基于周长面积分形维数的柑橘品种机器识别

柑橘品种自动识别是有针对性的柑橘病虫害防治、肥培管理、采摘等自动化作业的重要环节。为了评价宫川温州蜜柑、脐橙朋娜和沪溪无核椪柑3个柑橘品种机器自动识别的可行性，采集3个柑橘品种花萼面和侧面数字图像，以图像中柑橘轮廓像素数和区域像素数作为柑橘周长与面积，通过周长—面积方法计算其分形维数。将周长、面积和分形维数作为品种特征值，用小波神经网络识别3个品种，正确识别率分别为95%、95%、97.5%。试验结果表明，用周长、面积和分形维数3个特征值能有效识别3个柑橘品种，并具有较高的识别精度。

Machine recognition of citrus variety based on the fractal dimensions of perimeter-area

Citrus fruit variety recognition is an important issue for automated operations including diseases and insect pests prevention and cure, fertilization management and fruit picking. In order to evaluate the feasibility of automatic recognition of various fruits, samples of the citrus unshiu Marc.cv. unbergii Nakai, Skaggs Bonanza Navel orange and Luxi seedless Ponkan were studied. Images of calyx surfaces and the profile were acquired from sample fruits. Pixel numbers of fruit image contour and region were used as the perimeters and areas of fruits, and fractal dimensions of fruits were obtained by the perimeter-area method. Perimeters, areas and fractal dimensions were taken as the character values of three varieties of citrus fruits. A wavelet neural network model was presented to recognize different type of fruits based on these character values. The results showed that the correctnesses of the citrus unshiu Marc.cv. unbergii Nakai, Skaggs Bonanza Navel orange and Luxi seedless Ponkan were 95%, 95%, 97.5%, respectively. From the results we conclude that these three cultivars of citrus fruits can be automatically recognized and have a high correctness with three character values.