To develop and provide automatic identification of harfum algal bloom phytoplankton species. This on-site system will enable increased testing frequency, more rapid turn-around time, and improved consistency in species identification. The device and software solution will consists of:

• User defined alerts warning of presence of specific species and concentration levels

• Local and Cloud access to data

• Potentially generate data set for long term analysis/prediction

• Continuous training and update of AI engine.

There are a number of challenges with the current processes of identifying HAB species:

• Identification methods are often labour intensive which impacts how rapidly decisions can be made around feed management and other mitigation methods

• It is difficult to determine genus or species

• Other testing methods that require off-site laboratories increase turn-around time

• Collaborate with farms to collect imagery in order to train the artificial intelligence engine

• Once the AI is trained, then there will be a period of field-testing and pre-commercial implementation

• Develop the solution for both saltwater and freshwater algal species

We deployed the devices on farms in Australia (salmon, prawns and barramundi) and tutorials were provided to companies. The methods used typically fits into operational processes. Images of algae taken by the farms are identified by the farms or the LPAS experts via uploading images to the cloud. To date the LPAS data collection phase has received>4,750 images of phytoplankton from wild samples taken at aquaculture sites (Figure 1). This brings the total number of images to >9500 when combined with laboratory-based data collection.

• “real-time” and accurate data

• Enables faster decision-making and implementation of mitigation measures

• Improved fish welfare outcomes for farmed aquaculture species susceptible to HABs

BELINDA YAXLEY