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dc.contributor.authorLi, Hui
dc.contributor.authorChen, Yingyi
dc.contributor.authorLi, Wensheng
dc.contributor.authorWang, Qingbin
dc.contributor.authorDuan, Yanqing
dc.contributor.authorChen, Tao
dc.identifier.citationLi H, Chen Y, Li W, Wang Q, Duan Y, Chen T (2021) 'An adaptive method for fish growth prediction with empirical knowledge extraction', Biosystems Engineering, 212, pp.336-346.en_US
dc.description.abstractFish growth prediction provides important information for optimising production in aquaculture. Fish usually exhibit different growth characteristics due to the variations in the environment, the equipment used in different fish workshops and inconsistent application by operators of empirical rules varying from one pond to another. To address this challenge, the aim of this study is to develop an adaptive fish growth prediction method in response to feeding decision. Firstly, the practical operational experience in historical feeding decisions for different fish weights is extracted to establish the feeding decision model. Then, a fish weight prediction model is established by regression analysis methods based on historical fish production data analysis. The feeding decision model is integrated as the input information of the fish weight prediction model to obtain fish weight prediction. Furthermore, an adaptive fish growth prediction strategy is proposed by continuously updating model parameters using new measurements to adapt to specific characteristics. The proposed adaptive fish growth prediction method with empirical knowledge extraction is evaluated by the collected production data of spotted knifejaw (Oplegnathus punctatus). The results show that established models can achieve a good balance between goodness-of-fit and model complexity, and the adaptive prediction method can adapt to specific fish pond’s characteristics and provide a more effective way to increase fish weight prediction accuracy. The proposed method provides an important contribution to achieving adaptive fish growth prediction in a real time from the view of aquaculture practice for spotted knifejaw.en_US
dc.description.sponsorshipUK-China collaborative research programme “Advancing digital precision aquaculture in China (ADPAC)” [UK BBSRC grant BB/S020896/1 and Innovate UK grant 104904; National Key R&D Program of China, Grant no. 2017YFE0122100].en_US
dc.rightsGreen - can archive pre-print and post-print or publisher's version/PDF
dc.rightsAttribution-NonCommercial-NoDerivatives 4.0 International*
dc.subjectknowledge extractionen_US
dc.subjectadaptation modelsen_US
dc.subjectSubject Categories::D435 Aquacultureen_US
dc.titleAn adaptive method for fish growth prediction with empirical knowledge extractionen_US
dc.contributor.departmentUniversity of Surreyen_US
dc.contributor.departmentChina Agricultural Universityen_US
dc.contributor.departmentLaizhou Mingbo Aquatic Products Co., Ltden_US
dc.contributor.departmentUniversity of Bedfordshireen_US
dc.identifier.journalBiosystems Engineeringen_US
dc.description.note12m embargo from pub date when known

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