Abstract
We present the potential application of Machine Learning (ML) to fish farm in a similar approach used in agriculture to control crop growing and predict diseases. The agriculture concept of Precision Agriculture is now applied to fish farm by applying control-engineering principles to fish production; Precision Fish Farming (PFF) aims to improve the farmer's ability to monitor, control, and document biological processes. PFF can help the industry because it takes into consideration the boundary conditions and potentials that are unique to farming operations in the aquatic environment. The proposed solution improves commercial aquaculture and makes it possible to transition to knowledge-based production regime as opposed to experience-based. We apply a data mining approach to identify and evaluate the impact on the growth and mortality of fish in hatcheries. The use of ML techniques, combined with regulation, can increase the productivity and welfare of aquaculture living organisms.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
Antonucci, F., Costa, C.: Precision aquaculture: a short review on engineering innovations. Aquacult. Int. 28(1), 41–57 (2020)
Arafat, A., Akter, T., Ahammed, M., Ali, M., Nahid, A.: A dataset for internet of things based fish farm monitoring and notification system. Data Brief. 33, 106457 (2020)
Bijlsma, L., Berntssen, M., Merel, S.: A refined nontarget workflow for the investigation of metabolites through the prioritization by in silico prediction tools. Analyt. Chemis. 91(9), 6321–6328 (2019)
Chang, C.-C., Wang, Y.-P., Cheng, S.-C.: Fish segmentation in sonar images by mask R-CNN on feature maps of conditional random fields. Sensors 21(22), 7625 (2021)
Chen, C.-H., Wu, Y.-C., Zhang, J.-X., Chen, Y.-H.: IoT-based fish farm water quality monitoring system. Sensors 22(17), 6700 (2022)
Davis, A., Mukherjee, S., Wills, P., Ouyang, B.: Path planning algorithms for robotic aquaculture monitoring. In: Big Data IV: Learning, Analytics, and Applications, Proceedings Volume 12097, Orlando (2022)
Føre, M., et al.: Precision fish farming: a new framework to improve production in aquaculture. Biosyst. Eng. 173, 176–193 (2018)
Fuentes-Perez, J., Sanz-Ronda, F.: A custom sensor network for autonomous water quality assessment in fish farms. Electronics 10(18), 2192 (2021)
Kassem, T., Shahrour, I., El Khatabi, J., Raslan, A.: Smart and sustainable aquaculture farms. Sustainability 13(19), 1–16 (2021)
Kim, H.-S., Ko, W., Yang, J.: An effective risk management method with accurate water quality measuring system in fish farm. J. Eng. Appl. Sci. 13(11 SI), 8733–8736 (2018)
Kirankumar, P., Keertana, G., Sivarao, S., Vijaykumar, B., Shah, S.: Smart monitoring and water quality management in aquaculture using IOT and ML. In: 2021 IEEE International Conference on Intelligent Systems, Smart and Green Technologies, pp. 32–36. IEEE, Visakhapatnam (2021)
Li, D., Wang, G., Du, L., Zheng, Y., Wang, Z.: Recent advances in intelligent recognition methods for fish stress behavior. Aquacult. Eng. 96, 102222 (2022)
Masum, A.K.M.: implementation of internet of things (IoT)-based aquaculture system using machine learning approaches. Comput. Sci. J. Moldova 29(3), 320–329 (2021)
Ouyang, B., et al.: initial development of the hybrid aerial underwater robotic system (HAUCS): internet of things (IoT) for aquaculture farms. IEEE Internet Things J. 8(18), 14013–14027 (2021)
Parra, L., Sendra, S., García, L., Lloret, J.: Design and deployment of low-cost sensors for monitoring the water quality and fish behavior in aquaculture tanks during the feeding process. Sensors 18(3), 750 (2018)
Paul, B., Agnihotri, S., Kavya, B., Tripathi, P., Babu, C.: Sustainable smart aquaponics farming using IoT and data analytics. J. Inf. Technol. Res. 15(1), 1–27 (2022)
Saberioon, M., Cisar, P.: Automated within tank fish mass estimation using infrared reflection system. Comput. Electron. Agricult. 150, 484–492 (2018)
Sidoruk, M., Cymes, I.: Effect of water management technology used in trout culture on water quality in fishponds. Water 10(9), 1264 (2018)
Wada, M., Hatanaka, K., Natsir, M.: Development of automated sea-condition monitoring system for aquaculture in Indonesia. Sensors Mater. 31(3), 773–784 (2019)
Zambrano, A., Giraldo, L., Quimbayo, J., Medina, B., Castillo, E.: Machine learning for manually-measured water quality prediction in fish farming. Plos One 16(8), e0256380 (2021)
Hunter, M.C., Smith, R.G., Schipanski, M.E., Atwood, L.W., Mortensen, D.: Agriculture in 2050: recalibrating targets for sustainable intensification. BioSci. 67(4), 386–391 (2017)
Food and agriculture organization. https://www.fao.org/home/en/
Acknowledgments
This work was supported by EEA Grants Blue Growth Programme (Call #5), Project PT-INNOVATION-0069 – Fish2Fork.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2023 The Author(s), under exclusive license to Springer Nature Switzerland AG
About this paper
Cite this paper
Pires, A.R., Ferreira, J.C., Klakegg, Ø. (2023). The Future in Fishfarms: An Ocean of Technologies to Explore. In: Abraham, A., Bajaj, A., Gandhi, N., Madureira, A.M., Kahraman, C. (eds) Innovations in Bio-Inspired Computing and Applications. IBICA 2022. Lecture Notes in Networks and Systems, vol 649. Springer, Cham. https://doi.org/10.1007/978-3-031-27499-2_30
Download citation
DOI: https://doi.org/10.1007/978-3-031-27499-2_30
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-031-27498-5
Online ISBN: 978-3-031-27499-2
eBook Packages: Intelligent Technologies and RoboticsIntelligent Technologies and Robotics (R0)