Automatic adaptive weighted fusion of features-based approach for plant disease identification

IF 2.1 Q3 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE Journal of Intelligent Systems Pub Date : 2023-01-01 DOI:10.1515/jisys-2022-0247
Kirti, N. Rajpal, V. P. Vishwakarma
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引用次数: 1

Abstract

Abstract With the rapid expansion in plant disease detection, there has been a progressive increase in the demand for more accurate systems. In this work, we propose a new method combining color information, edge information, and textural information to identify diseases in 14 different plants. A novel 3-branch architecture is proposed containing the color information branch, an edge information branch, and a textural information branch extracting the textural information with the help of the central difference convolution network (CDCN). ResNet-18 was chosen as the base architecture of the deep neural network (DNN). Unlike the traditional DNNs, the weights adjust automatically during the training phase and provide the best of all the ratios. The experiments were performed to determine individual and combinational features’ contribution to the classification process. Experimental results of the PlantVillage database with 38 classes show that the proposed method has higher accuracy, i.e., 99.23%, than the existing feature fusion methods for plant disease identification.
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基于特征自适应加权融合的植物病害识别方法
摘要随着植物病害检测的迅速发展,对更精确的系统的需求也在不断增加。在这项工作中,我们提出了一种结合颜色信息、边缘信息和纹理信息的方法来识别14种不同植物的疾病。提出了一种新的包含颜色信息分支、边缘信息分支和纹理信息分支的三分支结构,利用中心差分卷积网络(CDCN)提取纹理信息。选择ResNet-18作为深度神经网络(DNN)的基础架构。与传统的深度神经网络不同,权重在训练阶段自动调整,并提供所有比例中的最佳比例。进行实验以确定单个和组合特征对分类过程的贡献。PlantVillage数据库38个分类的实验结果表明,与现有的特征融合方法相比,该方法具有更高的植物病害识别准确率,达到99.23%。
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来源期刊
Journal of Intelligent Systems
Journal of Intelligent Systems COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE-
CiteScore
5.90
自引率
3.30%
发文量
77
审稿时长
51 weeks
期刊介绍: The Journal of Intelligent Systems aims to provide research and review papers, as well as Brief Communications at an interdisciplinary level, with the field of intelligent systems providing the focal point. This field includes areas like artificial intelligence, models and computational theories of human cognition, perception and motivation; brain models, artificial neural nets and neural computing. It covers contributions from the social, human and computer sciences to the analysis and application of information technology.
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