2025-12-03 中国科学院（CAS）
The technical pipeline of TillerPET, with thorough exposition of the proposed modules. (Image by IGDB)
＜関連情報＞
- https://english.cas.cn/newsroom/research_news/life/202512/t20251217_1137292.shtml
- https://www.sciencedirect.com/science/article/pii/S2214514125002673
TillerPET: 収穫後の刈り株からのイネの分げつ数とコンパクト性のハイスループットin situ表現型解析 TillerPET: High-throughput in-situ phenotyping of rice tiller number and compactness from post-harvest stubble
Letian Zhou, Zhixin Tang, Songliang Cao, Xiaonan Hu, Wei Zhou, Xuhui Zhu, Xiaodong Bai, Hao Lu, Fan Chen, Weijuan Hu
The Crop Journal Available online: 7 November 2025
DOI:https://doi.org/10.1016/j.cj.2025.09.022
Abstract
For fast in-situ assessment of tiller phenotypes in rice breeding, we introduce the TillerPET model, an improved transformer-based deep learning solution that permits phenotyping the number and compactness of rice tillers in images of post-harvest rice stubble. A rice tiller phenotype dataset covering three years of field data and four experimental sites across China was constructed to train and validate the model. TillerPET reports an R2 of 0.941 for counting tiller number, demonstrating state-of-the-art performance on the proposed RTP dataset. Beyond its minimal errors in estimating tiller number, TillerPET also achieves an R2 of 0.978 for characterizing tiller compactness. The two phenotypic parameters exhibit a high degree of consistency with expert breeders, offering reliable phenotypic indicators to guide further breeding.
