## 4 Conclusions  
In this paper, we first analyzed the phenotypic characteristics and body parameters of tilapia and preliminarily concluded that there exists a close relationship between tilapia head circumference, chest circumference, and tail circumference and their own body weight and abdominal fat weight. Therefore, a tilapia part segmentation dataset was constructed for the first time, encompassing four distinct segments for each tilapia: the head, trunk, fins, and tail. To achieve accurate segmentation of tilapia body parts, a segmentation method based on improved Deeplabv3+ is proposed, which greatly addressed the issues of unclear boundary segmentation, multiple fish occlusions, and misclassification of small objects in complex backgrounds. In this method, we made improvements to both the encoder and decoder. In the encoder, we introduced the CBAM module to enhance the spatial relationship between different parts of tilapia after feature extraction and to make the target boundary information more complete. In the decoder, a SENet module was added to adaptively assign weights to different parts of the tilapia channel to improve the segmentation accuracy of the network. The experimental results demonstrated that, when tested on the tilapia part segmentation dataset, the IoU values for the background, head, fins, trunk, and tail were 99.09%, 88.83%, 77.97%, 89.66%, and 90.06%, respectively. Compared to Deeplabv3+, the model, with the addition of the dual-attention module, improved the mPA, mIoU, and mRecall values by 1.96, 3.23, and 4.54 percentage points, respectively. The proposed model achieved high accuracy in segmenting tilapia body parts and obtained clearer object boundaries. In the process of phenotypic measurement research, high-quality segmentation results are indispensable. Only through precise segmentation of different biological parts can we effectively delve into the intricate interplay between their morphology and functionality. Furthermore, the outcomes of accurate segmentation serve as an essential foundation for forthcoming phenotypic measurement endeavors.  
Conceptualization, G.F. and M.C.; methodology, H.W.; software, H.W.; validation, G.F., Z.L. and H.W.; formal analysis, G.F. and H.W.; resources, G.F.; data curation, G.F. and H.W.; writing--original draft preparation, G.F., H.W. and Z.L.; writing--review and editing, G.F., H.W. and Z.L.; supervision, Z.L. All authors have read and agreed to the published version of the manuscript.  
This work was supported by the National Key Research and Development Program of China (2022 YFD2400800) and JiangSu Modern Agricultural Industry Key Technology Innovation Planning, NO. CX (20) 2028.  
Not applicable.  
Experimental data related to this paper can be requested from the authors by email if any researcher is in need of the dataset, email: [EMAIL_ADDRESS].  
The authors are very grateful to [PERSON] of Nantong Longyang Aquatic Co. in Jiangsu Province, China, for providing us with the experimental data and the data collection site.  
The authors declare no conflict of interest.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Model** & **SENet** & **CBAM** & **mIoU (\%)** & **mPA (\%)** \\ \hline \multirow{4}{*}{Deeplabv3+} & & & 88.46 & 93.98 \\  & ✓ & & 89.08 & 94.21 \\  & ✓ & ✓ & 89.11 & 94.08 \\  & ✓ & ✓ & 91.69 & 95.94 \\ \hline \hline \end{tabular}
\end{table}
Table 4: mIoU and mPA values in different situations.