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Faculty of Veterinary and Agricultural Sciences

Analytics, Modelling and Technology

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The Analytics, Modelling and Technology research group focuses on creating opportunities for innovation in big data to support the global efforts towards sustainable intensification of agriculture.

We are a multidisciplinary group of researchers with backgrounds in the soil and crop sciences, geographic information systems, remote sensing, engineering and computer-based modelling.

Working in collaboration with various local and international research institutions and industry partners, we work to develop new scientific insights as well as industry-relevant solutions in an agricultural production environment that is technology and data rich. We don’t just ask ‘why’; we also ask ‘why not?’.

News and events

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Contact the team

    Group co-leaders

  1. Dr Alexis Pang

    Lecturer, Precision Agriculture and Soil Science

    • alexis.pang@unimelb.edu.au
    • +613 8344 7190
  2. Dr Shu Kee (Raymond) Lam

    Senior Lecturer, Climate Change and Biogeochemistry

    • shukee.lam@unimelb.edu.au
    • +613 9035 9619

The Analytics, Modelling and Technology research group focuses on creating opportunities for innovation in big data to support the global efforts towards sustainable intensification of agriculture.

  • State-of-the-art instruments for trace gas measurements using micrometeorological techniques

    Real-time, continuous field-scale measurement of greenhouse gas emissions from farms.

  • Soil node – Melbourne TrACEES Platform

    The Melbourne Trace Analysis for Chemical, Earth and Environmental Sciences (TrACEES) Platform’s Soil node has a strong capability in soils and environmental research and testing.

  • HyperSens Lab

    The Hyperspectral and Thermal Remote Sensing Laboratory (HyperSens) is established jointly between the School of Agriculture and Food (SAF), Faculty of Veterinary and Agricultural Sciences (FVAS), and the Melbourne School of Engineering (MSE), Department of Infrastructure Engineering.

Our researchers and graduate research students

    Group members

  1. Professor Deli Chen

    Professor of Soil Science; Redmond Barry Distinguished Professor

    Deli Chen has expertise in water and nutrient dynamics in plant-soil systems, GIS based agroecosystem modelling and decision support systems for optimal irrigation and fertilizer management; and the measures, models and mitigates greenhouse gas emissions from land sources, impact of climate on agro-ecosystems, agricultural ‘big data’ and sustainable indices.

    • Find an Expert profile
    • delichen@unimelb.edu.au
  2. Professor Pablo Zarco-Tejada

    Professor of Precision Agriculture

    Pablo Zarco-Tejada’s research focuses on remote sensing, precision agriculture and vegetation stress detection using hyperspectral and thermal imagery acquired by manned and unmanned aircraft systems. Recent projects include the application of artificial intelligence methods for early disease detection and the use of enabling analytics for grain crop monitoring applications.

    • Find an Expert profile
    • pablo.zarco@unimelb.edu.au
  3. Dr Lola Suárez Barranco

    Research Fellow, Hyperspectral Remote Sensing

    Lola Suárez is an expert in hyperspectral remote sensing, imaging spectroscopy and radiative transfer modelling. She works in plant trait retrieval to assess crop stress in agricultural crops and forest inventory metrics for forest monitoring. She has experience working with varied crop species and ecosystem types at different scales. Her interest is the use of spectroscopy and physical models to understand and measure vegetation dynamics.

    • Find an Expert profile
    • l.suarez@unimelb.edu.au
  4. Dr Shu Kee (Raymond) Lam

    Senior Lecturer, Climate Change and Biogeochemistry

    Shu Kee (Raymond) Lam's research focuses on soil carbon and nitrogen dynamics in agroecosystems, including soil-plant interactions under climate change (elevated atmospheric CO2 concentration) and mitigation of greenhouse gas emissions using urease and nitrification inhibitors. He also has expertise in global data synthesis (including meta-analysis).

    • Find an Expert profile
    • shukee.lam@unimelb.edu.au
  5. Dr Alexis Pang

    Lecturer, Precision Agriculture and Soil Science

    Alexis Pang’s research focuses on agricultural production systems modelling under different farming landscapes and climate change scenarios; application of GIS and remote sensing for Precision Agriculture. Recent work includes evaluation of machine-learning techniques for wheat yield prediction and APSIM modelling of rice production under projected future climates.

    • Find an Expert profile
    • alexis.pang@unimelb.edu.au
  6. Dr Xia (Emma) Liang

    Research Fellow, Sustainable Agriculture

    Xia (Emma) Liang’s research focuses on reactive nitrogen assessment, nitrogen footprint quantification, sustainable agriculture indicators and food credentialing at multiple scales, from local through to global levels. She also has expertise in global data synthesis and GIS. Her work is an exciting new research field nationally and internationally.

    • Find an Expert profile
    • liang.xia@unimelb.edu.au
  7. Graduate researchers

  8. Baobao Pan

    PhD candidate

    Baobao Pan’s PhD research integrates data mining, machine learning and agroecosystem process-based modelling to improve the prediction of soil N dynamics in terrestrial systems.

    • Find an Expert profile
    • bpan@student.unimelb.edu.au
  9. Andrew Longmire

    PhD candidate

    Andrew Longmire’s PhD research applies remote sensing technology to examine canopy nitrogen concentration.

    • Find an Expert profile
    • longmirea@student.unimelb.edu.au
  10. Xiuming Zhang

    PhD candidate

    Xiuming Zhang’s PhD research assesses the costs and benefits of the mitigation of ammonia emissions in Australia and China.

    • Find an Expert profile
    • xiumingz@student.unimelb.edu.au
  11. Ke Zhang

    PhD candidate

    Ke Zhang’s PhD research develops critical nitrogen dilution curve in agricultural systems based on canopy chlorophyll index.

    • Find an Expert profile
    • zhang.k3@unimelb.edu.au

The Analytics, Modelling and Technology research group focuses on creating opportunities for innovation in big data to support the global efforts towards sustainable intensification of agriculture.

Modelling, machine learning and meta-analysis

To achieve efficient agricultural nitrogen (N) management, our group applies process-based agroecosystem simulation models to simulate, predict and evaluate the impacts of management practices on N losses through different pathways, thereby developing decision-support tools. More recently, we integrated agricultural big data with machine learning to revamp existing parameters of agroecosystem models and improve the reliability of model prediction in N processes. We have also applied meta-analytic techniques to national and international datasets on soil-plant carbon and N dynamics in agricultural systems and their mitigation and adaptation to climate change.

Sustainable nitrogen indices

Australia has a reputation globally for production of food that is ‘clean and green’, but this branding is neither meaningfully defined nor readily verified. To substantiate this claim scientifically, our group has ongoing research, integrating vast datasets and resource-use models, for building the world’s first evidence-based N indices for (Australian) agricultural products that are embedded with an environmental cost-benefit analysis. The indices will help consumers to choose food products with low environmental footprints, incentivise farmers to adopt more sustainable N management practices and benchmark Australian agricultural production against international practices. Our group has also developed anew indicator, reactive nitrogen spatial intensity (NrSI), to identify Nr emission hotspots, indicate the potential environmental impacts of Nr, and assist management recommendations.

Remote sensing, GIS and precision agriculture

Rapidly advancing sensor systems on ground, air and space-borne platforms delivering hyperspectral and thermal remote sensing imagery at high spatio-temporal resolutions provide massive volumes of data to be analysed in novel ways, unveiling detailed insights into crop performance and their variability across large areas. Simultaneously, modern agricultural production systems generate large volumes of spatially referenced information such as yield, grain quality, soil moisture and weather data. This represents rich opportunities to develop smart farm systems that will allow growers make increasingly precise decisions to maximize yield potential while optimizing water and nutrients. Our group is working on developing new techniques and algorithms using hyperspectral and thermal remote sensing technology to detect biotic and abiotic crop stress under drought, heat, frost and nutrient deficiencies before symptoms are visible.

Current projects

  • Emissions avoidance of soil carbon from lands undergoing practice change

    Tropical rainforests and peatlands store huge amounts of carbon.

    03 Dec 2020 Project

Completed projects

  • Evaluation of machine learning approach for wheat yield prediction

    The application of machine learning algorithms to monitoring wheat crop and predicting yield has yet to be comprehensively investigated.

    09 Dec 2020 Project
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