Juhyoung Sung, Sungyoon Cho, Da-Eun Jung, Jongwon Kim, Jeonghwan Park, Kiwon Kwon, Young Myoung Ko, Journal of Internet Computing and Services, Vol. 24, No. 1, pp. 61-69, Feb. 2023
10.7472/jksii.2023.24.1.61, Full Text:
Keywords: Gaussian process, thermal growth coefficient, growth prediction model, growth data, Water Temperature

Abstract

Recently, as the fishery resources are depleted, expectations for productivity improvement by 'rearing fishery' in land farms are greatly rising. In the case of land farms, unlike ocean environments, it is easy to control and manage environmental and breeding factors, and has the advantage of being able to adjust production according to the production plan. On the other hand, unlike in the natural environment, there is a disadvantage in that operation costs may significantly increase due to the artificial management for fish growth. Therefore, profit maximization can be pursued by efficiently operating the farm in accordance with the planned target shipment. In order to operate such an efficient farm and nurture fish, an accurate growth prediction model according to the target fish species is absolutely required. Most of the growth prediction models are mainly numerical results based on statistical analysis using farm data. In this paper, we present a growth prediction model from a stochastic point of view to overcome the difficulties in securing data and the difficulty in providing quantitative expected values for inaccuracies that existing growth prediction models from a statistical point of view may have. For a stochastic approach, modeling is performed by introducing a Gaussian process regression method based on water temperature, which is the most important factor in positive growth. From the corresponding results, it is expected that it will be able to provide reference values ​​for more efficient farm operation by simultaneously providing the average value of the predicted growth value at a specific point in time and the confidence interval for that value.

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