# Non-Destructive Estimation of Leaf Size and Shape Characteristics in Advanced Progenies of Coffea arabica L. from Intraspecific and Interspecific Crossing

## TL;DR

This study explores non-destructive methods to estimate leaf size and shape in coffee plants using statistical models and morphometric indicators.

## Contribution

The study demonstrates the effectiveness of the Montgomery model for estimating leaf size in coffee progenies, both individually and in groups.

## Key findings

- The Montgomery model with an α value of 0.67000 provided accurate leaf size estimation for coffee progenies.
- Leaf width-to-length ratio and ellipticity index significantly influence leaf size estimation.
- The principle of similarity model is effective only on a per-progeny basis.

## Abstract

Non-destructive measurement of leaf size based on leaf length and/or width is a simple, economical, and precise methodology. Leaf morphometric indicators were measured on 55 coffee progenies obtained from intraspecific and interspecific crosses. The estimation of parameters in the models and the testing of hypotheses related to these were performed. The relationships between leaf width and length, the ellipticity index, and leaf size were subsequently analyzed with a partitioning algorithm. The groups were then compared using Hotelling’s T2 test. In coffee, the Montgomery model allowed for an adequate estimation of leaf size for each progeny, hybridization type, and grouped data. An α value of 0.67000 for the Montgomery model was consistent. This finding indicates that it is a suitable model for both individual and groups of progenies. The model based on the “principle of similarity” was found to be suitable only on a per-progeny basis. Certain characteristics, such as the leaf width-to-length ratio, ellipticity index, and leaf size, modify the parameter fit to inherent values. Similarly, leaves with a higher width-to-length ratio were the most elliptical for coffee, according to the groupings found. The estimation of coffee leaf size improves if the selected model considers whether they come from specific progenies or groups of progenies.