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

Mallikarjuna, S. B. and Shivakumara, Palaiahnakote and Khare, Vijeta and Basavanna, M. and Pal, Umapada and Poornima, B. (2022) Multi-gradient-direction based deep learning model for arecanut disease identification. CAAI Transactions on Intelligence Technology, 7 (2). pp. 156-166. ISSN 2468-6557, DOI

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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.

Item Type: Article
Funders: None
Uncontrolled Keywords: Deep learning; Image analysis; Pattern recognition
Subjects: Q Science > QA Mathematics > QA75 Electronic computers. Computer science
Divisions: Faculty of Computer Science & Information Technology > Department of Computer System & Technology
Depositing User: Ms. Juhaida Abd Rahim
Date Deposited: 05 Oct 2023 04:23
Last Modified: 05 Oct 2023 04:23

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