A scalable and efficient UAV-based pipeline and deep learning framework for phenotyping sorghum panicle morphology from point clouds.
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| Title: | A scalable and efficient UAV-based pipeline and deep learning framework for phenotyping sorghum panicle morphology from point clouds. |
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| Authors: | James C; School of Agriculture and Food Sustainability, The University of Queensland, Brisbane, Australia., Chandra SS; School of Electrical Engineering and Computer Science, The University of Queensland, Brisbane, Australia., Chapman SC; School of Agriculture and Food Sustainability, The University of Queensland, Brisbane, Australia. |
| Source: | Plant phenomics (Washington, D.C.) [Plant Phenomics] 2025 May 19; Vol. 7 (2), pp. 100050. Date of Electronic Publication: 2025 May 19 (Print Publication: 2025). |
| Publication Type: | Journal Article |
| Journal Info: | Publisher: Science Partner Journals Country of Publication: United States NLM ID: 101769942 Publication Model: eCollection Cited Medium: Internet ISSN: 2643-6515 (Electronic) Linking ISSN: 26436515 NLM ISO Abbreviation: Plant Phenomics Subsets: PubMed not MEDLINE |
| Database: | MEDLINE Ultimate |
| ISSN: | 2643-6515 |
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| DOI: | 10.1016/j.plaphe.2025.100050 |
