Integration of Morphometric and Machine Learning Approaches Strengthens Yield Prediction and Genetic Divergence Assessment in Annona reticulata under Semi-Arid Conditions.

Abstract

This study integrated morphometric characterization and machine-learning modelling to identify key predictors of yield in Annona reticulata under semi-arid conditions. Thirty-one canopy, fruit, seed, and biochemical traits were evaluated across 62 genotypes, revealing substantial phenotypic diversity, particularly in structural attributes such as tree growth nature and branch angle. Principal Component Analysis and hierarchical clustering differentiated genotypes into three ideotypes representing high-yielding, structurally stable, and quality-oriented groups. Random Forest modelling and SHapley Additive exPlanations (SHAP) interpretation consistently highlighted leaf breadth, leaf length, fruit shape, and pulp-associated traits as dominant yield predictors, underscoring the coordinated influence of source-sink balance. Integration of SHAP importances with trait stability (CV%) further revealed that moderately variable traits provide reliable selection indices. These findings demonstrate that yield performance is governed by multivariate trait networks rather than isolated descriptors. The proposed framework provides a robust basis for precision phenotyping and strategic parent selection to develop high-yielding, nutritionally enriched, and climate-resilient custard apple cultivars.