Canopy spectral reflectance can indicate both crop nutrient and canopy structural information. Differences in canopy structure can affect spectral reflectance. However, a non-imaging spectrometer cannot distinguish such differences while monitoring crop nutrients, because the results are likely to be influenced by the canopy structure. In addition, nitrogen application rate is one of the main factors influencing the canopy structure of crops. Strong correlations exist between indices of canopy structure and leaf nitrogen, and thus, these can be used to compensate for the spectral monitoring of nitrogen content in wheat leaves. In this study, canopy structural indices (CSI) such as wheat coverage, height, and textural features were obtained based on the RGB and height images obtained by the RGB-D camera. Moreover, canopy spectral reflectance was obtained by an ASD hyperspectral spectrometer, based on which two vegetation indices—ratio vegetation index (RVI) and angular insensitivity vegetation index (AIVI)—were constructed. With the vegetation indices and CSIs as input parameters, a model was established to predict the leaf nitrogen content (LNC) and leaf nitrogen accumulation (LNA) of wheat based on partial least squares (PLS) and random forest (RF) regression algorithms. The results showed that the RF model with RVI and CSI as inputs had the highest prediction accuracy for LNA, the coefficient of determination (R²) reached 0.79, and the root mean square error (RMSE) was 1.54 g/m². The vegetation indices and coverage were relatively important features in the model. In addition, the PLS model with AIVI and CSI as input parameters had the highest prediction accuracy for LNC, with an R² of 0.78 and an RMSE of 0.35%, among the vegetation indices. In addition, parts of both the textural and height features were important. The results suggested that PLS and RF regression algorithms can effectively integrate spectral and canopy structural information, and canopy structural information effectively supplement spectral information by improving the prediction accuracy of vegetation indices for LNA and LNC.

The accurate estimation of nitrogen accumulation is of great significance to nitrogen fertilizer management in wheat production. To overcome the shortcomings of spectral technology, which ignores the anisotropy of canopy structure when predicting the nitrogen accumulation in wheat, resulting in low accuracy and unstable prediction results, we propose a method for predicting wheat nitrogen accumulation based on the fusion of spectral and canopy structure features. After depth images are repaired using a hole-filling algorithm, RGB images and depth images are fused through IHS transformation, and textural features of the fused images are then extracted in order to express the three-dimensional structural information of the canopy. The fused images contain depth information of the canopy, which breaks through the limitation of extracting canopy structure features from a two-dimensional image. By comparing the experimental results of multiple regression analyses and BP neural networks, we found that the characteristics of the canopy structure effectively compensated for the model prediction of nitrogen accumulation based only on spectral characteristics. Our prediction model displayed better accuracy and stability, with prediction accuracy values (R²) based on BP neural network for the leaf layer nitrogen accumulation (LNA) and shoot nitrogen accumulation (SNA) during a full growth period of 0.74 and 0.73, respectively, and corresponding relative root mean square errors (RRMSEs) of 40.13% and 35.73%.

An instrument developed to monitor and diagnose crop growth can quickly and non-destructively obtain crop growth information, which is helpful for crop field production and management. Focusing on the problems with existing two-band instruments used for crop growth monitoring and diagnosis, such as insufficient information available on crop growth and low accuracy of some growth indices retrieval, our research team developed a portable three-band instrument for crop-growth monitoring and diagnosis (CGMD) that obtains a larger amount of information. Based on CGMD, this paper carried out studies on monitoring wheat growth indices. According to the acquired three-band reflectance spectra, the combined indices were constructed by combining different bands, two-band vegetation indices (NDVI, RVI, and DVI), and three-band vegetation indices (TVI-1 and TVI-2). The fitting results of the vegetation indices obtained by CGMD and the commercial instrument FieldSpec HandHeld2 was high and the new instrument could be used for monitoring the canopy vegetation indices. By fitting each vegetation index to the growth index, the results showed that the optimal vegetation indices corresponding to leaf area index (LAI), leaf dry weight (LDW), leaf nitrogen content (LNC), and leaf nitrogen accumulation (LNA) were TVI-2, TVI-1, NDVI (R₇₃₀, R₈₁₅), and NDVI (R₇₃₀, R₈₁₅), respectively. R² values corresponding to LAI, LDW, LNC and LNA were 0.64, 0.84, 0.60, and 0.82, respectively, and their relative root mean square error (RRMSE) values were 0.29, 0.26, 0.17, and 0.30, respectively. The addition of the red spectral band to CGMD effectively improved the monitoring results of wheat LAI and LDW. Focusing the problem of vegetation index saturation, this paper proposed a method to construct the wheat-growth-index spectral monitoring models that were defined according to the growth periods. It improved the prediction accuracy of LAI, LDW, and LNA, with R² values of 0.79, 0.85, and 0.85, respectively, and the RRMSE values of these growth indices were 0.22, 0.23, and 0.28, respectively. The method proposed here could be used for the guidance of wheat field cultivation.