A combination of artificial intelligence and scientific ingenuity looks set to be the next step forward in protecting Aotearoa New Zealand’s lakes and rivers from invasive aquatic weeds.

Lake Dunstan has recently been the focus of protests surrounding aquatic weed buildup.

Management and detection of invasive submerged weeds cost millions of dollars annually, but National Institute of Water and Atmospheric Research (NIWA) researchers have developed a way to detect and identify submerged weeds. 

This technology will enable agencies to survey far larger areas more efficiently than is currently possible, and potentially lead to much faster responses to new incursions.

Invasive submerged weeds can degrade water quality, exacerbate silt and flooding, reduce the number of native animals and plants and play havoc with irrigation water delivery and hydroelectric power schemes.

NIWA has developed a portable invasive species detector module that can be strapped to survey boats. The prototype is housed in a small waterproof case with an underwater video camera attached. 

Inside is a computer containing an artificial intelligence-based detector that has been trained to identify targeted invasive weed species and log their locations in real-time.

Principal technician Jeremy Bulleid has implemented a deep learning neural network - an artificial intelligence function - to train a computer model to recognise two of New Zealand’s worst invasive weeds - lagarosiphon and hornwort - and record their GPS locations. 

These data can then be exported to a mapping programme to enable control or eradication strategies to be implemented.

"The deep learning process enables us to replace the human eyes and brain with a video camera and a computer by running a detection application that has been taught what to look for," Jeremy says. 

Training a detector requires significant computing power and, depending on the complexity of the search environment and ‘target species’, may take days or even weeks. 

However, once training is completed, the trained detector is efficient and can be embedded into the computer located inside the detector module for real-time detection.

NIWA has successfully processed video imagery captured from an autonomous boat in a flume facility in Hamilton planted out with three different submerged plant species.

The research is still in its early days and requires further fieldwork, data collection and software development to evaluate its true potential.

However, NIWA freshwater ecologist Dr Daniel Clements says early detection and prevention is critical for achieving effective freshwater biosecurity outcomes.

"If you can detect high-risk invasive species early, before they are widespread, and implement effective management strategies, you minimise the long-term economic, environmental, social, recreational, and cultural impacts caused by these species,” Daniel says. 

"The development of these detector modules will enable a rapid and cost-effective detection and mapping that can be used over large areas."

Currently, most invasive species surveillance work is carried out by specialist divers.

Daniel says the new technology has the potential to shift diver expertise from detection effort to implementing control strategies.

"Real gains could be made by operating the modules from fully autonomous surface vessels that can be programmed and deployed without constant supervision.

"Eradicating a freshwater invasive weed by detecting it early is much more feasible and cost-effective than dealing with a widespread incursion in the long term."