柑桔黄龙病的可见-近红外光谱特征

柑桔黄龙病是一种以木虱为载体的细菌病原，目前还没有行之有效的治疗方法，对世界柑桔产业构成了严重的威胁。探索快速检测柑桔黄龙病的方法，对该病的诊断、评估及进一步的控制都具有重要意义。该研究采用了快速、无损的光谱方法对该病害特征进行初步探索。实验针对健康及染病植株的叶片及冠层，分别在实验室条件及田间环境下测量了其可见-近红外光谱反射率，以分析寻找二者的光谱差异。对原始光谱数据进行了平滑、聚类平均等预处理，并求取了一阶微分以确定其红边位置(red edge position, REP)。为了应对一阶微分在REP处的多个波峰现象，采用了线性外插值法及拉格朗日插值法量化REP。研究结果显示，健康及染病样本的反射率在可见光、近红外具有明显的差异。相比于健康样本，染病样本因其呈现的黄化现象，使其反射率在可见光区较高;又因黄龙病菌会明显阻碍叶片对水分的吸收而使其反射率在近红外较低。REP同样显示了潜在的区分能力，其明显随着染病程度的加深逐渐向红波段移动。在染病程度差异较大的数据集中，REP平均值相差达20 nm;而在染病程度差异较小的数据集中，阈值分割法的分类精度也高达90%以上，且线性外插值法的分类精度略高于拉格朗日插值法。本研究成果为利用光谱技术快速无损检测柑桔黄龙病提供了可靠的理论依据。

Visible-NIR Spectral Feature of Citrus Greening Disease

Citrus greening (Huanglongbing, or HLB) is a devastating disease caused by Candidatus liberibacter which uses psyllids as vectors. It has no cure till now, and poses a huge threat to citrus industry around the world. In order to diagnose, assess and further control this disease, it is of great importance to first find a quick and effective way to detect it. Spectroscopy method, which was widely considered as a fast and nondestructive way, was adopted here to conduct a preliminary exploration of disease characteristics. In order to explore the spectral differences between the healthy and HLB infected leaves and canopies, this study measured the visible-NIR spectral reflectance of their leaves and canopies under lab and field conditions, respectively. The original spectral data were firstly preprocessed with smoothing(or moving average) and cluster average procedures, and then the first derivatives were also calculated to determine the red edge position(REP). In order to solve the multi-peak phenomenon problem, two interpolation methods(three-point Lagrangian interpolation and four-point linear extrapolation) were adopted to calculate the REP for each sample. The results showed that there were obvious differences at the visible & NIR spectral reflectance between the healthy and HLB infected classes. Comparing with the healthy reflectance, the HLB reflectance was higher at the visible bands because of the yellowish symptoms on the infected leaves, and lower at NIR bands because the disease blocked water transportation to leaves. But the feature at NIR bands was easily affected by environmental factors such as light, background, etc. The REP was also a potential indicator to distinguish those two classes. The average REP was slowly moving toward red bands while the infection level was getting higher. The gap of the average REPs between the healthy and HLB classes reached to a maximum of 20 nm. Even in the dataset with relatively lower variation, the classification accuracy of threshold segmentation method by the REP could reach to more than 90%. The four-point linear extrapolation method had slightly better result than the three-point Lagrangian interpolation method. This study provided useful theoretical foundation to detect HLB by spectral reflectance.