Fine-tuning improves deep learning-based digital phenotyping of lettuce genetic resources.

Authors

Eunju Lee, Yuseok Jeong, Hyo Jun Bae, Yoon-Jung Lee, Seong-Hoon Kim, Young-Wang Na, Hwan-Suk Cheong, Inchan Choi

Published in

Frontiers in plant science. Volume 17. Pages 1970840. Epub Oct 09, 2026.

Abstract

Morphological characterization of agricultural genetic resources relies largely on visual assessment by experts, and applying the same criteria repeatedly to large collections is labor-intensive. Image-based deep learning can support this process, but systematic evidence on training strategies suited to genebank trait data remains limited.
This study targeted six morphological traits of lettuce genetic resources characterized according to the test guidelines of the International Union for the Protection of New Varieties of Plants (UPOV). Using genebank images accumulated from 2012 to 2025, six convolutional neural networks were trained with three training strategies under identical conditions, and their performance was compared.
Fine-tuning achieved the highest mean macro F1-score in 35 of the 36 backbone-trait combinations and significantly outperformed training from scratch and feature extraction, whereas no single backbone was superior across all traits. Misclassifications were concentrated in morphologically adjacent classes rather than randomly distributed, and for the ordinal traits, errors spanning two or more class levels were rare.
These patterns suggest that the remaining classification difficulty was associated in part with the morphological continuity between neighboring classes. The attainable performance also differed among traits, distinguishing traits for which the training strategy alone achieved relatively high performance from those for which it did not. These results provide empirical evidence for selecting training strategies and for determining the role of model-assisted characterization on a trait-by-trait basis in genebanks.

PMID:
42920599
Bibliographic data and abstract were imported from PubMed on 24 Oct 2026.

Read full publication at:
Please sign in to see all details.

Stats

1-terrible, 9-excellent. How would you rate this publication? Sign in in to submit your rating.

Post a comment

You need to be signed in to post comments. You can sign in here.

Comments

There are no comments yet.