Ji Jiangtao, Lu Xinyi, Ma Hao, Jin Xin, Jiang Shijie, Cui Hongwei, Lu Xiaoxuan, Yang Yaqing
College of Agricultural Equipment Engineering, Henan University of Science and Technology, Luoyang, Henan, China.
Front Plant Sci. 2025 Jun 2;16:1609650. doi: 10.3389/fpls.2025.1609650. eCollection 2025.
Leaf water content is a key physiological indicator of plant growth and health status. Constructing leaf water content estimation models based on spectroscopy is an effective method for monitoring plant physiological conditions.
To improve the accuracy of leaf water content estimation and develop models applicable to different plants, this study collected 1,680 groups of hyperspectral and water content data from peach tree leaves. Estimation models were established using two methods: "constructing vegetation indices" and "selecting characteristic wavelengths." The accuracy and number of wavelengths used in each model were systematically evaluated. The optimal model was used to predict the water content of each pixel in the hyperspectral images, achieving visualization of leaf water distribution. Additionally, 244 groups of hyperspectral and water content data from apple tree and lettuce leaves were collected to validate the generalization ability of the optimal model.
Results showed that the optimal models established using the two methods were the linear regression model based on the vegetation index NISDI (3 wavelengths, R = 0.9636, RMSEP=0.0356), and the CARS-RF model (12 wavelengths, R = 0.9861, RMSEP=0.0219). Although the accuracy of the two models was similar, the latter used four times more wavelengths than the former, so the former was chosen as the optimal model. Using the optimal model to estimate the water content of apple tree leaves, the R and RMSEP were 0.9504 and 0.1226, respectively. For lettuce containing only leaf tissue, the R and RMSEP were 0.8211 and 0.1771, respectively.
These results indicate that the model has some generalization ability and can accurately estimate the water content of leaves of woody plants in the same family, with some performance degradation across different growth forms. The study results achieved accurate estimation of leaf water content for three types of plants and also provided a reference for establishing plant leaf water content estimation models with generalization ability.
叶片含水量是植物生长和健康状况的关键生理指标。基于光谱构建叶片含水量估算模型是监测植物生理状况的有效方法。
为提高叶片含水量估算的准确性并开发适用于不同植物的模型，本研究收集了1680组桃树叶片的高光谱和含水量数据。采用“构建植被指数”和“选择特征波长”两种方法建立估算模型。系统评估了每个模型的准确性和所用波长数量。使用最优模型预测高光谱图像中每个像素的含水量，实现叶片水分分布的可视化。此外，收集了244组苹果树和生菜叶片的高光谱和含水量数据，以验证最优模型的泛化能力。
结果表明，使用两种方法建立的最优模型分别是基于植被指数NISDI的线性回归模型（3个波长，R = 0.9636，RMSEP = 0.0356）和CARS - RF模型（12个波长，R = 0.9861，RMSEP = 0.0219）。虽然两个模型的准确性相似，但后者使用的波长数量是前者的四倍，因此选择前者作为最优模型。使用最优模型估算苹果树叶片的含水量，R和RMSEP分别为0.9504和0.1226。对于仅含叶片组织的生菜，R和RMSEP分别为0.8211和0.1771。
这些结果表明该模型具有一定的泛化能力，能够准确估算同一家族木本植物叶片的含水量，在不同生长形式下性能有所下降。研究结果实现了对三种植物叶片含水量的准确估算，也为建立具有泛化能力的植物叶片含水量估算模型提供了参考。
