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A Lightweight Deep Learning Model for Forecasting the Fishing Ground of Purpleback Flying Squid (Sthenoteuthis oualaniensis) in the Northwest Indian Ocean

文献类型： 外文期刊

作者： Zhang, Shengmao 1 ; Chen, Junlin 1 ; Han, Haibin 1 ; Tang, Fenghua 1 ; Cui, Xuesen 1 ; Shi, Yongchuang 1 ;

作者机构： 1.Chinese Acad Fishery Sci, Minist Agr & Rural Affairs, East China Sea Fisheries Res Inst, Key Lab Fisheries Remote Sensing, Shanghai 200090, Peoples R China

2.Laoshan Lab, Qingdao 266237, Peoples R China

关键词： deep learning; lightweight; Northwest Indian Ocean; remote sensing data; Sthenoteuthis oualaniensis

期刊名称：APPLIED SCIENCES-BASEL （ 影响因子：2.5； 五年影响因子：2.7 ）

ISSN：

年卷期： 2025 年 15 卷 3 期

页码：

收录情况： SCI

摘要： The purpleback flying squid (Sthenoteuthis oualaniensis) is an economically significant cephalopod species in the Northwest Indian Ocean. Predicting its fishing grounds can provide a crucial foundation for fishery management and production. In this research, we collected data from China's light-purse seine fishery in the Northwest Indian Ocean from 2016 to 2020 to train and validate the AlexNet and VGG11 models. We designed a data partitioning method (DPM) to divide the training set into three scenarios, namely DPM-S1, DPM-S2, and DPM-S3. Firstly, DPM-S1 was employed to select the base model (BM). Subsequently, the optimal BM was lightweighted to obtain the optimal model (OM). The OM, known as the AlexNetMini model, has a model size that is one-third of that of the BM-AlexNet model. Our results also showed the following: (1) the F1-scores for AlexNet and AlexNetMini across the datasets DPM-S1, -S2, and -S3 were 0.6957, 0.7505, and 0.7430 for AlexNet and 0.6992, 0.7495, and 0.7486 for AlexNetMini, suggesting that both models exhibited comparable predictive performance; (2) the optimal dropout values for the AlexNetMini model were 0 and 0.2, and the optimal training set proportion was 0.8; (3) AlexNetMini utilized both DPM-S2 and DPM-S3, yielding comparable outcomes. However, given that the training duration for DPM-S3 was relatively shorter, DPM-S3 was selected as the preferred method for data partitioning. The findings of our study indicated that the lightweight model for the purpleback flying squid fishing ground prediction, specifically AlexNetMini, demonstrated superior performance compared to the original AlexNet model, particularly in terms of efficiency. Our study on the lightweight method for deep learning models provided a reference for enhancing the usability of deep learning in fisheries.

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