Identification of Potato Plant Leaf Diseases Using a Digital Image Approach Using Algorithms K-Nearest Neighbor (KNN)
Identifikasi Penyakit Daun Tanaman Kentang Dengan Pendekatan Citra Digital Menggunakan Algoritma K-Nearest Neighbor (KNN)
DOI:
https://doi.org/10.21070/ups.5248Keywords:
K-Nearest Neighbor, Identification, Potato, Digital ImageAbstract
Potato or in Latin Solanum Tuberosum L is one of the most widely developed wet tubers such as boiled, fried, baked or vegetable dishes. Potato plants are also very susceptible to high rainy weather which causes the emergence of groups of plant disrupting organisms (OPT). OPT that attack the leaves of potato plants are late blight and early blight. OPT attacks on the leaves of potato plants can be identified using digital images. This system was designed using Matlab program to identify it with K-Nearest Neighbor (KNN) method. Disease identification is detected based on texture features and color features. In texture afeatures consist of (IDM, Entropy, Variance, ASM, Correlation) and color features consist of (Mean, Standard Deviation, Skewness, Kurtosis). The stages that will be carried out for identification are image input, pre-processing, feature extraction, color extraction, classification by KNN method, and image type information will come out.
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References
“Produksi Kentang Menurut Provinsi , Tahun 2015-2019,” vol. 2019, p. 2019, 2019.
B. P. dan P. Pertanian, Pengenalan Penyakit yang Menyerang Pada Tanaman Kentang. 2014.
P. U. Rakhmawati, Y. M. Pranoto, and E. Setyati, “KLASIFIKASI PENYAKIT DAUN KENTANG BERDASARKAN,” pp. 1–8, 2018.
M. Svm and D. A. N. Knn, “Klasifikasi jenis umbi berdasarkan citra menggunakan svm dan knn,” vol. 12, no. 1, 2020.
Semangun, H, Penyakit-Penyakit Tanaman Hortikultura. Yogyakarta : Gadjah Mada University Press. 112-123 Hal, 1989.
O. Pythiales, “Penyakit Busuk Daun Kentang,” 1936.
D. Indonesia, P. Penyakit, H. Daun, and D. Indonesia, “Teknologi Budidaya Kentang pada Musim Hujan,” pp. 55–58, 2015.
C. Series, “Beef Image Classification using K-Nearest Neighbor Algorithm for Identification Quality and Freshness Beef Image Classification using K-Nearest Neighbor Algorithm for Identification Quality and Freshness,” 2019, doi: 10.1088/1742-6596/1179/1/012184.
S. Ferdiana, R. Enggar, and R. Dijaya, “Otomatisasi klasifikasi kematangan buah Mengkudu berdasarkan warna dan tekstur,” vol. 3, no. 1, pp. 17–23, 2017.
M. Islam, A. Dinh, and K. Wahid, “Detection of Potato Diseases Using Image Segmentation and Multiclass Support Vector Machine,” pp. 8–11, 2017.
P. Teknik, I. Universitas, and D. Nuswantoro, “KLASIFIKASI BIDANG KERJA LULUSAN MENGGUNAKAN ALGORITMA K-NEAREST NEIGHBOR,” vol. 10, no. April, pp. 31–43, 2014.
R. D. Kusumanto, A. N. Tompunu, and S. Pambudi, “Klasifikasi Warna Menggunakan Pengolahan Model Warna HSV Klasifikasi Warna Menggunakan Pengolahan Model Warna HSV Abstrak,” no. September, 2011.
D. Hariyanto, “STUDI PENENTUAN NILAI RESISTOR MENGGUNAKAN SELEKSI WARNA MODEL HSI PADA CITRA 2D,” pp. 13–22. “PENYAKIT_PENYAKIT_TANAMAN_HORTIKULTURA_D.pdf.”.
E. Kamilah, R. Venantius, H. Ginardi, and C. Fatichah, “Klasifikasi penyakit noda pada citra daun tebu berdasarkan ciri tekstur dan warna menggunakan segmentation-based gray level cooccurrence matrix dan LAB color moments,” vol. 3, no. 1, pp. 1–10, 2017.
Mungki Astiningrum, Putra Prima Arhandi, and Nabilla Aqmarina Ariditya, “Identifikasi Penyakit Pada Daun Tomat Berdasarkan Fitur Warna Dan Tekstur,” J. Inform. Polinema, vol. 6, no. 2, pp. 47–50, 2020, doi: 10.33795/jip.v6i2.320.
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