Potato Industry Reaps Benefits of New Computer Vision Technology

Computer Scientists from the University of Lincoln Centre for Vision and Robotics Research will be presenting a prototype  computer vision system which can identify sub-standard potatoes:

A prototype computer vision system to identify sub-standard potatoes will be presented by computer scientists from the University of Lincoln at the triennial conference ‘Crop Protection in Northern Britain’, taking place in Dundee this week.

The new machine vision system uses off-the-shelf hardware to enable affordable detection, identification and quantification of common defects affecting potatoes.

The British potato industry is worth around £3.5billion a year and potatoes account for 40% of the carbohydrate consumed in the UK.

The main factor affecting consumer preference is physical appearance, with clear unblemished skin a significant selling point. Potatoes with defects, diseases and blemishes are generally avoided. Most potatoes are sorted into different grades by hand, often resulting in mistakes and losses.

The University of Lincoln team from the Centre for Vision and Robotics Research worked with the Potato Council to produce a low-cost system which can assist quality control (QC) staff and improve consistency, speed and accuracy of defect identification and quantification.

Director of the Centre for Vision and Robotics Research, Dr. Tom Duckett, said: “The system relies on initial input by an expert, identifying blemishes, diseases, as well as good specimens, from sample batches of potatoes. The graphical user interface was developed to allow the software to be used by quality control experts from the industry. The system can be trained to recognise different defect types and will analyse potatoes in near-real-time – a significant improvement on previous research in this area.”

The system developed uses off-the-shelf hardware, including a low-cost vision sensor and a standard desktop computer with a graphics processing unit, together with software algorithms to enable detection, identification and quantification of common defects affecting potatoes. The system uses state-of-the-art image processing and machine learning techniques to automatically learn the appearance of different defect types. It also incorporates an intuitive graphical user interface to enable easy set-up of the system by quality control staff working in the industry.

Post-graduate students from the Centre for Vision and Robotics Research played a major role in the project with Michael Barnes (PhD student) undertaking underpinning theoretic research, while Jamie Hutton (MSc student) was responsible for researching and developing the real-time prototype for industry testing. The team worked with Glyn Harper from Sutton Bridge Crop Storage Research – the leading post-harvest applied research facility for agricultural storage in the UK. It is owned by the Agriculture & Horticulture Development Board and operated by its Potato Council division.

Crop Protection in Northern Britain 2012 takes place on 28 and 29 February. It will bring together farmers, agricultural and horticultural advisers to discuss environmental management, crop protection and associated topics which are prevalent in northern environments (Scotland, Northern England, Northern Ireland, and Northern Europe.)

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