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

作　　者：S.B.Mallikarjuna Palaiahnakote Shivakumara Vijeta Khare M.Basavanna Umapada Pal B.Poornima 

机构地区：[1]Department of Computer Science and Engineering,Bapuji Institute of Engineering and Technology,Davanagere,Affiliated to Visvesvaraya Theological University,Belagavi,Karnataka,India [2]Faculty of Computer Science and Information Technology,University of Malaya,Kuala Lumpur,Malaysia [3]Adani Institute of Infrastructure Engineering,Ahmedabad,India [4]Department of Computer Science,Davanagere University,Davanagere,Karnataka,India [5]Computer Vision and Pattern Recognition Unit,Indian Statistical Institute,Kolkata,West Bengal,India

出　　处：《CAAI Transactions on Intelligence Technology》2022年第2期156-166,共11页智能技术学报（英文）

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 con-volutional 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 

分 类 号：TP3[自动化与计算机技术—计算机科学与技术]