Feature Extraction using Histogram of Oriented Gradients for Image Classification in Maize Leaf Diseases


  • Vincent Mbandu Ochango School of Computing and Information Technolog, Murang’a University of Technology, Murang’a, Kenya
  • Geoffrey Mariga Wambugu School of Computing and Information Technolog, Murang’a University of Technology, Murang’a, Kenya
  • John Gichuki Ndia School of Computing and Information Technolog, Murang’a University of Technology, Murang’a, Kenya




Feature extraction, ORB, HOG, KAZE, Image classification, machine learning, classifier


The paper presents feature extraction methods and classification algorithms used to classify maize leaf disease images. From maize disease images, features are extracted and passed to the machine learning classification algorithm to identify the possible disease based on the features detected using the feature extraction method. The maize disease images used include images of common rust, leaf spot, and northern leaf blight and healthy images. An evaluation was done for the feature extraction method to see which feature extraction method performs best with image classification algorithms. Based on the evaluation, the outcomes revealed Histogram of Oriented Gradients performed best with classifiers compared to KAZE and Oriented FAST and rotated BRIEF. The random forest classifier emerged the best in terms of image classification, based on four performance metrics which are accuracy, precision, recall, and F1-score. The experimental outcome indicated that the random forest had 0.74 accuracy, 0.77 precision, 0.77 recall, and 0.75 F1-score.


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How to Cite

Vincent Mbandu Ochango, Wambugu, G. M. ., & Ndia, J. G. . (2022). Feature Extraction using Histogram of Oriented Gradients for Image Classification in Maize Leaf Diseases . International Journal of Computer and Information Technology(2279-0764), 11(4). https://doi.org/10.24203/ijcit.v11i4.244