基于无人机多光谱影像的棉花SPAD值及叶片含水量估测

利用多光谱快速准确获取棉花花铃期旱胁迫性状对棉花后期产量和纤维品质具有重要帮助。为实现棉花关键抗旱指标的快速检测，该研究以253份棉花品种为材料，设置干旱胁迫和正常灌溉2个处理，在花铃期通过大疆精灵4多光谱无人机获得图像，分析花铃期干旱胁迫和正常处理棉花的光谱反射率，结合地面调查叶绿素相对值(S_PAD)和叶片含水量(L_WC)数据，结合神经网络算法中的径向基函数进行模型的预测。结果显示：棉花干旱胁迫前后除红光反射值略上升，其余4个光谱均小于正常处理；通过径向基函数模型估测S_PAD和L_WC的最佳估测模型都为二次函数，决定系数R²分别为0.848 8和0.936 6，均方根误差R_MSE分别为2.005和0.930，相对误差R_E分别为0.004和0.011；将2个模型应用于试验区影像，对S_PAD及L_WC预测值进行聚类分析，根据S_PAD聚类结果，试验棉花旱情等级划分为特旱、重旱、...

Estimation of cotton SPAD value and leaf water content based on UAV multispectral images

Abstract: An accurate and rapid detection has been one of the most important steps to monitor the drought stress status of cotton at flowering and boll-forming stage in the cotton yield and quality. This study aims to realize the rapid detection of key drought resistance indexes of cotton. 253 cotton resources were taken as the raw materials in this experiment. Two treatments were set for the drought stress and normal irrigation. The images were also obtained by the DJI Jingling 4 multispectral UAV at the flowering and boll-forming stage. A systematic analysis was made to determine the spectral reflectance of drought stress and normal treatment cotton at the flowering and boll-forming stage. The predicted model was established to combined with the relative value of chlorophyll (SPAD) and leaf water content (LWC) data of ground survey using the radial basis function in the neural network algorithm. The results showed that the red-light reflection value of cotton increased slightly before and after drought stress, whereas, the other four spectra were less than normal treatment. The best estimation models were cubic function to predict the SPAD by radial basis function. The best estimation models of LWC were quadratic function. The ideal model of quadratic function SPAD was y=0.010 6x2-0.673x+61.045, where determination coefficient R2 was 0.848 8, root mean square error RMSE was 2.005 and relative error RE was 0.004. The ideal model of LWC was y=-0.017 6x2+0.709 7x+3.249 3, where R2 was 0.936 6, RMSE was 0.930, and RE was 0.011. The improved model was also applied to discriminate the drought image in the experimental area. The SPAD and LWC prediction values were clustered and analyzed during this time. The drought classification based on SPAD clustering results was that extreme drought, severe drought, moderate drought, light drought and no drought, and the drought classification based on LWC clustering results was that extreme drought, severe drought, moderate drought, drought, light drought and no drought. The obtained models and classification effects were better conform to the characteristics of cotton drought stress. The finding can provide a strong reference for the rapid and accurate acquisition during cotton drought monitoring.