Penggunaan Vis-NIR untuk Deteksi Serangan Huanglongbing pada Daun Jeruk

Raden Arief Firmansyah, Kudang Boro Seminar, Widodo .

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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