This project demonstrates the power of autonomous monitoring and artificial intelligence to transform large-scale pest surveillance. By combining connected sensing networks, satellite communications and advanced image analytics, the system delivers continuous, real-time intelligence that helps growers and researchers respond faster to emerging pest threats across broadacre farming regions.
Collaborators
This project was delivered in collaboration with Adelaide University (AU), and the Grains Research and Development Corporation (GRDC).
The Challenge
Tracking destructive broadacre pests (such as earwigs, millipedes, and slaters) in remote agricultural regions has traditionally been a bottleneck of manual labor. Monitoring these pests requires continuous hardware infrastructure in deep, remote regions. Manually examining a single night's worth of monitoring imagery would take an entomologist 4.5 months of continuous work (analyzing one image per minute).
Our Solution
We engineered and deployed a robust, solar-powered IoT infrastructure consisting of five networks of ten automated cameras across South Australia and Victoria. This hardware ecosystem operates on local Narrowband RF networks with Starlink satellite backhauls, feeding into a cloud backend. Every single night, the system generates, automatically processes, and tags 45,000 surveillance images. Our cloud-based AI applies automated image annotation and stitching, constantly refined by expert entomologist feedback loops.
The Impact
By condensing 4.5 months of manual analysis into an automated nightly pipeline, we provide grain growers and scientists with an encrypted, real-time web portal. By linking automated pest counts directly to localized weather and soil conditions, we have removed the guesswork from modern pest management and enabled rapid, data-backed interventions.