Toru Fujiwara

The paper proposes a nonlinear two-step model integrating proxy intermediate omics traits to improve phenotypic prediction in soybean, showing better accuracy than linear models.

The paper examines how soil nutrition affects the root-associated microbiome of rice across seasons and conditions, finding nitrogen-fixing bacteria dominate in non-fertilized fields and a machine learning model can predict soil fertilization status from microbiome data.

This study uses UAV remote sensing and deep learning to estimate soybean biomass traits and extract latent features for breeding applications.

This study evaluates drought tolerance stability in soybean using time-series multispectral data and genomic prediction models, finding higher accuracy with changing irrigation treatments.