Learning to discriminate: Can machines learn to identify pest species from photos?

3 July 2019

Eibe Frank

Machine learning has many applications in the real world, from medical diagnoses to detection of contaminants in soil samples. And now, researchers are investigating whether it can be used to protect New Zealand’s biosecurity.

Pest species are a particular threat to our unique biodiversity, but how do we identify what is a pest and what is a harmless member of the local fauna and flora? University of Waikato Professor of Computer Science Eibe Frank, together with Dr Varvara Vetrova at Canterbury University and Associate Professor Michael Cree at Waikato, is working on a program to help everyday Kiwis identify pest species using their smartphone.

Professor Frank will discuss some of the underlying machine learning technology at an upcoming public lecture on 16 July, drawing connections to work on statistical species identification that pre-dates the computer age. He will address how we can use machine learning to identify species automatically, by learning from photos that have been labelled by experts.

Professor Frank’s research focus is machine learning, data mining and artificial intelligence (AI) and their applications for the real world. As a PhD student, he was instrumental in the development of WEKA, the University’s award-winning open-source machine-learning software platform, and he continues to support its development and international use.

“It turns out learning to discriminate species from data is one of the oldest, if not the oldest, application of machine learning,” says Professor Frank. Ronald Fisher, a statistician and biologist, described a method for linear discriminant analysis in 1936 and applied it to the classification of species of iris flowers. His method is now a classic technique for supervised machine learning – learning from expert-labelled observations – but it was published years before computer scientist Alan Turing discussed the idea of learning machines in his seminal 1950 paper on ‘Computing Machinery and Intelligence’.

Professor Frank says until recently, expert knowledge was required to define features that can be input into machine-learning models to establish a representation of the problem that makes it amenable to machine learning. “However, recent developments in the field of artificial neural networks, sometimes referred to as ‘deep learning’, have changed this. Artificial neural networks can automatically learn a set of features to represent images, often yielding more accurate image classifiers than those based on ‘hand-crafted’ image features. This opens up new opportunities for automatic species identification based on photos taken with digital cameras.”

Professor Frank says AI can often be overhyped or oversold, but insists there are many applications where machine learning can improve outcomes for people and increase productivity for organisations. “Currently, New Zealand is lacking university graduates with significant knowledge in this area, but my colleagues in the Waikato Machine Learning Group and I are working hard to change this.”

The lecture, ‘Learning to discriminate species from data: Then and now’, is on Tuesday 16 July at 5.45pm in the Gallagher Academy of Performing Arts, with refreshments served from 5.15pm. The lecture is part of the University’s Hamilton Public Lecture Series and is free and open to the public. Register your attendance.

Latest stories

Related stories

Michèle Prinsep

Waikato academic ranked in top one per cent in the world for research citations

A University of Waikato researcher who identifies compounds in marine species which could be used…

Emry Daniels

Recognition for Pacific student who quit his day job for a design degree

Video gaming, tech gadgets and te reo Māori inspired 41-year-old Emry Daniels to quit his…

Professor Troy Baisden

Professor named new president of the New Zealand Association of Scientists (NZAS)

Professor Troy Baisden, who is based in School of Science at the University of Waikato,…

Sharna McCleary

Science student uses mushrooms to help clean up Whakatāne canal

Oyster mushrooms are helping to clean up an historically contaminated timber processing site in Whakatāne,…

Carolina Short

Design tutor’s font makes it big on Google

A fun project created to engage her design students has led University of Waikato Tutor…

Project to investigate earthquake frequency and activity on Hamilton’s faults

Newly discovered hidden faults in Hamilton, an area once thought devoid of any active faults,…

University of Waikato researchers awarded $5.6m from Marsden Fund

Four University of Waikato researchers were today awarded Marsden Fund grants for 2019.

Jessica Pasisi

Research aims to shed light on health and wellbeing of Niuean people

Waikato University PhD student, Jessica Pasisi, is seeking to better understand the mental health and…

Research uncovers sense of belonging for refugee and immigrant families through early childhood education

Two major studies are putting refugee and immigrant children in early childhood education at the…

Waikato students can “breathe easy” thanks to new scholarship

A new scholarship is available to University of Waikato students thanks to the generosity of…

Dr Robert Townsend

University research aims to break down barriers for disabled athletes

Major research is underway at the University of Waikato designed to offer more sport and…

Legal technology project to transform legal education in New Zealand

The world that we are living in today will not be the world our children…