A systematic literature review of gray level co-occurence matrix on plants
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Aug 24, 2023
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Gray Level Co-Occurrence Matrix, Plant, Classification, Data Collection
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Abstract
The Gray Level Co-Occurrence Matrix method includes contrast, correlation, energy and homogeneity then is processed using an artificial neural network method for its classification. This literature tries to learn about the process of the GLCM method. This is done to understand the methods that researchers use to collect data from various sources, process the data that has been collected, and classify the data so that it becomes information that is easier to understand. researchers collect, screen, and review the research found using a Systematic Literature Review approach. Researchers pooled research from ScienceDirect, Google Scholar, and Elsevier by selecting studies published from 2020 to 2023. The purpose of the researchers conducting this literature review was to understand the GLCM method in parks, gain an understanding of data collection techniques, methods, and study the results of the research. previously. This study collects and summarizes 12 studies. The study was conducted regarding the method of data collection, the methods used, and the results of the research.
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Sadad, A., Utami, E., & Dwi Hartanto, , A. . (2023). A systematic literature review of gray level co-occurence matrix on plants. Journal of Intelligent Decision Support System (IDSS), 6(3), 121-128. https://doi.org/10.35335/idss.v6i3.153
References
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Anggiratih, E., Siswanti, S., Octaviani, S. K., & Sari, A. (2021). Klasifikasi Penyakit Tanaman Padi Menggunakan Model Deep Learning Efficientnet B3 dengan Transfer Learning. Jurnal Ilmiah SINUS, 19(1), 75. https://doi.org/10.30646/sinus.v19i1.526
Anggraini, R. A., Wati, F. F., Shidiq, M. J., Suryadi, A., Fatah, H., & Kholifah, D. N. (2020). Identification of Herbal Plant Based on Leaf Image Using Glcm Feature and K-Means. Jurnal Techno Nusa Mandiri, 17(1), 71–78. https://doi.org/10.33480/techno.v17i1.1087
Ashraf, T., & Khan, Y. N. (2020). Weed density classification in rice crop using computer vision. Computers and Electronics in Agriculture, 175(February), 105590. https://doi.org/10.1016/j.compag.2020.105590
Citra, K., Daun, P., & Padi, T. (2023). Klasifikasi Citra Penyakit Daun Tanaman Padi Menggunakan CNN dengan Arsitektur VGG-19. Jurnal Sains Dan Informatika, 9(1), 37–45. https://doi.org/10.22216/jsi.v9i1.2175
Cybex. (2019). Budidaya Tanaman Padi. http://cybex.pertanian.go.id/Mobile/Artikel/88796/Budidaya-Tanaman- Padi/.
Durach, C. F., Kembro, J., & Wieland, A. (2017). A New Paradigm for Systematic Literature Reviews in Supply Chain Management. Journal of Supply Chain Management, 53(4), 67–85. https://doi.org/https://doi.org/10.1111/jscm.12145
Fikriah, F. K., Burhanis Sulthan, M., Mujahidah, N., & Khoirur Roziqin, M. (2022). Naïve Bayes untuk Klasifikasi Penyakit Daun Bawang Merah Berdasarkan Ekstraksi Fitur Gray Level Cooccurrence Matrix (GLCM). Jurnal Komtika (Komputasi Dan Informatika), 6(2), 133–141. https://doi.org/10.31603/komtika.v6i2.7925
Hanifah Fitri Yuniar. (2021). Pengendalian Penyakit Hawar Daun Bakteri (Hdb) Atau Penyakit Kresek Pada Tanaman Padi. https://pertanian.kulonprogokab.go.id/Detil/676/Pengendalian-Penyakit-%0A%0AHawar-Daun-Bakteri-Hdb.%0A
Haris, N. A. (2020). Kombinasi Ciri Bentuk dan Ciri Tekstur Untuk Identifikasi Penyakit Pada Tanaman Padi. JATISI (Jurnal Teknik Informatika Dan Sistem Informasi), 7(2), 237–250. https://doi.org/10.35957/jatisi.v7i2.239
Ilhamy, R. S., & Sanjaya, U. P. (n.d.). Algoritma K-Nearest Neighbors ( KNN ) untuk Klasifikasi Citra Buah Pisang dengan Ekstraksi Ciri Gray Level Co-Occurrence. 17(2), 88–93.
Kusanti, J., Penyakit, K., Padi, D., & Haris, A. (2018). Klasifikasi Penyakit Daun Padi Berdasarkan Hasil Ekstraksi Fitur GLCM Interval 4 Sudut. Jurnal Informatika: Jurnal Pengembangan IT (JPIT), 03(01), 1–6.
Löfstedt, T., Brynolfsson, P., Asklund, T., Nyholm, T., & Garpebring, A. (2019). Gray-level invariant Haralick texture features. PLOS ONE, 14(2), e0212110. https://doi.org/10.1371/journal.pone.0212110
Mohtar Khoiruddin, Apri Junaidi, W. A. S. (2022). Klasifikasi Penyakit Daun Padi Menggunakan Convolutional Neural Network. Data Institut Teknologi Telkom Purwokerto, 2(1), 37–45. https://www.kaggle.com/tedisetiady/leaf-rice-disease-
Peer Review Aplikasi Ekstraksi Fitur GLCM Deteksi Kerapatan Vegetasi.pdf. (n.d.).
Priyangka, A. A. J. V., & Kumara, I. M. S. (2021). Classification Of Rice Plant Diseases Using the Convolutional Neural Network Method. Lontar Komputer : Jurnal Ilmiah Teknologi Informasi, 12(2), 123. https://doi.org/10.24843/lkjiti.2021.v12.i02.p06
Rizal, R. A., Susanto, M., & Chandra, A. (2020). Classification Of Borax Content In Tomato Sauce Through Images Using GLCM. SinkrOn, 4(2), 6. https://doi.org/10.33395/sinkron.v4i2.10508
Rosiva Srg, S. A., Zarlis, M., & Wanayumini, W. (2022). Identifikasi Citra Daun dengan GLCM (Gray Level Co-Occurence) dan K-NN (K-Nearest Neighbor). MATRIK : Jurnal Manajemen, Teknik Informatika Dan Rekayasa Komputer, 21(2), 477–488. https://doi.org/10.30812/matrik.v21i2.1572
Sari, W. S., & Sari, C. A. (2022). Klasifikasi Bunga Mawar Menggunakan Knn Dan Ekstraksi Fitur Glcm Dan Hsv. Skanika, 5(2), 145–156. https://doi.org/10.36080/skanika.v5i2.2951
Sharma, N., & Verma, A. (2013). Performance Comparison of Texture based Approach for Identification of Regions in Satellite Image. International Journal of Computer Applications, 74(2), 10–15. https://doi.org/10.5120/12856-9410
Shrivastava, V. K., & Pradhan, M. K. (2021). Rice plant disease classification using color features: a machine learning paradigm. Journal of Plant Pathology, 103(1), 17–26. https://doi.org/10.1007/s42161-020-00683-3
Soleh, M. I. (2020). Penggunaan Pestisida Dalam Perspektif Produksi Dan Keamanan Pangan. https://tanamanpangan.pertanian.go.id/Detil-%0A%0AKonten/Iptek/16.%0A
Tampinongkol, F. (2023). Identifikasi Penyakit Daun Tomat Menggunakan Gray Level Co-occurrence Matrix (GLCM) dan Support Vector Machine (SVM). Techno Xplore : Jurnal Ilmu Komputer Dan Teknologi Informasi, 8(1), 08–16. https://doi.org/10.36805/technoxplore.v8i1.3578
Thoriq. (2022). Bimtek Pengelolaan Penanaman Padi. https://sekarwangi.desa.id/Artikel/2022/7/25/Bimtek-Pengelolaan- Penanaman-Padi.
Utaminingrum, F., Sarosa, S. J. A., Karim, C., Gapsari, F., & Wihandika, R. C. (2022). The combination of gray level co-occurrence matrix and back propagation neural network for classifying stairs descent and floor. ICT Express, 8(1), 151–160. https://doi.org/10.1016/j.icte.2021.05.010
van Dinter, R., Tekinerdogan, B., & Catal, C. (2021). Automation of systematic literature reviews: A systematic literature review. Information and Software Technology, 136, 106589. https://doi.org/https://doi.org/10.1016/j.infsof.2021.106589
Veronica, L., Al Amin, I. H., Hartono, B., & Kristianto, T. (2019). Ekstraksi Fitur Tekstur Menggunakan Matriks GLCM pada Citra dengan Variasi Arah Obyek. Prosiding SENDI_U, 978–979.
Waail Al Wajieh, M., & Luqman Al-Farisi, B. (2023). Classification of Longan Types Using The Back-Propagation Neural Network Algorithm Based on Leaf Morphology With Shape Characteristics. Proceedings of Malikussaleh International Conference on Multidisciplinary Studies (MICoMS), 3, 00035. https://doi.org/10.29103/micoms.v3i.196
Widodo, R., Widodo, A. W., & Supriyanto, A. (2018). Pemanfaatan Ciri Gray Level Co-Occurrence Matrix (GLCM) Citra Buah Jeruk Keprok (Citrus reticulata Blanco) untuk Klasifikasi Mutu. Jurnal Pengembangan Teknologi Informasi Dan Ilmu Komputer, 2(11), 5769–5776. https://j-ptiik.ub.ac.id/index.php/j-ptiik/article/view/3420
Yuliany, S., Aradea, & Andi Nur Rachman. (2022). Implementasi Deep Learning pada Sistem Klasifikasi Hama Tanaman Padi Menggunakan Metode Convolutional Neural Network (CNN). Jurnal Buana Informatika, 13(1), 54–65. https://doi.org/10.24002/jbi.v13i1.5022
Al Rivan, M. E., Rachmat, N., & Ayustin, M. R. (2020). Klasifikasi Jenis Kacang-Kacangan Berdasarkan Tekstur Menggunakan Jaringan Syaraf Tiruan. Jurnal Komputer Terapan, 6(1 SE-), 89–98. https://doi.org/10.35143/jkt.v6i1.3546
Anggiratih, E., Siswanti, S., Octaviani, S. K., & Sari, A. (2021). Klasifikasi Penyakit Tanaman Padi Menggunakan Model Deep Learning Efficientnet B3 dengan Transfer Learning. Jurnal Ilmiah SINUS, 19(1), 75. https://doi.org/10.30646/sinus.v19i1.526
Anggraini, R. A., Wati, F. F., Shidiq, M. J., Suryadi, A., Fatah, H., & Kholifah, D. N. (2020). Identification of Herbal Plant Based on Leaf Image Using Glcm Feature and K-Means. Jurnal Techno Nusa Mandiri, 17(1), 71–78. https://doi.org/10.33480/techno.v17i1.1087
Ashraf, T., & Khan, Y. N. (2020). Weed density classification in rice crop using computer vision. Computers and Electronics in Agriculture, 175(February), 105590. https://doi.org/10.1016/j.compag.2020.105590
Citra, K., Daun, P., & Padi, T. (2023). Klasifikasi Citra Penyakit Daun Tanaman Padi Menggunakan CNN dengan Arsitektur VGG-19. Jurnal Sains Dan Informatika, 9(1), 37–45. https://doi.org/10.22216/jsi.v9i1.2175
Cybex. (2019). Budidaya Tanaman Padi. http://cybex.pertanian.go.id/Mobile/Artikel/88796/Budidaya-Tanaman- Padi/.
Durach, C. F., Kembro, J., & Wieland, A. (2017). A New Paradigm for Systematic Literature Reviews in Supply Chain Management. Journal of Supply Chain Management, 53(4), 67–85. https://doi.org/https://doi.org/10.1111/jscm.12145
Fikriah, F. K., Burhanis Sulthan, M., Mujahidah, N., & Khoirur Roziqin, M. (2022). Naïve Bayes untuk Klasifikasi Penyakit Daun Bawang Merah Berdasarkan Ekstraksi Fitur Gray Level Cooccurrence Matrix (GLCM). Jurnal Komtika (Komputasi Dan Informatika), 6(2), 133–141. https://doi.org/10.31603/komtika.v6i2.7925
Hanifah Fitri Yuniar. (2021). Pengendalian Penyakit Hawar Daun Bakteri (Hdb) Atau Penyakit Kresek Pada Tanaman Padi. https://pertanian.kulonprogokab.go.id/Detil/676/Pengendalian-Penyakit-%0A%0AHawar-Daun-Bakteri-Hdb.%0A
Haris, N. A. (2020). Kombinasi Ciri Bentuk dan Ciri Tekstur Untuk Identifikasi Penyakit Pada Tanaman Padi. JATISI (Jurnal Teknik Informatika Dan Sistem Informasi), 7(2), 237–250. https://doi.org/10.35957/jatisi.v7i2.239
Ilhamy, R. S., & Sanjaya, U. P. (n.d.). Algoritma K-Nearest Neighbors ( KNN ) untuk Klasifikasi Citra Buah Pisang dengan Ekstraksi Ciri Gray Level Co-Occurrence. 17(2), 88–93.
Kusanti, J., Penyakit, K., Padi, D., & Haris, A. (2018). Klasifikasi Penyakit Daun Padi Berdasarkan Hasil Ekstraksi Fitur GLCM Interval 4 Sudut. Jurnal Informatika: Jurnal Pengembangan IT (JPIT), 03(01), 1–6.
Löfstedt, T., Brynolfsson, P., Asklund, T., Nyholm, T., & Garpebring, A. (2019). Gray-level invariant Haralick texture features. PLOS ONE, 14(2), e0212110. https://doi.org/10.1371/journal.pone.0212110
Mohtar Khoiruddin, Apri Junaidi, W. A. S. (2022). Klasifikasi Penyakit Daun Padi Menggunakan Convolutional Neural Network. Data Institut Teknologi Telkom Purwokerto, 2(1), 37–45. https://www.kaggle.com/tedisetiady/leaf-rice-disease-
Peer Review Aplikasi Ekstraksi Fitur GLCM Deteksi Kerapatan Vegetasi.pdf. (n.d.).
Priyangka, A. A. J. V., & Kumara, I. M. S. (2021). Classification Of Rice Plant Diseases Using the Convolutional Neural Network Method. Lontar Komputer : Jurnal Ilmiah Teknologi Informasi, 12(2), 123. https://doi.org/10.24843/lkjiti.2021.v12.i02.p06
Rizal, R. A., Susanto, M., & Chandra, A. (2020). Classification Of Borax Content In Tomato Sauce Through Images Using GLCM. SinkrOn, 4(2), 6. https://doi.org/10.33395/sinkron.v4i2.10508
Rosiva Srg, S. A., Zarlis, M., & Wanayumini, W. (2022). Identifikasi Citra Daun dengan GLCM (Gray Level Co-Occurence) dan K-NN (K-Nearest Neighbor). MATRIK : Jurnal Manajemen, Teknik Informatika Dan Rekayasa Komputer, 21(2), 477–488. https://doi.org/10.30812/matrik.v21i2.1572
Sari, W. S., & Sari, C. A. (2022). Klasifikasi Bunga Mawar Menggunakan Knn Dan Ekstraksi Fitur Glcm Dan Hsv. Skanika, 5(2), 145–156. https://doi.org/10.36080/skanika.v5i2.2951
Sharma, N., & Verma, A. (2013). Performance Comparison of Texture based Approach for Identification of Regions in Satellite Image. International Journal of Computer Applications, 74(2), 10–15. https://doi.org/10.5120/12856-9410
Shrivastava, V. K., & Pradhan, M. K. (2021). Rice plant disease classification using color features: a machine learning paradigm. Journal of Plant Pathology, 103(1), 17–26. https://doi.org/10.1007/s42161-020-00683-3
Soleh, M. I. (2020). Penggunaan Pestisida Dalam Perspektif Produksi Dan Keamanan Pangan. https://tanamanpangan.pertanian.go.id/Detil-%0A%0AKonten/Iptek/16.%0A
Tampinongkol, F. (2023). Identifikasi Penyakit Daun Tomat Menggunakan Gray Level Co-occurrence Matrix (GLCM) dan Support Vector Machine (SVM). Techno Xplore : Jurnal Ilmu Komputer Dan Teknologi Informasi, 8(1), 08–16. https://doi.org/10.36805/technoxplore.v8i1.3578
Thoriq. (2022). Bimtek Pengelolaan Penanaman Padi. https://sekarwangi.desa.id/Artikel/2022/7/25/Bimtek-Pengelolaan- Penanaman-Padi.
Utaminingrum, F., Sarosa, S. J. A., Karim, C., Gapsari, F., & Wihandika, R. C. (2022). The combination of gray level co-occurrence matrix and back propagation neural network for classifying stairs descent and floor. ICT Express, 8(1), 151–160. https://doi.org/10.1016/j.icte.2021.05.010
van Dinter, R., Tekinerdogan, B., & Catal, C. (2021). Automation of systematic literature reviews: A systematic literature review. Information and Software Technology, 136, 106589. https://doi.org/https://doi.org/10.1016/j.infsof.2021.106589
Veronica, L., Al Amin, I. H., Hartono, B., & Kristianto, T. (2019). Ekstraksi Fitur Tekstur Menggunakan Matriks GLCM pada Citra dengan Variasi Arah Obyek. Prosiding SENDI_U, 978–979.
Waail Al Wajieh, M., & Luqman Al-Farisi, B. (2023). Classification of Longan Types Using The Back-Propagation Neural Network Algorithm Based on Leaf Morphology With Shape Characteristics. Proceedings of Malikussaleh International Conference on Multidisciplinary Studies (MICoMS), 3, 00035. https://doi.org/10.29103/micoms.v3i.196
Widodo, R., Widodo, A. W., & Supriyanto, A. (2018). Pemanfaatan Ciri Gray Level Co-Occurrence Matrix (GLCM) Citra Buah Jeruk Keprok (Citrus reticulata Blanco) untuk Klasifikasi Mutu. Jurnal Pengembangan Teknologi Informasi Dan Ilmu Komputer, 2(11), 5769–5776. https://j-ptiik.ub.ac.id/index.php/j-ptiik/article/view/3420
Yuliany, S., Aradea, & Andi Nur Rachman. (2022). Implementasi Deep Learning pada Sistem Klasifikasi Hama Tanaman Padi Menggunakan Metode Convolutional Neural Network (CNN). Jurnal Buana Informatika, 13(1), 54–65. https://doi.org/10.24002/jbi.v13i1.5022
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