Digital Agriculture, Food and Wine

The Digital Agriculture, Food, and Wine group works 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 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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Emerging technologies based on artificial intelligence (AI) can be developed for any field of applied research.

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.

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.