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Fish Biomass Estimation Under Occluded Features: A Framework Combining Imputation and Regression

文献类型： 外文期刊

作者： Yang, Yaohui 1 ; Zhang, Lijun 2 ; Liu, Zhixiang 1 ; Luo, Tuyan 3 ; Bao, Baolong 4 ; Zhou, Liping 5 ; Xu, Jingxiang 2 ;

作者机构： 1.Shanghai Ocean Univ, Coll Informat Technol, Shanghai 201306, Peoples R China

2.Shanghai Ocean Univ, Coll Engn Sci & Technol, Shanghai 201306, Peoples R China

3.Fujian Acad Agr Sci, Inst Agr Qual Stand & Testing Technol, Fuzhou 350003, Peoples R China

4.Shanghai Ocean Univ, Key Lab Explorat & Utilizat Aquat Genet Resources, Minist Educ, Shanghai 201306, Peoples R China

5.Shanghai Univ, Sch Comp Engn & Sci, Shanghai 200444, Peoples R China

关键词： fish biomass estimation; feature occlusion; missing data imputation; regression models; precision aquaculture

期刊名称：FISHES （ 影响因子：2.4； 五年影响因子：2.4 ）

ISSN：

年卷期： 2025 年 10 卷 7 期

页码：

收录情况： SCI

摘要： In biomass estimation based on size-related features, regression models are commonly used to predict fish mass. However, in real-world scenarios, fish are often partially occluded by others, resulting in missing or corrupted features. To address this issue, we propose a robust framework that integrates feature imputation with regression. Missing features are first reconstructed through imputation, followed by regression for biomass prediction. We evaluated various imputation and regression methods and found that the autoencoder achieved the best performance in imputation. Among regression models, SVR, Extra Trees, and MLP performed best in their respective categories. These three models, combined with the autoencoder, were selected to construct the final framework. Experimental results demonstrate that the proposed framework significantly improves performance. For instance, the RMSE of SVR, Extra Trees, and MLP decreased from 21.10 g, 2.49 g, and 18.40 g to 6.53 g, 1.95 g, and 5.09 g, respectively.

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