# Non-Contact Tilapia Mass Estimation Method Based on Underwater Binocular Vision
## 4 Discussion  
In some previous studies using fish body length to estimate fish mass, it was necessary to manually mark the fish contour. However, such marking is not only less accurate but also time-consuming and laborious [23; 24]. Moreover, the free swimming of the fish in the water increases the difficulty of this operation. There are also combinations of features [25] to estimate fish mass and the use of fish body area [26] to estimate mass. In this study, in order to improve the accuracy of labeling the mouth and tail end of the fish, the image is segmented first. The body part of the fish is accurately segmented, and then the segmented image is labeled. And underwater image enhancement is performed before image stereo matching to further improve the matching accuracy. With the rapid development of deep learning, the SAM model released by Meta-AI has been able to perform semantic segmentation without the researcher providing any training samples. The segmentation in this study was directly based on this platform. For the segmented fish body, we can more clearly label the coordinates of the fish's muzzle and caudal fin base. And use the pixel coordinates combined with the depth information to estimate the body length of the fish more accurately.  
The experimental results show that the mass of the tilapia can be accurately estimated using the single characteristic of fish body length. However, as the tilapia grows it may lead to larger errors. In this study, it was found that, at the later stage of tilapia growth, the growth rate of the body length of the tilapia was lower than the growth rate of its own mass. At this point, a single body length can no longer accurately estimate the mass of tilapia. It is necessary to consider adding other characteristic parameters such as body height and body width to improve the estimation of tilapia weight to further improve the accuracy of tilapia weight estimation.  
Although this study created a dataset containing images of tilapia, the dataset was primarily collected under farm tank conditions. These experimental conditions resulted in a homogenous experimental context and fish species limitations. In the future, in order to enhance the general applicability of the method, it could be tested in more diverse natural water environments. A wider range of species of fish and their images under different background conditions could be collected to verify the applicability and generalizability  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Method** & **MRE/\%** & **R\({}^{2}\)** \\ \hline Based on area and area squared & 8.46 & 0.99 \\ YOLOv4 DLN & 7.44 & 0.94 \\ Our method & 5.90 & 0.99 \\ \hline \hline \end{tabular}
\end{table}
Table 7: Comparison of results of different methods for estimating fish mass.  
Figure 14: Comparison between estimated and manually measured fish mass.  
of the system. In this paper, the coordinates of the key points of the fish's muzzle and caudal fin base are extracted using manual labeling, and there is some human error in the calculation of the fish's body length. In the subsequent research, the model can be built using neural networks with deep learning methods. More complex datasets can be trained so as to achieve automatic detection and labeling of the endpoints of the snout and the base of the caudal fin for fully automated fish quality estimation. Currently, the system relies on a laptop computer as the platform for image processing and computation, and the images are captured by a binocular camera. Given the relatively large size of the laptop, the integration of the system components and the binocular camera into a smaller embedded device can be considered in the future. In this way, the size and weight of the system can be reduced, making it more suitable for use in practical application scenarios. When acquiring image depth information, some advanced devices can be used, such as the ZED depth camera, which can avoid the complex calibration and ranging process.