## 4 Discussion
### Comparison of the Application Effects of the AlexNet and VGG11 Models  
There are differences in the performance of the AlexNet and VGG11 models in different domains, and the better model can be decided by comparing their metrics. For example, [PERSON] et al. [23] found that the AlexNet model far outperformed the VGG11 model in terms of accuracy. When it came to identifying automotive tire defects, the AlexNet model achieved an average accuracy rate of 71.40%, while the VGG11 model only reached 37.58%. In the identification of COVID-19, the AlexNet model's accuracy, recall, precision, and F1-score are comprehensively higher than the VGG11 model [24]. There are also studiesreporting that the VGG11 model is better than AlexNet. [PERSON] et al. [25] argued that the AlexNet model has a simple structure and poor robustness on face perception, while the VGG11 model performs better than the AlexNet model. There are also studies showing that the AlexNet and VGG11 models have similar recognition results, but, based on the consideration of time, the AlexNet model was finally chosen. [PERSON] et al. [26] compared the prediction performance of the AlexNet and VGG11 models in the fish recognition process and found that there is not much difference in the accuracy, F1-score, and other indices between the two, but the AlexNet model has a shorter running time. [PERSON] et al. [27] obtained similar results in the study of marine pollution image classification. In this study, the AlexNet model was more stable than the VGG11 model, with higher accuracy and shorter training time. Therefore, AlexNet is more suitable for predicting purpleback flying squid fishing grounds in the Northwest Indian Ocean. The primary distinction between the two types of models discussed in this paper is the size of the convolutional kernels. In AlexNet, the first two layers made use of relatively larger convolutional kernels. Specifically, the kernel size of the first layer was \(11\times 11\), while that of the second layer was \(5\times 5\). In contrast, the VGG11 model employed uniform \(3\times 3\) kernels throughout the whole network. In terms of training performance, the AlexNet model outperformed the VGG11 model. Up to now, no research has been discovered concerning the influence of the convolutional kernel size on the fishery ground prediction model. In many applications of CNN models, a sizeable convolutional kernel is usually split into several small convolutional kernels [8; 26]. Still, according to the effective perceptual field (ERF) theory [28], the ERF increases linearly with the kernel size and sub-linearly with the depth. [PERSON] et al. [29] also demonstrated that using large convolutional kernels instead of splitting them into several small convolutional kernels results in a larger ERF, dramatically improving the CNN performance. This may be one of the reasons for the better performance of the AlexNet model compared to the VGG11 model in this study. In addition, some studies have shown that a higher recognition rate can also be obtained if we adjust the size of the convolution kernel according to the image characteristics. [PERSON] et al. [30] believed that an initial application of a larger convolution kernel is advantageous for the extraction of contour features, followed by the utilization of a smaller convolution kernel to eliminate detailed features. This approach is suggested to enhance the recognition rate in leaf identification tasks. In the study of hand-drawn sketch recognition, it is also found that a satisfactory recognition rate can be obtained by adjusting the size of the convolutional kernel according to the law of sketch recognition and using a larger first-layer convolutional kernel instead of a smaller one [31]. In the present study, the temperature fields of the fishing grounds that the purpleback flying squid inhabits also belong to the relatively large-scale features.