## 4 Conclusions  
In this paper, we propose a new hyperspectral imaging technique for predicting the LNC of corn. Before building the prediction model, we introduce the GLCM method for extracting texture features, a matrix function of the distance and angle of different pixel points, using the joint probability distribution of the simultaneous occurrence of different grayscale pixel points to reflect texture information. It has good results in identifying texture features of corn leaves, improving the prediction accuracy of the spectral image model, and reducing the complexity of the model. In this paper, a fusion of texture features and spectral features is used for modeling. Comparing the eight models, the PLSR model based on fused features has the best prediction of the nitrogen content of corn leaves (\(\text{R}_{\text{P}}{}^{2}\) = 0.987, RMSEP = 0.047). The PLSR model is an extension of the multiple linear regression model, which can effectively overcome the covariance problem of hyperspectral data and retain the original spectral information. It improves the accuracy and generalization of nitrogen content prediction in corn leaves. Compared with articles in the literature, the advantage of this study is that the combination of spectra and images using the expandability of hyperspectral techniques will be a promising method for predicting plant nutrient elements. Therefore, the following work will expand the study to a broader range of nutrient elements, which has far-reaching implications for modeling the growth dynamics of corn.  
Conceptualization, Z.W., Y.G., W.W.; methodology, L.W.; software, L.W.; validation, L.W.; formal analysis, L.W.; investigation, L.W., X.B.; resources, X.B.; data curation, L.W.; writing-original draft preparation, L.W.; writing-review and editing, Y.G., Z.W.; visualization, L.W.; supervision, Z.W.; project administration, Z.W.; funding acquisition, Z.W., W.W. All authors have read and agreed to the published version of the manuscript.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multirow{2}{*}{**Model**} & \multicolumn{2}{c}{**Original Spectrum**} & \multicolumn{2}{c}{**Spectral Features**} & \multicolumn{2}{c}{**Texture Features**} & \multicolumn{2}{c}{**Fusion Features**} \\ \cline{2-9}  & **SVR** & **PLSR** & **SVR** & **PLSR** & **SVR** & **PLSR** & **SVR** & **PLSR** \\ \hline \(\text{R}_{\text{P}}{}^{2}\) & 0.704 & 0.876 & 0.807 & 0.877 & 0.805 & 0.852 & 0.927 & 0.987 \\ \hline RMSEP & 0.248 & 0.143 & 0.192 & 0.142 & 0.190 & 0.268 & 0.120 & 0.047 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Predicting results of various characteristic models for leaf nitrogen content in crop.  
**Funding:** This work was financially supported by the \"Revitalizing Liaoning Talents Plan\" of Liaoning, funding number XLYC1902006.  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** No applicable.  
**Data Availability Statement:** The data presented in this study are available upon request from the corresponding author. The data are not publicly available due to their use in subsequent studies.  
**Conflicts of Interest:** The authors declare no conflict of interest.