AITP-YOLO: improved tomato ripeness detection model based on multiple strategies
The enhanced model features a reduced model size while exhibiting superior detection capabilities, enabling more efficient and precise recognition of tomato stages amidst complicated backgrounds, hence offering a valuable technical reference for automated tomato harvesting technology.
Authors
- Z. L. Yang (ORCID: https://orcid.org/0009-0009-6590-1540)
- Lijia Xu (ORCID: https://orcid.org/0000-0001-5100-4042)
- Wenyuan Huang (ORCID: https://orcid.org/0000-0002-9838-9127)
- Yu‐Cheng Liao (ORCID: https://orcid.org/0000-0002-1482-2086)
- Peiling Wang (ORCID: https://orcid.org/0000-0003-4202-7570)
- Ziao Chen (ORCID: https://orcid.org/0000-0001-5213-835X)
- Jiong Mu
Institutions
- Sichuan Agricultural University (CN)
Publication Details
- Journal
- Frontiers in Plant Science
- Published
- 2025-05-26
- DOI
- https://doi.org/10.3389/fpls.2025.1596739
- Citations
- 9
- Primary Topic
- Spectroscopy and Chemometric Analyses
- Type
- article
- Field-Weighted Citation Impact
- 6.77