Visible-near infrared spectroscopy for detection of Huanglongbing in citrus orchards

Sankaran, Sindhuja; Mishra, Ashish; Maja, Joe Mari; Ehsani, Reza

doi:10.1016/j.compag.2011.03.004

Cited by 159 publications

(79 citation statements)

References 17 publications

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“…However, despite the relatively high accuracies obtained using symptomatic leaves, as with the fluorescence studies, the overall accuracies based on reflectance were significantly reduced to only 41%-67% when asymptomatic leaves were evaluated [14]. Again, in the study by Sankaran et al [13], where accuracies of 92%-95% were obtained with symptomatic leaves, evaluation of asymptomatic samples resulted in low overall accuracies of 38%, largely the result of high percentages of false positives.…”

Section: Introductionmentioning

confidence: 85%

“…Classifiers were based on spectral bands and vegetative indexes. Overall accuracies greater than 80% were obtained using a quadratic discriminate analysis approach on symptomatic leaves [13]. In a study utilizing visible and near-infrared spectroscopy in the laboratory, overall accuracies of 92%-95% were obtained in the discrimination between healthy and HLB-positive leaf samples [13].…”

Section: Introductionmentioning

confidence: 98%

“…For example, in a study by Sankaran et al [13], visible and near-infrared spectroscopy were used to detect HLB infection under field and laboratory conditions. Classifiers were based on spectral bands and vegetative indexes.…”

Section: Introductionmentioning

confidence: 99%

“…Overall accuracies greater than 80% were obtained using a quadratic discriminate analysis approach on symptomatic leaves [13]. In a study utilizing visible and near-infrared spectroscopy in the laboratory, overall accuracies of 92%-95% were obtained in the discrimination between healthy and HLB-positive leaf samples [13]. In more recent studies, combinations of visible, near-infrared, mid-infrared, and thermal imaging have been used to discriminate between healthy and HLB-positive leaves, or between healthy, citrus canker, and HLB-positive leaves.…”

Section: Introductionmentioning

confidence: 99%

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Feasibility of Using the Optical Sensing Techniques for Early Detection of Huanglongbing in Citrus Seedlings

et al. 2017

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A vision sensor was introduced and tested for early detection of citrus Huanglongbing (HLB). This disease is caused by the bacterium Candidatus Liberibacter asiaticus (CLas) and is transmitted by the Asian citrus psyllid. HLB is a devastating disease that has exerted a significant impact on citrus yield and quality in Florida. Unfortunately, no cure has been reported for HLB. Starch accumulates in HLB infected leaf chloroplasts, which causes the mottled blotchy green pattern. Starch rotates the polarization plane of light. A polarized imaging technique was used to detect the polarization-rotation caused by the hyper-accumulation of starch as a pre-symptomatic indication of HLB in young seedlings. Citrus seedlings were grown in a room with controlled conditions and exposed to intensive feeding by CLas-positive psyllids for eight weeks. A quantitative polymerase chain reaction was employed to confirm the HLB status of samples. Two datasets were acquired; the first created one month after the exposer to psyllids and the second two months later. The results showed that, with relatively unsophisticated imaging equipment, four levels of HLB infections could be detected with accuracies of 72%-81%. As expected, increasing the time interval between psyllid exposure and imaging increased the development of symptoms and, accordingly, improved the detection accuracy.

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Section: Introductionmentioning

confidence: 85%

Section: Introductionmentioning

confidence: 98%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Feasibility of Using the Optical Sensing Techniques for Early Detection of Huanglongbing in Citrus Seedlings

et al. 2017

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“…Haiyan Zhao et al [4] used the PLS discriminant analysis to distinguish and analyze the geographical origin of wheat, correct classification rates were above 85%. Based on the analysis above and a great number of references [5][6][7], it is entirely feasible to build classification model according to the requirements of actual operation for wheat classification.…”

Section: Introductionmentioning

confidence: 99%

Modeling research on wheat grain in different quality categories using multilayer support vector machine

Hui¹,

Sun²,

Gao³

2015

Proceedings of the 2015 2nd International Workshop on Materials Engineering and Computer Sciences

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Abstract. Near Infrared Reflectance (NIR) spectroscopy is a 'green' nondestructive testing technology and it has been widely used in grain crop analysis. The experimental data were collected using 161 wheat samples from the major wheat-producing area in China. The original spectral data was represented by four characteristic variables extracted by Partial Least Squares based Dimension Reduction (PLSDR). Besides, Mahalanobis distance method, second derivative and SNV were used to preprocess spectra. A two-tier classification model based on SVM algorithm was used to achieve the classification of wheat quality. The experimental results indicated that the two-tier SVM classification model was effective in identifying the quality of wheat grain with the recognition rates of common, strong-gluten, middle-gluten and weak-gluten wheat samples being 93.3%, 87.5%, 72.7% and 92.3%, respectively, and the rejection rates of them being 90.0%, 97.4%, 100.0% and 95.2%, respectively. The model realized rapid and accurate classification of wheat, besides it could be applied to the detection system of wheat quality.

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Sensors III: Spectral Sensing and Data Analysis

Sinha

Khot

Gao

et al. 2021

Agriculture Automation and Control

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Visible-near infrared spectroscopy for detection of Huanglongbing in citrus orchards

Cited by 159 publications

References 17 publications

Feasibility of Using the Optical Sensing Techniques for Early Detection of Huanglongbing in Citrus Seedlings

Feasibility of Using the Optical Sensing Techniques for Early Detection of Huanglongbing in Citrus Seedlings

Modeling research on wheat grain in different quality categories using multilayer support vector machine

Sensors III: Spectral Sensing and Data Analysis

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