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基于高光谱成像技术检测脐橙溃疡
引用本文:李江波,饶秀勤,应义斌,王东亭.基于高光谱成像技术检测脐橙溃疡[J].农业工程学报,2010,26(8):222-228.
作者姓名:李江波  饶秀勤  应义斌  王东亭
作者单位:浙江大学生物系统工程与食品科学学院,杭州,310029
基金项目:国家自然科学基金(30825027);国家科技支撑计划(2008BADA1B04)
摘    要:为了研究从带有不同缺陷的柑橘类水果中快速识别出溃疡果的有效方法,基于高光谱成像技术,该文提出特征波段主成分分析法及波段比算法。以脐橙为研究对象,选取包括溃疡在内常见的10类脐橙果皮缺陷果及正常果。首先,提取并分析11类果皮感兴趣区域(ROI)光谱曲线并结合主成分分析法确定5个最佳波段(630、685、720、810和 875 nm);然后基于特征波段做主成分分析,选取第5主成分(PC-5)作为分类识别图像,识别率达到80%。为了进一步提高溃疡识别率,该文又提出采用特征波段主成分分析法与波段比算法相结合的方法,基于此算法溃疡正确识别率提高到95.4%。试验结果表明,基于高光谱成像技术可以有效地对带有溃疡病斑的脐橙分类识别。

关 键 词:主成分分析,缺陷检测,影像识别,溃疡,高光谱图像,双波段比,脐橙
收稿时间:2010/3/24 0:00:00
修稿时间:2010/6/19 0:00:00

Detection of navel oranges canker based on hyperspectral imaging technology
Li Jiangbo,Rao Xiuqin,Ying Yibin,Wang Dongting.Detection of navel oranges canker based on hyperspectral imaging technology[J].Transactions of the Chinese Society of Agricultural Engineering,2010,26(8):222-228.
Authors:Li Jiangbo  Rao Xiuqin  Ying Yibin  Wang Dongting
Abstract:Feature bands principal component analysis method and band ratio algorithm were proposed to fast differentiate citrus canker from normal fruit skin and other surface defects based on hyperspectral imaging technology. Navel oranges with cankerous, normal and other nine kinds of common defects were studied. First, region of interests (ROIs) spectral features of various defected peel areas were analyzed and combined with principal component analysis method to determined five optimal bands (i.e. 630, 685, 720, 810 and 875 nm). Next, principal component analysis was again performed based on feature wavelengths and the fifth principal component (PC-5) was used to classify and identify canker lesions on navel oranges. The overall classification success rate was 80% regardless of the presence of other confounding defects. In order to improve classification success rate, feature bands principal component analysis method and band ratio algorithm were combined to detect canker on the surface of navel oranges with an accuracy of 95.4%. The study results show that the hyperspectral imaging technology can be used to effectively classify and identify navel oranges with canker lesions.
Keywords:principal component analysis  defects inspection  image recognition  hyperspectral imaging  dual-band ratio  canker  navel oranges
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