刘燕德,肖怀春,孙旭东,曾体伟,张智诚,刘宛坤.基于高光谱成像的柑橘黄龙病无损检测[J].农业机械学报,2016,47(11):231-238.
Liu Yande,Xiao Huaichun,Sun Xudong,Zeng Tiwei,Zhang Zhicheng,Liu Wankun.Non-destructive Detection of Citrus Huanglong Disease Using Hyperspectral Image Technique[J].Transactions of the Chinese Society for Agricultural Machinery,2016,47(11):231-238.
摘要点击次数: 2226
全文下载次数: 2679
基于高光谱成像的柑橘黄龙病无损检测   [下载全文]
Non-destructive Detection of Citrus Huanglong Disease Using Hyperspectral Image Technique   [Download Pdf][in English]
投稿时间:2015-12-13  
DOI:10.6041/j.issn.1000-1298.2016.11.032
中文关键词:  柑橘  黄龙病  高光谱成像技术  最小二乘支持向量机  偏最小二乘判别分析
基金项目:国家高技术研究发展计划(863计划)项目(SS2012AA101306)、江西省科技支撑计划项目(20121BBF60054)、南方山地果园智能化管理技术与装备协同创新中心项目(赣教高字[2014]60号)和江西省优势科技创新团队项目(20153BCB24002)
作者单位
刘燕德 华东交通大学 
肖怀春 华东交通大学 
孙旭东 华东交通大学 
曾体伟 华东交通大学 
张智诚 华东交通大学 
刘宛坤 华东交通大学 
中文摘要:采用高光谱成像技术,结合最小二乘支持向量机(LS-SVM)和偏最小二乘判别分析(PLS-DA)2种方法,探索柑橘黄龙病快速无损检测的可行性。在380~1080nm光谱范围内,采集正常、轻度黄龙病、中度黄龙病、重度黄龙病和缺素5种柑橘叶片的高光谱图像。采用方差分析方法,分析了正常、轻度黄龙病、中度黄龙病、重度黄龙病和缺素5种叶片的叶绿素、淀粉和可溶性糖含量间的差异,表明3指标可作为判别黄龙病的指示性指标。采用偏最小二乘法,建立了叶绿素、可溶性糖及淀粉3指标含量的定量分析数学模型,模型预测均方根误差分别为7.46、5.51、5.88,提供了柑橘黄龙病高光谱成像快速检测依据。提取高光谱图像感兴趣区域的平均光谱,通过分析正常、轻度黄龙病、中度黄龙病、重度黄龙病和缺素5种叶片的代表性光谱,在750nm处吸光度存在差异。采用2阶导数处理样品光谱,消除了450~650nm和800~1000nm波段的基线漂移,放大了有效光谱信息。采用主成分分析(PCA)和连续投影算法(SPA)筛选柑橘黄龙病LS-SVM定性判别模型的输入变量,建立了LS-SVM定性判别模型,同时与PLS-DA进行对比。采用未参与建模的预测集样品评价模型性能,结果表明PLS-DA模型判别柑橘黄龙病的准确率更高,模型误判率为5.6%。实验结果表明,高光谱成像技术结合偏最小二乘判别分析方法可实现柑橘黄龙病快速无损检测与黄龙病病情等级判别。
Liu Y  e  Xiao Huaichun  Sun Xudong  Zeng Tiwei  Zhang Zhicheng  Liu Wankun
East China Jiaotong University,East China Jiaotong University,East China Jiaotong University,East China Jiaotong University,East China Jiaotong University and East China Jiaotong University
Key Words:citrus  Huanglong disease  hyperspectral imaging technique  LS-SVM  PLS-DA
Abstract:In order to explore the feasibility of the quick non destructive detection of citrus Huanglong disease, the hyperspectral image technique combined with least square support vector machine (LS-SVM) and partial least squares discriminate analysis (PLS-DA) were used. The hyperspectral images of the normal, the Huanglong disease of slight, moderate and serious, the lack element citrus leaves were collected in wavelength range of 380~1080nm. By using variance analysis method, the differences in content of chlorophyll, soluble sugar and starch of leaves of the normal, the Huanglong disease of slight, moderate, serious and the lack element were analyzed, and the chlorophyll, soluble sugar and starch were the indicator which could be used to discriminate Huanglong disease. The partial least squares (PLS) method was adopted to establish the mathematical model of quantitative analysis of chlorophyll, soluble sugar and starch, and root mean square error of forecast model were 7.46, 5.51, 5.88 respectively, which provided the basis for rapid detection of citrus Huanglong disease hyperspectral images. The average spectrum of hyperspectral images was extracted in interested area. The differences in absorbance at 750nm was found by analyzing five kinds of leaves of representative spectrum of the normal, the Huanglong disease of slight, moderate and serious, the lack element. The 2 order derivative was used to process the sample spectrum, the baseline drift in 450~650nm and 800~1000nm band was eliminated and the effective spectral information was enlarged. Using principal component analysis (PCA) and successive projections algorithm (SPA) to screen the input variables of the model of least squares support vector machine (LS-SVM) qualitative discrimination of citrus Huanglong disease, the LS-SVM model was built for qualitative discrimination and compared with the partial least squares qualitative discriminate model (PLS-DA) at the same time. The prediction sample set which was used to evaluate the performance of model was not used to establish the model. The results showed that the accuracy of PLS-DA model of citrus Huanglong disease was higher, three leaves of lack element were misclassified as serious Huanglong disease, and the misclassification rate was 56%. The experimental results showed that the hyperspectral image technology combined with PLS-DA can achieve rapid and nondestructive detection of citrus Huanglong disease and the degree of Huanglong disease.

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