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

该项目的目的是分析供人食用的大田豆类产品是否存在成虫(豆种甲虫)和幼虫危害。这项研究将审查、测试和开发最先进的计算机视觉算法，适用于最有效地检测、选择和分类豆子。将提出一种方法/示范来满足行业的要求。这将使用计算机精确度和机器学习技术，以便自动分析从一些豆类作物样本中产生的图像。这些将被归类为完好或损坏。还将使用现有系统对样本进行手工分析，以校准和测试基于自动视觉的方法的有效性。这项研究将考虑利用可用于快速分析损害和昆虫存在的技术开发手持仪器的潜力。还将考虑将该技术用于识别食品加工中的污染物，如有毒浆果和昆虫。该项目的创新方面包括从数字图像中检测和分类样品的全自动、坚固和准确的系统，将最先进的视觉技术应用于食品技术，以及开发从简单相机或手机拍摄的图像中分类样品的原型系统。

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.