本研究以不同水稻品种及施氮水平处理的水稻田块作为试验对象,利用无人机高光谱成像系统在水稻分蘖期、拔节孕穗期、抽穗开花期和乳熟期采集冠层高光谱数据。通过提取10个植被指数与8个微分指数共18个光谱参数,并与水稻产量进行相关性分析,筛选出相关性最高的8个光谱参数作为模型输入变量。进而,采用偏最小二乘回归(PLSR)和随机森林(RF)两种机器学习算法分别构建不同生育期的水稻产量预测模型。结果表明,不同生育期水稻产量估测效果存在显著差异,表现为拔节孕穗期>抽穗开花期>乳熟期>分蘖期。其中,拔节孕穗期和抽穗开花期为产量估测的优势生育期。在拔节孕穗期,PLSR模型的预测精度优于RF模型;而在抽穗开花期,RF模型的预测精度则优于PLSR模型。综合来看,在拔节孕穗期采用PLSR模型预测产量效果表现最佳,其验证集决定系数(R²)达0.856,均方根误差(RMSE)为0.970 t/hm²。

This study focused on paddy fields with different rice varieties and nitrogen application levels. Canopy hyperspectral data were collected during the tillering, jointing-booting, heading-flowering, and milk ripening stages using an unmanned aerial vehicle (UAV)-borne hyperspectral imaging system. A total of 18 spectral parameters, including 10 vegetation indices and 8 differential indices, were extracted and subjected to correlation analysis with rice yield. The eight spectral parameters with the highest correlations were selected as input variables for model construction. Subsequently, two machine learning algorithms, partial least squares regression (PLSR) and random forest (RF), were employed to construct rice yield prediction models for different growth stages, respectively. Results indicated significant differences in rice yield estimation performance across growth stages, ranked as follows: jointing-booting stage>heading-flowering stage>milk ripening stage>tillering stage. Among these, the jointing-booting and heading-flowering stages were identified as the optimal growth stages for yield estimation. At the jointing-booting stage, the PLSR model outperformed the RF model in prediction accuracy; conversely, at the heading-flowering stage, the RF model exhibited superior prediction accuracy compared to the PLSR model. Overall, the PLSR model performed best at the jointing-booting stage, achieving a coefficient of determination (R²) of 0.856 and a root mean square error (RMSE) of 0.970 t/hm² on the validation set.