Application of K-Means Clustering for Detection Downy Mildew at Madura Corn Plant Using Digital Image Processing

 Abstract views: 95

Authors

  • Imron Rosyadi NR Universitas Madura
  • Erwin Prasetyowati Universitas Madura
  • Badar Said Universitas Madura
  • Syaiful Arifin Universitas Madura
  • Mohammad Syafiir Ridoni Universitas Madura

DOI:

https://doi.org/10.36456/tibuana.6.2.7845.147-152

Abstract

The development and cultivation of corn is necessary in line with the increasing consumption of food ingredients and industrial needs, especially food products made from corn. In the development of maize in Indonesia, the main obstacle is the disturbance of Plant Pest Organisms (OPT), especially diseases, one of which is downy mildew. This disease can be identified by a change in color, so we need a way to find out the difference between the color of healthy leaves and the color of leaves that have changed due to downy mildew. One solution that can be used is image processing. Therefore the aim of this study was to detect downy mildew based on leaf color in corn plants based on digital image processing, to produce precise and objective results. The algorithm used is the K-Means Clustering algorithm. This study uses 50 images of training data and 25 images of test data. Based on the simulation of downy mildew disease identification using K-Means Clustering it achieves an accuracy rate of 85%.

 

Downloads

Download data is not yet available.

References

Budhi, R. K., Prayitno, A., & Elvina, S. (2019). Pengenalan Pola Daun untuk Pendeteksi Dini Penyakit Tanaman Jagung Menggunakan Deteksi Tepi Sobel. In Seminar Nasional APTIKOM. https://publikasi.dinus.ac.id/index.php/semnastik/article/download/2880/1758

Giri, K. J., Peer, M. A., & Nagabhushan, P. (2014). A Robust Color Image Watermarking Scheme Using Discrete Wavelet Transformation. International Journal of Image, Graphics and Signal Processing, 7(1), 47–52. https://doi.org/10.5815/ijigsp.2015.01.06 DOI: https://doi.org/10.5815/ijigsp.2015.01.06

Hermawati, D. T. (2016). Kajian Ekonomi antara Pola Tanam Monokultur dan Tumpangsari Tanaman Jagung, Kubis dan Bayam (Issue 1). https://journal.uwks.ac.id/index.php/inovasi/article/download/590/545

Kurniawan, A. F., Prasetyo, J., & Suharjo, R. (2017). Identifikasi Dan Tingkat Serangan Penyebab Penyakit Bulai Di Lampung Timur, Pesawaran, Dan Lampung Selatan (Vol. 5, Issue 3). https://jurnal.fp.unila.ac.id/index.php/JA/article/view/1824 DOI: https://doi.org/10.23960/jat.v5i3.1824

Lasena Y, M. Y. (2020). Clustering Komoditi Unggulan Daerah Provinsi Gorontalo Menggunakan Algoritma K-Means. Jambura Journal of Electrical and Electronics Engineering (JJEEE), 2(1), 14–18. https://ejurnal.ung.ac.id/index.php/jjeee/article/view/4392/1734 DOI: https://doi.org/10.37905/jjeee.v2i1.4392

Prasetyowati, E., & Rofiq, A. A. (2016). Penilaian Kinerja Keuangan Koperasi Pada Dinas Koperasi Dan UMKM Pamekasan Dengan K-Means. Simantec, 5(2).

Prasetyowati, E., Rosyadi NR, I., & Rachmatullah, S. (2023). Penerapan K-Means Algorithm Untuk Mengidentifikasi Supplier Bahan Baku Pada Komoditas Agrikultur Di Kabupaten Pamekasan. Simantec, 5(2), 147-156. DOI: https://doi.org/10.21107/simantec.v11i2.18810

Purwanto, D. S., Nirwanto, H., & Wiyatiningsih, S. (2016). Model Epidemi Penyakit Tanaman: Hubungan Faktor Lingkungan Terhadap Laju Infeksi Dan Pola Sebaran Penyakit Bulai (Peronosclerospora maydis) Pada Tanaman Jagung Di Kabupaten Jombang. Berkala Ilmiah Agroteknologi - Plumula, 5(2), 138–152. http://ejournal.upnjatim.ac.id/index.php/plumula/article/view/764

Rahman, S. (2015). Analisis Nilai Tambah Agroindustri Chips Jagung. Jurnal Aplikasi Teknologi Pangan, 4(3), 108–111. https://jatp.ift.or.id/index.php/jatp/article/view/136 DOI: https://doi.org/10.17728/jatp.v4i3.136

Ulhaq, M. A., & Masnilah, R. (2019). Pengaruh Penggunaan Beberapa Varietas dan Aplikasi Pseudomonas fluorescens untuk Mengendalikan Penyakit Bulai (Peronosclerospora maydis) pada Tanaman Jagung (Zea mays L.). Jurnal Pengendalian Hayati, 2(1), 1. https://doi.org/10.19184/jph.v2i1.17131 DOI: https://doi.org/10.19184/jph.v2i1.17131

Zheng, X., Lei, Q., Yao, R., Gong, Y., & Yin, Q. (2018). Image segmentation based on adaptive K-means algorithm. Eurasip Journal on Image and Video Processing, 2018(1). https://doi.org/10.1186/s13640-018-0309-3 DOI: https://doi.org/10.1186/s13640-018-0309-3

Downloads

Published

2023-07-31

How to Cite

Rosyadi NR, I., Prasetyowati, E. ., Said, B. ., Arifin, S., & Ridoni, M. S. (2023). Application of K-Means Clustering for Detection Downy Mildew at Madura Corn Plant Using Digital Image Processing. Tibuana, 6(2), 147–152. https://doi.org/10.36456/tibuana.6.2.7845.147-152

Issue

Section

Article