以次郎甜柿为研究对象, 通过图像分析的方法提取其表面缺陷图像的颜色参数和纹理参数，对其应用模糊模式识别方法，提出了次郎甜柿表面缺陷识别的模糊视觉集。在此基础上设计和计算了不同缺陷的视觉参数隶属度函数值。贴近度检验表明，对不同缺陷的视觉参数隶属度函数值和隶属度区间的划分符合模糊聚类的判别要求。验证性实验也表明，用模糊视觉集隶属度函数及相关参数区间作为柿果表面缺陷识别的工具对其进行机器视觉的识别，在识别率方面具有较高的准确性。

‘Jiro’ sweet persimmon surface defects were studied in this research. The color parameters and the texture parameters of the images were analyzed by the method of fuzzy pattern recognition, and the fuzzy vision set of the sweet persimmon′s surface defect recognition was proposed, on the ground of that we designed and computed the vision membership degree function value of the different defects on the fruit surface. The closeness examination indicated that the division of different surface defects′ membership degree function value and membership degree interval can fit the requirements of fussy cluster. The confirmatory experiment also showed that the fuzzy machine vision membership degree function and the relevant parameters interval as the tool of persimmon′s surface defect recognition have a high accuracy in the aspect of recognition rate.