Detection of Huanglongbing Disease in Citrus Using Fluorescence Spectroscopy

Sankaran, Sindhuja; Ehsani, Reza

doi:10.13031/2013.41241

Cited by 36 publications

(24 citation statements)

References 0 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…Disease is transmitted depending on severity of disease and disease development stage [24,37]. Environmental factors and the population of the vectors also affect the dynamic disease transmission in plants [14,56,57]. Sometimes the symptoms would be obvious in the crown and do not appear on the bottom side of the tree.…”

Section: Discussionmentioning

confidence: 99%

Detection and Differentiation between Laurel Wilt Disease, Phytophthora Disease, and Salinity Damage Using a Hyperspectral Sensing Technique

2016

View full text Add to dashboard Cite

Laurel wilt (Lw) is a fatal disease. It is a vascular pathogen and is considered a major threat to the avocado industry in Florida. Many of the symptoms of Lw resemble those that are caused by other diseases or stress factors. In this study, the best wavelengths with which to discriminate plants affected by Lw from stress factors were determined and classified. Visible-near infrared (400-950 nm) spectral data from healthy trees and those with Lw, Phytophthora, or salinity damage were collected using a handheld spectroradiometer. The total number of wavelengths was averaged in two ranges: 10 nm and 40 nm. Three classification methods, stepwise discriminant (STEPDISC) analysis, multilayer perceptron (MLP), and radial basis function (RBF), were applied in the early stage of Lw infestation. The classification results obtained for MLP, with percent accuracy of classification as high as 98% were better than STEPDISC and RBF. The MLP neural network selected certain wavelengths that were crucial for correctly classifying healthy trees from those with stress trees. The results showed that there were sufficient spectral differences between laurel wilt, healthy trees, and trees that have other diseases; therefore, a remote sensing technique could diagnose Lw in the early stage of infestation.

show abstract

Section: Discussionmentioning

confidence: 99%

Detection and Differentiation between Laurel Wilt Disease, Phytophthora Disease, and Salinity Damage Using a Hyperspectral Sensing Technique

2016

View full text Add to dashboard Cite

show abstract

“…LDA is an effective subspace technique because it optimizes the Fisher score and has no need to adjust the free parameters [29,30]. NB classifier is a probabilistic classifier based on Bayes' theory with independent assumptions between features [31,32], which was also used for citrus disease recognition [33]. The SVM classifier is developed on the basis of the statistical learning theory to give the largest distance between the margins of the training data set with a hyperplane, which can be achieved by solving a convex quadratic programming problem using a kernel function [34].…”

Section: Discriminant Analysis Methods For Disease Detectionmentioning

confidence: 99%

Hyperspectral Reflectance Imaging Combined with Multivariate Analysis for Diagnosis of Sclerotinia Stem Rot on Arabidopsis Thaliana Leaves

et al. 2019

View full text Add to dashboard Cite

Sclerotinia stem rot (SSR) is one of the most destructive diseases in the world caused by Sclerotinia sclerotiorum (S. sclerotiorum), resulting in significant yield loss. Early and high-throughput detection would be critical to prevent SSR from spreading. This study aimed to propose a feasible method for SSR detection based on the hyperspectral imaging coupled with multivariate analysis. The performance of different detecting algorithms were compared by combining the extreme learning machine (ELM), K-nearest neighbor algorithm (KNN), linear discriminant analysis (LDA), naïve Bayes classifier (NB) and the support vector machine (SVM) with the random frog (RF), successive projection algorithm (SPA) and sequential forward selection (SFS). The similarity of selected optimal wavelengths by three different feature selection methods indicated a high correlation between selected wavelengths and SSR. Compared with KNN, LDA, NB, and SVM, three wavelengths (455, 671 and 747 nm) selected by SFS-CA combined with ELM could achieve relatively better results with the overall accuracy of 93.7% and the lowest false negative rate of 2.4%. These results demonstrated the potential of the presented method using hyperspectral reflectance imaging combined with multivariate analysis for SSR diagnosis.

show abstract

“…Walaupun metode tersebut memberikan hasil akurat, biaya mahal dan proses pengujian yang butuh waktu menjadi kelemahan metode tersebut (Mishra et al 2012;Pourreza et al 2014). Oleh karena itu, dikembangkan metode alternatif yang bersifat cepat dan murah tetapi efektif mendeteksi HLB, antara lain menggunakan spektrosokopi fluorescence (Sankaran and Ehsani 2012), spektrosokopi inframerah (Cardinali et al 2012), spektrosokopi Vis-NIR Sankaran dan Ehsani 2011;Mishra et al 2012), spektrosokopi MIR (Sankaran et al 2010), Pencitraan dengan Vis-NIR dan termal (Sankaran et al 2013), pencitntraan secara mikroskop (Kim et al 2009) dan pencintraan dengan filter polarisasi (Pourreza et al 2014). Di Indonesia, teknologi deteksi HLB selain PCR menggunakan LAMP (Loop-mediated isothermal amplification) yang dikembangkan oleh Balitjestro (Badan Penelitian Jeruk dan Buah Subtropika).…”

Section: Abstract: Huanglongbing Visible-near Infrared Spectroscopyunclassified

The use of Vis-NIR for Detecting Huanglongbing Disease on Leaves of Citrus Orchard

Firmansyah¹,

Seminar

Widodo

2017

JTEP

View full text Add to dashboard Cite

Jurnal Keteknikan Pertanian (JTEP) terakreditasi berdasarkan SK Dirjen Penguatan Riset dan Pengembangan Kementerian Ristek Dikti Nomor I/E/KPT/2015 tanggal 21 September 2015. Selain itu, JTEP juga telah terdaftar pada Crossref dan telah memiliki Digital Object Identifier (DOI) dan telah terindeks pada ISJD, IPI, Google Scholar dan DOAJ. JTEP terbit tiga kali setahun yaitu pada bulan April, Agustus dan Desember berisi 12 naskah untuk setiap monuron baik dalam edisi cetak maupun edisi online. Jurnal berkala ilmiah ini berkiprah dalam pengembangan ilmu keteknikan untuk pertanian tropika dan lingkungan hayati. Penulis makalah tidak dibatasi pada anggota PERTETA tetapi terbuka bagi masyarakat umum. Lingkup makalah, antara lain: teknik sumberdaya lahan dan air, alat dan mesin budidaya pertanian, lingkungan dan bangunan pertanian, energi alternatif dan elektrifikasi, ergonomika dan elektronika pertanian, teknik pengolahan pangan dan hasil pertanian, manajemen dan sistem informasi pertanian. Makalah dikelompokkan dalam invited paper yang menyajikan isu aktual nasional dan internasional, review perkembangan penelitian, atau penerapan ilmu dan teknologi, technical paper hasil penelitian, penerapan, atau diseminasi, serta research methodology berkaitan pengembangan modul, metode, prosedur, program aplikasi, dan lain sebagainya. Penulisan naskah harus mengikuti panduan penulisan seperti tercantum pada website dan naskah dikirim secara elektronik (online submission) melalui http:// journal.ipb.ac.id/index.php/jtep. Penanggungjawab:Ketua

show abstract

Detection of Huanglongbing Disease in Citrus Using Fluorescence Spectroscopy

Cited by 36 publications

References 0 publications

Detection and Differentiation between Laurel Wilt Disease, Phytophthora Disease, and Salinity Damage Using a Hyperspectral Sensing Technique

Detection and Differentiation between Laurel Wilt Disease, Phytophthora Disease, and Salinity Damage Using a Hyperspectral Sensing Technique

Hyperspectral Reflectance Imaging Combined with Multivariate Analysis for Diagnosis of Sclerotinia Stem Rot on Arabidopsis Thaliana Leaves

The use of Vis-NIR for Detecting Huanglongbing Disease on Leaves of Citrus Orchard

Contact Info

Product

Resources

About