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May 19, 2017
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May 19, 2017
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Abstract

Abstract
Huanglongbing is citrus disease which is a major threat for citrus orchard. Neither disease has a cure nor an efficient means of control. Early detection is important to prevent development and spread of the disease. The most effective detection used DNA test by PCR. However, identification used DNA test required sample preparation, time-consuming and expensive. The objective of this study was to build detection of healthy and HLB-infected leaves software. The leaf samples collected from citrus orchard in Situgede village, Bogor. Sample
leaves divided into three group, Huanglongbing-infected leaves, healthy leaves and asymptomatic leaves. All samples was tested by PCR for verification visual symptoms of huanglongbing. Vis-NIR spectrometer with a spectra range of 339 to 1022nm was used to acquisition HLB-infected and healthy leaves spectral data. MSC, SNV, baseline correction, first and second derivative were used for pretreatment method. Artificial neural network was used to build classification model. X-loading plot from principal component analysis was used to obtain sensitive wavelength. Classification for healthy and HLB-infected classs used sensitive wavelength baseline correction-based had the best performance and high accuracy (100%). The classification model was embedded in software PC-desktop based which was used visual basic programming language. Asymptomatic leaves spectral from HLB-positive tree were used to testing classification model. Model classified data into HLB-infected group, which was consistent with PCR test. The result from this study indicated that developed software could be used to HLB detection in early stage of disease.

Abstrak
Huanglongbing adalah penyakit jeruk yang merupakan ancaman utama bagi budidaya jeruk. Tidak ada pengendalian yang tepat untuk Huanglongbing. Deteksi dini penting untuk mencegah penyebaran dan pengembangan penyakit ini. Deteksi dini yang paling efektif menggunakan tes DNA dengan PCR. Namun, identifikasi menggunakan tes DNA memerlukan persiapan sampel, memakan waktu dan mahal. Tujuan dari
penelitian ini adalah membangun perangkat lunak deteksi daun sehat dan terinfeksi HLB. Sampel daun dikumpulkan dari kebun jeruk di Desa Situ Gede, Bogor. Sampel daun dibagi menjadi tiga kelompok, daun yang terinfeksi HLB, daun sehat dan daun belum bergejala. Semua sampel telah diuji dengan PCR untuk verifikasi gejala visual Huanglongbing. Spektrometer Vis-NIR dengan rentang spektrum dari 339-1022nm digunakan
untuk mengumpulkan data spektrum daun terinfeksi HLB dan sehat. MSC, SVN, baseline correction, turunan pertama dan kedua dari spektra digunakan sebagai metode praperlakuan. Jaringan syaraf tiruan digunakan untuk membangun model klasifikasi Plot X-loading dari analisis komponen utama digunakan untuk mendapatkan panjang gelombang sensitif. Klasifikasi terhadap kategori daun sehat dan sakit menggunakan panjang gelombang sensitif berbasis baseline correction memiliki nilai akurasi 100 % dan kinerja terbaik. Model klasifikasi yang ditanam pada perangkat lunak berbasis komputer desktop menggunakan bahasa pemrograman visual
basic. Data spektrum daun belum bergejala dari pohon positif terinfeksi HLB digunakan untuk menguji model klasifikasi. Model mengklasifikasikan data tersebut ke kelompok terinfeksi HLB, yang konsinten dengan hasil pengujian PCR yang juga mengelompokkan pada daun terinfeksi HLB. Hasil penelitian ini menunjukkan bahwa perangkat lunak dapat digunakan untuk deteksi HLB pada tahap awal perkembangan penyakit.

Keywords

huanglongbing visible-near infrared spectroscopy artificial neural network citrus

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References

  1. Bove JM, Genomique DR, Pathogene P, Recherche C De, Bordeaux I De, Bourlaux E, Cedex O, Df A, Pereira A, Araraquara P. 2006. HUANGLONGBING : A Destructive, Newly- Emerging, Century-Old Disease of Citrus. J.
  2. Plant Pathol. 88(1):7–37.
  3. Cardinali MC do B, Boas PRV, Milori DMBP, Ferreira EJ, Silva MF e, Machado MA, Bellete BS, Silva MF das GF da. 2012. Infrared spectroscopy: A potential tool in huanglongbing and citrus variegated chlorosis diagnosis. Talanta 91:1–6.
  4. Carter GA, Knapp AK. 2001. Leaf optical properties in higher plants: linking spectral characteristics to stress and chlorophyll concentration. Am. J. Bot. 88(4):677–684.
  5. Ceccato P, Flasse S, Tarantola S, Jacquemoud S, Gregoire JM. 2001. Detecting vegetation leaf water content using reflectance in the optical domain. Remote Sens. Environ. 77(1):22–33.
  6. Cen H, He Y. 2007. Theory and application of near infrared reflectance spectroscopy in determination of food quality. Trends food Sci. Technol. 18(2):72–83.
  7. Etxeberria E, Gonzalez P, Dawson W, Spann T. 2007. An Iodine-Based Starch Test to Assist in Selecting Leaves for HLB Testing.
  8. Fan J, Chen C, Brlansky RH, Gmitter FG, Li ZG. 2010. Changes in carbohydrate metabolism in Citrus sinensis infected with “Candidatus Liberibacter asiaticus”. Plant Pathol. 59(6):1037– 1043.
  9. Granitto PM, Navone HD, Verdes PF, Ceccatto H a. 2002. Weed seeds identification by machine vision. Comput. Electron. Agric. 33(2):91–103.
  10. Heise HM, Winzen R. 2002. Fundamental Chemometric Methods. In: Siesler H., Ozaki Y, Kawata S, Heise H., editors. Near-Infrared Spectroscopy Principles, Instruments,
  11. Applications. Weinheim: Wiley-VCH. p. 125–162.
  12. Iftikhar Y, Rauf S, Shahzad U, Zahid MA. 2016. Huanglongbing : Pathogen detection system for integrated disease management – A review. J. Saudi Soc. Agric. Sci. 15(1):1–11.
  13. Kim DG, Burks TF, Schumann AW, Zekri M, Zhao X. 2009. Detection of Citrus Greening Using Microscopic Imaging. CIGR J. XI.
  14. Li X, He Y. 2008. Discriminating varieties of tea plant based on Vis/NIR spectral characteristics and using artificial neural networks. Biosyst. Eng. 99(3):313–321.
  15. Luo X, Takahashi T, Kyo K, Zhang S. 2012. Wavelength selection in vis / NIR spectra for detection of bruises on apples by ROC analysis. J. Food Eng. 109(3):457–466.
  16. Mishra AR, Karimi D, Ehsani R, Lee WS. 2012. Identification of Citrus Greening (Hlb) Using a Vis-Nir Spectroscopy Technique. Trans. Asabe 55(2):711–720.
  17. Nasr G, Badr E, Joun C. 2002. Cross Entropy Error Function in Neural Networks: Forecasting Gasoline Demand. FLAIRS Conf.:381–384.
  18. Nurhadi. 2015. PENYAKIT HUANGLONGBING TANAMAN JERUK ( Candidatus Liberibacter asiaticus ): ANCAMAN DAN STRATEGI PENGENDALIAN Huanglongbing Disease (
  19. Candidatus Liberibacter asiaticus ) on Citrus : Threats and Control Strategy. Pengemb. Inov. Pertan. 8(1):21–32.
  20. Osborne BG, Fearn T, Hindle PH, Hindle PT. 1993. Practical NIR Spectroscopy with Applications in Food and Beverage Analysis. Singapore: Longman Scientific & Technical (Longman food technology).
  21. Ozaki Y, Morita S, Du Y. 2007. Spectral Analysis. In: Ozaki Y, McClure WF, Christy AA, editors. Near- Infrared Spectroscopy In Food Science And Technology. New Jersey: Wiley-Interscience. p.47–72.
  22. Pholpho T, Pathaveerat S, Sirisomboon P. 2011. Classification of longan fruit bruising using visible spectroscopy. J. Food Eng. 104(1):169–172.
  23. Poole G. 2008. Visible/near-infrared spectroscopy for discrimination of HLB-infected citrus leaves from healthy leaves. In: Proc. Intl. Research Conf. on Huanglongbing. St. Paul, Minn: Plant Management Network. p. 178–180.
  24. Pourreza A, Lee WS, Raveh E, Ehsani R, Etxeberria E. 2014. Citrus Huanglongbing Detection Using Narrow-Band Imaging and Polarized Illumination. Trans. Asabe 57(1):259–272.
  25. Rady A, Guyer D. 2015. Utilization of visible/nearinfrared
  26. spectroscopic and wavelength selection methods in sugar prediction and potatoes classification. J. Food Meas. Charact. 9(1):20– 34.
  27. Sankaran S, Ehsani R. 2011. Visible-near infrared spectroscopy based citrus greening detection: Evaluation of spectral feature extraction techniques. Crop Prot. 30(11):1508–1513. Sankaran S, Ehsani R. 2012. Detection of Huanglongbing Disease in Citrus Using Fluorescence Spectroscopy. Trans. Asabe 55(1):313–320.
  28. Sankaran S, Ehsani R, Etxeberria E. 2010. Mid-infrared spectroscopy for detection of Huanglongbing (greening) in citrus leaves. Talanta 83:574–581.
  29. Sankaran S, Maja JM, Buchanon S, Ehsani R. 2013. Huanglongbing (citrus greening) detection using visible, near infrared and thermal imaging techniques. Sensors (Basel). 13(2):2117–30.
  30. Sankaran S, Mishra A, Maja JM, Ehsani R. 2011. Visible-near infrared spectroscopy for detection of Huanglongbing in citrus orchards. Comput. Electron. Agric. 77(2):127–134.
  31. Schneider H. 1968. Anatomy of greeningdiseased
  32. sweet orange shoots. Phytopathology 58(1):1155–1160.
  33. Sims DA, Gamon JA. 2002. Relationships between
  34. leaf pigment content and spectral reflectance across a wide range of species, leaf structures and developmental stages. Remote Sens. Environ. 81(2):337–354.
  35. Taufik M, Khaeruni A, Pakki T. 2010. Deteksi Keberadaan Citrus Vein Phloem Degeneration (CVPD) dengan Teknik PCR ( Polymerase Chain Reaction ) di Sulawesi Tenggara. J. HPT Trop. 10(1):73–79.
  36. West JS, Bravo C, Oberti R, Lemaire D, Moshou D, McCartney HA. 2003. The Potential of Optical Canopy Measurement for Targeted Control of Field Crop Diseases. Annu. Rev. Phytopathol 41(1):593–614.
  37. Windham WR, Poole GH, Park B, Heitschmidt G, Hawkins SA, Albano JP, Gottwald TR, Lawrence KC. 2011. Rapid screening of Huanglongbinginfected citrus leaves by near-infrared reflectance spectroscopy. Trans. ASABE 54:2253–2258.
  38. Yuan HC, Xiong FL, Huai XY. 2003. A method for
  39. estimating the number of hidden neurons in feedforward
  40. neural networks based on information entropy. Comput. Electron. Agric. 40(1):57–64.