Mid-infrared spectroscopy for detection of Huanglongbing (greening) in citrus leaves.
In recent years, Huanglongbing (HLB) also known as citrus greening has greatly affected citrus orchards in Florida. This disease has caused significant economic and production losses costing about $750/acre for HLB management. Early and accurate detection of HLB is a critical management step to control the spread of this disease. This work focuses on the application of mid-infrared spectroscopy for the detection of HLB in citrus leaves. Leaf samples of healthy, nutrient-deficient, and HLB-infected trees were processed in two ways (process-1 and process-2) and analyzed using a rugged, portable mid-infrared spectrometer. Spectral absorbance data from the range of 5.15-10.72 μm (1942-933 cm(-1)) were preprocessed (baseline correction, negative offset correction, and removal of water absorbance band) and used for data analysis. The first and second derivatives were calculated using the Savitzky-Golay method. The preprocessed raw dataset, first derivatives dataset, and second derivatives dataset were first analyzed by principal component analysis. Then, the selected principal component scores were classified using two classification algorithms, quadratic discriminant analysis (QDA) and k-nearest neighbor (kNN). When the spectral data from leaf samples processed using process-1 were used for data analysis, the kNN-based algorithm yielded higher classification accuracies (especially nutrient-deficient leaf class) than that of the other spectral data (process-2). The performance of the kNN-based algorithm (higher than 95%) was better than the QDA-based algorithm. Moreover, among different types of datasets, preprocessed raw dataset resulted in higher classification accuracies than first and second derivatives datasets. The spectral peak in the region of 9.0-10.5 μm (952-1112 cm(-1)) was found to be distinctly different between the healthy and HLB-infected leaf samples. This carbohydrate peak could be attributed to the starch accumulation in the HLB-infected citrus leaves. Thus, this study demonstrates the applicability of mid-infrared spectroscopy for HLB detection in citrus.
Citrus Research and Education Center, IFAS, University of Florida, 700 Experiment Station Road, Lake Alfred, FL 33850, USA.
Sindhuja
S
Sankaran
Reza
R
Ehsani
Edgardo
E
Etxeberria
D000465:Algorithms; D002957:Citrus / Q000378:metabolism*; D015233:Models, Statistical; D010363:Pattern Recognition, Automated; D018515:Plant Leaves / Q000378:metabolism*; D025341:Principal Component Analysis; D012230:Rhizobiaceae / Q000235:genetics; D013053:Spectrophotometry / Q000379:methods; D013055:Spectrophotometry, Infrared / Q000379:methods*; D013213:Starch / Q000737:chemistry
D013213:Starch