About
28
Publications
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Introduction
I am good at analyzing time-series data of crop growth from the aspect of quantitative genetics; e.g. genomic prediction and GWAS. I also utilize UAVs in measuring crop growth processes and can perform simple image analysis. Now I am mainly working on applying statistical and machine learning methods to crop model outputs and cultivation trial databases.
Current institution
Additional affiliations
April 2022 - present
National Agriculture and Food Research Organization (NARO)
Position
- Fixed-term researcher
April 2018 - present
Education
April 2016 - March 2018
Publications
Publications (28)
High-dimensional multi-omics microbiome data play an important role in elucidating microbial community interactions with their hosts and environment in critical diseases and ecological changes. Although Bayesian clustering methods have recently been used for the integrated analysis of multi-omics data, no method designed to analyze multi-omics micr...
A capacity for reliable germination under elevated temperatures is a crucial factor in maintaining the stability of bread wheat (Triticum aestivum) yields in the context of climate change. Although the environment of the parent plant during growth is a known factor affecting seed germinability, the effect of this environment on the heat tolerance o...
Intermediate omics traits, which mediate the effects of genetic variation on phenotypic traits, are increasingly recognised as valuable components of genetic evaluation. In particular, rhizosphere microbiota play a crucial role in plant health and productivity; however, their complex interactions with host genetics remain challenging to model. Alth...
High-dimensional multi-omics microbiome data plays an important role in elucidating microbial communities’ interactions with their hosts and environment in critical diseases and ecological changes. Although Bayesian clustering methods have recently been used for the integrated analysis of multi-omics data, no method designed to analyze multi-omics...
High-throughput phenotyping serves as a framework to reduce chronological costs and accelerate breeding cycles. In this study, we developed models to estimate the phenotypes of biomass-related traits in soybean (Glycine max) using unmanned aerial vehicle (UAV) remote sensing and deep learning models. In 2018, a field experiment was conducted using...
The evaluation of plant and animal growth, separately for genetic and environmental effects, is necessary for genetic understanding and genetic improvement of environmental responses of plants and animals. We propose to extend an existing approach that combines nonlinear mixed-effects model (NLMEM) and the stochastic approximation of the Expectatio...
Elevated temperatures during the flowing stage can induce spikelet sterility in rice, posing a major threat to production under climate change. Projecting the impacts and developing effective strategies are critical, but our understanding of regional, seasonal, and long-term trends in rice heat exposure remains limited. Previous studies on spikelet...
High throughput phenotyping serves as a framework to reduce chronological costs and accelerate breeding cycles. In this study, we developed models to estimate the phenotypes of biomass-related traits in soybean (Glycine max) using unmanned aerial vehicle (UAV) remote sensing and deep learning models. In 2018, a field experiment was conducted using...
Key message
We proposed models to predict the effects of genomic and environmental factors on daily soybean growth and applied them to soybean growth data obtained with unmanned aerial vehicles.
Abstract
Advances in high-throughput phenotyping technology have made it possible to obtain time-series plant growth data in field trials, enabling genoty...
Elevated temperatures during the flowing stage contribute to heat-induced spikelet sterility in rice, posing a major threat to production considering climate change projections. Developing effective strategies for stable rice production through breeding and crop management is critical; however, our understanding of regional, seasonal, and long-term...
High-dimensional multi-omics microbiome data plays an important role in elucidating microbial communities' interactions with their hosts and environment in critical diseases and ecological changes. Although Bayesian clustering methods have recently been used for the integrated analysis of multi-omics data, no method designed to analyze multi-omics...
Advances in high-throughput phenotyping technology have made it possible to obtain time-series plant growth data in field trials, enabling genotype-by-environment interaction (G×E) modeling of plant growth. Although the reaction norm is an effective method for quantitatively evaluating G×E and has been implemented in genomic prediction models, no r...
Plant response to drought is an important yield-related trait under abiotic stress, but the method for measuring and modeling plant responses in a time series has not been fully established. The objective of this study was to develop a method to measure and model plant response to irrigation changes using time-series multispectral (MS) data. We eva...
The evaluation of plant and animal growth, separately for genetic and environmental effects, is necessary for genetic understanding and genetic improvement of environmental responses of plants and animals. We propose to extend an existing approach that combines nonlinear mixed-effects model (NLMEM) and the stochastic approximation of the Expectatio...
This study investigated a method to evaluate the drought tolerance stability of a genotype in a single environmental trial by capturing the plant response to irrigation changes. Genotypes that exhibit stable phenotypes under various drought stress conditions are required for stable crop production. However, considerable time and money are required...
Background
The rapid and accurate identification of a minimal-size core set of representative microbial species plays an important role in the clustering of microbial community data and interpretation of clustering results. However, the huge dimensionality of microbial metagenomics datasets is a major challenge for the existing methods such as Diri...
Increasing the water use efficiency of crops is an important agricultural goal closely related to the root system —the primary plant organ for water and nutrient acquisition. In an attempt to evaluate the response of root growth and development of soybean to water supply levels, 200 genotypes were grown in a sandy field for 3 years under irrigated...
Microbiota are a major component of agroecosystems. Root microbiota, which inhabit the inside and surface of plant roots, play a significant role in plant growth and health. As next-generation sequencing technology allows the capture of microbial profiles without culturing the microbes, profiling of plant microbiota has become a staple tool in plan...
Multispectral (MS) imaging enables the measurement of characteristics important for increasing the prediction accuracy of genotypic and phenotypic values for yield‐related traits. In this study, we evaluated the potential application of temporal MS imaging for the prediction of aboveground biomass (AGB) in soybean [Glycine max (L.) Merr.]. Field ex...
With the widespread use of high-throughput phenotyping systems, growth process data are expected to become more easily available. By applying genomic prediction to growth data, it will be possible to predict the growth of untested genotypes. Predicting the growth process will be useful for crop breeding, as variability in the growth process has a s...
Our previous study described stage-specific responses of ‘Norin 61’ bread wheat to high temperatures from seedling to tillering (GS1), tillering to flowering (GS2), flowering to full maturity stage (GS3), and seedling to full maturity stage (GS1–3). The grain development phase lengthened in GS1 plants; source tissue decreased in GS2 plants; rapid s...
With the widespread use of high-throughput phenotyping systems, growth process data are expected to become more easily available. By applying genomic prediction to growth data, it will be possible to predict the growth of untested genotypes. Predicting the growth process will be useful for crop breeding, as variability in the growth process has a s...
Background: The rapid and accurate identification of a minimal-size core set of representative microbial species plays an important role in the clustering of microbial community data and interpretation of the clustering results. However, the huge dimensionality of microbial metagenomics data sets is a major challenge for the existing methods such a...
Multi-spectral (MS) imaging enables the measurement of characteristics important for increasing the prediction accuracy of genotypic and phenotypic values for yield-related traits. In this study, we evaluated the potential application of temporal MS imaging for the prediction of above-ground biomass (AGB) and determined which developmental stages s...
The application of remote sensing in plant breeding can provide rich information about the growth processes of plants, which leads to better understanding concerning crop yield. It has been shown that traits measured by remote sensing were also beneficial for genomic prediction (GP) because the inclusion of remote sensing data in multitrait models...
Bread wheat (Triticum aestivum) is less adaptable to high temperatures than other major cereals. Previous studies of the effects of high temperature on wheat focused on the reproductive stage. There are few reports on yield after high temperatures at other growth stages. Understanding growth-stage-specific responses to heat stress will contribute t...
Microbiota are a major component of agroecosystems. Root microbiota, which inhabit the inside and surface of plant roots, play a significant role in plant growth and health. As next-generation sequencing technology allows the capture of microbial profiles without culturing the microbes, profiling of plant microbiota has become a staple tool in plan...
Genomic prediction (GP) is expected to become a powerful technology for accelerating the genetic improvement of complex crop traits. Several GP models have been proposed to enhance their applications in plant breeding, including environmental effects and genotype-by-environment interactions (G×E). In this study, we proposed a two-step model for pla...
