Abstract:Rice plant height is a core parameter for phenotypic analysis and growth state assessment, and its high-throughput detection is of significant importance for breeding and production. UAV LiDAR, with its high-precision advantages, has become a research hotspot for rice plant height detection. However, existing studies generally constructed linear regression models based on a single feature, which led to insufficient accuracy and generalization ability in complex application scenarios such as multi-variety and multi-breeding material analysis. Therefore, a multi-feature fusion strategy was adopted to build a nonlinear regression model for rice plant height, aiming to improve estimation accuracy. Field experiments were conducted in Zengcheng, Guangzhou, and Yazhou, Sanya, where plant height and laser point cloud time-series data were collected for five rice varieties and 225 breeding materials. A multi-dimensional feature system, including height percentiles, statistical parameters, and canopy profile area, was constructed. Linear and nonlinear machine learning algorithms were used to establish the detection model for rice plant height. The results showed that the accuracy of the multi-feature nonlinear prediction model was higher than that of the linear prediction model, with the highest coefficient of determination (R2) reaching 0.969 and the root mean square error (RMSE) as low as 4.73 cm. Compared with the single-feature linear model (R2= 0.905, RMSE was 8.231 cm), the R2 was increased by 7.2% and RMSE was decreased by 42.5%. Further studies on generalization ability indicated that the model built using data from the five varieties in Zengcheng, Guangzhou, showed significantly lower generalization ability compared with the model constructed from the 225 breeding materials in Yazhou, Sanya, confirming that the diversity of rice samples can effectively improve model robustness. The research result can provide a high-precision and highly applicable general technical framework for high-throughput phenotypic analysis of rice plant height.
