Integration of Morphometric and Machine Learning Approaches Strengthens Yield Prediction and Genetic Divergence Assessment in Annona reticulata under Semi-Arid Conditions.
Vikas Yadav, Daya Shankar Mishra, Jagadish Rane, V.V. Apparao, Levitikos Dembure, Prakashbhai Ravat, Nezif Abajebal Abadura, P Satheesh Kumar, Bismark Anokye, Anand Sahil, Poonam Devi, Peter Amoah
This study combines morphometric data and machine learning to identify yield predictors in Annona reticulata under semi-arid conditions, revealing key traits and grouping genotypes by performance and structure.
bioRxiv · May 18, 2026 · PreprintPlant Biology