Implementation of Gray Level Coocurence Matrix on the Leaves of Rice Crops
DOI:
https://doi.org/10.26555/jiteki.v16i1.16630Keywords:
GLCM, Leaves, RiceAbstract
Rice is one of the cultivation plants that are very important for human survival. The success of rice harvesting affects the level of farmers' income. However, farmers often suffer losses as a result of illness in rice. Rice plants infected with the disease will show symptoms in the form of patches that have certain patterns and colors on some parts of the body of rice plants, such as stems, leaves, and roots. Disease symptoms that emerge on the leaves are most easily identified because the leaves have a wider cross-section than other body parts of rice. Therefore, in this study, the leaf was used as an initial step parameter for disease detection in rice. This research aimed to identify diseases that exist in rice plants using the method of Gray Level Co-occurrence Matrix (GLCM). The GLCM method is a feature extraction method. The disease detection process on the leaves of the rice plants was done by retrieving the original image for the initial step; then, the original image was segmented before converted to greyscale imagery. After that, feature extraction was carried out using the GLCM features: Entropy, Eccentricity, Contrast, Energy, Correlation, Homogeneity. The results showed 90% accuracy results using GLCM extraction. The recognition of the emerging diseases on rice leaves can help to identify the type of disease infecting the rice plants.
References
Y. Sari, A. R. Baskara, and F. Arya, “Klasifikasi grade daun padi sebagai penentu pemupukan urea dengan metode ekstraksi fitur,†Prosiding Seminar Nasional Lingkungan Lahan Basah, vol. 4, no. 1, pp. 148–151, April 2019. Google Scholar
A. Candra, “Prototype Sistem Kontrol Air Sawah Otomatis Berdasarkan Level Air Berbasis Mikrokontroler Atmega8535 Pada Desa Bontoraja Kabupaten Bulukumba,†JEECOM, vol. 2, no. 1, pp. 22–33, 2020. Google Scholar
S. Sudewi, A. Ala, Baharuddin, and M. Farid BDR., “Keragaman Organisme Pengganggu Tanaman (OPT) pada Tanaman Padi Varietas Unggul Baru (VUB) dan Varietas Lokal pada Percobaan Semi Lapangan,†J. Agrik., vol. 31, no. 1, pp. 15–24, 2020. DOI: 10.24198/agrikultura.v31i1.25046
N. Trisna, Y. Elva, and A. I. Jmhur, “Implementasi Sistem Pakar Diagnosa Penyakit Tanaman Padi Dengan Menggunakan Metode Forward Chaining,†Jurnal Informasi Komputer Logika, vol. 1, no. 3, pp. 1–10, 2019. Google Scholar
J. Kusanti and N. A. Haris, “Klasifikasi Penyakit Daun Padi Berdasarkan Hasil Ekstraksi Fitur GLCM Interval 4 Sudut,†J. Pengemb. IT, vol. 03, no. 01, pp. 1–6, 2018. DOI: 10.30591/jpit.v3i1.669
D. Rosadi and A. Hamid, “Sistem Pakar Diagnosa Penyakit Tanaman Padi Menggunakan Metode Forward Chaining,†J. Comput. Bisnis, vol. 8, no. 1, pp. 43–48, 2014. Google Scholar
S. Wulandari, M. F. Noor, A. K. Wardhana, and Kusrini, “Sistem Pakar Diagnosa Hama Dan Penyakit Tanaman Padi Dengan Metode Bayes,†J. Inf., vol. 5, no. 2, pp. 59–64, 2019. DOI: 10.46808/informa.v5i2.83
A. Priatmoko and E. Harahap, “Implementasi Algoritma DES Menggunakan MATLAB,†J. Mat., vol. 16, no. 1, pp. 11–19, 2017. DOI: 10.29313/jmtm.v16i1.3360
I. D. Kurniawati and I. A. Kusumawardhani, “Implementasi Algoritma Canny dalam Pengenalan Wajah menggunakan Antarmuka GUI Matlab,†Institution of Engineering and Technology, 2017. Google Scholar
A. P. Manullang, A. Prahutana, and R. Santoso, “Penerapan Metode Simple Additive Weighting (Saw) Dan Weighted Product (Wp) Dalam Sistem Penunjang Pemilihan Laptop Terfavorit Menggunakan Gui Matlab,†J. Gaussian, vol. 7, no. 2018, pp. 11–22, 2018. Google Scholar
E. Nurraharjo, “Implementasi Pemrograman Interfacing MATLAB-Arduino,†J. Teknol. Inf. Din., vol. 20, no. 2, pp. 100–105, 2015. Google Scholar
E. Maria, Yulianto, Y. P. Arinda, Jumiaty, P. Nobel, "Segmentasi Citra Digital Bentuk Daun Pada Tanaman Di Politani Samarinda Menggunakan Metode Thresholding," Jurnal Rekayasa Teknologi Informasi (JURTI), vol. 2, no. 1, 2018. DOI: 10.30872/jurti.v2i1.1377
I. Amalia, Indrawati, and Y. M. Amin, “Ekstraksi Fitur Citra Songket Berdasarkan Tekstur Menggunakan Metode Gray Level Co-occurrence Matrix (GLCM),†J. Infomedia, vol. 3, no. 2, pp. 64–68, 2018. DOI: 10.30811/jim.v3i2.715
M. Latief and R. Yusuf, "Gorontalo Medicinal Plants Image Identification System Using Artificial Neural Network with Back Propagation," IJITEE (International Journal of Information Technology and Electrical Engineering), vo. 2, no. 2, 2018. DOI: 10.22146/ijitee.42154
Priyanka and D. Kumar, “Feature Extraction Using and Selection Kidney Ultrasound Images Using GLCM and PCA,†Procedia Comput. Sci., vol. 167, no. 2019, pp. 1722–1731, 2020, DOI: 10.1016/j.procs.2020.03.382
C. Liu and X. Zheng, “Comparative Investigation on Objective Evaluation Methods for Fabric Smoothness,†FIBRES Text. East. Eur., vol. 2, no. 140, pp. 43–49, 2020. Google Scholar DOI: 10.5604/01.3001.0013.7313
K. H. Thanoon, “Proposed Algorithm for Using GLCM Properties to Distinguishing Geometric Shapes,†Raf. J. Comp. Math’s, vol. 13, no. 1, pp. 32–47, 2019. DOI: 10.33899/csmj.2020.163501
R. Andrian, D. Maharani, M. Ardhi, and A. Junaidi, “Butterfly identification using gray level co-occurrence matrix (glcm) extraction feature and k-nearest neighbor (knn) classification,†Sci. J. Inf. Syst. Technol. Available, vol. 6, no. 1, pp. 11–21, 2020, DOI: 10.26594/register.v6i1.1602
P. Mohanaiah, P. Sathyanarayana, and L. Gurukumar, “Image Texture Feature Extraction Using GLCM Approach,†Int. J. Sci. Res. Publ., vol. 3, no. 5, pp. 1–5, 2013. Google Scholar
H. A. Al-beiruti and H. A. Jeiad, “OPG Images Automatic Segmentation and Feature Extraction for Dental Lesion Diagnosis Purposes,†Int. J. Sci. Res., vol. 7, no. 11, pp. 717–724, 2018, DOI: 10.21275/ART20192396. Semantic Scholar
Downloads
Published
How to Cite
Issue
Section
License
Authors who publish with JITEKI agree to the following terms:
- Authors retain copyright and grant the journal the right of first publication with the work simultaneously licensed under a Creative Commons Attribution License (CC BY-SA 4.0) that allows others to share the work with an acknowledgment of the work's authorship and initial publication in this journal.
- Authors are able to enter into separate, additional contractual arrangements for the non-exclusive distribution of the journal's published version of the work (e.g., post it to an institutional repository or publish it in a book), with an acknowledgment of its initial publication in this journal.
- Authors are permitted and encouraged to post their work online (e.g., in institutional repositories or on their website) prior to and during the submission process, as it can lead to productive exchanges, as well as earlier and greater citation of published work.
This work is licensed under a Creative Commons Attribution 4.0 International License
