Vizintin, A.; Zagorscak, M.; Turk, E.; Kriznik, M.; Petek, M.; Stare, K.; Wurzinger, B.; Shaikh, M. A.; Heselmans, G.; Sollinger, J.; Lindenbergh, P.-J.; Graveland, R.; Oome, S.; Prat, S.; Bachem, C.; Teige, M.; Doevendans, B.; Ribarits, A.; Zrimec, J.; Gruden, K.

Accurate pre-harvest prediction of crop yield informs variety selection, optimizes management, and accelerates breeding. As potato is the world's leading non-grain staple, here we evaluate a diverse panel of varieties in a three-year field trial across five European locations. Canopy development and environmental parameters are monitored throughout the growing season using drone-based imaging, in-field sensors and gene expression measurements, while tuber yield and quality traits are quantified at harvest. We show that these data enable the identification of climate-resilient, high-yielding genotypes and support the development of machine learning models that explain over 80% of yield variance in independent test sets. Strikingly, measurements collected within the first two months after planting achieve predictive performance comparable to models trained on full-season data. Model interrogation further shows that simplified five-parameter linear equations capture over 70% of yield variability. Our framework thus demonstrates the potential of integrative field phenotyping and data-driven modeling to improve variety selection across heterogeneous environments.