Potato yield can be predicted using drone-captured and environmental measurements early in the growing season

Accurate pre-harvest prediction of crop yield informs variety selection, optimizes management, and accelerates breeding. As potato is the worlds 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 variation 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 over 70% of yield variation can already be predicted based on a simple five-parameter linear equation. Our framework thus demonstrates the potential of integrative field phenotyping and data-driven modeling to improve variety selection across heterogeneous environments.