Smart Image-Based Deep Learning System for Automated Quality Grading of Phalaenopsis Seedlings in Outsourced Production
Hong-Dar Lin, Zheng-Yuan Zhang, Chou-Hsien Lin, Haitao Wang, Fei Fei, Yongkai Zhu · 2025
Read Smart Image-Based Deep Learning System for Automated Quality Grading of Phalaenopsis Seedlings in Outsourced Production by Hong-Dar Lin, Zheng-Yuan Zhang, Chou-Hsien Lin, Haitao Wang, Fei Fei, Yongkai Zhu (2025) in the Tower of Babel, a free online library of public-domain works preserved in clean, searchable text. No sign-up. The full text is served as a plain-text file at a predictable URL, where text is present: https://antarctic-labs.com/tower-of-babel/pmc-12737025.txt.
Description
Hong-Dar Lin and co-authors build an automated system to grade orchid seedlings, replacing slow hand inspection. Phalaenopsis orchids are a major export for Taiwan, and farms outsource young seedlings to contract growers.
The system photographs each plant from above with depth sensing and from several side angles. Detection models locate defects and roots, and simpler classifiers count the faults and assign a grade. The team tested two grading routes: a three-stage method that shows its reasoning step by step, and a direct method that grades in one pass. The direct route scored higher, with top accuracy near ninety percent on their measure, while the staged route stayed close behind. The authors say the system is reliable enough for export inspection lines.
Catalog record
| CREATOR | Hong-Dar Lin, Zheng-Yuan Zhang, Chou-Hsien Lin, Haitao Wang, Fei Fei, Yongkai Zhu |
|---|---|
| YEAR | 2025 |
| COLLECTION | PAPERS |
| FORMAT | TXT |
| FILE SIZE | 98 KB |
| VERSION | 1.0 |
| LICENSE | CC BY 4.0 |
| RIGHTS | CC BY |
| SOURCE | Europe PMC |