Maize Leaf Disease Image Classification Using Bag of Features

Main Article Content

Wahyudi Setiawan
Mohammad Syarief
Novi Prastiti


Image classification is an image grouping based on similarities features. The features extraction stage is a crucial factor for classifying an image. In the conventional image classification, the features commonly used are morphology, color, and texture with various derivative features. The type and number of appropriate features will affect the classification results. In this study, image classification by using the Bag of Features (BOF) method which can generate features automatically. It consists of 4 stages: feature point location by using grid method, feature extraction by using Speed Up Robust Feature (SURF), clustering word-visual vocabularies by using k-means, and classification by using Support Vector Machine (SVM). The classification data are maize leaf images from the PlantVillage-Dataset. The data consists of 3 types of images: RGB, grayscale, and segmentation images. Each type includes four classes: healthy, Cercospora, common rust, and northern leaf blight. There are 50 images for each class. We used two scenarios of testing for each type of data: training and validation, 70% and 80% images for training, and the rest for validation. Experimental results showed that the validation accuracies of RGB, grayscale, and segmentation images were 82%, 77%, and 85%.


Download data is not yet available.

Article Details

How to Cite
W. Setiawan, M. Syarief, and N. Prastiti, “Maize Leaf Disease Image Classification Using Bag of Features”, INFOTEL, vol. 11, no. 2, pp. 48-54, Jun. 2019.


[1] S. Arivazhagan, R. N. Shebiah, S. S. Nidhyanandhan, and L. Ganesan, “Fruit Recognition using Color and Texture Features Fruit Recognition using Color and Texture Features,” no. October 2010.
[2] S. Chatterjee, D. Dey, and S. Munshi, “Mathematical Morphology aided Shape, Texture and Colour Feature Extraction from Skin Lesion for Identification of Malignant Melanoma,” in International Conference on Condition Assessment Techniques in Electrical Systems (CATCON), 2015, pp. 200–203.
[3] C. Busch and M. Eberle, “Morphological Operations for Color – Coded Images.”
[4] N. Bagri and P. K. Johari, “A Comparative Study on Feature Extraction using Texture and Shape for Content-Based Image Retrieval,” Int. J. Adv. Technol., vol. 80, pp. 41–52, 2015.
[5] H. Al Hiary, S. Bani Ahmad, M. Reyalat, M. Braik, and Z. ALRahamneh, “Fast and Accurate Detection and Classification of Plant Diseases,” Int. J. Comput. Appl., vol. 17, no. 1, pp. 31–38, 2011.
[6] L. Cao, X. San, Y. Zhao, and G. Chen, “Application of machine vision technology in the diagnosis of maize disease,” IFIP Adv. Inf. Commun. Technol., vol. 370 AICT, no. PART 3, pp. 188–194, 2012.
[7] E. Alehegn, “Maize Leaf Diseases Recognition and Classification Based on Imaging and Machine Learning Techniques,” 2017.
[8] S. P. Mohanty, D. Hughes, and M. Salathé, “Using Deep Learning for Image-Based Plant Disease Detection,” Front. Plant Sci., vol. 7, no. 10, pp. 1–7, 2016.
[9] K. R. Aravind, P. Raja, K. V Mukesh, R. Aniirudh, R. Ashiwin, and C. Szczepanski, “Bag of Features and Multiclass Support Vector Machine,” 2018 2nd Int. Conf. Inven. Syst. Control, no. Icisc, pp. 1191–1196, 2018.
[10] A. Foncubierta-Rodríguez, A. de Herrera, and H. Müller, “Medical image retrieval using bag of meaningful visual words: unsupervised visual vocabulary pruning with PLSA,” Proc. 1st ACM Int. Work. Multimed. Index. Inf. Retr. Healthc., pp. 75–82, 2013.
[11] M. R. Zare, W. C. Seng, and A. Mueen, “Automatic classification of medical X-ray images,” Malaysian J. Comput. Sci., vol. 26, no. 1, pp. 9–22, 2013.
[12] Y. Hu, Z. Li, P. Li, Y. Ding and Y. Liu, “Accurate and Fast Building Detection Using Binary Bag-of-Features,” ISPRS - Int. Arch. Photogramm. Remote Sens. Spat. Inf. Sci., vol. XLII-1/W1, no. June, pp. 613–617, 2017.
[13] L. Weizman and J. Goldberger, “Detection of Urban Zones in Satellite Images using Visual Words,” IGARSS 2008 - 2008 IEEE Int. Geosci. Remote Sens. Symp., pp. V-160-V–163, 2008.
[14] D. Q. Xiao, J. Z. Feng, T. Y. Lin, C. H. Pang, and Y. W. Ye, “Classification and recognition scheme for vegetable pests based on the BOF-SVM model,” Int. J. Agric. Biol. Eng., vol. 11, no. 3, pp. 190–196, 2018.
[15] K. Venugoban and A. Ramanan, “Image Classification of Paddy Field Insect Pests Using Gradient-Based Features,” Int. J. Mach. Learn. Comput., vol. 4, no. 1, pp. 1–5, 2014.
[16] H. Bay, T. Tuytelaars, and L. Van Gool, “SURF: Speeded up robust features,” Lect. Notes Comput. Sci. (including Subser. Lect. Notes Artif. Intell. Lect. Notes Bioinformatics), vol. 3951 LNCS, pp. 404–417, 2006.
[17] M. Zhang and R. Askin, “Small Blob Detection in Medical Images,” no. May, 2015.
[18] A. Ming and H. Ma, “A blob detector in color images,” Proc. 6th ACM Int. Conf. Image video Retr. - CIVR ’07, pp. 364–370, 2007.
[19] R. Lakemond, C. Fookes, and S. Sridharan, “Affine adaptation of local image features using the Hessian matrix,” 6th IEEE Int. Conf. Adv. Video Signal Based Surveillance, AVSS, 2009, no. September, pp. 496–501, 2009.
[20] D.-C. Cheng, “Haar Wavelet Analysis Haar Wavelets,” pp. 1–17, 2011.
[21] L. February, “Chapter 1 Overview,” 2010.
[22] S. Ehsan, A. F. Clark, N. ur Rehman, and K. D. McDonald-Maier, “Integral images: Efficient algorithms for their computation and storage in resource-constrained embedded vision systems,” Sensors (Switzerland), vol. 15, no. 7, pp. 16804–16830, 2015.
[23] K. S. Sujatha, G. M. Karthiga, and B. Vinod, “Evaluation of Bag of Visual Words for Category Level Object Recognition,” Int. J. Electron. Signals Syst., vol. 1, no. 4, pp. 104–110, 2012.
[24] A. Kowalczyk, Support Vector Machine. Morrisville, North Carolina: syncfusion, 2017.
[25] Burhanuddin, “Preferensi Penyakit Karat Daun ( Puccinia Polysora Undrew ) Pada Tanaman Jagung,” Pros. Semin. Nas. Serelia, pp. 395–405, 2015.