# A Multi-Step Image Pre-Enhancement Strategy for a Fish Feeding Behavior Analysis Using Efficientnet
## 2 Methodology
### Data Collection  
The experiment was conducted within a real aquaculture system at the Hai'an aquaculture base, which houses approximately 500 Koi fish, each weighing between 1200 and 1500 g. Throughout this study, the aquaculture pond environment was consistently maintained with dissolved oxygen levels at 7.1 \(\pm\) 1.0 mg/L and a water temperature within the range of 12-15 \({}^{\circ}\)C. Prior to the commencement of the experiment, the fish were trained to feed at a fixed location to facilitate subsequent data collection. Feeding was carried out twice daily, at 9:30 a.m. and 5:30 p.m., with each feeding amount set to 1.5-2.0% of the total weight of the fish stock.  
The image collection system ensures optimal shooting effects through the rational layout of light sources and cameras, as well as the use of wide-angle lenses, as illustrated in Figure 2. Firstly, the light source is located directly above the breeding area, approximately 1.2 m high, which can evenly illuminate the entire water surface and fish bodies, thereby ensuring the clarity and detail presentation of the images. Secondly, the camera is vertically placed above the breeding pool at a height of approximately 1.2 m, approximately 1 m above the surface of the aquaculture pool. This height and angle can cover the entire breeding pool, ensuring that all fish activities are captured, avoiding dead corners and blind spots, which is conducive to a comprehensive observation and analysis of fish behavior and activities.  
The video data collected were encoded in MP4 format, featuring a frame rate of 30 frames per second and a resolution of 1280 \(\times\) 720 pixels. Image processing was performed using Python, with the PyTorch library utilized for constructing neural networks. The captured videos were segmented into 452 sub-videos, each containing 300 frames. Video frames were extracted from the collected video data at intervals of 100 frames to construct a dataset of fish stock images. Subsequently, the collected dataset of fish stock images was categorized and labeled into four classes based on the fish's feeding behaviors and the amount of feed dispensed.  
Figure 2: The structure of the image acquisition system.  
As shown in Table 1, the behaviors of the fish vary significantly under different feeding states. At the initial stage of feeding, the fish exhibit a strong desire to feed, competing for bait while causing disturbances such as splashes and noise. Approximately 1-2 min later, as the bait begins to diminish, a portion of the fish ceases feeding, and the overall feeding intensity of the stock [37, 38] decreases to a moderate level. After another minute, with a further reduction in bait, only a few fish continue to show feeding interest, and the school begins to disperse further. Around 4 min into the process, almost all fish lose their desire to feed and start to disperse, swimming slowly along the bottom. Images representing different categories are illustrated in Figure 3.  
To enhance the diversity of the data samples and thereby improve model performance, several preprocessing operations are performed on the images of the fish stock. Initially, the images are subjected to random aspect ratio cropping, where they are randomly cropped into various sizes and aspect ratios. Under this premise, these images are then randomly rotated by changing their rotation angles and centers. Subsequently, the rotated images are trimmed to a uniform size. Finally, the images of the fish stock undergo random alterations in brightness, contrast, saturation, and hue.