High speed vision-based quality grading of oranges

We describe a novel system for grading oranges into three quality bands, according to their surface characteristics. This processing operation is currently the only non-automated step in citrus packing houses. The system must handle fruit with a wide range of size (55-100 mm), shape (spherical to highly eccentric), surface coloration and defect markings. Furthermore, the point of stem attachment (the calyx) must be recognised in order to distinguish it from defects. A neural network classifier on rotation invariant transformations (Zernike moments) is used to recognise radial colour variation, that is shown to be a reliable signature of the stem region. This application requires both high throughput (5-10 oranges per second) and complex pattern recognition. Three separate algorithmic components are used to achieve this, together with state-of-the-art processing hardware and novel mechanical design. The grading is achieved by simultaneously imaging the fruit from six orthogonal directions as they are propelled through an inspection chamber. In the first stage processing colour histograms from each view of an orange are analysed using a neural network based classifier. Views that may contain defects are further analysed in the second stage using five independent masks and a neural network classifier. The computationally expensive stem detection process is then applied to a small fraction of the collected images. The succession of oranges constitute a pipeline, and, time saved in the processing of defect free oranges is used to provide additional time for other oranges. Initial results are presented from a performance analysis of this system