Maize Leaf Disease Image Classification Using Bag of Features
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Abstract
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%.
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