The Digital Agriculture, Food, and Wine group work on the implementation and integration of new and emerging technologies, including but not limited to artificial intelligence tools on agricultural and food applications from farm to the palate.
Digital Agriculture (DA) deals with implementing and integrating digital data, sensors, and tools on agricultural, food, and wine applications from the paddock/vineyard to consumers. These technologies can range from big data, sensor technology, sensor networks & IoT, remote sensing, robotics, unmanned aerial vehicles (UAV).
Data processing is performed using new and emerging technologies, such as computer vision, machine learning, and artificial intelligence, among others.
The latest advances made by the DAFW group for crop monitoring/decision making, assessment of the quality of produces, sensory analysis for consumer perception and animal stress, and welfare assessment.
News and events
Contact the team
Dr Alexis Pang
Teaching specialist in Precision Agriculture
Computing and Information Systems
Emerging technologies based on artificial intelligence (AI) can be developed for any field of applied research.
Examples of the DAFW outputs are:
- Deep and machine learning modelling based on remote sensing for Livestock identification and welfare assessment
- Assessment of aroma profiles in cocoa plantations based on aerial photogrammetry, canopy architecture and AI
- Assessment of big data related to environmental factors affecting dairy cow stress and milk productivity and quality
- Remote sensing and AI to assess crop water status
- Use of robotics and remote sensing to assess the intensity of beer sensory descriptors , consumers acceptability , proteins and other physicochemical parameters
- Use of biometrics from consumers to assess acceptability of beer , and insect-based snacks
- A portable electronic nose (e-nose) coupled with AI to assess aromas in beer, smoke taint in wines after bushfires and detecting pest and diseases in crops , and
- NIR and machine learning to assess physicochemical parameters and sensory descriptors of beer , and physicochemical parameters in chocolate , detection of pest and diseases in crops, assessment of berry cell death and plant water status, among others.
Deep and machine learning modelling based on remote sensing for Livestock identification and welfare assessment
Development and application of computer vision techniques coupled with machine and deep learning for identification and assessment of welfare of livestock such as cattle, sheep and pigs as well as prediction of produce quality traits and yield. This also includes deployment of artificial intelligence models using Jetson technology.
UAV‐based remote sensing and GIS mapping of crops and produce assessment
UAV‐based remote sensing and GIS mapping of processed data for irrigation scheduling, plant water status assessment, nutrient assessment, pest and disease early prediction and smoke contamination.
Artificial intelligence/machine learning agriculture, food and animal sciences
Machine learning based modelling and artificial intelligence applications for agriculture, food and animal sciences
Robotics, sensory evaluation/biometrics and machine learning modelling for brewages
Integration of Robotics, sensory analysis of food and brewages with biometrics and machine learning algorithms to understand consumer preferences and quality of food and brewage products.
Computer application development for agriculture, food and wine sciences
Mobile computer applications development to be used for agriculture, food and wine sciences.
Advanced analytical platforms for plant physiology, climate change, sensory technologies and robotics
The DAFW group has expertise in the use and maintenance of state-of-the-art instrumentation to obtain direct measurements of plant physiology and through remote sensing.
Meet the researchers who make up the Digital Agriculture, Food and Wine group.
Associate Professor Sigfredo Fuentes
Associate Professor in Digital Agriculture, Food and Wine Sciences
Sigfredo Fuentes’ scientific interests range from climate change impacts on agriculture, development of new computational tools for plant physiology, food, and wine science, new and emerging sensor technology, proximal, short and long-range remote sensing using robots and UAVs, machine learning and artificial intelligence.
Dr Claudia Gonzalez Viejo
Research Fellow in Digital Agriculture, Food and Wine Sciences
Claudia Gonzalez Viejo’s research interests lie on the development of emerging technologies based on artificial intelligence such as robotics, sensors, computer vision, biometrics and machine learning modelling and their application in the field of agricultural, food and beverage sciences and engineering.
Dr Eden Tongson
Postdoctoral Fellow in Digital Agriculture, Food and Wine Sciences
Eden Tongson’s research interests are in the areas of genetics and throughput phenotyping of crops and the implementation of digital tools and machine learning in agriculture and food. She is also a professional, scientific illustrator and digital artist for peer-reviewed journal articles and scientific books.
Dr Nir Lipovetzky
Senior Lecturer in Computing and Information Systems
Nir Lipovetzky’s research interests span across AI planning, search, learning, verification, and intention recognition with a special focus on how to introduce different approaches to the problem of inference in sequential decision problems, and applications to autonomous systems. He's involved in the development of the Lightweight Automated Planning ToolKiT (LAPKT), aimed to make your life easier if your purpose is to create, use or extend basic to advanced Automated Planners.
Professor Frank R. Dunshea
Redmond Barry Distinguished Professor & Chair of Agriculture
Professor Dunshea has focused much of his recent research on biomedicine and fuctional foods. Professor Dunshea’s research has an impressive breadth and quality, and he has published over 750 journal, conference, book or technical articles. His research has had a high scientific impact. In addition, the results of much of his research have been rapidly adopted by industry. Professor Dunshea has maintained a balanced approach to research, combining fundamental with applied research, providing commercial and public good outcomes.
The Digital Agriculture, Food and Wine group (DAFW) deals with the implementation and integration of digital data, sensors, technology, and tools with artificial intelligence (AI) for agricultural applications from the farm or vineyard to consumers.
Due to complexities involving agriculture, food, and wine sciences, many people consider these practices part science, part art. However, we attribute these complexities to intricate interactions that need to be taken into consideration and understood. These are related to complex processes happening in the soil, the root system, the plant, and canopies interacting with the atmosphere throughout the season.
The recent implementation of unmanned aerial vehicles (UAVs) or drones and remote sensing opened up a variety of technologies that were developed for image analysis through computer vision, more robust modeling techniques through machine learning and artificial intelligence that can be applied to agriculture and food process.
Our group has made many advances in researching these potential techniques for practical applications in the industry and many more industries related to animal production and food science.
What is the difference between Precision Agriculture (PA) and Digital Agriculture (DA)?
Precision agriculture has been around for more than 30 years and relates to the technology implemented in agricultural applications such as satellites, GPS guided agricultural machinery, among others. The DAFW group creates intelligent and smart tools to interpret data and do practical and tangible applications using machine learning, robotics, and artificial intelligence. The DAFW group has been developing DA practical tools that can be readily applied to the industry.