Professor Scott Chapman

Professor in Crop Physiology

School of Agriculture and Food Sustainability
Faculty of Science
scott.chapman@uq.edu.au
+61 7 54601 152
+61 7 54601 108

Overview

Summary of Research:

  • My current research at UQ is as Professor in this School (teaching AGRC3040 Crop Physiology) and as an Affiliate Professor of QAAFI. Since 2020, with full-time appointment at UQ, my research portfolio has included multiple projects in applications of machine learning and artificial intelligence into the ag domain. This area is developing rapidly and across UQ, I am engaging with faculty in multiple schools (ITEE, Maths and Physics, Mining and Mech Engineering) as well as in the Research Computing Centre to develop new projects and training opportunities at the interface of field agriculture and these new digital analytics.
  • My career research has been around genetic and environment effects on physiology of field crops, particularly where drought dominates. Application of quantitative approaches (crop simulation and statistical methods) and phenotyping (aerial imaging, canopy monitoring) to integrate the understanding of interactions of genetics, growth and development and the bio-physical environment on crop yield. In recent years, this work has expanded more generally into various applications in digital agriculture from work on canopy temperature sensing for irrigation decisions (CSIRO Entrepreneurship Award 2022) through to applications of deep-learning to imagery to assist breeding programs.
  • Much of this research was undertaken with CSIRO since 1996. Building on an almost continuous collaboration with UQ over that time, including as an Adjunct Professor to QAAFI, Prof Chapman was jointly appointed (50%) as a Professor in Crop Physiology in the UQ School of Agriculture and Food Sciences from 2017 to 2020, and at 100% with UQ from Sep 2020. He has led numerous research projects that impact local and global public and private breeding programs in wheat, sorghum, sunflower and sugarcane; led a national research program on research in ‘Climate-Ready Cereals’ in the early 2010s; and was one of the first researchers to deploy UAV technologies to monitor plant breeding programs. Current projects include a US DoE project with Purdue University, and multiple projects with CSIRO, U Adelaide, La Trobe, INRA (France) and U Tokyo. With > 8500 citations, Prof Chapman is currently in the top 1% of authors cited in the ESI fields of Plant and Animal Sciences and in Agricultural Sciences.

Research Interests

  • Applications of deep learning in crop phenotyping
  • Use of simulation models in plant breeding programs and managing climate change
  • Deployment of IoT, UAV and remote sensing technologies in research and commercial field scales

Research Impacts

Optimization of genotype evaluation methods in breeding programs

  • By 2005, completed two sugarcane projects that radically changed the priorities and evaluation methods of Australian breeding programs such that the delivery of new varieties now happens 3 to 5 years earlier. The major outcome was a confidential industry report. Supervised similar research for Advanta sunflower breeding in Argentina to reorganise and accelerate preliminary testing program.
  • Led the public sector’s most extensive global collaborative study of wheat variety performance (>200 trials). This has assisted the delivery of better spring-wheat varieties into developing countries and into Australia.
  • Extended research to use “environment characterization”, which I co-developed in the late 90s. The basic methodology to better identify stable varieties in the face of drought stress, has been adopted by international seed companies and local breeding programs in a range of crops.
  • From 2009 to 2017, led the development of applications of ‘Pheno-Copter’ autonomous aerial robot platform at CSIRO based on hardware and software processing systems to allow capture and analysis of high-throughput image information from field crop experiments in wheat, sorghum, sugarcane and cotton.
  • Since 2019/2020, have begun to lead two new research projects funded by GRDC involving both UQ and CSIRO. One project (AG-FE-ML) with partners in France (INRAe/ARVALIS) and Japan (U Tokyo) is in the applications of deep learning/feature extraction on agricultural imagery to allow automated segmentation of plant parts from images and to enable counting of reproductive structures (heads/panicles/grains) that are associated with grain yield of crops. The second project (INVITA) is applying a range of technologies (in-field sensors, cameras, satellite imagery, computer simulation) and methods (multi-variate statistics and machine learning) to attempt to improve the prediction of differences in yields among crop genotypes in the National Variety Trials. This research aims to allow the interpolation of results across the national production areas.

Exploiting crop adaptation traits through experiments and simulation studies

  • Supervised and co-investigated to demonstrate the adaptive yield and quality value of major wheat genes around the world (dwarfing and disease genes) and across Australia (water soluble carbohydrates, transpiration efficiency and tillering genes)
  • As a co-investigator, developed a unique platform (to the public sector) in the simulation modelling of crop growth and plant breeding programs. This platform has attracted >$6 million co-investment (ARC and private company) and provides the full capability to model the breeding systems of major crops. It continues development in the current ARC CoE for Plant Success.
  • Co-published pioneering research on the simulation of genetic controls of leaf growth processes within crop models. This original contribution has opened novel opportunities for the high-throughput simulation, testing and improvement of fully-specified physiological, breeding and statistical methodologies that are applied in plant breeding.
  • As lead PI (wheat) and co-PI (sorghum), ran experiments and improved models to analyse potential of genetic variation in heat tolerance to cope with current and future climates in Australian environments.

Qualifications

  • PhD, The University of Queensland
  • B Agr Sc 1st class honors, University of Queensland

Publications

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Supervision

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Available Projects

  • We have multiple opportunities for agricultural and maths/IT/engineering students to enrol or be co-supervised in research with our teams.

    Please contact me or carla.gho@uq.edu.au

View all Available Projects

Publications

Book Chapter

  • Potgieter, Andries B., Watson, James, George-Jaeggli, Barbara, McLean, Gregory, Eldridge, Mark, Chapman, Scott C., Laws, Kenneth, Christopher, Jack, Chenu, Karine, Borrell, Andrew, Hammer, Graeme and Jordan, David R. (2019). The use of hyperspectral proximal sensing for phenotyping of plant breeding trials. Fundamentals, sensor systems, spectral libraries, and data mining for vegetation. (pp. 127-148) edited by Prasad S. Thenkabail, John G. Lyon and Alfredo Huete. Boca Raton FL, USA: CRC Press. doi: 10.1201/9781315164151-5

  • Potgieter, Andries B., Watson, James, George-Jaeggli, Barbara, McLean, Gregory, Eldridge, Mark, Chapman, Scott C., Laws, Kenneth, Christopher, Jack, Chenu, Karine, Borrell, Andrew, Hammer, Graeme L. and Jordan, David R. (2018). The use of hyperspectral proximal sensing for phenotyping of plant breeding trials. Fundamentals, sensor systems, spectral libraries, and data mining for vegetation. (pp. 127-147) Boca Raton, FL, United States: CRC Press. doi: 10.1201/9781315164151-5

  • Chapman, Scott C., Zheng, Bangyou, Potgieter, Andries B., Guo, Wei, Baret, Frederic, Liu, Shouyang, Madec, Simon, Solan, Benoit, George-Jaeggli, Barbara, Hammer, Graeme L. and Jordan, David R. (2018). Visible, near infrared, and thermal spectral radiance on-board UAVs for high-throughput phenotyping of plant breeding trials. Biophysical and biochemical characterization and plant species studies. (pp. 275-299) edited by Prasad S. Thenkabail, John G. Lyon and Alfredo Huete. Boca Raton, FL, United States: CRC Press. doi: 10.1201/9780429431180-10

  • Hammer, Graeme, Messina, Charlie, van Oosterom, Erik, Chapman, Scott, Singh, Vijaya, Borrell, Andrew, Jordan, David and Cooper, Mark (2016). Molecular breeding for complex adaptive traits: how integrating crop ecophysiology and modelling can enhance efficiency. Crop systems biology: narrowing the gaps between crop modelling and genetics. (pp. 147-162) edited by Xinyou Yin and Paul C. Struik. Cham, Switzerland: Springer International Publishing. doi: 10.1007/978-3-319-20562-5_7

  • Hammer, Graeme, McLean, Greg, Doherty, Al, van Oosterom, Erik and Chapman, Scott (2016). Sorghum crop modeling and its utility in agronomy and breeding. Sorghum: state of the art and future perspectives. (pp. 1-25) edited by Ignacio Ciampitti and Vara Prasad. Madison, WI United States: American Society of Agronomy and Crop Science Society of America. doi: 10.2134/agronmonogr58.2014.0064

  • Bustos-Korts, Daniela, Malosetti, Marcos, Chapman, Scott and van Eeuwijk, Fred (2015). Modelling of genotype by environment interaction and prediction of complex traits across multiple environments as a synthesis of crop growth modelling, genetics and statistics. Crop Systems Biology: Narrowing the Gaps Between Crop Modelling and Genetics. (pp. 55-82) edited by Xinyou Yin and Paul C. Struik. Cham, Switzerland: Springer International Publishing. doi: 10.1007/978-3-319-20562-5_3

  • Hathorn, Adrian and Chapman, Scott C. (2014). Historical and prospective applications of ‘quantitative genomics’ in utilising germplasm resources. Genomics of plant genetic resources: volume 1. managing, sequencing and mining genetic resources. (pp. 93-110) edited by Roberto Tuberosa, Andreas Graner and Emile Frison. Dordrecht, Netherlands : Springer Netherlands. doi: 10.1007/978-94-007-7572-5_5

  • Hathorn, Adrian, Chapman, Scott and Dieters, Mark (2014). Simulated breeding with QU-GENE graphical user interface. Crop breeding: methods and protocols. (pp. 131-142) edited by Delphine Fleury and Ryan Whitford. New York, NY, United States: Humana Press. doi: 10.1007/978-1-4939-0446-4_11

  • Reynolds, Matthew P., Hays, Dirk and Chapman, Scott (2010). Breeding for adaptation to heat and drought stress. Climate Change and Crop Production. (pp. 71-91) CABI Publishing.

  • Rebetzke, Greg J., Chapman, Scott C., Lynne Mcintyre, C., Richards, Richard A., Condon, Anthony G., Watt, Michelle and Van Herwaarden, Anthony F. (2009). Grain Yield Improvement in Water-Limited Environments. Wheat Science and Trade. (pp. 215-249) Oxford, UK: Wiley-Blackwell. doi: 10.1002/9780813818832.ch11

  • Cooper, Mark, van Eeuwijk, Fred, Chapman, Scott C., Podlich, Dean W. and Loffler, Carlos (2006). Genotype-by-environment interactions under water-limited conditions. Drought Adaptation in Cereals. (pp. 51-96) edited by Jean-Marcel Ribaut. New York, NY, United States: Food Products Press.

  • Cooper, M., Podlich, D., Micallef, K. P., Smith, O. S., Jensen, N. M., Chapman, S. C. and Kruger, N. L. (2002). Complexity, quantitative traits and plant breeding: a role for simulation modelling in the genetic improvement of crops. Quantitative Genetics, Genomics and Plant Breeding. (pp. 143-166) edited by M. S. Kang. Wallingford, UK: CAB International.

  • Cooper, M., Podlich, D., Jensen, N., Chapman, S. C. and Hammer, G. L. (1999). Modelling plant breeding programs. Trends in Agronomy. (pp. 33-64) Trivandrum, India: Research Trends.

Journal Article

Conference Publication

Other Outputs

Grants (Administered at UQ)

PhD and MPhil Supervision

Current Supervision

Completed Supervision

Possible Research Projects

Note for students: The possible research projects listed on this page may not be comprehensive or up to date. Always feel free to contact the staff for more information, and also with your own research ideas.

  • We have multiple opportunities for agricultural and maths/IT/engineering students to enrol or be co-supervised in research with our teams.

    Please contact me or carla.gho@uq.edu.au