k-Nearest Neighbor and Feature Extraction on Detection of Pest and Diseases of Cocoa

  • Mohammad Yazdi Pusadan Universitas Tadulako
  • Syahrullah Universitas Tadulako
  • Merry Universitas Tadulako
  • Ahmad Imam Abdullah Universitas Tadulako
Keywords: Cocoa pods, Detection of pests, and diseases of cocoa pods, HSV, KNN, Confusion Matrix, K-Fold Cross-Validation.

Abstract

Knowledge and utilization of digital images are growing rapidly not only in the fields of medicine and industry but also in the field of agriculture. This knowledge can apply it to a computer-based program that is used to detect agricultural products more effectively and efficiently. this research aims to build a system to detect the types of pests and diseases of cocoa pods because in general, an inspection of pests and diseases of cocoa pods is still manual based on the visual analysis of the color of the pods visually by the human eye which has limitations, which requires more energy to sort, the level of human consistency. In terms of assessing the symptoms of pests and fruit diseases, it is not guaranteed, because humans can experience fatigue, and humans also assess symptoms of pests and fruit diseases, sometimes it is subjective. This study utilizes digital image processing techniques to extract the color features of digital images of cocoa pods, the method used to extract the color features of Hue, Saturation, Value (HSV), and the classification algorithm used by K-Nearest Neighbor. The data used as many as 150 images divided into 70% training data and 30% testing data. Based on the results of trials using k values ​​of 5,7,11 and 13 in the holdout method, the best accuracy is 84.44% with a value of k = 5. And in the k-5 cross-validation test, the best accuracy is also found at k = 5 with a value accuracy of 99.33%.

 

Downloads

Download data is not yet available.

References

U. S. U. Syafrizal Lubis, “Identifikasi Desease pada Buah Kakao Menggunakan Hue Saturation Value dan Moment Invariant dengan Algoritma Backproga,” 2018.

G. H. Yogiswara, R. Magdalena, and H. F. T. S. Putra, “Identifikasi Jenis Desease Pada Kakao Dengan Pengolahan Citra Digital Dan K-nearest Neighbor,” in e-Proceeding of Engineering, 2016, vol. 5, no. 1, pp. 371–377.

K. T. Ikhsan Nur Rahmani, Danang Lelono, “Klasifikasi Kakao Berbasis e-nose dengan Metode Neuro Fuzzy,” vol. 8, no. 1, 2018, doi: 10.22146/ijeis.25512.

N. Wijaya and A. Ridwan, “Klasifikasi Jenis Buah Apel Dengan,” Sisfokom, vol. 08, no. 1, pp. 74–78, 2019.

F. R. Lestari et al., “Deteksi Desease tanaman jeruk siam berdasarkan citra daun menggunakan segmentasi warna rgb-hsv,” pp. 276–283, 2018.

D. S. Tan, R. N. Leong, A. F. Laguna, and C. A. Ngo, “A Method for Detecting and Segmenting Infected Part of Cacao Pods,” no. March, 2016.

P. H. S. Lestari, Zeni Dwi, Nur Nafi’iyah, “Sistem Klasifikasi Jenis Pisang Berdasarkan Ciri Warna HSV Menggunakan Metode K-NN,” pp. 11–15, 2019.

H. Khotimah and M. Nafi, Nur, “Klasifikasi Kematangan Buah Mangga Berdasarkan Citra HSV dengan KNN,” vol. 1, no. 2, pp. 4–7, 2019.

E. Budianita, Jasril, and L. Handayani, “Implementasi Pengolahan Citra dan Klasifikasi K- Nearest Neighbour Untuk Membangun Aplikasi Pembeda Daging Sapi dan Babi,” vol. 12, no. 2, pp. 242–247, 2015.

R. K. N. C. H. K. Naveena, “Identification and Classification of Fruit Diseases,” vol. 6, no. 7, pp. 11–14, 2016.

F. F. AZIZAH, LAILA MARIFATUL, SITTI FADILLAH UMAYAH, “Deteksi Kecacatan Permukaan Buah Manggis Menggunakan Metode Deep Learning dengan Konvolusi Multilayer,” vol. 21, no. 2, pp. 230–236, 2018, doi: 10.18196/st.212229.

R. T. Basri, Harli, Indrabayu, Areni, Intan Sari, “Image Processing System for Early Detection of Cocoa Fruit Pest Attack Image Processing System for Early Detection of Cocoa Fruit Pest Attack,” 2019, doi: 10.1088/1742-6596/1244/1/012003.

S. Nurmuslimah, “Implementasi metode,” vol. 2, no. 2, pp. 91–98, 2016.

N. Harivinod, P. Pooja, N. K. H, B. S. Ashritha, and G. G. Hegde, “Cocoa Care - An Android Application for Cocoa Disease Identification,” pp. 440–444.

D. S. Gaikwad and K. J. Karande, “Image Processing Approach for Grading And Identification Of Diseases On Pomegranate Fruit : An Overview,” vol. 7, no. 2, pp. 519–522, 2016.

and E. S. W. U Lestari, R A Kumalasanti, “Identifying the Quality System of Cocoa Beans to Increase Productivity Using Backpropagation Neural Network Algorithm : A Case Study at Identifying the Quality System of Cocoa Beans to Increase Productivity Using Backpropagation Neural Network Algorithm :,” 2019, doi: 10.1088/1742-6596/1413/1/012033.

A. L. dan Y. Adhitya, “Classifying Physical Morphology of Cocoa Beans Digital Images using Multiclass Ensemble Least-Squares Support Vector Machine Classifying Physical Morphology of Cocoa Beans Digital Images using Multiclass Ensemble Least-Squares Support Vector Machine,” 2018.

S. A. Veites-campos and R. Ramírez-betancour, “Identification of Cocoa Pods with Image Processing and Artificial Neural Networks,” no. 7, 2018.

R. E. Angelia and N. B. Linsangan, “Fermentation Level Classification of Cross Cut Cacao Beans Using k-NN Algorithm.”

Published
2022-07-03
How to Cite
Mohammad Yazdi Pusadan, Syahrullah, Merry, & Ahmad Imam Abdullah. (2022). k-Nearest Neighbor and Feature Extraction on Detection of Pest and Diseases of Cocoa. Jurnal RESTI (Rekayasa Sistem Dan Teknologi Informasi), 6(3), 471 - 480. https://doi.org/10.29207/resti.v6i3.4064
Section
Artikel Rekayasa Sistem Informasi