Potassium (K) is one of the most important elements influencing cotton metabolism, quality, and yield. Due to the characteristics of strong fluidity and fast redistribution of the K in plants, it leads to rapid transformation of the K lack or abundance in plant leaves; therefore, rapid and accurate estimation of potassium content in leaves (LKC, %) is a necessary prerequisite to solve the regulation of plant potassium. In this study, we concentrated on the LKC of cotton in different growth stages, an estimation model based on the combined characteristics of wavelet decomposition spectra and image was proposed, and discussed the potential of different combined features in accurate estimation of the LKC. We collected hyperspectral imaging data of 60 main-stem leaves at the budding, flowering, and boll setting stages of cotton, respectively. The original spectrum (R) is decomposed by continuous wavelet transform (CWT). The competitive adaptive reweighted sampling (CARS) and random frog (RF) algorithms combined with partial least squares regression (PLSR) model were used to determine the optimal decomposition scale and characteristic wavelengths at three growth stages. Based on the best “CWT spectra” model, the grayscale image databases were constructed, and the image features were extracted by using color moment and gray level co-occurrence matrix (GLCM). The results showed that the best decomposition scales of the three growth stages were CWT-1, 3, and 9. The best growth stage for estimating LKC in cotton was the boll setting stage, with the feature combination of “CWT-9 spectra + texture,” and its determination coefficients (R²val) and root mean squared error (RMSEval) values were 0.90 and 0.20. Compared with the single R model (R²val = 0.66, RMSEval = 0.34), the R²val increased by 0.24. Different from our hypothesis, the combined feature based on “CWT spectra + color + texture” cannot significantly improve the estimation accuracy of the model, it means that the performance of the estimation model established with more feature information is not correspondingly better. Moreover, the texture features contributed more to the improvement of model performance than color features did. These results provide a reference for rapid and non-destructive monitoring of the LKC in cotton.

钾 (K) 是影响棉花代谢、品质和产量的最重要元素之一。由于植物中钾的流动性强、重新分配快的特点，导致植物叶片中钾缺乏或丰富的快速转化；因此，快速准确地估算叶片钾含量（LKC，%）是解决植物钾调控的必要前提。本研究针对棉花不同生育阶段的LKC，提出了一种基于小波分解光谱和图像组合特征的估计模型，并探讨了不同组合特征在准确估计LKC中的潜力。我们分别采集了棉花萌芽期、开花期和结铃期的 60 片主茎叶的高光谱成像数据。原始频谱（R）通过连续小波变换（CWT）分解。采用竞争性自适应重加权采样 (CARS) 和随机青蛙 (RF) 算法结合偏最小二乘回归 (PLSR) 模型确定三个生长阶段的最佳分解规模和特征波长。基于最佳“CWT光谱”模型，构建灰度图像数据库，利用色矩和灰度共生矩阵（GLCM）提取图像特征。结果表明，三个生育期的最佳分解尺度为CWT-1、3、9。棉花LKC估算的最佳生育期为结铃期，具有“CWT-9光谱+质地”的特征组合。 ,” 及其决定系数 ( 采用竞争性自适应重加权采样 (CARS) 和随机青蛙 (RF) 算法结合偏最小二乘回归 (PLSR) 模型确定三个生长阶段的最佳分解规模和特征波长。基于最佳“CWT光谱”模型，构建灰度图像数据库，利用色矩和灰度共生矩阵（GLCM）提取图像特征。结果表明，三个生育期的最佳分解尺度为CWT-1、3、9。棉花LKC估算的最佳生育期为结铃期，具有“CWT-9光谱+质地”的特征组合。 ,” 及其决定系数 ( 采用竞争性自适应重加权采样 (CARS) 和随机青蛙 (RF) 算法结合偏最小二乘回归 (PLSR) 模型确定三个生长阶段的最佳分解规模和特征波长。基于最佳“CWT光谱”模型，构建灰度图像数据库，利用色矩和灰度共生矩阵（GLCM）提取图像特征。结果表明，三个生育期的最佳分解尺度为CWT-1、3、9。棉花LKC估算的最佳生育期为结铃期，具有“CWT-9光谱+质地”的特征组合。 ,” 及其决定系数 ( 基于最佳“CWT光谱”模型，构建灰度图像数据库，利用色矩和灰度共生矩阵（GLCM）提取图像特征。结果表明，三个生育期的最佳分解尺度为CWT-1、3、9。棉花LKC估算的最佳生育期为结铃期，具有“CWT-9光谱+质地”的特征组合。 ,” 及其决定系数 ( 基于最佳“CWT光谱”模型，构建灰度图像数据库，利用色矩和灰度共生矩阵（GLCM）提取图像特征。结果表明，三个生育期的最佳分解尺度为CWT-1、3、9。棉花LKC估算的最佳生育期为结铃期，具有“CWT-9光谱+质地”的特征组合。 ,” 及其决定系数 (R² val) 和均方根误差 (RMSEval) 值为 0.90 和 0.20。与单个 R 模型相比（R² val = 0.66，RMSEval = 0.34），R² val 增加了 0.24。与我们的假设不同，基于“CWT光谱+颜色+纹理”的组合特征并不能显着提高模型的估计精度，这意味着用更多特征信息建立的估计模型的性能并没有相应更好。此外，纹理特征比颜色特征对模型性能的提升贡献更大。这些结果为棉花LKC的快速无损监测提供了参考。