Automated Apple Stem‐end/calyx Identification

Machine vision methods are widely used in apple defect detection and quality grading applications. Currently, 2D near‐infrared (NIR) imaging technology is used to detect apple defects based on the difference in image intensity of defects from normal apple tissue. However, it is difficult to accurately differentiate an apple's stem‐end/calyx from a true defect due to their similar 2D NIR images, which presents a major technical challenge to the successful application of this machine vision technology. In this research, we used a novel two‐step 3D data analysis strategy to differentiate apple stem‐ends/calyxes from true defects according to their different 3D shape information. In the first step, a 2D NIR imaging was extended to a 3D reconstruction using a shape‐from‐shading (SFS) approach. After successfully obtaining 3D information, a quadratic facet model was introduced to conduct the 3D concave shape fitting such that the identification of apple stem‐ends and calyxes could be achieved based on their different 3D structures. Significant improvement in terms of the detection rate could be obtained based on 3D shape fitting in comparison to the traditional 2D intensity fitting approach. Samples of the reconstructed 3D apple surface maps as well as the identified stem‐ends/calyxes were shown in the results, and an overall 90.15% detection rate was achieved, compared to the 58.62% detection rate of the traditional 2D intensity fitting approach.