Novel computer vision techniques for food quality analysis - identification of Bruchus rufimanus (bean seed beetle) damage in field beans (Vicia faba) for export for human consumption

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The project will aim to analyse field bean produce for human consumption for the the presence of adult Bruchus rufimanus (bean seed beetle) and larval damage. The study will review, test and develop computer state of the art vision algorithms suitable for detecting, selecting and classifying the beans most effectively. A methodology/demonstrator will be proposed to meet the industry's requirements. This will employ computer cision and machine learning techniques in order to automatically analyse images generated from samples from a number of bean crops. These will be classified as good or damaged. Samples will also be analysed by hand using the existing system to calibrate and test the effectiveness of the automated vision based approaches. The study will consider the potential to develop hand-held instruments using the technology that can be employed for rapid analysis of damage and insect presence. Potential uses of the technology for identification of contaminants in food processing, such as poisonous berries and insects will also be considered. The innovative aspects of this project include a fully automatic, robust and accurate system for detecting and classifying samples from digital images, the application of state-of-the-art vision techniques to food technology and development of a prototype system to classify samples from images taken with a simple camera or handset.