Submitted to: ASABE Annual International Meeting
Publication Type: Abstract Only
Publication Acceptance Date: 7/13/2011
Publication Date: 8/12/2011
Citation: Mizushima, A., Lu, R. 2011. Development of a cost-effective machine vision system for in-field sorting and grading of apples: fruit orientation and size estimation. ASABE Annual International Meeting. Paper No. 1110723.
Interpretive Summary: In-field presorting would allow growers to separate fresh market-grade apples from inferior apples that are only suitable for processed products (juice, applesauce, canning, dried, etc.), thus achieving cost savings in postharvest sorting, grading, and storage. The objective of this research was to develop technology for automatic in-field presorting and segregation of undersized and defective fruit from fresh market-grade apples. To achieve this goal, a prototype machine vision system was built for in-field presorting of apples, using a low-cost digital camera, coupled with energy-saving LED lights and a two-lane conveyor with generic bi-con rollers. Image processing algorithms were developed to estimate apple orientation and size. Laboratory tests were conducted for ‘Delicious’ (D), ‘Empire’ (EM), ‘Golden Delicious’ (GD), and ‘Jonagold’ (JG) apples at the speed of four fruit per second. The orientation estimation algorithm showed more than 85% success rate for elongated D and GD apples, whereas it performed less satisfactorily (less than 70% success rate) for round-shaped EM and JG apples. The machine vision system had the mean error of 1.79 mm for fruit size estimations, and 98% or more of the test apples were estimated within ±5 mm. The system had 4.3% classification error for undersized apples (i.e., less than 63.5 mm in size), versus 15.1% for a mechanical sizing machine. The low cost hardware configuration would meet the required presorting speed of four apples per second and allow implementing additional color grading and defects detection functions for the in-field presorting system.
Technical Abstract: 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 did not performed so well 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.