Overview of canola silique segmentation and counting.

<p>(A) Sparse reconstruction. (B) Dense reconstruction. (C) Point cloud preprocessing. (D) Point cloud augmentation. (E) Segmentation model prediction result. (F) Segmented Silique Point Cloud. (G) Silique instance clustering result.</p>

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Bibliographic Details
Main Author:	Jiajun Liu (692586) (author)
Other Authors:	Bei Zhou (5219792) (author), Jie Liu (15128) (author), Xike Zhang (2848757) (author), Jiangshu Wei (22634246) (author), Yao Zhang (134381) (author), Junjie Wu (393541) (author), Changping Wu (52884) (author), Di Hu (12438) (author)
Published:	2025
Subjects:	Space Science Biological Sciences not elsewhere classified Information Systems not elsewhere classified throughput plant phenotyping plant phenotypic traits made publicly available based spatial clustering semantic segmentation tasks instance segmentation task div >< p canola silique segmentation counting </ p segmentation accuracy precision segmentation canola siliques counting accuracy automatic counting study proposes precision agriculture model complexity method achieves excellent balance efficient solution crop breeding contrastive learning baseline models achieves 94 accurate analysis 72m parameters 6432 /</
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Summary:	<p>(A) Sparse reconstruction. (B) Dense reconstruction. (C) Point cloud preprocessing. (D) Point cloud augmentation. (E) Segmentation model prediction result. (F) Segmented Silique Point Cloud. (G) Silique instance clustering result.</p>

Overview of canola silique segmentation and counting.

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