Extreme learning machine with feature extraction using GLCM for phosphorus deficiency identification of cocoa plants


Basri Basri(1*); Muhammad Assidiq(2); Harli A. Karim(3); Andi Nuraisyah(4);

(1) Universitas Al Asyariah Mandar
(2) Universitas Al Asyariah Mandar
(3) Universitas Al Asyariah Mandar
(4) Universitas Al Asyariah Mandar
(*) Corresponding Author

  

Abstract


This study aims to analyze the implementation of the Extreme Learning Machine (ELM) Algorithm with Gray Level Co-Occurrence Matrix (GLCM) as an Image Feature Extraction method in identifying phosphorus deficiency in cocoa plants based on leaf characteristics. Characteristic images of cocoa leaves were placed under normal conditions and phosphorus deficiency, each with 250 datasets. The feature extraction process by GLCM was analyzed using the ELM parameter approach in the form of Network Node_Hidden variations and several Activation Functions. The method of this case study was conducted with data collection, algorithm development to validation, and measurement using ROC. It was found that the best accuracy when testing the dataset was 95.14% on the node_hidden 50 networks using the Multiquadric Activation Function. These results indicate that the feature extraction model with GLCM using Contrast, Correlation, Angular Second Moment, and Inverse Difference Momentum properties can be maximized on Multiquadric Activation Function.


Keywords


Extreme Learning Machine; GLCM feature extraction; Phosphorus deficiency; Cocoa.

  
  

Full Text:

PDF
  

Article Metrics

Abstract view: 446 times
PDF view: 84 times
     

Digital Object Identifier

doi  https://doi.org/10.33096/ilkom.v14i2.1226.112-119
  

Cite

References


I. M. Fahmid, H. Harun, M. M. Fahmid, and N. Busthanul, “Competitiveness, production, and productivity of cocoa in Indonesia,” in IOP Conference Series: Earth and Environmental Science, 2018, vol. 157, no. 1, p. 12067.

Basri, Harli, Indrabayu, I. S. Areni, and R. Tamin, “Image processing system for early detection of cocoa fruit pest attack,” J. Phys. Conf. Ser., vol. 1244, p. 12003, Jun. 2019.

Basri, R. Tamin, H. A. Karim, Indrabayu, and I. S. Areni, “Mobile image processing application for cacao’s fruits pest and disease attack using deep learning algorithm,” ICIC Express Lett., vol. 14, no. 10, 2020.

R. R. Waliyansyah, K. Adi, and J. E. Suseno, “Implementasi metode Gray Level Co-occurrence Matrix dalam identifikasi jenis daun tengkawang,” J. Nas. Tek. Elektro Dan Teknol. Inf., vol. 7, no. 1, pp. 50–56, 2018.

Ahmad Izzuddin and M. Rizal Wahyudi, “Pengenalan pola daun untuk membedakan tanaman padi dan gulma menggunakan metode Principal Components Analysis (PCA) dan Extreme Learning Machine (ELM),” ALINIER J. Artif. Intell. Appl., vol. 1, no. 1, pp. 44–51, 2020.

Z. Effendi, I. O. Yosephine, and M. H. A. Sembiring, “Deteksi unsur hara makro N, P, dan K pada daun tanaman kelapa sawit (Elaeis Guineensis Jacq) dengan menggunakan metode Image Processing berdasarkan filter sobel,” J. Agro Estate, vol. 2, no. 1, pp. 42–49, 2018.

F. Zikra, K. Usman, and R. Patmasari, “Detection of chili disease based on leaf image using Gray Level Co-Occurence Matrix Method and Support Vector Machine,” in Prosiding Seminar Nasional Darmajaya, 2021, vol. 1, pp. 105–113.

A. A. Paturrahman, “Analisis pengenalan pola daun berdasarkan fitur canny edge detection dan fitur GLCM menggunakan metode klasifikasi K-Nearest Neighbor (K-NN),” Publ. Tugas Akhir S-1 PSTI FT-UNRAM, 2020.

M. Lihawa and Z. Ilahude, “Ekstraksi ciri spora patogen citra penyakit pada tanaman jagung berbasis tekstur derajat keabuan menggunakan Gray Level Co-occurence Matrix,” J. Technopreneur, vol. 6, no. 2, pp. 101–108, 2018.

W. Li et al., “A gingivitis identification method based on contrast‐limited adaptive histogram equalization, gray‐level co‐occurrence matrix, and extreme learning machine,” Int. J. Imaging Syst. Technol., vol. 29, no. 1, pp. 77–82, 2019.

M. Maksum Vivin Umrotul, N. Dian, and C. Rini, “Image X-ray classification for COVID-19 detection using GCLM-ELM,” J. Mat. MANTIK, pp. 74–85, 2021.

P. K. Paruchuri, V. Gomathy, E. A. Devi, S. Sankhwar, and S. K. Lakshmanaprabu, “An intelligent COVID-19 classification model using optimal grey-level co-occurrence matrix features with extreme learning machine,” Int. J. Comput. Appl. Technol., vol. 65, no. 4, pp. 334–342, 2021.

M. I. Afandi, E. Kurniawan, and S. K. Wijaya, “Rapid grading of mangosteen peel defect using Extreme Learning Machine,” in The 2021 International Conference on Computer, Control, Informatics and Its Applications, 2021, pp. 60–65.

R. Fitriansyah, H. Sukmaningtyas, and M. I. Katili, “International Journal of A llied Medical Sciences and C linical Research ( IJAMSCR ),” vol. 7, no. 4, 2019.

L. C. Ngugi, M. Abelwahab, and M. Abo-Zahhad, “Recent advances in image processing techniques for automated leaf pest and disease recognition–A review,” Inf. Process. Agric., vol. 8, no. 1, pp. 27–51, 2021.

S. R. Sulistiyanti, F. X. Setyawan, and M. Komarudin, “Pengolahan Citra, Dasar dan Contoh Penerapannya.” Teknosain, 2016.

F. Liantoni, “Deteksi tepi citra daun mangga menggunakan algoritma Ant Colony Optimization,” in Seminar Nasional Sains dan Teknologi Terapan III, 2015, vol. 3, pp. 411–418.

M. Astiningrum, P. P. Arhandi, and N. A. Ariditya, “Identifikasi penyakit pada daun tomat berdasarkan fitur warna dan tekstur,” in Seminar Informatika Aplikatif Polinema, 2019, pp. 227–230.

T. Fawcett, “An introduction to ROC analysis,” Pattern Recognit. Lett., vol. 27, no. 8, pp. 861–874, 2006.

W. Rahmawati, “Implementasi extreme learning machine dalam prediksi interaksi protein HIV-1 dengan manusia berdasarkan barisan asam amino.” Fakultas Sains dan Teknologi Universitas Islam Negeri Syarif Hidayatullah, Jakarta, 2020.


Refbacks



Copyright (c) 2022 Basri Basri, Muhammad Assidiq, Harli A. Karim, Andi Nuraisyah

Creative Commons License
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.