Abstract: To enhance the accuracy of image scoring and consistency in screening for the propagation potential of succulent leaves, we propose a breeding scoring algorithm based on multi-channel feature fusion of “surface-outline-color”. This algorithm first employs an image segmentation model to automatically detect and locate areas of succulent leaves with breeding potential within the original image, completing preliminary screening. Subsequently, it extracts leaf area, contour, color, and high-level semantic features to construct a unified multidimensional fusion vector, which is then input into a scoring model to generate breeding scores. Among these, the area feature reflects developmental fullness, the contour feature indicates morphological regularity, and the color feature measures health status. Meanwhile, semantic features extracted by a pre-trained visual model supplement deeper growth patterns and semantic associations that traditional metrics struggle to capture, further enhancing the model’s discriminative power and generalization capability. Experimental results demonstrate that this method significantly improves the efficiency and accuracy of leaf screening. Compared to traditional methods, the Pearson correlation coefficient and area under the curve improved by an average of 0.093 8 and 0.065 3, respectively, across multiple succulent datasets. The mean squared logarithmic error decreased by approximately 0.012 1, demonstrating enhanced accuracy and robustness. This effectively supports the intelligent breeding of succulent plants.

Key words: succulent plant, leaf selection, breeding score algorithm, surface-contour-color feature, image recognition, deep learning