薛建新,张淑娟,张晶晶.壶瓶枣自然损伤的高光谱成像检测[J].农业机械学报,2015,46(7):220-226.
Xue Jianxin,Zhang Shujuan,Zhang Jingjing.Application of Hyperspectral Imaging for Detection of Natural Defective Features in Huping Jujube Fruit[J].Transactions of the Chinese Society for Agricultural Machinery,2015,46(7):220-226.
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壶瓶枣自然损伤的高光谱成像检测   [下载全文]
Application of Hyperspectral Imaging for Detection of Natural Defective Features in Huping Jujube Fruit   [Download Pdf][in English]
投稿时间:2015-01-25  
DOI:10.6041/j.issn.1000-1298.2015.07.032
中文关键词:  壶瓶枣 自然损伤 高光谱成像 检测
基金项目:国家自然科学基金资助项目(31271973)和山西省自然科学基金资助项目(2012011030-3)
作者单位
薛建新 山西农业大学 
张淑娟 山西农业大学 
张晶晶 山西农业大学 
中文摘要:采用高光谱成像技术(450~1000nm)对壶瓶枣的5种自然损伤(缩果病、裂纹、虫害、黑斑病、鸟啄伤)进行识别研究。利用高光谱成像系统采集了5种自然损伤及完好枣一共663个壶瓶枣样本的高光谱图像,并提取相应的感兴趣区域(ROI),得到了样本的光谱数据。应用偏最小二乘回归(PLSR)、连续投影算法(SPA)从全波段中分别提取了9条、10条特征波长,利用Kennard-Stone算法将各类样本按照3∶1的比例随机分成训练集(500个)和测试集(163个),并对其建立最小二乘支持向量机(LS-SVM)判别模型,结果表明使用SPA-LS-SVM建立的壶瓶枣自然损伤模型的整体判别正确识别率为93.2%。运用主成分分析(PCA)对由SPA提取出的10条特征波长(535、595、657、672、685、749、826、898、964、999nm)所对应的单波段图像进行数据压缩,分别采用Sobel算子、区域生长算法Regiongrow并结合主成分图像识别出163个壶瓶枣样本的边缘与自然损伤特征区域,得出平均正确识别率达到90.8%。研究结果表明:采用高光谱成像技术可以对壶瓶枣的自然损伤进行光谱判别和图像识别。
Xue Jianxin  Zhang Shujuan  Zhang Jingjing
Shanxi Agricultural University,Shanxi Agricultural University and Shanxi Agricultural University
Key Words:Huping jujube Natural defects Hyperspectral imaging Detection
Abstract:Hyperspectral imaging technology covered the range of 450~1000nm was employed to detect natural defects (shrink, crack, insect damage, black rot and peck injury) of Huping jujube fruit. 663 sample images were acquired which included five types of natural defects and sound samples. After acquiring hyperspectral images of Huping jujube 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 (500 samples) and test set (163 samples) according to the proportion of 3∶1. Partial least squares regression (PLSR) and successive projections algorithm (SPA) were conducted to select optimal sensitive wavelengths (SWs), as a result, 9SWs and 10SWs were selected, respectively. And then, least squares support vector machine (LS SVM) discriminate model was established by using the selected wavebands. The results showed that the discriminate accuracy of the SPA-LS-SVM method was 93.2%. Then, images corresponding to ten sensitive bands (535, 595, 657, 672, 685, 749, 826, 898, 964, 999nm) selected by SPA were executed to PCA. Finally, the images of PCA were employed to identify the location and area of natural defects feature through imaging processing. Using Sobel operator, region growing algorithm and the images of PCA, the edge and defect feature of 163 Huping jujube fruits could be recognized, the detect precision was 90.8%. This investigation demonstrated that hyperspectral imaging technology could detect the natural defects of shrink, crack, insect damage, black rot and peck injury in Huping jujube fruit in spectral analysis and feature detection, which provided a theoretical reference for the natural defects nondestructive detection of jujube fruit.

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.

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