基于模糊综合评判的可视化叶色模型数据标准化

为提高可视化过程中叶色模型数据的准确性，该文以玉米叶片叶色数据为对象，利用模糊综合评判法建立了叶绿素含量准确性评价体系，并对不同叶色数据进行了转换和验证，结果表明：采用模糊综合评价指标所测SPAD值转换后的叶绿素含量与RGB值转换后的叶绿素含量绝对误差小于0.111?mg/g；叶色模型数据准确程度偏向于2级，即比较准确；所建立的叶绿素含量准确性评价体系能有效规范叶色模型的数据采集，具有可行性。

Data normalization of leaf color based on fuzzy comprehensive evaluation for visualization model

In order to improve the accuracy of data which was used in producing the model of leaf color in the process of visualization, taking the data of leaf color from maize as a research object, an evaluation system on the accuracy of chlorophyll data was created with the method of fussy comprehensive evaluation. Meanwhile, data of leaf color in different standard were translated and checked in this paper. The results showed that the absolute error between chlorophyll value translated from SPAD which was measured with the index of fuzzy comprehensive evaluation and the value translated from RGB was less than 0.111 mg/g, the accuracy degree of measured leaf-color model data was proved to reach level 2 which also represented exactness. The accuracy of chlorophyll data measure method under the evaluation system can meet the requirement for normalization of data acquisition of leaf color in the process of visualization.