@article{Zhou2025, 
author = {Letian Zhou and Zhixin Tang and Songliang Cao and Xiaonan Hu and Wei Zhou and Xuhui Zhu and Xiaodong Bai and Hao Lu and Fan Chen and Weijuan Hu},
title = {TillerPET: High-throughput in-situ phenotyping of rice tiller number and compactness from post-harvest stubble},
year = {2025},
journal = {The Crop Journal},
volume = {13},
number = {6},
pages = {1928-1938},
keywords = {Plant phenotyping, Plant counting, Rice tillers, Tiller compactness, Post-harvest stubble},
url = {https://www.sciopen.com/article/10.1016/j.cj.2025.09.022},
doi = {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.}
}