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

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.

doi:10.64898/2026.03.09.709817

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bioRxiv · 10.64898/2026.03.09.709817

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

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Abstract

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.

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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.. 2026-03-11. Potato yield can be predicted by using drone-captured and environmental measurements early in the growing season. https://doi.org/10.64898/2026.03.09.709817

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