Comparative Analysis of Feature Extraction Methods for Cotton Leaf Diseases Detection
DOI:
https://doi.org/10.21015/vtse.v11i3.1626Abstract
Cotton leaf diseases must be accurately detected and classified to reduce plant diseases and output losses. Feature extraction strategies for automated cotton leaf disease diagnosis are compared in this study. The research uses HOG, SIFT, SURF, GLCM, and Gabor wavelets filter feature extractor to extract features. We gathered and preprocessed 2400 cotton leaf images of healthy and diseases, Angular Leaf Spot, Bacterial Blight, Cotton curl leaf disease (CLCuD), as well as Alternaria Disease. K-means clustering separates leaf areas and improves feature extraction in image segmentation. Discriminative features are extracted using the mentioned methods, and Support Vector Machine (SVM) classifier is employed for disease identification. The comparative analysis based on Accuracy, Precision, and Sensitivity reveals the Gabor Wavelet Filter Feature Extractor as the top performer, achieving 92% accuracy on the test dataset containing bacterial blight, curl virus, alternaria, and healthy leaves. While HOG, SIFT, SURF, and GLCM methods also perform well, they are outperformed by the Gabor Wavelet method. This study shows Gabor wavelet-based features can accurately identify and classify cotton leaf illnesses, helping farmers fight plant diseases. The results underscore the need of choosing proper feature extraction methods for autonomous plant disease diagnostic systems.
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