茶树叶齿的大小、形状和排列方式是茶树种质资源鉴定评价的重要标准之一,但长期以来,这一标准的判定主要依赖技术人员的经验,存在较大的主观性和不确定性。在现有的图像识别算法基础上,引入根据叶片几何形态特征设计的自定义卷积算子进行优化,提出了一套基于叶片图像分析的茶树叶齿特征量化方法。试验验证结果表明,该方法能够快速准确地获取叶片面积、周长和叶齿数量等基本参数,并能通过自定义算子对叶齿锐度、叶齿深度和叶齿密度等描述性指标进行量化。量化结果的变异系数均小于1%,具有重复性强、稳定性高的特点。相较于人工主观辨识,该方法的测定时间不超过30 s,有效提高了茶树叶片叶齿形态特征评价的准确性和工作效率,为茶树种质资源的量化评价提供了新的方法和思路。
Abstract
The size, shape, and arrangement of tea leaf serrations are important criteria for assessing and evaluating tea germplasm resources. However, for a long time, the determination of these criteria has mainly relied on the experience of technicians, resulting in subjective judgments and uncertainties. In this study, a custom convolutional operator based on the geometric morphological features of leaves was introduced, and the existing image recognition algorithms were optimized. A quantification method for tea leaf serrations based on leaf image analysis was proposed. Through experimental validation, the results show that this method can rapidly and accurately obtain basic parameters such as leaf area, perimeter, and number of serrations. It can also quantify descriptive indicators such as serration sharpness, serration depth, and serration density using custom operators. The coefficients of variation for the quantified results are all less than 1%, indicating strong repeatability and high stability. Compared to manual subjective identification, the measurement time of this method does not exceed 30 s, effectively enhancing the accuracy and efficiency of evaluating the morphological characteristics of tea leaf serrations. It provided a new approach and perspective for the quantitative evaluation of tea germplasm resources.
关键词
茶树叶片 /
形态特征量化 /
叶齿密度 /
叶齿锐度 /
叶齿深度
Key words
morphological quantification /
serration density /
serration depth /
serration sharpness /
tea leaf
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基金
重庆市农业科学院市级财政科研项目（cqaas2023sjczzd009）
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