Author
Rosa Martínez
#BlueEconomy, #BE, #BlueGrowth, #Acuiculture4.0, #feeding, #efficiency, #IA, #machinelearning, #Industry4.0, #feedingprocess, #geneticmodels, #smartmodels
Success and profitability of the aquaculture production depends on the use of refined techniques and the appropriate decision-making during cultivation period. In national aquaculture, the most interesting species productivity such as seabream (Sparus aurata) or sea bass (Dicentrarchus Labrax) has not grown during the last years due to the need for technical and scientist advances to solve the bottlenecks. Digital technologies still have a long way to go even though there are many systems of remote observation and marine data collection. Artificial Intelligence technology may be helpful, identifying models in feeding activities and introducing strategies for fish farmers as efficient use of feed and comfort maintenance of the fishes.
Aquaculture systems are complex because of biological variability of the chemical and physical processes. Therefore, the processes and interactions knowledge of a system is vital to reduce the environmental degradation in fishes intensive production. There are mathematics and stochastic space-time models that allow both the evaluation of the intake of a fish stock although requires establishing a relationship between food intake and impact factors[1]; and the spread of diseases between and within farms; as well as the yield and grown production species estimation considering different factors (behaviour, grown, distribution and feed conversion efficiency among others) [2],[3].
However, it is difficult to solve this problem with traditional mathematics models due to the complex and no lineal relationship between food intake and other factors. Then, the need for AI models arises, making easier the daily management farm operations, feeding rate determination, dissolved oxygen levels prediction or even the analysis of the effects of management strategies. In the literature, we find samples such as:
Beyond these models there are genetics algorithms (one of the most popular heuristics approximations to the processes optimization and making decisions based on multiple criteria) that have been used both for determination of optimal cultivation strategies that maximize economic performance and determination of feed sequences to improve fattening period.
Author: Rosa Martínez, CTN - Marine Technology Centre - Centro Tecnológico Naval y del Mar
Sources:
[1] Chen, L., Yang, X., Sun, C., Wang, Y., Xu, D., & Zhou, C. (2020). Feed intake prediction model for group fish using the MEA-BP neural network in intensive aquaculture. Information Processing in Agriculture, 7(2), 261–271. https://doi.org/10.1016/j.inpa.2019.09.001
[2] Føre, M., Alver, M., Alfredsen, J. A., Marafioti, G., Senneset, G., Birkevold, J., Willumsen, F. V., Lange, G., Espmark, Å., & Terjesen, B. F. (2016). Modelling growth performance and feeding behaviour of Atlantic salmon (Salmo salar L.) in commercial-size aquaculture net pens: Model details and validation through full-scale experiments. Aquaculture, 464, 268–278. https://doi.org/10.1016/j.aquaculture.2016.06.045
[3] Seginer, I. (2016). Growth models of gilthead sea bream (Sparus aurata L.) for aquaculture: A review. Aquacultural Engineering, 70, 15–32. https://doi.org/10.1016/j.aquaeng.2015.12.001
[4] Palaiokostas, C. (2021). Predicting for disease resistance in aquaculture species using machine learning models. Aquaculture Reports, 20(March), 100660. https://doi.org/10.1016/j.aqrep.2021.100660
[5] Besson, M., Allal, F., Chatain, B., Vergnet, A., Clota, F., & Vandeputte, M. (2019). Combining Individual Phenotypes of Feed Intake with Genomic Data to Improve Feed Efficiency in Sea Bass. Frontiers in Genetics, 10(MAR), 219. https://doi.org/10.3389/fgene.2019.00219
[6] Manoharan, H., Teekaraman, Y., Kshirsagar, P. R., Sundaramurthy, S., & Manoharan, A. (2020). Examining the effect of aquaculture using sensor-based technology with machine learning algorithm. Aquaculture Research, 51(11), 4748–4758. https://doi.org/10.1111/are.14821
[7] Li, D., & Liu, C. (2020). Recent Advances and Future Outlook for Artificial Intelligence in Aquaculture. Smart Agriculture, 2(3), 1–20. https://doi.org/10.12133/J.SMARTAG.2020.2.3.202004-SA007