Klasifikasi penyakit noda pada citra daun tebu berdasarkan ciri tekstur dan warna menggunakan segmentation-based gray level co-occurrence matrix dan lab color moments

Authors

  • Evy Kamilah Ratnasari Teknik Informatika - Universitas Dr. Soetomo Surabaya
  • Raden Venantius Hari Ginardi Teknik Informatika - Institut Teknologi Sepuluh Nopember
  • Chastine Fatichah Teknik Informatika - Institut Teknologi Sepuluh Nopember

DOI:

https://doi.org/10.26594/register.v3i1.575

Keywords:

color moments, GLCM, segmentation, spot disease, sugarcane leaf image, citra daun tebu, penyakit noda, segmentasi

Abstract

 

Penyakit noda pada daun tanaman tebu menampakkan gejala berupa lesi atau bercak. Lesi tersebut menghambat proses fotosintesis daun dan dapat mengakibatkan menurunnya produksi gula. Oleh karena itu, dalam meningkatkan kualitas produksi gula dibutuhkan diagnosa dini untuk mengambil keputusan penanganan penyakit yang cepat dan tepat, sehingga dapat meminimalisir kerusakan daun yang signifikan akibat penyebaran penyakit tersebut. Sayangnya keterbatasan keberadaan ahli penyakit tanaman tebu yang berpotensi dalam mendiagnosa penyakit noda tidak dapat mengatasi hal tersebut. Penelitian ini mengusulkan diagnosa penyakit noda tanaman tebu menggunakan metode pemrosesan citra berdasarkan fitur tekstur Segmentation-based Gray Level Co-Occurrence Texture (SGLCM) dan LAB color moments. Metode yang diajukan terdiri dari ekstraksi ciri warna pada citra masukan yang akan menghasilkan 12 fitur warna dan ekstraksi ciri tekstur pada citra masukan yang tersegmentasi dan menghasilkan 24 fitur tekstur, kemudian gabungan fitur warna dan tekstur tersebut digunakan sebagai masukan klasifikasi k-Nearest Neighbor (kNN) untuk mengenali jenis penyakit noda pada citra daun tanaman tebu. Jenis penyakit noda terdiri dari noda cincin, noda karat, dan noda kuning yang memiliki karakteristik berbeda. Klasifikasi penyakit noda pada tanaman tebu  menggunakan metode tersebut dapat menghasilkan akurasi tertinggi 93%.

 

 

 

The sugarcane spot disease attack the sugarcane with appear as spots on the leaves, so this spots prevent the vital process of photosynthesis to take place and caused sugar production losses. Early diagnosis of this spot disease can improve the quality of sugar production. The diagnosis result can be used as decision reference to control the disease fast and accurately to minimize attack severe that can caused significant damage. Unfortunately, experts who are able to identify the diseases are often unavailable. This research attempted to identify the three sugarcane spot diseases (ring spot, rust spot, and yellow spot) using Segmentation-based Gray Level Co-Occurrence Texture (SGLCM) and LAB color moments. The SGLCM obtain 24 texture features of segmented image and color moments obtain 12 color features. This method achieved at least 93% accuracy when identifying the diseases using kNN classifier.

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Published

2017-01-01

How to Cite

[1]
E. K. Ratnasari, R. V. H. Ginardi, and C. Fatichah, “Klasifikasi penyakit noda pada citra daun tebu berdasarkan ciri tekstur dan warna menggunakan segmentation-based gray level co-occurrence matrix dan lab color moments”, Register: Jurnal Ilmiah Teknologi Sistem Informasi, vol. 3, no. 1, pp. 1–10, Jan. 2017.

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