【摘要】 叶绿素含量能够反映绿色蔬菜健康状况并能够促进蔬菜的生长和发育，而白菜作为1种重要的蔬菜作物，其生长状态的监测对于提高产量和品质具有重要意义。本文通过多光谱无人机构建了9种颜色特征与24种光谱图像组合，并使用手持SPAD叶绿素仪同步获得了田间白菜冠层SPAD值。使用4种机器学习的方法，包括偏最小二乘回归、支持向量回归、BP神经网络和一维卷积神经网络来构建田间白菜SPAD值预测模型。通过决定系数（R2）、均方根误差（RMSE）和平均绝对误差（MAE）来评估模型的精度。结果表明：颜色特征、可见光与多光谱图像特征相结合的预测精度，相比于单一特征来说具有较高的预测精度。其中，基于支持向量回归的田间白菜冠层SPAD预测模型精度最高，其R2=0.785,RMSE=4.320,MAE=3.451。综合分析可得出的结论是，选择多种可见光与多光谱图像特征组合作为输入变量，并使用支持向量回归的预测方法，可以显著提高SPAD值预测的准确性，为快速准确地监测田间白菜SPAD值提供新的技术支持。更多还原

【Abstract】 Chlorophyll content can reflect the health status of green vegetables and promote the growth and development of vegetables. As an important vegetable crop, monitoring the growth status of Chinese cabbage is of great significance for improving yield and quality. In this study, nine color features and 24 spectral image combinations were constructed using a multispectral unmanned aerial vehicle, and the SPAD values of Chinese cabbage canopy were obtained simultaneously using a handheld SPAD chlorophyll meter. Four machine learning methods, including partial least squares, support vector regression, BP neural network, and 1D-convolutional neural network, were used to construct the Chinese cabbage SPAD value estimation model. The accuracy of the model was evaluated by the determination coefficient(R2), root mean square error(RMSE), and mean absolute error(MAE). The results showed that the prediction accuracy combining color features, visible light, and multispectral image features was higher than that of a single feature. Among them, the support vector machine-based Chinese cabbage canopy SPAD prediction model showed the highest accuracy with R2=0.785, RMSE=4.320, and MAE=3.451. The conclusion drawn from the comprehensive analysis was that selecting multiple visible light and multispectral image feature combinations as input variables and using the support vector machine model can significantly improve the accuracy of SPAD value estimation, which provids new technical support for rapid and accurate monitoring of Chinese cabbage SPAD values.更多还原