Maize Leaf Disease Image Classification Using Bag Of Features

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Wahyudi Setiawan Mohammad Syarief Novi Prastiti


Image classification is a grouping of images based on specific categories. This study discussed the maize leaf disease image classification using Bag-Of-Features (BOF) approach, which is a collection of methods stages to perform feature extraction and classification of the images. The methods we used were Speeded-Up Robust Features (SURF) for feature extraction,  k-means clustering and Support Vector Machine for classification. The maize leaf images used in this research are from the PlantVillage-Dataset. The data consists of 3 types of images: RGB, gray scale and segmentation images. Each type includes 4 disease classes: healthy, cercospora, common rust, and northern leaf blight. There are 50 images for each class. We used 2 scenarios of testing for each type of the data: training and validation; 60 images for training, and the rests (140 images) for validation. Experi-mental results showed that the training accuracies of RGB, gray scale, and segmentation images were 98.25%, 95%, and 74.75% respectively; and the validation accuracies were 72%, 72.75%, and 80.5%.


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SETIAWAN, Wahyudi; SYARIEF, Mohammad; PRASTITI, Novi. Maize Leaf Disease Image Classification Using Bag Of Features. JURNAL INFOTEL, [S.l.], v. 11, n. 2, july 2019. ISSN 2460-0997. Available at: <>. Date accessed: 19 sep. 2019. doi:


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