柑橘黄龙病高光谱早期鉴别及病情分级  

Early detection and grading of citrus huanglongbing using hyperspectral imaging technique

作　　者：梅慧兰[1,2,3] 邓小玲[1,2,3] 洪添胜[1,2,3] 罗霞[1,2,3] 邓晓玲[4] 

机构地区：[1]华南农业大学工程学院,广州510642 [2]华南农业大学南方农业机械与装备关键技术教育部重点实验室,广州510642 [3]国家柑橘产业技术体系机械研究室,广州510642 [4]华南农业大学资源环境学院柑橘黄龙病研究室,广州510642

出　　处：《农业工程学报》2014年第9期140-147,共8页Transactions of the Chinese Society of Agricultural Engineering

基　　金：国家自然科学基金(青年基金)(31201129);现代农业产业技术体系建设专项资金(CARS-27);广东省教育厅高校优秀青年创新人才培养计划(2012LYM_0028);教育部高等学校博士学科点专项科研基金(20124404120006)

摘　　要：为实现柑橘黄龙病的早期、快速确诊,有效阻止病害蔓延,达到早期防治、保障柑橘生产的目的,该文研究基于高光谱成像的柑橘黄龙病早期无损检测及病情分级,并对多种预处理方法的建模结果进行探讨。试验获取370～1 000 nm健康、不同染病程度及缺锌共5类柑橘叶片的高光谱图像,用遥感图像处理平台(environment for visualizing images,ENVI)得到各类样本感兴趣区域的光谱反射率平均值。运用一阶微分、移动窗口拟和多项式平滑(savitzky-golay,SG)进行数据处理,结合偏最小二乘判别分析(partial least squares-discriminate analysis,PLS-DA)构建黄龙病的早期鉴别及病情分级模型。结果表明:建立的3个判别模型,验证集相关系数均不低于0.9548。其中,经SG平滑及一阶微分预处理所建立的模型分类效果最佳,总体预测准确率达96.4%,预测均方根误差0.1344。该研究为柑橘病害早期诊断和预警提供了新方法,也为黄龙病病害程度遥感监测提供了基础。Timely, accurate, rapid diagnosis and grading of citrus Huanglongbing （HLB）, a devastating disease severely influencing the citrus industry in the world, plays a very important role in guaranteeing the yield, the quality of citrus fruits, and the benefits of citrus growers. Based on a hyperspectral imaging technique, this paper not only focused on the method of early nondestructive detection and grading of citrus HLB disease, but also tried to discuss the influence of different data preprocessing methods on the modeling results. What is more, the varying reflection spectral characteristics of citrus leaves in diverse disease degrees were analyzed in the paper based on measured hyperspectral data. Hyperspectral images of five kinds of citrus leaves, including the healthy, infected with different degrees with HLB, and those with zinc deficiency, were acquired through experiments by a hyperspectral imaging system with the wavelength range of 370-1 000 nm, and then the average spectral reflectance data of region of interests of different kinds of leaf samples were obtained by utilizing the environment for visualizing images（ENVI）. By taking advantage of a partial least squares-discriminate analysis （PLS-DA） method, three models of the early diagnosis and grading of HLB disease, tested with a leave-one-out cross-validation strategy, were established with original spectral data and data preprocessed by different data pretreatment methods, such as first derivative and moving window polynomial fitting smoothing （Savitzky-Golay smoothing,SG）. In the end, the predictive performances of all of the three models were compared and analyzed with the new validation data. As a result, the cross-validation correlation coefficients of three discriminate models were all greater than 0.9548, however, their prediction performances were not the same. The detection results of the first discriminate model, established with original data, was not satisfactory. The second discriminate model, set up with data pretreated by a

关 键 词：无损检测 分级 模型 高光谱成像 偏最小二乘判别 黄龙病 早期鉴别 HUANGLONGBING (HLB) 

分 类 号：S436.661[农业科学—农业昆虫与害虫防治] O433.4[农业科学—植物保护]