# Identification of intraspecific cultivar Melia azedarach ‘Mizhi’ based on complete chloroplast genome data and leaf anatomy

## TL;DR

This study identifies a new cultivar of Melia azedarach using chloroplast genome data and leaf anatomy to support accurate plant identification and sustainable development.

## Contribution

The study introduces precise molecular markers and multi-scale phenotypic data for cultivar identification and AI-assisted systems.

## Key findings

- Melia azedarach ‘Mizhi’ shows unique traits in chloroplast genome SNPs and leaf structures.
- Four complementary methods confirm the cultivar’s uniqueness for identification and protection.
- Generated data supports future AI-assisted and automated cultivar identification systems.

## Abstract

The Melia azedarach ‘Mizhi’ (cultivar ‘Mizhi’) is a recently developed cultivar within the genus Melia. It has an elegant tree form, dense foliage, and notable ornamental, ecological, and potential medicinal value, offering broad market prospects. As the number of cultivar Melia continues to increase, accurate identification of these cultivars becomes increasingly critical to ensure its proper utilization and successful large-scale promotion. This study aims to provide more evidence for cultivar identification, development and protection by analyzing shallow whole-genome sequencing data, leaf epidermal and cross-sectional structure. Finally, four complementary approaches: nuclear gene fragments, complete chloroplast genome data, leaf epidermal structure (include micromorphological and ultrastructural characteristic), and leaf cross-sectional structure, were used in this study. The results show that the Melia azedarach ‘Mizhi’ exhibits its uniqueness in all of the above aspects. Moreover, the SNP based on the chloroplast genome can directly reveal the differences between Melia azedarach ‘Mizhi’ and others. Overall, this study provides precise molecular markers for the identification and protection of Melia azedarach ‘Mizhi’, offering new technical tools and theoretical support for the sustainable development and utilization of Melia species. The standardized molecular markers and multi-scale phenotypic data generated in this study also provide high-quality source data for future AI-assisted and automated cultivar identification. This work lays a solid foundation for the development of objective, scalable, and data-driven identification systems in plant cultivar research.