TillerPET: High-throughput in-situ phenotyping of rice tiller number and compactness from post-harvest stubble

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.