Zoë Migicovsky

This study examines how grapevine rootstock genotypes and their associated rhizosphere microbiomes influence elemental composition in different vine compartments.

This paper introduces the Euler Characteristic Transform (ECT) for classifying plant shapes and predicting leaf venation from blade geometry using deep learning.

The paper introduces a Python-based method using pseudo-landmarks and Procrustes analysis to compare leaf shapes across species and development, showing shape transitions and genetic correlations.

This study uses topological data analysis and deep learning to show that grapevine leaf blade and vasculature shapes are predictively linked, suggesting a one-to-one relationship between the two tissues.

The paper quantifies variation in apple fruit shape and size across diverse accessions and finds they are independent traits.

This study compares diploid and triploid apple varieties across 10 traits, finding triploids have higher phenolic content but overall phenotypic differences are subtle and often insignificant.

The paper examines how terroir and rootstock influence leaf shape in grapevines, identifying a specific morphological feature affected by these factors.