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Multi‐gradient‐direction based deep learning model for arecanut disease identification

B. Mallikarjuna, S.; Shivakumara, Palaiahnakote; Khare, Vijeta; Basavanna, M.; Pal, Umapada; Poornima, B.

Multi‐gradient‐direction based deep learning model for arecanut disease identification Thumbnail


Authors

S. B. Mallikarjuna

Vijeta Khare

M. Basavanna

Umapada Pal

B. Poornima



Abstract



Arecanut disease identification is a challenging problem in the field of image processing. In this work, we present a new combination of multi-gradient-direction and deep convolutional neural networks for arecanut disease identification, namely, rot, split and rot-split. Due to the effect of the disease, there are chances of losing vital details in the images. To enhance the fine details in the images affected by diseases, we explore multi-Sobel directional masks for convolving with the input image, which results in enhanced images. The proposed method extracts arecanut as foreground from the enhanced images using Otsu thresholding. Further, the features are extracted for foreground information for disease identification by exploring the ResNet architecture. The advantage of the proposed approach is that it identifies the diseased images from the healthy arecanut images. Experimental results on the dataset of four classes (healthy, rot, split and rot-split) show that the proposed model is superior in terms of classification rate.

Citation

B. Mallikarjuna, S., Shivakumara, P., Khare, V., Basavanna, M., Pal, U., & Poornima, B. (2022). Multi‐gradient‐direction based deep learning model for arecanut disease identification. CAAI Transactions on Intelligence Technology, 7(2), 156–166. https://doi.org/10.1049/cit2.12088

Journal Article Type Article
Acceptance Date Feb 22, 2022
Publication Date Mar 25, 2022
Deposit Date Nov 15, 2024
Publicly Available Date Nov 19, 2024
Journal CAAI Transactions on Intelligence Technology
Print ISSN 2468-2322
Electronic ISSN 2468-2322
Publisher Wiley
Peer Reviewed Peer Reviewed
Volume 7
Issue 2
Pages 156–166
DOI https://doi.org/10.1049/cit2.12088

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