University of South Australia

Machine vision system for almond grading and safety

Researchers at UniSA have developed a world-first automated technique for simultaneously grading almond quality and detecting potentially serious mycotoxin contamination in kernels.

In 2019-2020, Australia’s almond crop was worth just over $1 billion, and the value of the sector is expected to expand to $1.5 billion in coming years, with Australian almond growing conditions among the best in the world.   

Given the local industry is now exporting to more than 50 nations, accurate and consistent grading of almonds is paramount, ensuring international markets can trust the Australian product.

Traditionally, almonds have been graded manually, with samples taken hourly from production lines to check for consistency of appearance, chips and scratches, double kernels, insect and mould damage, and other defects.  

This process, however, is labour intensive, slow, and subjective, all of which can lead to inaccurate and inconsistent grading, particularly from season to season due to staff turnover.

In conjunction with industry partner SureNut, researchers at the University of South Australia have developed a machine that dramatically improves the accuracy of almond grading, in addition to detecting potentially fatal contaminants common in almond kernels.

Funded through the Cooperative Research Centres Projects program, a research team led by Associate Professor Sang-Heon Lee combined two high definition cameras, a hyperspectral camera and purpose developed AI algorithms to create a system that can examine almond quality in far greater detail than the human eye.  

The system can accurately assess physical defects such as chips and scratches and detect harmful contaminants, including the presence of aflatoxin B1, a potent carcinogen that may be implicated in more than 20 per cent of global liver cancer cases.

“Our goal with this innovation was not to simply replicate what a human being could do, but to go far beyond that,” Associate Prof Lee says.

“So, in respect to physical appearance, this machine can detect defects more quickly and more accurately than manual grading, and by using two high definition cameras and a transparent viewing surface, it can also view both sides of the nut simultaneously.”

While this visual functionality alone puts the SureNut system at the forefront of innovation in this field, the addition of the hyperspectral camera for contamination detection is a ground-breaking world first.

UniSA engineering researchers, Dr Wilmer Ariza and Dr Gayatri Mishra, developed the hyperspectral system used on the SureNut machine, and Dr Ariza says almonds presented a unique challenge for the technology.

“We are the first team to successfully use hyperspectral imaging this way with almonds, even though other researchers have tried,” Dr Ariza says.

“Certain characteristics of the nut kernels – which we are keeping a secret – made the process extremely difficult, but we overcame that, and now we can deliver highly accurate analysis using hyperspectral imaging.


For more information:
Dan Lander
University of South Australia
Mobile: +61 0408 882 809
Email: dan.lander@unisa.edu.au


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