###### Abstract  
The nitrogen content is an important indicator affecting corn plants' growth status. Most of the standard hyperspectral imaging-based techniques for nondestructive detection of crop nitrogen content use a single feature as the input variable of the model, which reduces the generalization ability of the prediction model. To this end, a prediction model for the nitrogen content of corn leaves based on the fusion of image and spectral features is proposed. In this study, corn leaves at the modulation stage were studied, samples with different nitrogen levels were numbered, and their hyperspectral data in the wavelength range of 400-1100 nm were collected. The average spectrum of the models was used as valid spectral information. First-order derivatives, standard normal variables transformation (SNV), Savitzky-Golay (S-G) smoothing, and normalization were selected to preprocess the spectral features. The CARS-SPA algorithm was used to screen sensitive spectral variables. The gray level co-currency matrix (GLCM) was chosen to extract the texture image features of the test samples. Corn leaf spectral and texture image features were fused and modeled as target features. Partial least squares regression (PLSR) and support vector machine regression (SVR) were used to predict corn leaves' nitrogen content. The results showed that the image and spectral-based fusion models improved the prediction performance to some extent compared to the univariate models. The PLSR model based on feature fusion predicted the best results, in which the R\({}^{2}\) and RMSEP were 0.987 and 0.047. This method provides a reliable theoretical basis and technical support for developing nondestructive and accurate detection of nitrogen content in corn leaves. hyperspectral; fusion features; corn nitrogen content; machine learning; nondestructive detection +
Footnote †: journal: Medical Imaging  
With the continuous development of non-destructive testing technology, hyperspectral imaging has been widely used to detect plant LNC. The absorption and reflection of LNC to the waveband affect the material composition and structural information inside the crop. Hyperspectral-based plant nitrogen detection obtains the spectral characteristics of plants and canopies by using hyperspectral sensors without damaging the crop tissues and structures and then obtains the nitrogen content surplus or deficit in the plant quickly and accurately by analyzing the spectral information of plant leaves or canopies [5]. [PERSON] et al. [6] quantitatively predicted the LNCs of winter wheat by constructing several LNC spectral indices (LNCSIs), and the results confirmed the validity of LNCSIs for winter wheat LNC prediction. [PERSON] et al. [7] developed a regression model for rice LNC prediction by measuring its active and passive spectra. [PERSON] et al. [8] proposed a LNC prediction model based on UAV hyperspectral images of the corn canopy. [PERSON] et al. [9] constructed a winter wheat LNC estimation model based on a multi-angle composite vegetation index by acquiring spectral data of multiple leaf inclination angles. The above studies demonstrate the feasibility of the inversion of plant nutrient elements based on spectral features. However, the single spectral feature has limitations for detecting the distribution pattern of plant nitrogen content. It cannot dynamically predict the internal plant nitrogen content, which has limitations for further improving the robustness of the prediction model. Hyperspectral imaging is a combination of imaging and spectroscopic techniques. Although both spectral data and texture features can improve the accuracy of nitrogen nondestructive detection to some extent, a single consideration of image texture variables cannot predict the nitrogen content inside the plant. In contrast, a single consideration of spectral variables cannot characterize the overall spatial distribution of nitrogen [10,11].