Dr Alison C McCarthy

Contact information

  • Email address: Alison.McCarthy@unisq.edu.au
  • Official website address: https://www.facebook.com/CAEUSQ/
  • Postal address: University of Southern Queensland
    West Street
    Centre for Agricultural Engineering (P9 building)
    Toowoomba QLD 4350
  • City/region: Toowoomba
  • Country: Australia
  • Business phone: 0746312189
  • Mobile phone: 0458845858
  • Fax number: 0746311870

Professional affiliations

Full Member of the Association of Australian Cotton Scientists

Biography

Dr Alison McCarthy is an irrigation and mechatronic Research Fellow within the Centre for Agricultural Engineering at the University of Southern Queensland in Toowoomba.  She has a BEng (Hons) in Mechatronics and PhD in Irrigation Engineering from the University of Southern Queensland.  She has been involved with research projects in the cotton industry since 2010 funded by the Cotton Research and Development Corporation.  This research has led to the development of real-time adaptive control and low cost camera-based sensing systems to reduce labour in plant growth monitoring, and improve and potentially optimise the irrigation of field crops. Her current projects involve the variable-rate irrigation of cotton, dairy, horticulture and sugarcane crops via lateral move and centre pivot irrigation machines and surface irrigation systems.

During Alison’s PhD she developed a simulation framework ‘VARIwise’ to aid the development, evaluation and management of spatially and temporally varied site-specific irrigation control strategies. Her research interests include irrigation decision-making and machine vision.

Areas of expertise

  • Control and sensing systems for monitoring and irrigation of field crops
  • Management of  in-field spatial and temporal variability
  • Developing tools for growers to evaluate infield variability

Current research activities

  • Real-time adaptive control of irrigation via surface irrigation systems and large mobile irrigation machines for cotton (website)
  • On-the-go plant growth sensor development for site-specific monitoring of fodder and cotton growth
  • Remote monitoring and automatic detection of grain crop attributes for grain National Variety Trials (website)

Overview of irrigation automation using VARIwise

Site-specific irrigation enables the delivery of irrigation water where and when it is required in the field.  Commercially available hardware is available that can adjust the application from centre pivots and lateral moves; however, wide adoption of these systems is limited because of a lack of decision-support to determine irrigation application.

The irrigation control framework VARIwise was created to developed, simulate and compare site-specific irrigation control strategies (McCarthy et al. 2010).  This involves: (i) dividing the field into smaller, controllable sub-areas named ‘cells’; (ii) assigning soil and plant parameters to each cell; (iii) calibrating the corresponding crop model for each cell; and (iv) executing a crop production model within in each cell.  VARIwise has been used to determine the optimal data input types and resolution for each control strategy in simulation (McCarthy et al. 2011; McCarthy et al. 2012).

Two general types of adaptive control strategies have been implemented in VARIwise that can be applied to irrigation: sensor- and model-based.  Sensor-based control strategies use the difference between a measured and target variable to update the irrigation application, whilst model-based control strategies determine irrigation application that best achieves the desired future crop performance as predicted by a calibrated crop production model.

Generic adaptive control systems have three major components: (i) sensor data feedback; (ii) control strategy that uses data feedback to determine system input; and (iii) actuator hardware to adjust system inputs.  The transfer of data between these components where VARIwise incorporates the control strategy is shown in the figure below.  An on-the-go plant sensing system was developed to estimate plant height (for leaf area index calculation), flower count (for square count calculation) and boll count, as required to calibrate the industry crop production model incorporated in VARIwise.  This sensing system was standalone and platforms were developed that enabled mounting to on-farm vehicles (e.g. moped) and irrigation machines.

Field evaluations of the control strategies were conducted on a siphon and centre pivot irrigation system in Jondaryan, QLD utilising the plant sensing system, control strategies and irrigation control hardware. This demonstrated the implementation of adaptive control systems at commercial cotton cropping sites and indicated that plant data input was preferable to soil data input for model-based control strategies.  Yield improvements and water use reductions were achieved in both irrigation systems.  Higher water reductions were achieved in surface irrigation systems than overhead irrigation systems because of the larger volumes of irrigation water applied.  Adoption of these irrigation control systems would provide improved and automated irrigation management and labour savings to the industry.

Evaluation of automated site-specific irrigation control systems

Testing VRI on beans at Kalbar

Overview of automated variety trial monitoring using cameras

The National Variety Trials for grain crops in Australia currently require manual observations of flowering date and plant cover of each plot by Trial Service Providers. This often limits the frequency of variety observation and assessment and can produce inconsistent results across research stations. An automated remote crop monitoring system potentially provides an ongoing assessment of crop varieties with an Internet-enabled camera and image analysis algorithms. Existing image analysis algorithms have automatically determined when crops are flowering using spectral reflectance and colour segmentation techniques. However, these have not been developed for common Australian grain crops (e.g. wheat and chickpea). A camera-based remote crop monitoring system and image analysis algorithms are being developed to automatically capture images of the trial plots and identify flowers and extract plant cover.

Camera images have been captured at wheat and chickpea trial sites in Brookstead, Queensland, Australia from a camera system installed at the edge of the trial site. A high resolution smartphone-based camera system captured images to detect wheat flowers (2-4mm long, 0.5-1mm wide) and chickpea flowers (8-10mm long, 8-10mm wide) from the edge of the plot. Image analysis algorithms have been implemented to detect crop height and flowering. Crop height was detected using the length of the shadows in the images, time of image capture and day of the year. Spectral analysis of chickpea and wheat crop components (e.g. flowers, leaves, branches) was conducted using a tuneable diode filter and a DSLR camera to determine wavebands for flower detection. Chickpea flowers were detected using an amber filter and wheat flowers were detected using a red filter. A media article from GRDC Ground Cover: (Media article)