Nitrogen is an important indicator for monitoring wheat growth. The rapid development and wide application of non-destructive detection provide many approaches for estimating leaf nitrogen content (LNC) in wheat. Previous studies have shown that better results have been obtained in the estimation of LNC in wheat based on spectral features. However, the lack of automatically extracted features leads to poor universality of the estimation model. Therefore, a feature fusion method for estimating LNC in wheat by combining spectral features with deep features (spatial features) was proposed. The deep features were automatically obtained with a convolutional neural network model based on the PyTorch framework. The spectral features were obtained using spectral information including position features (PFs) and vegetation indices (VIs). Different models based on feature combination for evaluating LNC in wheat were constructed: partial least squares regression (PLS), gradient boosting decision tree (GBDT), and support vector regression (SVR). The results indicate that the model based on the fusion feature from near-ground hyperspectral imagery has good estimation effect. In particular, the estimation accuracy of the GBDT model is the best (R² = 0.975 for calibration set, R² = 0.861 for validation set). These findings demonstrate that the approach proposed in this study improved the estimation performance of LNC in wheat, which could provide technical support in wheat growth monitoring.

中文翻译：

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基于近红外高光谱图像光谱特征和深部特征融合的小麦叶片氮素含量估算

氮是监测小麦生长的重要指标。无损检测技术的迅速发展和广泛应用为估算小麦叶片氮含量（LNC）提供了许多方法。先前的研究表明，基于光谱特征估算小麦的LNC可获得更好的结果。但是，缺少自动提取的特征会导致估算模型的通用性差。因此，提出了一种将光谱特征与深部特征（空间特征）相结合的小麦LNC特征融合方法。深度特征是使用基于PyTorch框架的卷积神经网络模型自动获得的。使用包括位置特征（PFs）和植被指数（VIs）的光谱信息获得光谱特征。构建了基于特征组合的小麦LNC评估的不同模型：偏最小二乘回归（PLS），梯度提升决策树（GBDT）和支持向量回归（SVR）。结果表明，基于近地高光谱影像融合特征的模型具有良好的估计效果。特别是，GBDT模型的估计精度最高（R² = 0.975（用于校准组），R ² = 0.861（用于验证组）。这些发现表明，本研究中提出的方法改善了小麦中LNC的估算性能，这可以为小麦生长监测提供技术支持。