Digital Agriculture (DA) deals with the implementation and integration of 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, 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.
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Examples of the DAFW outputs are:
- 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.
UAV‐based remote sensing and GIS mapping
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
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
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.
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
Postdoctoral 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, high throughput phenotyping of crops and the implementation of digital tools and machine learning in agriculture and food sciences. She is also a professional scientific illustrator and digital artist for peer-reviewed journal articles and scientific books.
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. It relates to the technology implemented to agricultural applications based mainly on remote sensing from satellites, unmanned aerial and terrestrial vehicles (UAV and UTV respectively) with information deployed in GPS-guided agricultural machinery. Digital agriculture has allowed many advancements in PA recently, since DA deals with the analysis of data using new and emerging tools from artificial intelligence (AI) such as sensor networks data, robotics, remote sensing, computer vision and machine learning, among others. The DAFW group creates intelligent and smart tools to interpret data for practical and tangible applications using machine learning, robotics, and artificial intelligence. The DAFW group has been developing practical and user-friendly DA tools that can be readily applied in industry.
This project aims to test an artificial intelligence (AI) detection system to detect pests in different grain crops.Project
This project aims to develop rapid techniques to assess animal stress and welfare using non-invasive biometrics to analyse physiological responses (heart rate, respiration rate, startling, and temperature)Project
This project aims to develop robotics and sensors such as electronic noses coupled with other artificial intelligence tools as rapid methods to assess food and beverages from farm to the final product.Project
This project aims to test new and emerging technologies for in-field operations and management, ranging from proximal sensor network technology and remote sensing from UAV and satellite.Project
This project aims to understand the effects of food, beverage and packaging stimuli on the physiological, emotional, and sensory responses of consumers.Project