Three-dimensional vision for real-time produce grading

Produce is often sold by weight, so one of the roles of the grading system is to allocate each item to a particular chute for packing into fixed weight bundles. Accurate, high-speed weight measurement is difficult and expensive, so machine vision is used to estimate the weight of each item. Previous estimations relied on a single diameter measurement, which resulted in large errors. To ensure that the minimum weight was provided, each bundle was on average 30% overweight. By improving the accuracy of the estimation, and combining this with an improved chute allocation strategy, significant savings can be made. The weight estimation in the system under development is based on the projected area of each item. The error in weight estimation was further improved by measuring the projected area from two perpendicular views. With the produce being sorted at a rate of 12 to 15 items per second, there are significant challenges in obtaining and processing the simultaneous perpendicular views of each item. The two views are captured of the item through the use of mirrors, and a third direct view is also obtained for quality grading purposes.