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- W4380200344 abstract "Early diagnosis of crop plant disease is crucial since many of these diseases pose a considerable threat not only to global food security but also towards agricultural productivity. Aiming that, for detection of mungbean leaf diseases at the beginning stage, we introduce a novel approach based on Gabor Wavelet Transform (GWT) and Cubic SVM in this paper. We perform GWT to decompose the given images into eighteen directional sub-bands and then extract fusion of Gabor Wavelet (GW) based texture features from each detailed GWT coefficient sub-band. Finally, Cubic SVM was deployed to classify three different disease classes by using these GW based features, conducting cross validation at 10 fold. Outcomes of our experimental evaluation using our self-prepared dataset of mungbean leaf diseases exhibit that our proposed method yields overall a sensitivity of 91.11%, a specificity of 95.56%, a precision of 91.39% and an accuracy of 91.11%. Moreover, outcomes obtained from our comparative analysis confirm the supremacy of our proposed framework over 3 currently existing approaches." @default.
- W4380200344 created "2023-06-11" @default.
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- W4380200344 date "2023-01-01" @default.
- W4380200344 modified "2023-10-16" @default.
- W4380200344 title "Gabor Wavelet Based Fused Texture Features for Identification of Mungbean Leaf Diseases" @default.
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- W4380200344 doi "https://doi.org/10.1007/978-3-031-34619-4_3" @default.
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