Water status is critical since it affects photosynthetic efficiency and limits crop yield. Thus it is essential to calculate the water content of crop fast and nondestructively. In this work, hyperspectral reflectance in 900–1700 nm was used to estimate water content of corn leaves. The fractional order Savitzky-Golay derivation (FOSGD) was used to pretreat the hyperspectral data. The result indicated that the best performance of partial least square (PLS) model was obtained when the fractional order equaled to 1 and 2. In order to make a PLS model simpler, three wavelength selection approaches, variable importance in the projection (VIP), competitive adaptive reweighted sampling (CARS), and random frog were performed to extract the sensitive wavelengths. The result demonstrated that the PLS model with CARS and random frog wavelength selection did exhibit the best performance and extracted 23 feature wavelengths, with the coefficient of determination (R²) up to 0.91 for calibration and 0.92 for validation. The root mean squared error (RMSE) decreased to 0.049 for calibration and 0.044 for validation. Meanwhile, 80 percent of the feature wavelengths extracted by these two methods were similar. The findings of the current study may provide an effective way to predict the water content of corn leaves using a combination of FOSGD-CARS-PLS or FOSGD-random frog-PLS.

水状态至关重要，因为它会影响光合作用效率并限制农作物产量。因此，必须快速，无损地计算出作物的水分含量。在这项工作中，使用900-1700 nm的高光谱反射率来估算玉米叶片的水分含量。分数阶Savitzky-Golay推导（FOSGD）用于预处理高光谱数据。结果表明，当分数阶等于1和2时，偏最小二乘（PLS）模型获得了最佳性能。为了简化PLS模型，使用了三种波长选择方法，投影重要性（VIP），竞争性自适应加权采样（CARS）和随机青蛙进行了提取敏感波长。²）校准最高为0.91，验证最高为0.92。校准的均方根误差（RMSE）降至0.049，而验证的均方根误差（RMSE）降至0.044。同时，这两种方法提取的特征波长的80％是相似的。当前研究的结果可能提供一种结合使用FOSGD-CARS-PLS或FOSGD-random frog-PLS预测玉米叶片含水量的有效方法。