AUTHOR=Xue Mengjia , Huang Siyi , Xu Wenting , Xie Tianwu 

TITLE=Advanced deep learning models for phenotypic trait extraction and cultivar classification in lychee using photon-counting micro-CT imaging

JOURNAL=Frontiers in Plant Science

VOLUME=Volume 15 - 2024

YEAR=2024

URL=https://www.frontiersin.org/journals/plant-science/articles/10.3389/fpls.2024.1358360

DOI=10.3389/fpls.2024.1358360

ISSN=1664-462X

ABSTRACT=In contemporary agronomic research, there is a growing emphasis on non-destructive imaging and precise phenotypic characterization. A photon-counting micro-CT system has been developed, which is capable of imaging lychee fruit at the micrometer level and capturing a full energy spectrum, thanks to its advanced photon-counting detectors. For automatic measurement of phenotypic traits, seven CNN-based deep learning models including AttentionUNet, DeeplabV3+, SegNet, TransUNet, UNet, UNet++, and UNet3+ were developed.These models exhibit an excellent Dice, Recall, and Precision index ranging mostly between 0.90 and 0.99, while the Mean Intersection over Union (MIoU) is recorded mostly between 0.88 and 0.98. Additionally, machine learning techniques tailored for small-sample training were employed to identify key characteristics of various lychee species. This approach served both as a feature selection process and a means of classification, further enhancing the study's ability to discern and categorize distinct lychee varieties. Consequently, this research not only contributes to the advancement of non-destructive plant analysis but also opens new avenues for exploring the intricate phenotypic variations within plant species.