Click on “Download PDF” for the PDF version or on the title for the HTML version.

If you are not an ASABE member or if your employer has not arranged for access to the full-text, Click here for options.

Development of a cost-effective machine vision system for infield sorting and grading of apples: Fruit orientation and size estimation

Published by the American Society of Agricultural and Biological Engineers, St. Joseph, Michigan

Citation:  2011 Louisville, Kentucky, August 7-10, 2011  1110723.(doi:10.13031/2013.37288)
Authors:   Akira Mizushima, Renfu Lu
Keywords:   Machine vision, Apple, Sorting, Grading, Sizing, In-field

The objective of this research was to develop an in-field apple presorting and grading system to separate undersized and defective fruit from fresh market-grade apples. To achieve this goal, a cost-effective machine vision inspection prototype was built, which consisted of a low-cost color camera, LED (light-emitting diode) lights and a generic bi-cone conveyor. Algorithms were developed for image distortion correction and for real-time estimation of apple orientation, shape and size. The machine vision system was tested and evaluated for Delicious(D), Empire(EM), Golden Delicious(GD), and Jonagold(JG) apples at a speed of four fruit per second. The orientation estimation algorithm had 87.6% and 86.2% accuracies for D and GD apples, respectively, within 20of actual fruit orientation, whereas it performed less satisfactorily for round-shaped EM and JG apples. The machine vision system achieved good fruit size estimations with the overall root mean square error of 1.79 mm for the four varieties of apple, and it had a two-size grading error of 4.3%, versus 15.1% by a mechanical sizing machine. The system provides a cost effective means for sorting apples for size.

(Download PDF)    (Export to EndNotes)