Semantic Segmentation Method for High-Resolution Tomato Seedling Point Clouds Based on Sparse Convolution

Shizhao Li, Zhichao Yan, Boxiang Ma, Shaoru Guo, Hongxia Song

Open source

DOI: 10.3390/agriculture15010074
Published: 2024-12-31
Container: Agriculture
Publisher: MDPI AG
Open access: unknown

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Cite this work

BibTeX

@article{allodium:10.3390/agriculture15010074,
  title = {Semantic Segmentation Method for High-Resolution Tomato Seedling Point Clouds Based on Sparse Convolution},
  author = {Shizhao Li and Zhichao Yan and Boxiang Ma and Shaoru Guo and Hongxia Song},
  year = {2024},
  journal = {Agriculture},
  doi = {10.3390/agriculture15010074},
  url = {https://doi.org/10.3390/agriculture15010074}
}

RIS

TY  - JOUR
TI  - Semantic Segmentation Method for High-Resolution Tomato Seedling Point Clouds Based on Sparse Convolution
AU  - Shizhao Li
AU  - Zhichao Yan
AU  - Boxiang Ma
AU  - Shaoru Guo
AU  - Hongxia Song
PY  - 2024
JO  - Agriculture
DO  - 10.3390/agriculture15010074
UR  - https://doi.org/10.3390/agriculture15010074
ER  -

APA

Li, S., Yan, Z., Ma, B., Guo, S., & Song, H. (2024). Semantic Segmentation Method for High-Resolution Tomato Seedling Point Clouds Based on Sparse Convolution. Agriculture. https://doi.org/10.3390/agriculture15010074

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crossref · retrieved 2026-11-10T13:44:59.016Z