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 coe..., , # Data from: UAV-based spatiotemporal phenotyping and growth modeling for forecasting potato yield

[Access this dataset on Dryad](https://doi.org/10.5061/dryad.5qfttdzmn)

This dataset provides the data and reproducible code used for pre-harvest yield forecasting of potato using UAV-based remote sensing. Field experiments were conducted in 2023 and 2024. For each experimental plot, RGB and multispectral UAV imagery were acquired, and vegetation features were extracted from orthomosaic images and digital surface models (DSM). In addition, destructive sampling was conducted at multiple time points to measure tuber fresh weight (TW).

Using these UAV-derived features and observed TW data, a Random Forest regression model was developed to estimate TW at different harvest stages. Furthermore, Gompertz growth curves were fitted to the time-series of estimated TW in order to forecast yield at the expected harvest date.

This repository includes plot-level ground-truth TW datasets (.csv), UAV-..., ,