Rapid and accurate prediction of crop nitrogen content is of great significance for guiding precise fertilization. In this study, an unmanned aerial vehicle (UAV) digital camera was used to collect cotton canopy RGB images at 20 m height, and two cotton varieties and six nitrogen gradients were used to predict nitrogen content in the cotton canopy. After image-preprocessing, 46 hand features were extracted, and deep features were extracted by convolutional neural network (CNN). Partial least squares and Pearson were used for feature dimensionality reduction, respectively. Linear regression, support vector machine, and one-dimensional CNN regression models were constructed with manual features as input, and the deep features were used as inputs to construct a two-dimensional CNN regression model to achieve accurate prediction of cotton canopy nitrogen. It was verified that the manual feature and deep feature models constructed from UAV RGB images had good prediction effects. R² = 0.80 and RMSE = 1.67 g kg⁻¹ of the Xinluzao 45 optimal model, and R² = 0.42 and RMSE = 3.13 g kg⁻¹ of the Xinluzao 53 optimal model. The results show that the UAV RGB image and machine learning technology can be used to predict the nitrogen content of large-scale cotton, but due to insufficient data samples, the accuracy and stability of the prediction model still need to be improved.

中文翻译：

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利用无人机 RGB 图像预测棉花中的叶氮含量

快速准确预测作物氮含量对于指导精准施肥具有重要意义。本研究采用无人机（UAV）数码相机采集20 m高的棉花冠层RGB图像，利用2个棉花品种和6个氮梯度预测棉花冠层氮含量。经过图像预处理后，提取了 46 个手部特征，并通过卷积神经网络 (CNN) 提取了深度特征。偏最小二乘法和 Pearson 分别用于特征降维。线性回归、支持向量机和一维 CNN 回归模型以手动特征为输入构建，并将深度特征作为输入，构建二维CNN回归模型，实现棉花冠层氮的准确预测。验证了基于无人机RGB图像构建的人工特征模型和深度特征模型具有良好的预测效果。R新陆早 45 最优模型的² = 0.80 和 RMSE = 1.67 g kg ^{-1 ，新陆早 53 最优模型的 R 2} = 0.42 和 RMSE = 3.13 g kg ^-1。结果表明，无人机RGB图像和机器学习技术可用于预测大面积棉花氮含量，但由于数据样本不足，预测模型的准确性和稳定性仍有待提高。