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类食用的菜豆产品中是否存在豆种甲虫（Bruchus rufimanus）成虫及其幼虫的损害。该研究将审查、测试和开发适合于最有效地检测、选择和分类豆类的计算机视觉算法。将提出一种方法/示范，以满足行业的要求。这将采用计算机决策和机器学习技术，以自动分析从许多豆类作物样本生成的图像。这些将被分为完好或损坏。样品也将使用现有系统手工分析，以校准和测试基于自动视觉方法的有效性。该研究将考虑利用该技术开发手持式仪器的潜力，该仪器可用于快速分析损害和昆虫的存在。还将考虑该技术在食品加工中识别污染物（如有毒浆果和昆虫）的潜在用途。该项目的创新之处包括一个全自动、强大和准确的系统，用于从数字图像中检测和分类样品，将最先进的视觉技术应用于食品技术，以及开发一个原型系统，用于从简单的相机或手机拍摄的图像中分类样品。

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.