Image Segmentation Analysis Using Otsu Thresholding and Mean Denoising for the Identification Coffee Plant Diseases

Ami Rahmawati; Ita Yulianti; Siti Nurajizah

doi:10.34288/jri.v6i1.261

Authors

Ami Rahmawati Universitas Nusa Mandiri
Ita Yulianti Universitas Bina Sarana Informatika
Siti Nurajizah Universitas Bina Sarana Informatika

(*) Corresponding Author

DOI:

https://doi.org/10.34288/jri.v6i1.261

Keywords:

coffee, mean denoising, otsu thresholding, leaf rust, image segmentation

Abstract

In Indonesia, coffee is one of the plantation products with a relatively high level of productivity and is a source of foreign exchange income for the country. However, unfortunately, certain factors can threaten productivity and quality in cultivating coffee plants, one of which is rust leaf disease. This disease causes disturbances in photosynthesis, thereby reducing plant yields. Therefore, to maintain and control productivity in coffee cultivation, this research carried out the process of observing coffee leaf images through segmentation using the Otsu Thresholding and Mean Denoising methods. The entire series of processes in this research was carried out using the Python programming language and succeeded in providing output in the form of image comparisons showing areas affected by Rust Leaf disease using the Otsu thresholding method alone and the Otsu thresholding method combined with a non-local means denoising algorithm. The test results prove that the Otsu thresholding method with the non-local means denoising algorithm has a smaller MSE value. It is the most optimal method for handling coffee leaf disease image segmentation with an accuracy level of 88%. It is hoped that this research can support farmers in providing insight into early detection of coffee plant diseases and increasing productivity through visual analysis.

Downloads

Download data is not yet available.

Author Biographies

Ami Rahmawati, Universitas Nusa Mandiri

Dosen program studi Sistem Informasi

Ita Yulianti, Universitas Bina Sarana Informatika

Sistem Informasi Akuntansi Kampus Kota Sukabumi

Siti Nurajizah, Universitas Bina Sarana Informatika

Sistem Informasi Akuntansi Kampus Kabupaten Karawang

References

Alfian, D. (2021). Sistem Pendukung Keputusan Berbasis Metode Analytical Hierarchy Process (AHP) Dalam Pemilihan Biji Kopi Berkualitas. INTECOMS: Journal of Information Technology and Computer Science, 4(2), 192–201. https://doi.org/10.31539/intecoms.v4i2.2837

Ardiansyah, A., & Hasan, N. F. (2023). Deteksi dan Klasifikasi Penyakit Pada Daun Kopi Menggunakan Yolov7. Jurnal Sisfokom (Sistem Informasi Dan Komputer), 12(1), 30–35. https://doi.org/10.32736/sisfokom.v12i1.1545

Asyhari, A., Sari, F. Y., Efendi, N. R., Nurjanah, D., Septianti, O., Putra, B., Maulana, D., Intan, D., Bagas P., A., Apriyadi, N., Benard, M., Farida, F., Syafaat, A., & Veronika, I. (2020). Pemberdayaan Kelompok Petani Kopi Karang Rejo untuk Meningkatkan Pemanfaatan Daun Kopi Menjadi Layak Konsumsi. Jurnal Pengabdian …, 5(1), 279–286. http://ppm.ejournal.id/index.php/pengabdian/article/view/251

Fibriani, I., Widjonarko, Sarwono, C. S., & Dwika, F. (2020). Deteksi Penyakit Brown Eye Spot pada Daun Kopi Menggunakan Metode Euclidean Distance dan Hough Transform. Journal Of Electrical Engineering And Technology, 1(1).

Hakim, L., Kristanto, S. P., Shodiq, M. N., Yusuf, D., Setiawan, W. A., Informatika, T., Banyuwangi, N., Raya, J., & Km, J. (2020). Segmentasi Citra Penyakit Pada Batang Buah Naga Menggunakan Metode Ruang Warna L*a*B*. Seminar Nasional Terapan Riset Inovatif (SENTRINOV) Ke-6 ISAS Publishing Series: Engineering and Science, 6(1), 728–736.

Helmy Abdillah, M., Aufar, Y., & Romadoni, J. (2023). Deteksi Penyakit Berbasis Convolutional Neural Network (CNN) dan Peninjauan Kesuburan Tanah Sebagai Upaya Peningkatan Hasil Tanaman Kopi Robusta Di Desa Lok-Tunggul Kabupaten Banjar. Jurnal Abdi Insani, 10(2), 1059–1068. https://doi.org/10.29303/abdiinsani.v10i2.803

Marina, I., Howara, D., & Sulmi, S. (2022). Manajemen Persediaan Bahan Baku Kopi Bubuk Pada Ukm Sidole 986 Di Kota Palu. Jurnal Pembangunan Agribisnis (Journal of Agribusiness Development), 1(3), 83–90. https://doi.org/10.22487/jpa.v1i3.1524

Pratita, D. G., Selviyanti, E., & Sabran, S. (2022). Pelatihan Pembuatan Lilin Aromaterapi Berbahan Baku Kopi Pada Ibu PKK di Dusun Krajan, Patrang. Journal of Community Development, 3(1), 1–7. https://doi.org/10.47134/comdev.v3i1.57

R. Lumbanraja, F., Rosdiana, S., Sudarsono, H., & Junaidi, A. (2020). Sistem Pakar Diagnosis Hama Dan Penyakit Tanaman Kopi Menggunkan Metode Breadth First Search (Bfs) Berbasis Web. Explore: Jurnal Sistem Informasi Dan Telematika, 11(1), 1. https://doi.org/10.36448/jsit.v11i1.1452

Ramadhan, M., Anwar, B., Gunawan, R., & Kustini, R. (2021). Pada Tanaman Kopi Menggunakan Metode. Sistem Pakar Untuk Mendiagnosa Penyakit Pada Tanaman Kopi Menggunakan Metode Teorema Bayes, 4307(June), 115–121.

Ratnawati, L., & Sulistyaningrum, D. R. (2019). Penerapan Random Forest untuk Mengukur Tingkat Keparahan Penyakit. 8(2).

Saputra, F. E., Cahya Wihandika, R., & Widodo, A. W. (2021). Penentuan Kualitas Biji Kopi Menggunakan Local Ternary Patterns Dan RGB-HSV Color Moment Dengan Learning Vector Quantization. 5(6), 2299–2307. http://j-ptiik.ub.ac.id

Siska, R. K. W., Lubis, L., & Lisnawati, L. (2018). Serangan Karat Daun Kopi (Hemileia vastatrix B et Br) pada Tanaman Kopi Arabika di Perkebunan Rakyat Kabupaten Mandailing Natal Sumatera Utara. Talenta Conference Series: Agricultural and Natural Resources (ANR), 1(1), 82–86. https://doi.org/10.32734/anr.v1i1.101

Sugiarti, L. (2019). Identifikasi Hama Dan Penyakit Pada Tanaman Kopi Di Kebun Percobaan Fakultas Pertanian Universitas Winaya Mukti. Agro Wiralodra, 2(1), 16–22. https://doi.org/10.31943/agrowiralodra.v2i1.27

Sukmawati, L., & Sadikin, R. (2023). Segmentasi Jalan Berlubang Citra Jalan Raya Menggunakan Metode Thresholding Dan K-Means. Jurnal Teknik Komputer, 9(2), 89–95. https://doi.org/10.31294/jtk.v9i2.15211

Windiawan, R., Suharso, A., & Artikel, S. (2019). Identifikasi Penyakit pada Daun Kopi Menggunakan Metode Deep Learning VGG16 INFO ARTIKEL ABSTRAK. Exploreit, 13(2), 9–16. https://doi.org/10.35891/explorit

Yasin, M. A. N., & Al Maki, W. F. (2022). Klasifikasi Penyakit Pada Tanaman Kopi Menggunakan K-nearest Neighbor Dioptimasi Dengan Genetic Algorithm. EProceedings of Engineering, 9(3), 1913–1918.