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.

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The Digital Agriculture, Food and Wine Sciences group (DAFW), belonging to the Faculty of Veterinary and Agricultural Sciences (FVAS) at the University of Melbourne, has been working on the implementation of digital tools in agriculture.

These tools include remote sensing using satellite and unmanned aerial vehicles (UAVs); short-range and proximal remote sensing using computer applications (Apps), robotics, biological (trained dogs) and digital sensor technology and sensor networks to monitor the Soil-Plant-Atmosphere (SPA) interactions; implementation of the Internet of things (IoT) for data gathering, storage, and communication.

Data analysis is performed mainly through signal analysis, computer vision, and Machine Learning (ML) to obtain supervised and unsupervised learning models for data analysis and decision making. Model accuracies obtained by the DAFW group and published in peer-reviewed journals are higher than 85 per cent, with many over 90 per cent in the prediction of many important factors that are critical for decision-making from the farm to the consumer’s palate.