Potato yield can be predicted by 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 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. Significance statementThe ability to predict harvest crop yields from pre-harvest measurements can enable farmers and growers to make informed decisions on variety selection and management practices, while breeders can benefit from accelerated breeding cycles. We perform a panel of field trials with potato, the no. 1 global non-grain staple, across varying conditions and locations, recording various growth- and climate-related data, including gene expression, and post-harvest yield and quality of tubers. We demonstrate the potential of the field trial data to facilitate the analysis and selection of best-performing varieties across diverse conditions and locations, and to revolutionize farming by enabling early (already within 2 months) and straightforward (only a couple of key measured variables) yield predictions with high accuracy.