# TSINet: A Semantic and Instance Segmentation Network for 3D Tomato Plant Point Clouds
## 5 Conclusions  
Accurate organ-level segmentation is essential for phenotypic analysis and growth monitoring of plants. This study focuses on the segmentation of tomato plants using the Pheno4D dataset. We develop TSINet, an end-to-end dual-purpose segmentation network that can segment tomato plant point clouds in both instance and semantic ways. TSINet adopts an encoder-decoder architecture, consisting of a shared encoder and two parallel decoder branches dedicated to the respective segmentation tasks. A Geometry-Aware Adaptive Feature Extraction Block (GAFEB) and a Dual Attention-Based Feature Enhancement Module (DAFEM) are further incorporated to enhance feature representations. To address the limitations of the dataset, we employ extensive data augmentation, achieving a 20-fold increase in data volume, which enhances the model's generalization capability. According to the experimental results, our model performs exceptionally well, with semantic segmentation metrics reaching an average precision of 97.00%, recall of 96.17%, F1-score of 96.57%, and IoU of 93.43%. For instance segmentation, the model attains a mean precision (mPrec) of 81.54%, mean recall (mRec) of 81.69%, mean coverage (mCov) of 81.60%, and mean weighted coverage (mWCov) of 86.40%, surpassing mainstream point cloud segmentation baselines. These results highlight TSINet as an effective and practical solution for high-throughput, automated phenotyping of tomato plants, contributing to the advancement of intelligent agriculture. Conceptualization, L.Z. and X.L. ; methodology, S.M. ; software, S.M. ; validation, S.M. ; formal analysis, S.M. ; data curation, S.M. ; writing--original draft preparation, S.M. ; writing--review and editing, L.Z. and X.L. ; visualization, S.M. ; supervision, L.Z. and X.L. ; funding acquisition, X.L. All authors have read and agreed to the published version of the manuscript. This research was funded by the National Natural Science Foundation of China (No. 62202281) and Shandong Province College Student Innovation and Entrepreneurship Training Program (No. S202410434023). **Informed Consent Statement:** Not applicable. **Data Availability Statement:** The data that support the findings of this study are available from the corresponding author upon reasonable request. **Conflicts of Interest:** The authors declare no conflicts of interest. +++ ==WARNING: Truncated because of repetitions==
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