Estimating Leaf Nitrogen Content in Wheat Using Multimodal Features Extracted from Canopy Spectra

Abstract

The leaf nitrogen content (LNC) of wheat is one of key bases for wheat nitrogen fertilizer management and nutritional diagnosis, which is of great significance to the sustainable development of precision agriculture. The canopy spectrum provides an effective way to monitor the nitrogen content of wheat. Previous studies have shown that features extracted from the canopy spectrum, such as vegetation indices (VIs) and band positions (BPs), have successfully achieved the monitoring of crop nitrogen nutrition. However, the features mentioned above are spectral features extracted on the basis of linear or nonlinear combination models with a simple structure, which limits the general applicability of the model. In addition, models based on spectral features are prone to overfitting, which also reduces the accuracy of the model. Therefore, we propose an estimation model based on multimodal features (convolutional features and VIs, BPs) of the canopy spectrum, which aim to improve accuracy in estimating wheat LNC. Among these, the convolutional features (CFs) extracted by the designed convolutional neural network represent the deep semantic information of the canopy reflection spectrum, which can make up for the lack of robustness of the spectral features. The results showed that the accuracy of the model based on the fusion features (VIs + BPs + CFs) was higher than that of the feature of single modality. Moreover, the particle swarm optimization–support vector regression (PSO-SVR) model based on multimodal features had the best prediction effect (R2 = 0.896, RMSE = 0.188 for calibration, R2 = 0.793, RMSE = 0.408 for validation). Therefore, the method proposed in this study could improve performance in the estimation of wheat LNC, which provides technical support for wheat nitrogen nutrition monitoring.