Huanglongbing Citrus Greening Detection Using Visible, Near Infrared and Thermal Imaging TechniquesReportar como inadecuado




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Citrus Research and Education Center, IFAS, University of Florida, 700 Experiment Station Road, Lake Alfred, FL 33850, USA





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Abstract This study demonstrates the applicability of visible-near infrared and thermal imaging for detection of Huanglongbing HLB disease in citrus trees. Visible-near infrared 440–900 nm and thermal infrared spectral reflectance data were collected from individual healthy and HLB-infected trees. Data analysis revealed that the average reflectance values of the healthy trees in the visible region were lower than those in the near infrared region, while the opposite was the case for HLB-infected trees. Moreover, 560 nm, 710 nm, and thermal band showed maximum class separability between healthy and HLB-infected groups among the evaluated visible-infrared bands. Similarly, analysis of several vegetation indices indicated that the normalized difference vegetation index NDVI, Vogelmann red-edge index VOG and modified red-edge simple ratio mSR demonstrated good class separability between the two groups. Classification studies using average spectral reflectance values from the visible, near infrared, and thermal bands 13 spectral features as input features indicated that an average overall classification accuracy of about 87%, with 89% specificity and 85% sensitivity could be achieved with classification models such as support vector machine for trees with symptomatic leaves. View Full-Text

Keywords: citrus disease; visible-near infrared imaging; thermal imaging; support vector machine citrus disease; visible-near infrared imaging; thermal imaging; support vector machine





Autor: Sindhuja Sankaran, Joe Mari Maja, Sherrie Buchanon and Reza Ehsani *

Fuente: http://mdpi.com/



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