Huanglongbing (citrus greening) detection using visible, near infrared and thermal imaging techniques

Sindhuja Sankaran; Joe Mari Maja; Sherrie Buchanon; Reza Ehsani

doi:10.3390/s130202117

Back

Huanglongbing (citrus greening) detection using visible, near infrared and thermal imaging techniques

Journal article

Open access

Peer reviewed

Huanglongbing (citrus greening) detection using visible, near infrared and thermal imaging techniques

Sindhuja Sankaran, Joe Mari Maja, Sherrie Buchanon and Reza Ehsani

Sensors (Basel, Switzerland), Vol.13(2), pp.2117-2130

02/06/2013

DOI: https://doi.org/10.3390/s130202117

Handle:

https://hdl.handle.net/2376/106651

PMCID: PMC3649375

PMID: 23389343

Files and links (1)

url

https://doi.org/10.3390/s130202117View

Published (Version of record) Open

Abstract

Algorithms

Citrus - microbiology

Color

Imaging, Three-Dimensional - instrumentation

Imaging, Three-Dimensional - methods

Remote Sensing Technology - instrumentation

Plant Diseases - microbiology

Thermography - methods

Spectroscopy, Near-Infrared - methods

Thermography - instrumentation

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.

Metrics

15 Record Views

See more details

Details

Title: Huanglongbing (citrus greening) detection using visible, near infrared and thermal imaging techniques
Creators: Sindhuja Sankaran - Citrus Research and Education Center, IFAS, University of Florida, 700 Experiment Station Road, Lake Alfred, FL 33850, USA
Joe Mari Maja
Sherrie Buchanon
Reza Ehsani
Publication Details: Sensors (Basel, Switzerland), Vol.13(2), pp.2117-2130
Academic Unit: Biological Systems Engineering, Department of
Publisher: Switzerland
Identifiers: 99900546605801842
Language: English
Resource Type: Journal article