Wonky potatoes: A challenge for packers and processors

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Machinery lines may struggle - warning

WHILE British consumers are being urged to accept smaller, non-conformist potatoes, following recent drought impacts, packers and processors will need to ensure their production lines are equipped to handle the smaller tubers, it has been claimed.

Many areas of the UK have seen very low rainfall in 2022, and parts of England are in drought. The BBC has reported that potatoes, as well as onions, carrots, apples and Brussels sprouts are likely to be worst affected by the hot, dry weather. The National Farmers’ Union (NFU) has also appealed for shoppers to accept smaller vegetables and potatoes, as it said the driest July on record could mean many items will struggle to meet with supermarket quality standards — and may look 'different' to what consumers are used to.

Scorpion Vision is advising   processors and packers of fresh produce to consider upgrading to AI-powered vision technology. Processing systems guided by classic machine vision do not have the intelligence to handle non-uniform produce - the only way of maintaining line efficiencies in the face of variability is by implementing processing and inspection systems that combine AI with 3D vision. 

But while retailers are being urged to accept more out-of-spec produce and consumers to get used to wonky veg on shelf, processors and packers may need to check their machinery lines are able to cope with greater variability, according to Paul Wilson, Managing Director at Scorpion Vision, a UK-based provider of sophisticated machine vision automation systems.

Traditional systems for sorting, grading and inspecting use classic 3D vision to look for features in the product image. This can only look for features that conform to a pattern or shape that is expected. However, wonky vegetables don’t come in a fixed size, shape or colour, and this inherent variability translates to compromised processing performance. 

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AI-powered vision tech, an option that has only become commercially viable and available in the last few years, is one way of tackling this and helping to avoid unnecessary waste he said, pointing out that it has opened up a new world of opportunity for revolutionising the performance and efficiency of camera-driven processing systems. It has the ability to look at and analyse each individual vegetable before making a decision on how to process it. The machine simply needs to be shown some examples in a variety of conditions and it will learn what to look for, enabling it to formulate its own conclusion about what it is seeing. 

When a computer receives an image, machine vision software compares that image data with a neural network model. This process, called deep learning inference, allows computers to recognise very subtle differences.

Photo: blackieshoot

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