JAMES Cook University (JCU) in Queensland will oversee the development of a smart weed spraying robot that could reduce herbicide usage on sugarcane farms in Great Barrier Reef (GBR) catchment areas by at least 80 per cent.
The two-year project, funded by a $400,000 grant through the partnership between the Great Barrier Reef Foundation and the Australian Government’s Reef Trust, is a collaboration between JCU, AutoWeed, and Sugar Research Australia.
AutoWeed is a start-up agricultural technology company developing smart spot spraying systems, and Sugar Research Australia will help assess the water quality improvements the new technology promises.
Dr Mostafa Rahimi Azghadi, a senior engineering lecturer at JCU, will lead the project. He said the group’s effort would help reduce herbicide runoff, which was a serious threat to plants and animals in rivers, creeks, and coastal and inshore areas.
“Most herbicides, being mobile in soil, are carried in river runoff and have been detected in GBR ecosystems at concentrations high enough to affect organisms. Sugarcane farms are only 1.4 per cent of the GBR catchment area but contribute 95 per cent of the pesticide load draining to the GBR,” Dr Rahimi Azghadi said.
The project will rely on the pioneering “deep learning” technology being developed by JCU and AutoWeed to detect and spray priority sugarcane weeds.
AutoWeed co-founder and engineer, Jake Wood, said the new system would use stored images of weeds to detect and spray them without hitting non-target crops.
“Extending our AutoWeed spot spraying technology to sugarcane requires significant new research and development. We aim to reduce knockdown herbicide usage on sugarcane farms by at least 80 per cent,” Mr Wood said.
“This will incentivise water quality improvements in reef catchment areas by reducing weed management costs for farmers while also lowering the concentration of herbicides in runoff to support a healthy reef.”
In the first year of the project, hundreds of thousands of images of sugarcane farmers’ crops will be collected, labelled by a human expert, and fed into deep learning models to train the weed and crop detection system. Every time the spraying system is used it will collect more data, so the deep learning models can further improve their performance over time.
The second year will focus on developing and trialling the herbicide delivery component of the project.
“We’re aiming to design, develop, and trial the spot spraying method and fit it to a 24-metre wide, high-rise self-propelled boom to be used on a sugarcane farm,” Dr Rahimi Azghadi said.
JCU will manage the project while its researchers work closely with AutoWeed engineers to extend their proprietary technology to priority sugarcane weeds.
Mr Wood said their AutoWeed technology had previously targeted weeds in cattle farm pastures and broadacre crops, but sugarcane presented unique challenges.
“The project will use deep convolutional neural networks – the very same used by Facebook to detect faces and Google to optimise image searches – but it will be the first time it’s been applied to sugarcane,” he said.
Great Barrier Reef Foundation managing director Anna Marsden said poorer water quality caused by land-based runoff was a significant threat to the health of Australia’s irreplaceable ecosystem, the Great Barrier Reef.
“Sediment and pollutants running into the Reef’s waters smother coral and seagrass, are toxic to marine life and contribute to crown-of-thorns starfish outbreaks, and we must continue to work together and do better,” Ms Marsden said.
“We know that there is a high calibre of work being undertaken by farmers and the agricultural community to safeguard the future of the Reef, however, if we are to reach the targets set out by the Reef 2050 Water Quality Improvement Plan then we need to stimulate new ideas and out of the box solutions.
“This innovative project will add to the more than 60 Reef-saving projects we are delivering right now with over 65 project delivery partners.”
The two-year project will run until August 2022.