李修华1,2,3, 李民赞2*, Won Suk Lee3, Reza Ehsani4, Ashish Ratn Mishra4
1. 广西大学电气工程学院，广西 南宁 530004 2. 中国农业大学，现代精细农业系统集成研究教育部重点实验室，北京 100083 3. Department of Agricultural and Biological Engineering, University of Florida，Gainesville, FL 32611, USA 4. Citrus Research and Education Center, University of Florida，Lake Alfred, FL 33850, USA
Visible-NIR Spectral Feature of Citrus Greening Disease
LI Xiu-hua1,2,3, LI Min-zan2*, Won Suk Lee3, Reza Ehsani4, Ashish Ratn Mishra4
1. College of Electrical Engineering, Guangxi University, Nanning 530004, China 2. Key Laboratory of Modern Precision Agriculture System Integration Research, Ministry of Education, China Agricultural University, Beijing 100083, China 3. Department of Agricultural and Biological Engineering, University of Florida, Gainesville, FL 32611, USA 4. Citrus Research and Education Center, University of Florida, Lake Alfred, FL 33850, USA
Abstract：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.
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