Technology Partnership of Nagoya University, Inc.
World Class Research Contributing to Society
Dr. Daisuke Sugiura
Lecturer
Department of Plant Production Sciences
Graduate School of Bioagricultural Sciences
Nagoya University, Japan
BIO.
After graduating from the Department of Biological Sciences, Faculty of Science, The University of Tokyo in 2007, Dr. Sugiura received his M.S. and Ph.D. degrees from the Department of Biological Sciences, Graduate School of Science, The University of Tokyo. He then worked as a postdoctoral fellow in the Department of Biological Sciences, Graduate School of Science, The University of Tokyo (2012-2017), a visiting fellow in the John Evans lab at the Australian National University (2015 - 2016), and an assistant professor in the Graduate School of Bioagricultural Sciences, Nagoya University (2017-2021) before He has been in his current position since November 2021.
Micro controller-based plant phenotyping system for the evaluation of crop water use and biomass production.
Understanding water use characteristics of C3 and C4 crops is important for food security under climate change. Our recent work revealed that rapid stomatal closure could reduce unnecessary water loss and contribute to higher water use efficiency in major C4 compared to C3 Poaceae crops. However, high-end instruments for the evaluation of water use characteristics are usually low throughput. Here, we developed microcontroller-based plant phenotyping systems that enables high-throughput and low-cost evaluation of plant water use characteristics in both growth chambers and field. In the first topic, I will introduce the case study of the present systems to assess water use characteristics and stress tolerance in rice, maize, and soybean.
Evaluating field-scale biomass production is also important for breeding high-yielding crop varieties. We have developed a novel technique to determine rice LAI (leaf area index) nondestructively and accurately throughout growth period in paddy field by continuous measurements of near-infrared radiation (NIR) and photosynthetically active radiation (PAR) in rice canopy. Using this technique, it was revealed that maintaining high LAI throughout the growth period could lead to higher grain yield. Another important factor influencing rice grain yield is maintenance respiration which could reduce the amount of assimilates available for growth and yield. In the second topic, I will introduce microcontroller-based respiration measurement system that enables continuous measurements of rice respiration throughout night period.
Selected Publication
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Ozeki K, Miyazawa Y, Sugiura D* (2022). Rapid stomatal closure contributes to higher water use efficiency in C4 compared to C3 major Poaceae crops. Plant Physiology 189, 188-203.
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Fukuda S, Koba K, Okamura M, Watanabe Y, Hosoi J, Nakagomi K, Maeda H, Kondo M, Sugiura D* (2021). Novel technique for non-destructive LAI estimation by continuous measurement of NIR and PAR in rice canopy. Field Crops Research, 263, 108070.
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Sugiura D*, Terashima I, John R Evans (2020). A decrease in mesophyll conductance by cell wall thickening contributes to photosynthetic down-regulation. Plant Physiology, 183, 1600-1611.
Full list of Publication
Official Website
https://sites.google.com/site/daisukesugiura/home (*Japanese only)
Dr. Takashi Tanaka
Associate Professor
Laboratory of Crop Science
Faculty of Applied Biological Sciences
Gifu University, Japan
BIO.
After graduating from the Department of Bioresource Sciences, Faculty of Agriculture, Kyoto University in 2012, Dr. Tanaka received his M.S. and Ph.D. degrees from the Department of Agricultural Sciences, Graduate School of Agriculture, Kyoto University. He then worked as a Research Assistant at the Graduate School of Agriculture, Kyoto University (2016-2017) and Assistant Professor at the Faculty of Applied Biological Sciences, Gifu University (2017- 2022) before assuming his current position in April 2022. He has also served as a director of Sagri Corporation since 2021.
Data analytics for on-farm experimentation using precision agricultural technology
His current work focuses on developing data analytics for agronomy and crop science using statistical modelling, crop simulation model, and machine learning techniques. The outcomes of agronomic field experiments can be easily affected by spatial and temporal variations of environmental factors such as soil and weather. Since Sir R.A. Fisher introduced the theory of experimental design in the early 20th century, small-plot randomized experiments followed by analysis of variance (ANOVA) became a standard statistical approach for agronomic research. However, this methodology had limitations and challenges with regard to scalability of outcomes to real large farmers’ fields due to the impact of uncontrollable underlying environmental effects on crop. Meanwhile, the adoption of precision agriculture such as yield monitor and satellite/UAV remote sensing is increasingly enabling farmers to collect data in their own fields. Variable-rate application technology enables farmers to implement on-farm experimentations to understand crop responses to agronomic input treatments (e.g., fertilizers, seeds, and herbicides). However, observation derived from on-farm experimentation usually violates a conventional statistical assumption. He will talk about possibilities and issues in on-farm experimentations in regard to data analytics.
Selected Publication
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Zhou, X., Heuvelink, G., Kono, Y., Matsui, T.,Tanaka, T.S.T. (2022) Using linear mixed-effects modeling to evaluate the impact of edaphic factors on spatial variation in winter wheat grain yield in Japanese consolidated paddy fields. European Journal of Agronomy 133: 126447. doi:10.1016/j.eja.2021.126447
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Kakimoto, S., Mieno, T., Tanaka, T.S.T., Bullock, D.S. (2022) Causal forest approach for site-specific input management via on-farm precision experimentation. Computers and Electronics in Agriculture 199: 107164
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Tanaka, T.S.T. (2021) Assessment of design and analysis frameworks for on-farm experimentation through a simulation study of wheat yield in Japan. Precision Agriculture 22: 1601–1616 doi:10.1007/s11119-021-09802-1
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Zhou, X., Kono, Y., Win, A., Matsui, T.,Tanaka, T.S.T. (2021) Predicting within-field variability in grain yield and protein content of winter wheat using UAV-based multispectral imagery and machine learning approaches. Plant Production Science24(2):137–151. 10.1080/1343943X.2020.1819165
Full list of Publication
Google Scholar
https://scholar.google.com/citations?user=ayVWcJwAAAAJ&hl=en
ResearchGate
https://www.researchgate.net/profile/Takashi-Tanaka-6/research
Dr. Alex Woodley
Assistant Professor
Sustainable and Organic Soil Fertility
Department of Crop and Soil Sciences
NC State University
Dr. Woodley received his Ph.D. in Land Resource Science from the University of Guelph. He then was a NSERC postdoctoral fellow at the Harrow Research and Development Centre (AAFC). He joined the faculty at NCSU in spring 2018.
This research program is focused on the mitigation and adaptation to climate change in sustainable agricultural systems through improved soil productivity. Research initiatives include linking soil health indicators to productive agroecosystems, mitigation of soil greenhouse gas emissions, soil carbon sequestration and nutrient management of fertilizers, organic amendments and cover crops.
Evaluating climate-smart agricultural practices from lab to field : Current and Future Challenges and Opportunities
There is increasing focus on agricultures role in climate change from the public, industry and the government. In particular, the potential for agricultural soils to be a mitigation tool through soil carbon sequestration. Agricultural soils also emit ~74% of the human induced nitrous oxide emissions in the U.S. primarily through nitrogen fertilizer and manure application. Practices that may increase soil carbon sequestration may also increase nitrous oxide emissions and negate benefits, requiring a multifaceted approach to evaluating climate-smart best management. Measure and evaluating climate-smart practices is a complex task that requires innovation in field research design and analyzer technology, across a variety of scales from small plot to farm level basis. Ultimately, model refinement and data science advancements will drive regional and national inventories. Dr. Woodley will provide an overview of the technology they are using at NCSU to approach this challenge and highlight emerging opportunities in this space.
Full list of Publication
https://scholar.google.com/citations?user=KsCmZ8QAAAAJ&hl=en&oi=ao
Official Website
Dr. Cranos Williams
Goodnight Distinguished Professor of Agricultural Analytics
Platform Director, Data-Driven Plant Sciences Platform, NC Plant Sciences Initiative
NC State University
BIO.
Dr. Cranos Williams is the Goodnight Distinguished Professor of Agricultural Analytics at North Carolina State University with primary and secondary appointments in the electrical and computer engineering and plant and microbial biology departments, respectively. Dr. Williams also serves as the Platform Director of the Data-Driven Plant Sciences research platform of the North Carolina Plant Sciences Initiative and is the head of the EnBiSys Research Laboratory. He received his B.S. in electrical engineering from North Carolina A&T State University in 2001, and his M.S. and Ph.D. in electrical engineering from North Carolina State University in 2002 and 2008, respectively.
Dr. Williams has developed a highly collaborative, multidisciplinary research program that focuses on the development of computational and analytical solutions for modeling and understanding the combinatorial interactions of biomolecular, physiological, and structural processes that impact plant growth, development, and adaptation. Specific research contributions include: development of heterogeneous data management and analytics pipelines for decreasing waste and maximizing value across the sweetpotato value chain, multi-scale modeling and machine learning approaches to elucidate plant/microbiome relationships that increase plant resiliency, computer vision tracking of gene expression data over space and time to extract spatial and temporal metrics that assess response of plants to combinatorial stresses, dynamic modelling of gene regulatory networks in plants in response to iron deficiency stress, multiscale modelling of the plant secondary cell wall, system identification and experimental design for biological processes in the presence of bounded uncertainty, data-driven modularization of biological systems to identify individual components and modules in biochemical pathways that influence plant growth, development, and adaptation. The findings from these projects have provided better insight into the molecular mechanisms associated with stress response in plants, yielded models that predict wood formation in trees, used early stakeholder engagement to develop translatable IoT and big-data analytics frameworks that provide solutions to ag- stakeholders, and provided computational algorithms for spatial and temporal tracking of gene expression, molecular experimental design, and multi-scale modeling of biological processes. These findings will ultimately translate to strategies for improving tolerance of crop plants to pathogens and abiotic stresses, increasing the efficiency of biofuel production from plant biomass, and strategies for addressing food security challenges.
Harnessing the Ag data revolution for modeling plant and agronomic systems across scale
The next revolution in precision agriculture solutions will require an improved understanding of the complex regulatory mechanisms that are instrumental in plant growth, development, and adaptation. Key in these efforts is the ability to acquire and analyze data across biological scales (from molecular to phenotypic scales). High-throughput data that have been collected across biological scales include molecular data such as gene expression profiles and confocal imaging to data capturing plant physiology such as hyperspectral imaging and remote sensing. The diversity of these datasets (in combination with the complexity of plant systems) has created opportunities to develop novel computational intelligence and machine learning approaches that are capable of modeling plant systems within and across biological scales. In this presentation, we provide a brief overview of approaches for analyzing various types of high-throughput biological data. These approaches address the many challenges associated with analyzing biological data, including the need to mitigate high variation and/or uncertainty in data, the need for novel segmentation and feature extraction, and the integration of disparate datasets for making causal inferences across scale. The application of these approaches has led to scientific contributions such as the modeling of key gene regulatory mechanisms involved in plant stress response, the identification of emergent properties that link molecular activity to phenotypic outcomes, and the development of automated high-throughput phenotyping approaches for early detection of plant diseases. The continued acquisition of high-throughput data across scale and the continued development of novel machine learning and modeling tools will provide opportunities to further push the boundaries of our understanding of plant systems and will be key to a better understanding of how plants respond to complex environments.
Selected Publication
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S. Haque, N. Nelson, E. Lobaton, G. C. Yencho, K. Pecota, R. Mierop, M. Kudenov, M. Boyette, and C. Williams, "Computer vision approach to characterize size and shape phenotypes of horticultural crops using high- throughput imagery,", Computers and Electronics in Agriculture, vol. 182, 2021, https://doi.org/10.1016/j.compag.2021.106011.
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M. Matthews, J. Wang, R. Sederoff, V. L. Chiang, and C. Williams, “A multiscale model of lignin biosynthesis for predicting bioenergy traits in Populus trichocarpa,” Computational and Structural Biotechnology Journal, vol. 19, pp. 168-182, 2020.
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A. Koryachko, A. Matthiadis, S. Haque, D. Muhammad, J. Ducoste, J. Tuck, T. Long, C. Williams, “Dynamic modeling of the iron deficiency modulated transcriptome response in Arabidopsis thaliana roots,” in silico Plants, Volume 1, Issue 1, 2019, diz005, https://doi.org/10.1093/insilicoplants/diz005. (CW and TL co-corresponding authors)
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E. Buckner, I. Madison, H. Chou, C.E. Melvin, R. Sozzani, C. Williams, T.A. Long,“ High Resolution Spatiotemporal Imaging and Analysis of Dynamic Cell Cycle Progression Patterns Under Iron Deficiency and Heat Stress Conditions,” Frontiers in Plant Science, vol. 10, pp. 1487, 2019. (CW and TL co-corresponding authors)
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J. Wang, P. Naik, H. Chen, R. Shi, C. Lin, J. Jiu, C. Shuford, J. Ducoste, Q. Li, C. Williams, D. Muddiman, R. Sederoff, and V. Chiang, “Complete Proteomic-Based Enzyme Reaction and Inhibition Kinetics Reveal How Monolignol Biosynthetic Enzyme Families Affect Metabolic Flux and Lignin in Populus trichocarpa, ” The Plant Cell Online, vol. 26, no. 3, pp. 894-914, 2014.
Full list of Publication
https://ci.lib.ncsu.edu/profiles/cmwilli5
Official Website