摘要
In order to obtain the phenotypic parameters of apple quickly and accurately,which were commonly used as the basis of fruit sorting,a fast estimation method of apple phenotypic parameters based on three-dimensional(3D)reconstruction was proposed in this study.In this study,a three-dimensional model was constructed to estimate the phenotypic parameters of apple,such as volume,height,diameter,and fruit shape index.Firstly,an image acquisition system was built to capture sequence images of fruit with a binocular stereo vision system,and the images were extracted and matched using the Accelerated-KAZE algorithm to create the point cloud data.Secondly,the point cloud data were matched with the algorithm of Iterative Closest Point to establish a whole model of apple,and the surface reconstruction model of fruit was obtained by constructing irregular triangulation network.Finally,the apple phenotypic parameters were calculated by means of segmentation,surface complement and integral of the fruit model.Total of 200 apples were used as samples in the experiment.By this method,the phenotypic parameters of the apples were estimated based on their 3D reconstruction model,and the linear regression analysis was carried out between the estimated values and the real values.The results showed that R2 of the linear regression fitting of each parameter was higher than 0.90.Among them,the fitting of volume was the best with R2 of 0.97.In addition,the average errors of apple volume,height,fruit shape index,maximum diameter D and minimum diameter d were 8.73 cm3,1.43 mm,1.28%,0.90 mm,and 1.23 mm,respectively.According to the Chinese national standard of“fresh Apple”,the average error of the estimated result is within the range of allowable error.It indicates that the method of apple phenotypic parameter estimation based on 3D reconstruction has a high accuracy and practicability,and it can be used as the support for fruit sorting.
基金
supported by the National Key Research and Development Program of China Sub-project(Grant No.2018YFD0700302-02)
the National Natural Science Foundation of China(Grant No.61805073,51975186).
