Data from: UAV-based spatiotemporal phenotyping and growth modeling for forecasting potato yield

Monitoring spatial variations in plant growth and forecasting yield before
harvest provides valuable insights for optimizing agronomic
decision-making in potato cultivation. Although unmanned aerial vehicle
(UAV)-based remote sensing has recently enabled the development of tuber
fresh weight (TW) estimation models, their integration into practical
yield-forecasting systems remains limited. In this study, we developed
machine learning models to estimate tuber weights at multiple preharvest
time points using RGB and multispectral UAV imagery. Image-derived
features were extracted from the orthomosaic and digital surface model
(DSM) images for each plot, and a random forest regression model was
trained for TW estimation. The estimated values were subsequently used to
fit the Gompertz growth curves, which were then used to forecast the yield
at the expected harvest time. The correlation between the estimated and
observed values was strong in the UAV-based TW estimation, with
correlation coefficients exceeding 0.8 and coefficients of determination
(R²) above 0.6 at all time points. Yield forecasts based on fitted growth
curves achieved a correlation of 0.78 and an R² of -0.17 in 2023, and 0.70
and an R² of 0.47 in 2024. These results demonstrate that UAV-based
sampling combined with machine learning is a feasible approach for
monitoring spatiotemporal variations in tuber growth and forecasting
potato yield at the plot level prior to harvest.