AUTHOR=Lu Yuwei , Wang Rui , Hu Tianyu , He Qiang , Chen Zhou Shuai , Wang Jinhu , Liu Lingbo , Fang Chuanying , Luo Jie , Fu Ling , Yu Lejun , Liu Qian 

TITLE=Nondestructive 3D phenotyping method of passion fruit based on X-ray micro-computed tomography and deep learning

JOURNAL=Frontiers in Plant Science

VOLUME=Volume 13 - 2022

YEAR=2023

URL=https://www.frontiersin.org/journals/plant-science/articles/10.3389/fpls.2022.1087904

DOI=10.3389/fpls.2022.1087904

ISSN=1664-462X

ABSTRACT=Passion fruit is a tropical liana of the Passiflora family that is commonly planted throughout the world due to its abundance of nutrients and industrial value. Researchers are committed to exploring the relationship between phenotype and genotype to promote the improvement of passion fruit varieties. However, the traditional manual phenotyping methods have shortcomings in accuracy, objectivity, and measurement efficiency when obtaining large quantities of personal data on passion fruit, especially internal organization data. This study selected samples of passion fruit from three widely grown cultivars, which differed significantly in fruit shape, size, and other morphological traits. A Micro-CT system was developed to perform fully automated nondestructive imaging of the samples to obtain 3D models of passion fruit. A designed label generation method and segmentation method based on U-Net model were used to distinguish different tissues in the samples. Finally, fourteen traits, including fruit volume, surface area, length and width, sarcocarp volume, per-icarp thickness, and traits of fruit type, were automatically calculated. The experimental results show that the segmentation accuracy of the deep learning model reaches more than 0.95, the measurement accuracy of external traits of passion fruit is comparable to manual operations, and the measurement of internal traits is more reliable because of the nondestructive characteristics of our method. According to the statistical data of the whole samples, the Pearson analysis method was used to ana-lyze the possible correlation between the various traits. The principal component analysis was used to evaluate the comprehensive quality of the passion fruit samples. The results of this study will firstly provide a nondestructive method for more accu-rate and efficient automatic acquisition of comprehensive phenotypic traits of pas-sion fruit and have the potential to be extended to more fruit crops. The preliminary study of the correlation between the characteristics of passion fruit can also provide a particular reference value for molecular breeding and comprehensive quality evaluation.