# A Multi-Step Image Pre-Enhancement Strategy for a Fish Feeding Behavior Analysis Using Efficientnet
## 4 Conclusions  
This paper presents an innovative image pre-enhancement strategy designed to optimize the precision and efficiency of fish feeding behavior classification. Initially, a multi-step image pre-enhancement protocol is adopted, encompassing three main processing phases: Multi-Scale Retinex with Color Restoration (MSRCR), Multi-Metric Driven Contrast Limited Adaptive Histogram Equalization (mdc), and Unsharp Masking. This suite  
Figure 9: Feature visualization using the t-SNE method. (**a**) shows the classification results of the EfficientNet network model on images of fish feeding behavior, visualized in the form of t-SNE. (**b**) shows the classification results of the MIPS-EfficientNet network model on images of fish feeding behavior, also visualized through t-SNE. of pre-enhancement actions effectively rectifies common image quality issues, such as surface reflections, low contrast, and indistinct detail features. Subsequent to image pre-enhancement, a lightweight EfficientNet neural network model is further employed to accomplish the classification of fish feeding behaviors. This study involves integrating a Multi-Scale Image Preprocessing Strategy (MIPS) into the EfficientNet model and comparing its performance against the baseline, unprocessed EfficientNet model. The analysis focuses on the accuracy (acc) post model training and the time required for prediction. Moreover, to assess the universality of the MIPS preprocessing strategy and its impact on model performance, this strategy is also applied to other leading deep learning frameworks, such as ResNet-18, ResNet-50, and ResNeXt50. By comparing the accuracy and prediction time of these frameworks after applying MIPS preprocessing, this research aimed to comprehensively evaluate the potential of the preprocessing strategy in enhancing the efficiency and effectiveness of various deep learning models. The test results indicate that the proposed Multi-Step Image Pre-enhancement (MIPS) module significantly enhances the performance of various convolutional neural network (CNN) models in terms of classification accuracy. Particularly in comparison with the current advanced ResNet series models, this approach not only improves the classification accuracy but also substantially reduces the training times of the models. These achievements demonstrate that the method introduced in this paper is both effective and efficient in processing fish feeding behavior classification tasks. To validate the efficiency of the algorithm, we evaluated its practical application in the fish feeding process during experiments. By comparing it with other well-known convolutional neural networks (CNNs), we found that the MIPS-enhanced models performed exceptionally well on several key metrics. Below are some practical application examples. In terms of cost reduction, by integrating the algorithm to optimize the feeding process, feed waste was reduced from 25% to 10%, saving the fish farm approximately 500 kg of feed per month, which is equivalent to monthly savings of USD 1500 (assuming a feed cost of USD 3 per kilogram). Regarding feeding efficiency, the experiment revealed that manual feeding took about 2 h per session, while the automated feeding system, after implementing the algorithm, completed the process in 1.5 h, increasing feeding efficiency by 25%, allowing staff to focus on other critical tasks. In terms of productivity improvement, an experiment monitoring two ponds showed that the pond using the algorithm had a 10% higher fish growth rate and a 15% improvement in the feed conversion ratio (FCR), resulting in an average weight increase of 200 g per fish, enhancing market value and profitability. Regarding the environmental impact, the optimized feeding process reduced the occurrence of feed waste sinking to the bottom of the pond, thus reducing water pollution and the need for frequent pond cleaning, benefiting the environment and lowering maintenance costs for the fish farm.