Because cleaner grains make finer flour

A new computer program devised by British physicists can quickly spot tiny beetles, rodent droppings and ergot (a poisonous mould) in grain destined for flour and bread manufacture. The researchers reveal details of their work today in the Institute of Physics journal Measurement Science and Technology.

Professor Roy Davies and his colleagues in the Machine Vision Research Group at Royal Holloway, University of London, in Egham, Surrey, have found they can run their program on a conventional desktop computer to detect quickly and easily several common contaminants in wheat. The program identifies saw-toothed grain beetles (Oryzaephilus surinamensis), small rodent droppings and the toxic mould ergot among tens of thousands of grains of wheat. “The program will help millers, farmers, wholesalers, and other relevant parties to check batches of grain for infestation,” says Professor Davies.

The program analyses snapshot images of some 60,000 grains of wheat (about three kg) in just three minutes and identifies insects and certain other non-grain particles using a linear feature detector. At the chosen resolution of the imaging system the adult insects look like short rectangular bars.

The new system overcomes the problem of low contrast between insect and grain and also avoids confusing insects with the dark edges of wheat grains, a cause of false positive results. “The best results were obtained by integrating two of our previous detectors thus combining optimum detection sensitivity with fidelity to the original shape,” explains Professor Davies, “this ensures accurate recognition.”

In recent years, machine vision has been used for many applications such as inspection and surveillance, though several difficulties can arise in taking its use further. One of these is cost, another is the problem of making the necessary computer algorithms (programs) sufficiently effective when dealing with highly variable objects, such as food products and even people going about their daily lives.

The present research was funded by the Home-Grown Cereals Authority and carried out in conjunction with Central Science Laboratory (MAFF), York. It reveals how the system can be used for fast and inexpensive quality control of batches of cereal grain. According to Professor Davies, the basic science involved is highly generic and the system might also be used in fields as diverse as entomology for tracking insects, transport studies for tracking vehicles and trains on aerial views of the ground, or even in forensics for investigating fingerprints.

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