Comparison of Support Vector Machine (SVM), K-Nearest Neighbor (K-NN), and Stochastic Gradient Descent (SGD) for Classifying Corn Leaf Disease based on Histogram of Oriented Gradients (HOG) Feature Extraction

Firdaus Solihin, Departemen of Informatics, Faculty of Engineering, University of Trunojoyo, Madura, Bangkalan, Indonesia, Indonesia
Muhammad Syarief, Departemen of Informatics, Faculty of Engineering, University of Trunojoyo, Madura, Bangkalan, Indonesia, Indonesia
Eka Mala Sari Rochman, Departemen of Informatics, Faculty of Engineering, University of Trunojoyo, Madura, Bangkalan, Indonesia, Indonesia
Aeri Rachmad, Departemen of Informatics, Faculty of Engineering, University of Trunojoyo, Madura, Bangkalan, Indonesia, Indonesia

Abstract


Image classification involves categorizing an image's pixels into specific classes based on their unique characteristics. It has diverse applications in everyday life. One such application is the classification of diseases on corn leaves. Corn is a widely consumed staple food in Indonesia, and healthy corn plants are crucial for meeting market demands. Currently, disease identification in corn plants relies on manual checks, which are time-consuming and less effective. This research aims to automate disease identification on corn leaves using the Support Vector Machine (SVM), K-Nearest Neighbor (K-NN) with K=2, and Stochastic Gradient Descent (SGD) algorithms. The classification process utilizes the Histogram of Oriented Gradients (HOG) feature extraction method with a dataset of corn leaf images. The classification results achieved an accuracy of 71.44%, AUC of 79.16%, precision of 70.08%, recall of 71.44%, and f1 score of 67.11%. The highest accuracy was obtained by combining HOG feature extraction with the SGD algorithm.


Keywords


classification; corn-leaf; disease; HOG; K-NN; SGD; SVM

Full Text:

PDF

References


S. S. Putro, M. A. Syakur, E. M. S. Rochman, and A. Rachmad, "Comparison of backpropagation and ERNN methods in predicting corn production," Commun. Math. Biol. Neurosci, pp. 1-17, 2022.

Ministry of Trade of the Republic of Indonesia. (2022, Feb.) Analisis Perkembangan Harga Bahan Pangan Pokok di Pasar Domestik dan International. [Online].

M. S. Sudjono, "Penyakit Jagung dan Pengendaliannya," Balai Penelitian Tanaman Pangan Maros, vol. 1, pp. 34-36, 2015.

Subash Subedi, "A review on important maize diseases and their management in Nepal," Journal of Maize Research and Development, vol. 1, no. 1, pp. 28-52, 2015.

Yusniza Sulong, Abd Jamil Zakaria , Salmah Mohamed, Mohammad Hailmi Sajiili, and Norhayati Ngah, "Survey on Pest and Disease of Corn (Zea Mays Linn) grown at BRIS Soil Area," JOURNAL OF AGROBIOTECHNOLOGY, vol. 10, no. 1S, pp. 75-87, 2019.

J. Chen, W. Wang, D. Zhang, A. Zeb, and Y. A. & Nanehkaran, "Attention embedded lightweight network for maize disease recognition," Plant Pathology, vol. 70, no. 3, pp. 630-640, 2021.

J. Liu, M. Wang, and L., & Li, X. Bao, "EfficientNet based recognition of maize diseases by leaf image classification," Journal of Physics: Conference Series, vol. 1693, no. 1, 2020.

M. Syarief and W. Setiawan, "Convolutional neural network for maize leaf disease image classification," Telkomnika (Telecommunication Computing Electronics and Control), vol. 18, no. 3, pp. 1376-1381, 2020.

T Gladima Nisia and S Rajesh, "Extraction of High-level and Low-level feature for classification of Image using Ridgelet and CNN based Image Classification," IOP Publishing, In Journal of Physics: Conference Series, vol. 1911, no. 1, pp. 1-6, 2021.

Wei Zhou, Gao Shengyu, Ling Zhang, and Xin Lou, "Histogram of Oriented Gradients Feature Extraction From Raw Bayer Pattern Images," IEEE Transactions on Circuits and Systems II: Express Briefs PP, vol. 67, no. 5, pp. 946-950, 2020.

S V Shidlovskiy, A S Bondarchuk, S Poslavsky, and M V Shikhman, "Reducing dimensions of the histogram of oriented gradients (HOG) feature vector," Journal of Physics: Conference Series, vol. 1611, no. 1, pp. 1-7, 2020.

Reinaldo, Natanael Manurung, Juara Immanuel Simbolon, and Christnatalis, "Traffic sign detection using histogram of oriented gradients and max margin object detection ," IOP Conf. Series: Journal of Physics, vol. 1230, no. 1, pp. 1-9, 2018.

Masna Wati , Haviluddin, Novianti Puspitasari, Edy Budiman, and Robbi Rahim, "First-order Feature Extraction Methods for Image Texture and Melanoma Skin Cancer Detection," IOP Conf. Series: Journal of Physics, vol. 1230, no. 1, pp. 1-9, 2018.

Shijin Khumar and Dharun, "Extraction of Texture Features using GLCM and Shape Features using Connected Regions," International Journal of Engineering and Technology, vol. 8, no. 6, pp. 2926-2930, 2016.

Fazal Malik and Baharum Baharudin, "The Statistical Quantized Histogram Texture Features Analysis for Image Retrieval Based on Median and Laplacian Filters in the DCT Domain," The International Arab Journal of Information Technolog, vol. 10, no. 6, pp. 1-9, 2013.

Hartayuni Sain and Santi Wulan Purnami, "Combine Sampling Support Vector Machine for Imbalanced Data Classification," Procedia Computer Science, vol. 72, pp. 59-66, 2015.

Raquel Rodríguez-Pérez and Jürgen Bajorath, "Evolution of Support Vector Machine and Regression Modeling in Chemoinformatics and Drug Discovery," Journal of Computer-Aided Molecular Design , vol. 36, pp. 355–362, 2022.

Nello Cristianini and John Shawe-Taylor, An Introduction to Support Vector Machine and Other Kernel-Based Learning Methods.: Cambridge University Press, 2000.

Zhiliang Liu and Hongbing Xu, "Kernel Parameter Selection for Support Vector Machine Classification," Journal of Algorithms & Computational Technology , vol. 8, pp. 163-177, 2013.

Gufron, Bayu Surarso, and Rahmat Gernowo, "Implementation of the K-Nearest Neighbor Method to determine the Classification of the Study Program Oprational Budget in Higher Education," in 1st International Conference of Health, Science & Technology, 2019, pp. 201-204.

Sitefanus Hulu, Poltak Sihombing, and Sutarman, "Analysis of Performance Cross Validation Method and K-Nearest Neighbor in Classification Data," International Journal of Research and Review, vol. 7, no. 4, pp. 69-73, 2020.

Shadi Diab, "Optimizing Stochastic Gradient Descent in Text Classification Based on Fine-Tuning Hyper-Parameters Approach," International Journal of Computer Science and Information Security, vol. 16, no. 12, pp. 155-160, 2016.

Taarun Srinivas et al., "Novel Based Ensemble Machine Learning Classifiers for Detecting Breast Cancer," Mathematical Problems in Engineering, pp. 1-16, 2022.

Nijat Mehdiyev, David Enke, Peter Fettke, and Peter Loos, "Evaluating Forecasting Methods by Considering Different Accuracy Measures," Proceda Computer Science, vol. 95, pp. 264 – 271, 2016.

Rung-Ching Chen, Christine Dewi, Su-Wen Huang, and Rezzy Eko Caraka, "Selecting critical features for data classification based on machine learning methods," Journal of Big Data, pp. 1-26, 2020.

Tharwat, "Classification assessment methods," Applied Computing and, vol. 17, pp. 168-192, 2021.

Moka Uma Devi, "Categorizing The Age Group And Measuring Accuracy Of Fuzzy Model," International Journal of Electronics and Communication Engineering and Technology, vol. 10, no. 5, pp. 36-46, 2019.




DOI: https://doi.org/10.21831/elinvo.v8i1.55759

Refbacks

  • There are currently no refbacks.


Copyright (c) 2023 Elinvo (Electronics, Informatics, and Vocational Education)

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

Our Journal indexed by:

ISSN 2477-2399 (online) || ISSN 2580-6424 (print)

View My Stats

Flag Counter