Grain Yield Estimation of Rice Germplasm Resources Using Time-Series UAV Imagery and Dynamic Clustering Process

Traditional methods for measuring rice yield are often labor-intensive, time-consuming, and difficult to implement at scale. Conversely, remote sensing-based yield prediction models typically exhibit limited applicability across diverse genetic materials. In this study, we propose a high-precision yield prediction approach that integrates UAV-based time-series imagery with dynamic process clustering. Field experiments were conducted over two years involving 630 rice germplasm accessions in Rugao and Huaian, Jiangsu Province. UAV-mounted RGB and multispectral cameras were employed to acquire canopy imagery throughout the rice growth period. A range of features, including spectral reflectance, vegetation indices, canopy height (CH), and canopy volume (CV), were extracted from the UAV data. The K-Shape clustering algorithm was applied to dynamically group the temporal growth curves, enabling the construction of a cluster-based yield prediction model. Among the vegetation indices, the Enhanced Vegetation Index (EVI2) demonstrated the best performance (R2 = 0.73, RMSE = 599.53 kg/hm2). Models based on temporal features of CH and CV showed satisfactory accuracy (R2 = 0.70, RMSE = 640.96 kg/hm2). Notably, a dual-modal model combining vegetation indices with structural parameters significantly improved predictive performance (R2 = 0.80, RMSE = 511.42 kg/hm2). This study demonstrates that multi-feature cluster analysis enhances the accuracy and robustness of yield prediction models across diverse genotypes. The proposed methodology provides valuable technical support for high-yield rice breeding initiatives.