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Professor Scott Chapman
Professor

Scott Chapman

Email: 
Phone: 
+61 7 54601 108
Phone: 
+61 7 54601 152

Overview

Background

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.

Availability

Professor Scott Chapman is:
Available for supervision
Media expert

Qualifications

  • Bachelor (Honours), The University of Queensland
  • Doctor of Philosophy, The University of Queensland

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.

Works

Search Professor Scott Chapman’s works on UQ eSpace

299 works between 1988 and 2024

1 - 20 of 299 works

2024

Journal Article

Prediction accuracy and repeatability of UAV based biomass estimation in wheat variety trials as affected by variable type, modelling strategy and sampling location

Smith, Daniel T. L., Chen, Qiaomin, Massey-Reed, Sean Reynolds, Potgieter, Andries B. and Chapman, Scott C. (2024). Prediction accuracy and repeatability of UAV based biomass estimation in wheat variety trials as affected by variable type, modelling strategy and sampling location. Plant Methods, 20 (1) 129. doi: 10.1186/s13007-024-01236-w

Prediction accuracy and repeatability of UAV based biomass estimation in wheat variety trials as affected by variable type, modelling strategy and sampling location

2024

Journal Article

Estimating aboveground biomass dynamics of wheat at small spatial scale by integrating crop growth and radiative transfer models with satellite remote sensing data

Hu, Pengcheng, Zheng, Bangyou, Chen, Qiaomin, Grunefeld, Swaantje, Choudhury, Malini Roy, Fernandez, Javier, Potgieter, Andries and Chapman, Scott C. (2024). Estimating aboveground biomass dynamics of wheat at small spatial scale by integrating crop growth and radiative transfer models with satellite remote sensing data. Remote Sensing of Environment, 311 114277, 1-15. doi: 10.1016/j.rse.2024.114277

Estimating aboveground biomass dynamics of wheat at small spatial scale by integrating crop growth and radiative transfer models with satellite remote sensing data

2024

Conference Publication

Insights in the ability of high-resolution narrow band multispectral and thermal sensors to estimate cotton production in Australia

Devoto, F., Reynolds-Massey-Reed, S., Segura, Pinzon C., Bell, M., Mclaren, T., Awale, R., Camino, C., Bange, M., Woodgate, W., Chapman, S. and Potgieter, A. B. (2024). Insights in the ability of high-resolution narrow band multispectral and thermal sensors to estimate cotton production in Australia. 2024 IEEE International Geoscience and Remote Sensing Symposium, Athens, Greece, 7-12 July 2024. Piscataway, NJ, United States: IEEE. doi: 10.1109/igarss53475.2024.10642663

Insights in the ability of high-resolution narrow band multispectral and thermal sensors to estimate cotton production in Australia

2024

Journal Article

Seasonal dynamics of fallow and cropping lands in the broadacre cropping region of Australia

Xie, Zunyi, Zhao, Yan, Jiang, Ruizhu, Zhang, Miao, Hammer, Graeme, Chapman, Scott, Brider, Jason and Potgieter, Andries B. (2024). Seasonal dynamics of fallow and cropping lands in the broadacre cropping region of Australia. Remote Sensing of Environment, 305 114070, 1-14. doi: 10.1016/j.rse.2024.114070

Seasonal dynamics of fallow and cropping lands in the broadacre cropping region of Australia

2024

Journal Article

GrainPointNet: a deep-learning framework for non-invasive sorghum panicle grain count phenotyping

James, Chrisbin, Smith, Daniel, He, Weigao, Chandra, Shekhar S. and Chapman, Scott C. (2024). GrainPointNet: a deep-learning framework for non-invasive sorghum panicle grain count phenotyping. Computers and Electronics in Agriculture, 217 108485, 108485. doi: 10.1016/j.compag.2023.108485

GrainPointNet: a deep-learning framework for non-invasive sorghum panicle grain count phenotyping

2023

Journal Article

Preliminary results in innovative solutions for soil carbon estimation: integrating remote sensing, machine learning, and proximal sensing spectroscopy

Li, Tong, Xia, Anquan, McLaren, Timothy I., Pandey, Rajiv, Xu, Zhihong, Liu, Hongdou, Manning, Sean, Madgett, Oli, Duncan, Sam, Rasmussen, Peter, Ruhnke, Florian, Yüzügüllü, Onur, Fajraoui, Noura, Beniwal, Deeksha, Chapman, Scott, Tsiminis, Georgios, Smith, Chaya, Dalal, Ram C. and Dang, Yash P. (2023). Preliminary results in innovative solutions for soil carbon estimation: integrating remote sensing, machine learning, and proximal sensing spectroscopy. Remote Sensing, 15 (23) 5571, 1-17. doi: 10.3390/rs15235571

Preliminary results in innovative solutions for soil carbon estimation: integrating remote sensing, machine learning, and proximal sensing spectroscopy

2023

Conference Publication

Toward a unified framework for RGB and RGB-D visual navigation

Du, Heming, Huang, Zi, Chapman, Scott and Yu, Xin (2023). Toward a unified framework for RGB and RGB-D visual navigation. 36th Australasian Joint Conference on Artificial Intelligence, AJCAI 2023, Brisbane, QLD Australia, 28 November –1 December 2023. Singapore: Springer. doi: 10.1007/978-981-99-8391-9_29

Toward a unified framework for RGB and RGB-D visual navigation

2023

Other Outputs

Capacity building and knowledge transfer in seaweed mapping in Indonesia

Abdul Aziz, Ammar, Wicaksono, Prama, Arjasakusuma, Sanjiwana, Chapman, Scott, Langford, Zannie, Grunefeld, Swaantje, Azizan, Fathin Ayuni and Maishella, Amanda (2023). Capacity building and knowledge transfer in seaweed mapping in Indonesia. Melbourne, VIC, Australia: Australia-Indonesia Centre.

Capacity building and knowledge transfer in seaweed mapping in Indonesia

2023

Conference Publication

Advances in the study of biochemical, morphological and physiological traits of wheat and sorghum crops in australia using hyperspectral data and machine learning

Potgieter, A. B., Camino, C., Poblete, T., Zhi, X., Reynolds-Massey-Reed, S., Zhao, Y., Belwalkar, A., Ruizhu, J., George-Jaeggli, B., Chapman, S., Jordan, D., Wu, A., Hammer, G. L. and J, Zarco-Tejada P. (2023). Advances in the study of biochemical, morphological and physiological traits of wheat and sorghum crops in australia using hyperspectral data and machine learning. 2023 IEEE International Geoscience and Remote Sensing Symposium, Pasadena, CA USA, 16-21 July 2023. Piscataway, NJ USA: Institute of Electrical and Electronics Engineers. doi: 10.1109/igarss52108.2023.10282230

Advances in the study of biochemical, morphological and physiological traits of wheat and sorghum crops in australia using hyperspectral data and machine learning

2023

Journal Article

Modelling the impacts of diverse cover crops on soil water and nitrogen and cash crop yields in a sub-tropical dryland

Garba, Ismail I., Bell, Lindsay W., Chapman, Scott, deVoil, Peter, Kamara, Alpha Y. and Williams, Alwyn (2023). Modelling the impacts of diverse cover crops on soil water and nitrogen and cash crop yields in a sub-tropical dryland. Field Crops Research, 301 109019, 1-14. doi: 10.1016/j.fcr.2023.109019

Modelling the impacts of diverse cover crops on soil water and nitrogen and cash crop yields in a sub-tropical dryland

2023

Journal Article

Utilisation of unmanned aerial vehicle imagery to assess growth parameters in mungbean (Vigna radiata (L.) Wilczek)

Xiong, Yiyi, Chiau, Lucas Mauro Rogerio, Wenham, Kylie, Collins, Marisa and Chapman, Scott C. (2023). Utilisation of unmanned aerial vehicle imagery to assess growth parameters in mungbean (Vigna radiata (L.) Wilczek). Crop and Pasture Science, 75 (1) ARTN CP22335CO. doi: 10.1071/cp22335

Utilisation of unmanned aerial vehicle imagery to assess growth parameters in mungbean (Vigna radiata (L.) Wilczek)

2023

Journal Article

Coherent Terahertz laser feedback interferometry for hydration sensing in leaves

Kashyap, Mayuri, Torniainen, Jari, Bertling, Karl, Kundu, Urbi, Singh, Khushboo, Donose, Bogdan, Gillespie, Tim, Lim, Yah Leng, Indjin, Dragan, Li, Lian He, Linfield, Edmund, Davies, Giles, Dean, Paul, Smith, Millicent, Chapman, Scott, Bandyopadhyay, Aparajita, Sengupta, Amartya and Rakic, Aleksandar (2023). Coherent Terahertz laser feedback interferometry for hydration sensing in leaves. Optics Express, 31 (15), 23877-23888. doi: 10.1364/oe.490217

Coherent Terahertz laser feedback interferometry for hydration sensing in leaves

2023

Journal Article

Global wheat head detection challenges: winning models and application for head counting

David, Etienne, Ogidi, Franklin, Smith, Daniel, Chapman, Scott, de Solan, Benoit, Guo, Wei, Baret, Frederic and Stavness, Ian (2023). Global wheat head detection challenges: winning models and application for head counting. Plant Phenomics, 5 0059, 1-14. doi: 10.34133/plantphenomics.0059

Global wheat head detection challenges: winning models and application for head counting

2023

Conference Publication

Evaluating variation in stem strength in response to artificial drought stress amongst sorghum genotypes

Geetika, Geetika, Borrell, Andrew, Thornton, Erin, Hunt, Colleen, Chapman, Scott, Philp, Trevor, Fekybelu, Solomon, Potgieter, Andries, Godwin, Ian, Hammer, Graeme, Mace, Emma and Jordan, David (2023). Evaluating variation in stem strength in response to artificial drought stress amongst sorghum genotypes. Global Sorghum Conference Sorghum in the 21st Century, Montpellier, France, 5-9 June 2023.

Evaluating variation in stem strength in response to artificial drought stress amongst sorghum genotypes

2023

Other Outputs

Peningkatan kapasitas dan transfer pengetahuan dalam pemetaan rumput laut di Indonesia

Abdul Aziz, Ammar, Wicaksono, Pramaditya, Arjasakusuma, Sanjiwana, Chapman, Scott, Langford, Zannie, Grunefeld, Swaantje, Azizan, Fathin Ayuni and Maishella, Amanda (2023). Peningkatan kapasitas dan transfer pengetahuan dalam pemetaan rumput laut di Indonesia. Melbourne, VIC, Australia: Australia-Indonesia Centre.

Peningkatan kapasitas dan transfer pengetahuan dalam pemetaan rumput laut di Indonesia

2023

Journal Article

VegAnn, Vegetation Annotation of multi-crop RGB images acquired under diverse conditions for segmentation

Madec, Simon, Irfan, Kamran, Velumani, Kaaviya, Baret, Frederic, David, Etienne, Daubige, Gaetan, Samatan, Lucas Bernigaud, Serouart, Mario, Smith, Daniel, James, Chrisbin, Camacho, Fernando, Guo, Wei, De Solan, Benoit, Chapman, Scott C. and Weiss, Marie (2023). VegAnn, Vegetation Annotation of multi-crop RGB images acquired under diverse conditions for segmentation. Scientific Data, 10 (1) 302, 1-12. doi: 10.1038/s41597-023-02098-y

VegAnn, Vegetation Annotation of multi-crop RGB images acquired under diverse conditions for segmentation

2023

Other Outputs

Generic model to estimate wheat LAI

Chen, Qiaomin, Chapman, Scott, Zheng, Bangyou, Chenu, Karine and Chen, Qiaomin (2023). Generic model to estimate wheat LAI. The University of Queensland. (Dataset) doi: 10.48610/ac9642c

Generic model to estimate wheat LAI

2023

Journal Article

A generic model to estimate wheat LAI over growing season regardless of the soil-type background

Chen, Qiaomin, Zheng, Bangyou, Chenu, Karine and Chapman, Scott C. (2023). A generic model to estimate wheat LAI over growing season regardless of the soil-type background. Plant Phenomics, 5 0055, 0055. doi: 10.34133/plantphenomics.0055

A generic model to estimate wheat LAI over growing season regardless of the soil-type background

2023

Journal Article

Self-supervised plant phenotyping by combining domain adaptation with 3D plant model simulations: application to wheat leaf counting at seedling stage

Li, Yinglun, Zhan, Xiaohai, Liu, Shouyang, Lu, Hao, Jiang, Ruibo, Guo, Wei, Chapman, Scott, Ge, Yufeng, Solan, Benoit de, Ding, Yanfeng and Baret, Frédéric (2023). Self-supervised plant phenotyping by combining domain adaptation with 3D plant model simulations: application to wheat leaf counting at seedling stage. Plant Phenomics, 5 0041, 1-13. doi: 10.34133/plantphenomics.0041

Self-supervised plant phenotyping by combining domain adaptation with 3D plant model simulations: application to wheat leaf counting at seedling stage

2023

Conference Publication

Evaluating variation in stem strength in response to artificial drought stress amongst sorghum genotypes

Geetika, Geetika, Borrell, Andrew, Thornton, Erin, Reed, Sean, Hunt, Colleen, Chapman, Scott, Philp, Trevor, Fekybelu, Solomon, Potgieter, Andries, Godwin, Ian, Hammer, Graeme, Mace, Emma and Jordan, David (2023). Evaluating variation in stem strength in response to artificial drought stress amongst sorghum genotypes. Australian Summer Grains Conference, Gold Coast, QLD Australia, 13-15 March 2023.

Evaluating variation in stem strength in response to artificial drought stress amongst sorghum genotypes

Funding

Current funding

  • 2024 - 2029
    ARC Training Centre in Predictive Breeding for Agricultural Futures
    ARC Industrial Transformation Training Centres
    Open grant
  • 2023 - 2028
    Narrow orchard systems for future climates (administered by Agriculture Victoria)
    Agriculture Victoria
    Open grant
  • 2023 - 2024
    Developing applications of satellite imagery for modelling environmental and social impacts of climate change on seaweed farming in Indonesia (KONEKSI Grant administered by Griffith University)
    Griffith University
    Open grant
  • 2023 - 2028
    Australian Plan Phenomics Facility NCRIS 2022 (administered by The University of Adelaide)
    University of Adelaide
    Open grant
  • 2023 - 2025
    Proximal and remote sensing for low-cost soil carbon stock estimation
    Commonwealth Department of Industry, Science, Energy and Resources
    Open grant
  • 2023 - 2027
    Analytics for the Australian Grains Industry (AAGI)
    Grains Research & Development Corporation
    Open grant
  • 2021 - 2027
    Reducing lodging risk in sorghum to increase grower confidence and profitability
    Grains Research & Development Corporation
    Open grant
  • 2021 - 2025
    Evaluating Salinity Tolerance in Diverse Taro (Colocasia) Wild Relatives to enhance Food Security in the Pacific Islands
    Australia & Pacific Science Foundation
    Open grant
  • 2021 - 2025
    CropVision: A next-generation system for predicting crop production
    ARC Linkage Projects
    Open grant
  • 2020 - 2024
    INVITA A technology and analytics platform for improving variety selection
    Grains Research & Development Corporation
    Open grant
  • 2018 - 2024
    Enhancing Light Use Efficiency to break through yield potential barriers in grain crops
    Pioneer Hi-Bred International Inc.
    Open grant

Past funding

  • 2022 - 2023
    Lean design workshop to understand future challenges for horticulture production in tropical and subtropical regions of Australia
    Horticulture Innovation Australia Limited
    Open grant
  • 2021 - 2023
    Carbon ID: A remote sensing decision support tool to identify the impact of agricultural land management on soil carbon stock
    Queensland Department of Agriculture and Fisheries
    Open grant
  • 2020 - 2022
    AgAsk: A machine learning generated question-answering conversational agent for data-driven growing decisions.
    Grains Research & Development Corporation
    Open grant
  • 2020 - 2024
    CropPhen: Remote mapping of grain crop type and phenology
    Grains Research & Development Corporation
    Open grant
  • 2020 - 2022
    Machine learning to extract maximum value from soil and crop variability (GRDC project administered by The University of Adelaide).
    University of Adelaide
    Open grant
  • 2020 - 2022
    Machine learning applied to High-throughput feature extraction from imagery to map spatial variability
    Grains Research & Development Corporation
    Open grant
  • 2016 - 2019
    Automated Sorghum Phenotyping and Trait Development Platform
    Purdue University
    Open grant

Supervision

Availability

Professor Scott Chapman is:
Available for supervision

Before you email them, read our advice on how to contact a supervisor.

Available projects

  • See Research Interests

    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

Supervision history

Current supervision

Completed supervision

Media

Enquiries

Contact Professor Scott Chapman directly for media enquiries about:

  • ag tech
  • climate change and crop production
  • crop science
  • digital agriculture

Need help?

For help with finding experts, story ideas and media enquiries, contact our Media team:

communications@uq.edu.au