Whole surface image reconstruction for machine vision inspection of fruit

Abstract

Automated imaging systems offer the potential to inspect the quality and safety of fruits and vegetables consumed by the public. Current automated inspection systems allow fruit such as apples to be sorted for quality issues including color and size by looking at a portion of the surface of each fruit. However, to inspect for defects and contamination, the whole surface of each fruit must be imaged. The goal of this project was to develop an effective and economical method for whole surface imaging of apples using mirrors and a single camera. Challenges include mapping the concave stem and calyx regions. To allow the entire surface of an apple to be imaged, apples were suspended or rolled above the mirrors using two parallel music wires. A camera above the apples captured 90 images per sec (640 by 480 pixels). Single or multiple flat or concave mirrors were mounted around the apple in various configurations to maximize surface imaging. Data suggest that the use of two flat mirrors provides inadequate coverage of a fruit but using two parabolic concave mirrors allows the entire surface to be mapped. Parabolic concave mirrors magnify images, which results in greater pixel resolution and reduced distortion. This result suggests that a single camera with two parabolic concave mirrors can be a cost-effective method for whole surface imaging.