In order to promote the application of digital image processing technology in forest nutrition diagnosis and realize real-time monitoring of forest growth status and nutrient content, three prediction models of the total nitrogen content of young Aquilaria sinensis Lignum Resinatum based on image color and shape characteristics were constructed in this study, which provided a theoretical basis for nutrition diagnosis of young forest tree. Firstly, the optimal K value is determined according to the boundary distance and the size of the setting error, and the improved K-Means algorithm is used to extract the foreground image. Then, separate the three channels of R, G, and B of the foreground image and calculate the average value respectively. Then, the R, G, and B three channels of the foreground image are separated and the mean values are calculated respectively. According to the image color space conversion formula, the image is converted to HIS and Lab color space respectively, and the hue (H), saturation (S), brightness (I), brightness (L), red to green channel (a), yellow to blue channel (b), and a total of 9 color features are obtained. Find the minimum circumscribed rectangle of the foreground image, calculate the area (CA) of the foreground image, the area (RA), perimeter (RC), and rectangularity (RD) of the minimum circumscribed rectangle of the foreground image, and obtain four shape features in total. Finally, principal component analysis was performed on the color features, shape features, and color features + shape features, and the obtained three types of principal components were used as independent variables to construct a prediction model for the total nitrogen content of young A. sinensis, and the accuracy of the three models constructed was tested. Finally, the principal component analysis of color feature, shape feature, and color feature +shape feature was carried out respectively, and the three principal components obtained were used as independent variables to construct the prediction model of the total nitrogen content of young A. sinensis, and the accuracy of the three models was teste. The results show that improving the K value selection method can reduce the uncertainty of the K-Means clustering segmentation algorithm, enhance the segmentation efficiency of the algorithm, and achieve accurate segmentation of A. sinensis visible light images. The three models of the total nitrogen content of young agarwood constructed in this study had good prediction ability. The model accuracy based on single image parameters was basically the same, but the model based on shape features used fewer parameters and had higher relative modeling efficiency. The two-image parameter model uses more parameters than the single-image parameter model, but the fitting degree is better and the accuracy is higher. In practical applications, it can be selected according to different needs. In this study, different image features were used to build a total nitrogen model, which better realized the non-destructive estimation of the total nitrogen content of young trees, and provided a certain reference for precision forestry.