文章基本信息

标题：Multi‐gradient‐direction based deep learning model for arecanut disease identification
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作者：S. B. Mallikarjuna ; Palaiahnakote Shivakumara ; Vijeta Khare 等
期刊名称：CAAI Transactions on Intelligence Technology
电子版ISSN：2468-2322
出版年度：2022
卷号：7
期号：2
页码：156-166
DOI：10.1049/cit2.12088
语种：English
出版社：IET Digital Library
摘要：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.
关键词：deep learning;image analysis;pattern recognition