雷雨,何东健,周兆永,张海辉,苏东.苹果霉心病可见/近红外透射能量光谱识别方法[J].农业机械学报,2016,47(4):193-200.
Lei Yu,He Dongjian,Zhou Zhaoyong,Zhang Haihui,Su Dong.Detection of Moldy Core of Apples Based on Visible/Near Infrared Transmission Energy Spectroscopy[J].Transactions of the Chinese Society for Agricultural Machinery,2016,47(4):193-200.
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苹果霉心病可见/近红外透射能量光谱识别方法   [下载全文]
Detection of Moldy Core of Apples Based on Visible/Near Infrared Transmission Energy Spectroscopy   [Download Pdf][in English]
投稿时间:2015-11-01  
DOI:10.6041/j.issn.1000-1298.2016.04.026
中文关键词:  苹果  霉心病  能量光谱  连续投影算法  主成分分析  支持向量机
基金项目:国家高技术研究发展计划(863计划)项目(2013AA10230402)和陕西省科技统筹创新工程计划项目(2014KTCL02—15)
作者单位
雷雨 西北农林科技大学 
何东健 西北农林科技大学 
周兆永 西北农林科技大学 
张海辉 西北农林科技大学 
苏东 西北农林科技大学 
中文摘要:针对苹果霉心病从外表无法识别的难题,提出基于可见/近红外透射能量光谱进行快速无损识别的模型和方法。在200 ~ 1100nm波段内采集了200个苹果的透射能量光谱数据,随机选取140个样品作为训练集,剩余60个样品作为测试集。用平滑法和多元散射校正对光谱数据进行预处理。基于全光谱、连续投影算法(SPA)提取的12个特征波长、主成分分析(PCA)提取的9个主成分,分别建立了偏最小二乘判别法、误差反向传播人工神经网络和支持向量机(SVM)识别模型。实验结果说明,应用PCA—SVM建立的模型识别性能最优,该模型对测试集和训练集中霉心病果和健康果的识别正确率分别为99.3%和96.7%。基于SPA和PCA所建模型的输入变量数仅相当于基于全光谱所建模型输入变量数的0.99%和0.74%,极大降低了模型的复杂度。研究结果表明,该方法是可行的且具有较高识别准确度,为苹果在线内部品质分级和便携式苹果霉心病检测仪的研究提供了技术依据。
Lei Yu  He Dongjian  Zhou Zhaoyong  Zhang Haihui  Su Dong
Northwest A&F University,Northwest A&F University,Northwest A&F University,Northwest A&F University and Northwest A&F University
Key Words:apples  moldy core  energy spectra  successive projections algorithm  principal component analysis  support vector machine
Abstract:In order to solve the problem of identification moldy core of apples from the surface, a quick and non-destructive detection method was proposed based on visible/near infrared transmission energy spectroscopy. Visible/near infrared transmission energy spectra of 200 apples were collected in the wavelength range of 200 ~ 1100nm. Totally 140 samples were used for the calibration set, and 60 samples for the validation set. Smoothing method and multiple scattering correction were used to preprocess the original spectra. Totally 12 characteristic wavelengths and 9 principal components were selected by successive projections algorithm (SPA) and principal component analysis (PCA), respectively. Partial least squares discriminant analysis, error back propagation artificial neural networks, and support vector machine (SVM) measurement model were established based on SPA and PCA, respectively. The results showed that the best model was PCA—SVM, and its recognition accuracy rate reached 99.3% for the calibration set and 96.7% for the validation set. The models established based on SPA and PCA were much simpler than those based on full spectra, since the numbers of input variable of them were only about 0.99% and 0.74% of that of full spectra, respectively. The results showed that the method was available and had high identification accuracy. Meanwhile, the results would provide theoretical basis for the research and development of on-line detection of internal quality in apples and portable moldy core apple detector.

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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