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

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 ±20°of 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.