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

## TL;DR

This paper introduces deep learning models for analyzing lychee fruit traits using micro-CT imaging, enabling non-destructive classification of cultivars.

## Contribution

The study proposes a novel application of photon-counting micro-CT and deep learning for lychee phenotyping and cultivar classification.

## Key findings

- Seven CNN-based models achieved high performance with Dice, Recall, and Precision indices between 0.90 and 0.99.
- The Mean Intersection over Union (MIoU) consistently ranged between 0.88 and 0.98.
- The approach effectively enhanced the ability to discern and categorize distinct lychee varieties.

## Abstract

In contemporary agronomic research, the focus has increasingly shifted towards 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. Machine learning techniques tailored for small-sample training were employed to identify key characteristics of various lychee species.

These models demonstrate outstanding performance with Dice, Recall, and Precision indices predominantly ranging between 0.90 and 0.99. The Mean Intersection over Union (MIoU) consistently falls between 0.88 and 0.98. This approach served both as a feature selection process and a means of classification, significantly enhancing the study's ability to discern and categorize distinct lychee varieties.

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.