Benchmark Study of Point Cloud Semantic Segmentation Architectures on Strawberry Organs
With the increasing consumer demand for healthy and natural foods, strawberries have emerged as one of the most popular small berries globally. Consequently, careful investigation of the relationship between leaf photosynthetic activity (source strength) and fruit development (sink strength) during...
Saved in:
| Main Authors: | , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2025-06-01
|
| Series: | AgriEngineering |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2624-7402/7/6/181 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1849472675654664192 |
|---|---|
| author | Rundong Xu Hiroki Naito Fumiki Hosoi |
| author_facet | Rundong Xu Hiroki Naito Fumiki Hosoi |
| author_sort | Rundong Xu |
| collection | DOAJ |
| description | With the increasing consumer demand for healthy and natural foods, strawberries have emerged as one of the most popular small berries globally. Consequently, careful investigation of the relationship between leaf photosynthetic activity (source strength) and fruit development (sink strength) during strawberry growth provides important insights for maximizing the production potential of this crop. This objective necessitates accurate strawberry organ segmentation. Recently, advancements in deep learning (DL) have driven the development of numerous semantic segmentation models that have performed effectively on benchmark datasets. Nevertheless, their small-organ plant segmentation efficacy remains insufficiently explored. Consequently, this study evaluates eight representative point-based semantic segmentation models for the strawberry organ segmentation task: PointNet++, PointMetaBase, Point Transformer V2, Swin3D, KPConv, RandLA-Net, PointCNN, and Sparse UNet. The employed dataset comprises two components: the open-source LAST-Straw strawberry dataset and a custom Japanese strawberry dataset. Strawberry point cloud organs were categorized into four classes: leaf, stem, flower, and berry. The sparse convolution-based Sparse UNet achieved the highest mean intersection over union of 81.3, followed by the PointMetaBase model at 80.7. This study provides insights into the strengths and limitations of existing architectures, assisting researchers and practitioners in selecting appropriate models for strawberry organ segmentation tasks. |
| format | Article |
| id | doaj-art-ac6773296b8d46ecbc861911f63b083c |
| institution | Kabale University |
| issn | 2624-7402 |
| language | English |
| publishDate | 2025-06-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | AgriEngineering |
| spelling | doaj-art-ac6773296b8d46ecbc861911f63b083c2025-08-20T03:24:28ZengMDPI AGAgriEngineering2624-74022025-06-017618110.3390/agriengineering7060181Benchmark Study of Point Cloud Semantic Segmentation Architectures on Strawberry OrgansRundong Xu0Hiroki Naito1Fumiki Hosoi2Graduate School of Agricultural and Life Sciences, The University of Tokyo, 1-1-1 Yayoi, Bunkyo, Tokyo 113-8657, JapanGraduate School of Agricultural and Life Sciences, The University of Tokyo, 1-1-1 Yayoi, Bunkyo, Tokyo 113-8657, JapanGraduate School of Agricultural and Life Sciences, The University of Tokyo, 1-1-1 Yayoi, Bunkyo, Tokyo 113-8657, JapanWith the increasing consumer demand for healthy and natural foods, strawberries have emerged as one of the most popular small berries globally. Consequently, careful investigation of the relationship between leaf photosynthetic activity (source strength) and fruit development (sink strength) during strawberry growth provides important insights for maximizing the production potential of this crop. This objective necessitates accurate strawberry organ segmentation. Recently, advancements in deep learning (DL) have driven the development of numerous semantic segmentation models that have performed effectively on benchmark datasets. Nevertheless, their small-organ plant segmentation efficacy remains insufficiently explored. Consequently, this study evaluates eight representative point-based semantic segmentation models for the strawberry organ segmentation task: PointNet++, PointMetaBase, Point Transformer V2, Swin3D, KPConv, RandLA-Net, PointCNN, and Sparse UNet. The employed dataset comprises two components: the open-source LAST-Straw strawberry dataset and a custom Japanese strawberry dataset. Strawberry point cloud organs were categorized into four classes: leaf, stem, flower, and berry. The sparse convolution-based Sparse UNet achieved the highest mean intersection over union of 81.3, followed by the PointMetaBase model at 80.7. This study provides insights into the strengths and limitations of existing architectures, assisting researchers and practitioners in selecting appropriate models for strawberry organ segmentation tasks.https://www.mdpi.com/2624-7402/7/6/181deep learningpoint cloudsemantic segmentationstrawberry phenotyping |
| spellingShingle | Rundong Xu Hiroki Naito Fumiki Hosoi Benchmark Study of Point Cloud Semantic Segmentation Architectures on Strawberry Organs AgriEngineering deep learning point cloud semantic segmentation strawberry phenotyping |
| title | Benchmark Study of Point Cloud Semantic Segmentation Architectures on Strawberry Organs |
| title_full | Benchmark Study of Point Cloud Semantic Segmentation Architectures on Strawberry Organs |
| title_fullStr | Benchmark Study of Point Cloud Semantic Segmentation Architectures on Strawberry Organs |
| title_full_unstemmed | Benchmark Study of Point Cloud Semantic Segmentation Architectures on Strawberry Organs |
| title_short | Benchmark Study of Point Cloud Semantic Segmentation Architectures on Strawberry Organs |
| title_sort | benchmark study of point cloud semantic segmentation architectures on strawberry organs |
| topic | deep learning point cloud semantic segmentation strawberry phenotyping |
| url | https://www.mdpi.com/2624-7402/7/6/181 |
| work_keys_str_mv | AT rundongxu benchmarkstudyofpointcloudsemanticsegmentationarchitecturesonstrawberryorgans AT hirokinaito benchmarkstudyofpointcloudsemanticsegmentationarchitecturesonstrawberryorgans AT fumikihosoi benchmarkstudyofpointcloudsemanticsegmentationarchitecturesonstrawberryorgans |