3D reconstruction and morphological parameter measurement of apple using monocular structured light

Apple morphological parameters are often required for the high efficiency and less physical damage to apple surface during measurement. However, the low accuracy or high hardware cost have limited to the reconstruction technology using three-dimensional vision. This study aims to propose an automatic measurement of apple surface three-dimensional reconstruction and morphological parameters using monocular fringe projection. High-precision measurement was also realized to significantly reduce the system complexity and economic cost. An economical and reliable technical path was then provided for the automatic detection and grading of fruits after harvest. A combined coding strategy was combined with the phase shift and gray code, in order to improve the high accuracy and anti-interference. Phase-shift code was used to achieve the high-precision phase recovery, while Gray code was to solve the ambiguity problem in phase unwrapping. Sub-millimeter reconstruction accuracy was then achieved in a single projection. The experimental results of standard sphere reconstruction show that the absolute error was only 0.29 mm, and the relative error was as low as 0.37%, indicating its excellent single-view reconstruction. A high-precision rotating platform was introduced to obtain the fully three-dimensional information of the apple surface. The self-occlusion was also avoided in a single view. The multi-angle rotation of apple samples was adjusted for the high-precision point cloud data of some surfaces from multiple perspectives. Then, the iterative nearest point algorithm was used to accurately register and mosaic the point cloud data from different perspectives. The geometric shape of the occluded area was effectively restored to finally construct a complete and seamless three-dimensional digital model of the apple surface. The automatic extraction of morphological parameters was further developed for the reconstructed apple point cloud model. The key appearance quality indexes of apples were accurately calculated, including the deformity index, the maximum transverse diameter and volume, indicating the uniformity of fruit shape. A series of experiments were carried out to evaluate the performance of the system. The standard plane and sphere were reconstructed after verification. The results show that there was the remarkable reconstruction accuracy of the system, where the root mean square error of the point cloud fitting of standard sphere was less than 0.095 mm, and the plane reconstruction error was 0.387 mm. Furthermore, 40 samples of red Fuji apples were measured to verify the effectiveness of the system in practical application. The deformity index, the maximum diameter and volume were determined by linear regression, compared with the standard values by manual measurement. There was a high degree of consistency between the automatic and the manual measurement. The fitting determination coefficients R were 0.98, 0.97 and 0.98, respectively, and the root mean square errors were 0.53 mm (deformity index), 0.98 mm (maximum diameter) and 6.95 cubic centimeters (volume), respectively. Especially in volume measurement, the average relative error was only 1.09%, indicating the high measurement accuracy. The monocular fringe projection shared the significant advantages, compared with the commonly-used three-dimensional reconstruction, such as linear structured light scanning or binocular stereo vision. The optical path structure of the system was greatly simplified to maintain the sub-millimeter high accuracy, thus effectively controlling the overall cost. This finding can provide an innovative solution with the high precision, low cost and easy integration, in order to realize the rapid acquisition of three-dimensional morphological parameters in the nondestructive testing of fruit external quality. There are the application potential and popularization in modern agricultural equipment, especially in the field of automatic sorting.