The robot blemish spotter can reliably identify diseases
A “learning” computer system that sorts potatoes has been built using off-the-shelf technology by researchers at the University of Lincoln’s Robotics Lab.
The robot blemish spotter can reliably identify diseases such as silver scurf and common scab, researchers said.
The test system uses computer kit not dissimilar from systems many gamers will have in their homes.
The UK potato industry is worth about £3.5bn, but much of the sorting of produce is still done by hand.
TADD – or the Trainable Anomaly Detection and Diagnosis system – is able to “detect, identify and quantify many of the common blemishes affecting potatoes”, Dr Tom Duckett of the University of Lincoln told the BBC.
The key innovation, in Mr Duckett’s view, is the learning software which is trained by a human expert to identify diseased or damaged produce.
“Existing computer vision systems have to be programmed and calibrated. Our system is different it learns from some samples provided from a human expert,” he said.
TADD is only a vision system at present, but just spotting an off-colour spud is harder than it sounds.
“When potatoes get too much light they tend to go green, but in red potato varieties greening looks more black,” said Mr Duckett.
But because TADD uses artificial intelligence (AI) systems, it can be trained to deal with different varieties.
TADD was built using inexpensive, everyday computing gear.
“We used the lowest-quality, cheapest images sensor that we could – a webcamera costing £60. We are also using a standard desktop computer,” said Mr Duckett.
The system also makes use of a graphics processing unit (GPU) of the sort used to help process images in games.
“What a GPU is doing in a games context is turning information into graphics or images, whereas we’re using it in a reverse way to extract information from images,” said Mr Duckett.
Mr Duckett said he believed the smart system was highly adaptable: “We know we can apply this to other kinds of produce, for example carrots and apples and so on.”
In fact he says the team is working towards a “general purpose” anomaly detection system.
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