VIDEO GRADING OF ORANGES IN REAL-TIME

We describe a novel system for grading oranges into three quality band s, according to their surface characteristics. The system is designed to process fruit with a wide range of size (55-100 mm), shape (spheric al to highly eccentric), surface coloration and defect markings. This application requires both high throughput (5-10 oranges per second) an d complex pattern recognition. The grading is achieved by simultaneous ly imaging each item of fruit from six orthogonal directions as it is propelled through an inspection chamber. In order to achieve the requi red throughput, the system contains state-of-the-art processing hardwa re, a novel mechanical design, and three separate algorithmic componen ts. One of the key improvements in this system is a method for recogni sing the point of stem attachment (the calyx) so that it can be distin guished from defects. A neural network classifier on rotation invarian t transformations (Zernike moments) is used to recognise the radial co lour variation that is shown to be a reliable signature of the stem re gion. The succession of oranges processed by the machine constitute a pipeline, so time saved in the processing of defect free oranges is us ed to provide additional time for other oranges. Initial results are p resented from a performance analysis of this system.