| Predicting biomass of rice with intermediate traits: Modeling method combining crop growth models and genomic prediction models Y Toda, H Wakatsuki, T Aoike, H Kajiya-Kanegae, M Yamasaki, ... PLoS One 15 (6), e0233951, 2020 | 18 | 2020 |
| Genomic prediction modeling of soybean biomass using UAV‐based remote sensing and longitudinal model parameters Y Toda, A Kaga, H Kajiya‐Kanegae, T Hattori, S Yamaoka, M Okamoto, ... The Plant Genome 14 (3), e20157, 2021 | 15 | 2021 |
| Time‐series multispectral imaging in soybean for improving biomass and genomic prediction accuracy K Sakurai, Y Toda, H Kajiya‐Kanegae, Y Ohmori, Y Yamasaki, ... The Plant Genome 15 (4), e20244, 2022 | 8 | 2022 |
| Genomic prediction of green fraction dynamics in soybean using unmanned aerial vehicles observations Y Toda, G Sasaki, Y Ohmori, Y Yamasaki, H Takahashi, H Takanashi, ... Frontiers in Plant Science 13, 828864, 2022 | 8* | 2022 |
| Stage-specific characterization of physiological response to heat stress in the wheat cultivar Norin 61 S Matsunaga, Y Yamasaki, Y Toda, R Mega, K Akashi, H Tsujimoto International Journal of Molecular Sciences 22 (13), 6942, 2021 | 8 | 2021 |
| High throughput method of 16S rRNA gene sequencing library preparation for plant root microbial community profiling K Kumaishi, E Usui, K Suzuki, S Kobori, T Sato, Y Toda, H Takanashi, ... Scientific Reports 12 (1), 19289, 2022 | 7* | 2022 |
| Effects of irrigation on root growth and development of soybean: A 3-year sandy field experiment KT Bui, T Naruse, H Yoshida, Y Toda, Y Omori, M Tsuda, A Kaga, ... Frontiers in Plant Science 13, 1047563, 2022 | 5 | 2022 |
| Metabolome profiling of heat priming effects, senescence, and acclimation of bread wheat induced by high temperatures at different growth stages S Matsunaga, Y Yamasaki, R Mega, Y Toda, K Akashi, H Tsujimoto International Journal of Molecular Sciences 22 (23), 13139, 2021 | 4 | 2021 |
| Stochastic variational variable selection for high-dimensional microbiome data T Dang, K Kumaishi, E Usui, S Kobori, T Sato, Y Toda, Y Yamasaki, ... Microbiome 10 (1), 236, 2022 | 3 | 2022 |
| Random regression for modeling soybean plant response to irrigation changes using time-series multispectral data K Sakurai, Y Toda, K Hamazaki, Y Ohmori, Y Yamasaki, H Takahashi, ... Frontiers in Plant Science 14, 1201806, 2023 | 2 | 2023 |
| High-Throughput Phenotyping of Soybean Biomass: Conventional Trait Estimation and Novel Latent Feature Extraction Using UAV Remote Sensing and Deep Learning Models M Okada, C Barras, Y Toda, K Hamazaki, Y Ohmori, Y Yamasaki, ... Plant Phenomics 6, 0244, 2024 | | 2024 |
| Modeling soybean growth: A mixed model approach M Delattre, Y Toda, J Tressou, H Iwata PLOS Computational Biology 20 (7), e1011258, 2024 | | 2024 |
| Reaction norm for genomic prediction of plant growth: modeling drought stress response in soybean Y Toda, G Sasaki, Y Ohmori, Y Yamasaki, H Takahashi, H Takanashi, ... Theoretical and Applied Genetics 137 (4), 77, 2024 | | 2024 |
| Revealing the spatial characteristics of rice heat exposure in Japan through panicle temperature analysis Y Toda, Y Ishigooka, M Yoshimoto, T Takimoto, T Kuwagata, D Makowski, ... Journal of Agricultural Meteorology 80 (3), 79-89, 2024 | | 2024 |
| An integrative framework of stochastic variational variable selection for joint analysis of multi-omics microbiome data T Dang, Y Fuji, K Kumaishi, E Usui, S Kobori, T Sato, Y Toda, K Sakurai, ... bioRxiv, 2023.08. 18.553796, 2023 | | 2023 |
| Multi-Omics Integration for Modeling Drought Stress Response in Soybean H Iwata, Y Toda, Y Yamasaki, K Uchida, Y Ohmori, H Takahashi, M Tsuda, ... ASA, CSSA and SSSA International Annual Meetings (2019), 2019 | | 2019 |
| Using Multi-Omics Intermediate Traits in Genome Selection: Predictive Modeling and Visualization Methods H Iwata, Y Toda, Y Fuji, Y Ohmori, Y Yamasaki, H Takahashi, ... Plant and Animal Genome XXIX Conference (January 8-12, 2022), 0 | | |
Hisashi TSUJIMOTOArid Land Research Center, Tottori University在 tottori-u.ac.jp 的电子邮件经过验证
