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CADSI
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Our impact areas
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Nature
- AutoWeed: AI Sport Spraying for Smarter Weed Control
- Automated Detection and Counting of Flying-Foxes from Thermal Imagery
- Smart Detection of Marine Invasive Species for Biosecurity
- 3D LiDAR Mapping of Coral Restoration Gardens and Mangrove Ecosystems in Bali and Lombok, Indonesia
- Hyperspectral Satellite Monitoring of Post-Cyclone Water Quality in the Great Barrier Reef
- Automated Estimation of Snail Shell Volume and Morphology
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Economies
- Smart Sugarcane Health Monitoring Platform with Satellite Remote Sensing and Machine Learning
- AutoFish: AI Phenotyping for Tropical Aquaculture
- Detecting Variety Contamination in Sugarcane Plots
- Automating South Sea Pearl Phenotyping Through Masking and Deep Learning Algorithms
- Non-Destructive, High-throughput Phenotyping for Aquaculture: NIR Spectroscopy for the “Forensic” Chemical Analysis of Akoya Oyster and Red Algae
- Remote Sensing and Hydrodynamic Modelling for Flood-Driven Soil Erosion in Northern Queensland
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- The Place of AI in Higher Education Teaching, Learning and Assessment
- Beyond the Text: Exploring student engagement and perceptions of AI generated podcasts and infographics in higher education
- Guardrails or Handbrakes? How Australian Universities Frame AI in Policy, and the Dominance of Misconduct Narratives
- Equitable GenAI Co-Pilot for Engineering Education
- VEtheRBlocks: Teaching the Internet with AR and Toy Bricks
- Corrugation Control in Unsealed Roads: Mechanisms and Sustainable Solutions
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We lift productivity and sustainability across primary to quaternary industries by deploying AI for sensing, automation, optimisation and quality assurance. This includes smarter agriculture and aquaculture, resilient supply chains, advanced manufacturing, and data-driven regional development that grows jobs and skills.
Smart Sugarcane Health Monitoring Platform with Satellite Remote Sensing and Machine Learning
Sugar-AI is a James Cook University–led project using AI and satellite imagery to detect ratoon stunting disease in sugarcane. It supports earlier intervention, improved productivity, and scalable decision-making for growers.
Learn more about this project here
AutoFish: AI Phenotyping for Tropical Aquaculture
AutoFish is a James Cook University project using AI and imaging to automate phenotyping for aquaculture, focusing on barramundi. It supports faster, more consistent trait measurement for breeding and production decisions
Learn more about this project here
Detecting Variety Contamination in Sugarcane Plots
This project uses drone imagery, multispectral data, and machine learning to identify sugarcane varieties across large areas. It helps detect cross-contamination risks, supporting disease prevention and more targeted crop management
Learn more about this project here
Automating South Sea Pearl Phenotyping Through Masking and Deep Learning Algorithms
This project uses AI to automate pearl phenotyping by assessing traits such as size and shape. It supports faster grading, improved breeding programs, and innovation in the Australian pearling industry.
Learn more about this project here
Non-Destructive, High-throughput Phenotyping for Aquaculture: NIR Spectroscopy for the “Forensic” Chemical Analysis of Akoya Oyster and Red Algae
This project develops near-infrared spectroscopy (NIRS) models to enable fast, non-destructive quality assessment in aquaculture. It estimates key compounds in pearl oysters and seaweed extracts to support breeding, productivity, and commercial applications
Learn more about this project here
Corrugation Control in Unsealed Roads: Mechanisms and Sustainable Solutions
This project uses engineering, modelling, and AI to understand and reduce corrugation on unsealed roads. It supports safer, lower-maintenance road networks through predictive tools and more durable material design.
Learn more about this project here
