基于“面-廓-色”多通道特征融合的多肉叶片繁育评分算法

doi:10.3969/j.issn.0255-8297.2026.02.010

摘要/Abstract

摘要： 为了提升多肉叶片繁育潜力的图像评分精度与筛选一致性，提出一种基于“面-廓-色”多通道特征融合的繁育评分算法。该算法首先利用图像分割模型，从原图中自动检测并定位具备繁育潜力的多肉叶片区域，完成初步筛选。随后提取叶片的面积、轮廓、色泽与高层语义特征，构建统一的多维融合向量，并输入评分模型生成繁育得分。面积特征体现发育饱满度，轮廓特征反映形态规整性，色泽特征衡量健康状态；而视觉预训练模型提取的语义特征则补充了传统指标难以捕捉的深层生长模式与语义关联，进一步增强模型判别力与泛化性。实验结果表明，该方法显著提升了叶片筛选的效率与准确率。相比传统方法，在多个多肉数据集上，皮尔逊相关系数与曲线下面积平均提高0.093 8和0.065 3，平均平方对数误差降低约0.012 1，展现出更强的精度与鲁棒性，有效支持多肉植物的智能化选育。

关键词: 多肉植物, 叶片筛选, 繁育评分算法, 面-廓-色特征, 图像识别, 深度学习

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

中图分类号:

P751.1

张蕾, 李萄苑, 李明飞, 邓海敏, 谢诚. 基于“面-廓-色”多通道特征融合的多肉叶片繁育评分算法[J]. 应用科学学报, 2026, 44(2): 316-329.

ZHANG Lei, LI Taoyuan, LI Mingfei, DENG Haimin, XIE Cheng. Breeding Score Algorithm for Succulent Leaves based on Multi-channel Feature Fusion of Area, Contour, and Color[J]. Journal of Applied Sciences, 2026, 44(2): 316-329.

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