AI Case Study

The Northern Territory Department of Primary Industry and Resources achieves 95% accuracy identifying fish with machine learning

Australia's Northern Territory Department of Primary Industry and Resources has implemented machine learning to automate the counting of fish and other sea life by analysing underwater footage. The system has achieved 95-99% success rate in identifying fish.

Industry

Public And Social Sector

Government

Project Overview

"The Northern Territory Department of Primary Industry and Resources (DPIR) has turned to artificial intelligence (AI) to speed up its fish-counting exercises. Developed with Microsoft and built on Azure Cognitive Service, the open-source solution is published on GitHub, and had its first iteration running in a month. DPIR said the solution has already shown that the local golden snapper and black jewfish are overfished, and that the solution could be applied elsewhere across the territory."

Reported Results

The system now "can now identify fish with a 95 to 99 percent success rate" after being "widely deployed within six months".

Technology

Details not disclosed

Function

Strategy

Analytics

Background

Fish counting "used to be completed by DPIR [Department of Primary Industry and Resources] scientists watching hours of underwater footage filmed on reefs around Darwin... The scientists could not enter the water themselves, thanks to saltwater crocodiles and sharks not being completely onside with the idea of humans entering water."

Benefits

Data

Underwater footage of fish and other aquatic life