黄锋华,张淑娟,杨 一,满 尊,张学豪,吴玉香.油桃外部缺陷的高光谱成像检测[J].农业机械学报,2015,46(11):252-259.
Huang Fenghua,Zhang Shujuan,Yang Yi,Man Zun,Zhang Xuehao,Wu Yuxiang.Application of Hyperspectral Imaging for Detection of Defective Features in Nectarine Fruit[J].Transactions of the Chinese Society for Agricultural Machinery,2015,46(11):252-259.
摘要点击次数: 2467
全文下载次数: 1548
油桃外部缺陷的高光谱成像检测   [下载全文]
Application of Hyperspectral Imaging for Detection of Defective Features in Nectarine Fruit   [Download Pdf][in English]
投稿时间:2015-04-13  
DOI:10.6041/j.issn.1000-1298.2015.11.034
中文关键词:  油桃 外部缺陷 高光谱成像 无损检测 极限学习机
基金项目:国家自然科学基金资助项目(31271973、31171599)和山西省自然科学基金资助项目(2012011030-3)
作者单位
黄锋华 山西农业大学 
张淑娟 山西农业大学 
杨 一 山西农业大学 
满 尊 山西农业大学 
张学豪 山西农业大学 
吴玉香 山西农业大学 
中文摘要:采用高光谱(420~1000nm)成像技术对“中油9号”油桃的4种外部缺陷(裂纹果、锈病果、异形果和暗伤果)进行检测判别。对400个样本(4种外部缺陷样本和完好样本)运用偏最小二乘回归(PLSR)从全波段中分别提取了10条特征波长,分别为497、534、657、677、696、709、745、823、868、943nm。缺陷样本的高光谱图像经过主成分分析后,对876nm下的单波段图像通过掩膜、Sobel算子处理,并对主成分图像经过区域生长算法实现缺陷样本的缺陷区域分割。对光谱数据进行主成分分析得到前10个主成分值,并对图像数据采用灰度共生矩阵(GLCM)提取得到6项图像纹理指标(均值、对比度、相关性、能量、同质性、熵值)。将主成分值和纹理值融合建立极限学习机(ELM)模型对油桃外部缺陷进行检测判别。结果表明,该模型对缺陷样本的判别正确率为91.67%,完好样本的正确率为100%。
Huang Fenghua  Zhang Shujuan  Yang Yi  Man Zun  Zhang Xuehao  Wu Yuxiang
Shanxi Agricultural University,Shanxi Agricultural University,Shanxi Agricultural University,Shanxi Agricultural University,Shanxi Agricultural University and Shanxi Agricultural University
Key Words:Nectarine Defective feature Hyperspectral imaging Nondestructive detection Extreme learning machine
Abstract:Hyperspectral imaging, an emerging analytical technology for quality and safety inspections of agricultural and sideline products, combines the advantages of digital image or computer vision with spectroscopy technology in the whole system. Hyperspectral imaging can simultaneously acquire both spatial and spectral information, which deliver chemical, structural and functional information from the samples. In this work, hyperspectral imaging technology was applied to determine a classifier that can be used for nondestructive defection of the defective features in “No.9 of Zhongyou” nectarine fruit. There were 400 samples from a nectarine planting garden in the Wanan Village in Yuncheng City of Shanxi Province, China, including: crack(50), peel spots(50), malformation(50), hidden damage(50) and normal(200) samples. Hyperspectral imaging technology covered the range of 420~1000nm was employed to detect defects (crack, peel spots, malformation and hidden damage) of nectarine fruit. 400 RGB images were acquired through a total of 400 samples, which included four types of defective features and sound samples. After acquiring hyperspectral images of nectarine fruits, the spectral data were extracted from region of interest (ROI). Using Kennard-Stone algorithm, all kinds of samples were randomly divided into training set (280) and testing set (120). First of all, based on the calculation of partial least squares regression (PLSR), 10 wavelengths at 497nm, 534nm, 657nm, 677nm, 696nm, 709nm, 745nm, 823nm, 868nm and 943nm were selected as the optimal sensitive wavelengths (SWs), respectively. Subsequently, the image of the 876nm wavelength was named as the feature image, then principal component analysis (PCA), mask process, “Sobel” edge detector and “region grow” algorithm were carried out among defective and normal nectarines to extract the defective region. Moreover, ten principal components (PCs) were selected based on PCA, and seven textural feature variables (mean, contrast, correlation, energy, homogeneity and entropy) were extracted by using gray level co occurrence matrix (GLCM), respectively. Finally, the ability of hyperspectral imaging technique was tested by using the extreme learning machine (ELM) models. The ELM classification model was built based on the combination of PCs and textural features. The results show the correct discrimination accuracy of defective samples was 91.67%, and the correct discrimination accuracy of normal samples was 100%. The research revealed that the hyperspectral imaging technique is a promising tool for detecting defective features in nectarine, which could provide a theoretical reference and basis for designing classification system of fruits in further work.

Transactions of the Chinese Society for Agriculture Machinery (CSAM), in charged of China Association for Science and Technology (CAST), sponsored by CSAM and Chinese Academy of Agricultural Mechanization Science(CAAMS), started publication in 1957. It is the earliest interdisciplinary journal in Chinese which combines agricultural and engineering. It always closely grasps the development direction of agriculture engineering disciplines and the published papers represent the highest academic level of agriculture engineering in China. Currently, nearly 8,000 papers have been already published. There are around 3,000 papers contributed to the journal each year, but only around 600 of them will be accepted. Transactions of CSAM focuses on a wide range of agricultural machinery, irrigation, electronics, robotics, agro-products engineering, biological energy, agricultural structures and environment and more. Subjects in Transactions of the CSAM have been embodied by many internationally well-known index systems, such as: EI Compendex, CA, CSA, etc.

   下载PDF阅读器