environment, necessitating proper isolation of all devices to prevent damage. Although traditionally considered expensive, recent publications [14; 120] indicate that new low-cost devices have emerged on the market in recent years. Some recent papers have claimed the cost of sensors for water quality monitoring for a coastal aquaculture system is lower than EUR 6000 [14], EUR 2000 [120], or even less than EUR 500 if we focus on tank monitoring [110]. The final cost will be related to the included parameters, the location, and the number of monitored tanks/cages. The cost of the proposed control system in this paper depends on the cloud services for data storage and algorithm operation. It is impossible to offer the value of these services, given the high heterogeneity in these services and the possibility of using local storage. Nevertheless, estimated costs can be close to EUR 100/month, which are easily offset by savings in energy, feed, and enhanced fish performance.  
In future work, the proposed system will be tested in fish tanks and more low-cost sensors will be tested and calibrated. So far, some of the included sensors still require high maintenance needs and have membranes of selective electrodes such as the ones for DO and pH. The creation of physical sensors that require a low level of maintenance and have a low cost will be a top priority. Moreover, the adaptation of LoRa technology to create tree topology networks to connect a different group of cages, and the use of LoRa underwater as a possible alternative for underwater acoustic modes, are currently being studied.  
Conceptualization, L.P. and J.L.; methodology, L.P.; investigation, L.P., S.S., L.G. and J.L.; resources, L.P. and L.G.; data curation, L.P. and L.G.; writing--original draft preparation, L.P. and L.G.; writing--review and editing, S.S. and J.L.; visualization, S.S.; supervision, J.L.; funding acquisition, S.S. and J.L. All authors have read and agreed to the published version of the manuscript.  
This study forms part of the ThinkInAzul programme and was supported by MCIN with funding from European Union NextGenerationEU (PFRT-C17.I1) and by Generalitat Valenciana (THINKINAZUL/2021/002) and by the grant FJC2021-047073-I funded by MCIN/AEI/10.13039/501100011033 and by the European Union NextGenerationEU/PFRT.  
Not applicable.  
Data Availability Statement: The original contributions presented in this study are included in the article, further inquiries can be directed to the corresponding author.  
The authors declare no conflicts of interest.