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

Abstract

Abstract 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 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.

Code and data availability

The supplied blocks describe potato field trials, drone-derived vegetation metrics, environmental sensor data, gene expression measurements, and machine learning models, but contain no data or code availability statement, no public repository deposit, and no authors' URL for any dataset, image, script, or trained model

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