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Multi-Class Weed Recognition Using Hybrid CNN-SVM Classifier
The Convolutional Neural Network (CNN) is one of the widely used deep learning models that offers the chance to boost farming productivity through autonomous inference of field conditions. In this paper, CNN is connected to a Support Vector Machine (SVM) to form a new model CNN-SVM; the CNN models c...
Autores principales: | , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2023
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10458607/ https://www.ncbi.nlm.nih.gov/pubmed/37631689 http://dx.doi.org/10.3390/s23167153 |
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author | Wu, Yanjuan He, Yuzhe Wang, Yunliang |
author_facet | Wu, Yanjuan He, Yuzhe Wang, Yunliang |
author_sort | Wu, Yanjuan |
collection | PubMed |
description | The Convolutional Neural Network (CNN) is one of the widely used deep learning models that offers the chance to boost farming productivity through autonomous inference of field conditions. In this paper, CNN is connected to a Support Vector Machine (SVM) to form a new model CNN-SVM; the CNN models chosen are ResNet-50 and VGG16 and the CNN-SVM models formed are ResNet-50-SVM and VGG16-SVM. The method consists of two parts: ResNet-50 and VGG16 for feature extraction and SVM for classification. This paper uses the public multi-class weeds dataset DeepWeeds for training and testing. The proposed ResNet-50-SVM and VGG16-SVM approaches achieved 97.6% and 95.9% recognition accuracies on the DeepWeeds dataset, respectively. The state-of-the-art networks (VGG16, ResNet-50, GoogLeNet, Densenet-121, and PSO-CNN) with the same dataset are accurate at 93.2%, 96.1%, 93.6%, 94.3%, and 96.9%, respectively. In comparison, the accuracy of the proposed methods has been improved by 1.5% and 2.7%, respectively. The proposed ResNet-50-SVM and the VGG16-SVM weed classification approaches are effective and can achieve high recognition accuracy. |
format | Online Article Text |
id | pubmed-10458607 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-104586072023-08-27 Multi-Class Weed Recognition Using Hybrid CNN-SVM Classifier Wu, Yanjuan He, Yuzhe Wang, Yunliang Sensors (Basel) Article The Convolutional Neural Network (CNN) is one of the widely used deep learning models that offers the chance to boost farming productivity through autonomous inference of field conditions. In this paper, CNN is connected to a Support Vector Machine (SVM) to form a new model CNN-SVM; the CNN models chosen are ResNet-50 and VGG16 and the CNN-SVM models formed are ResNet-50-SVM and VGG16-SVM. The method consists of two parts: ResNet-50 and VGG16 for feature extraction and SVM for classification. This paper uses the public multi-class weeds dataset DeepWeeds for training and testing. The proposed ResNet-50-SVM and VGG16-SVM approaches achieved 97.6% and 95.9% recognition accuracies on the DeepWeeds dataset, respectively. The state-of-the-art networks (VGG16, ResNet-50, GoogLeNet, Densenet-121, and PSO-CNN) with the same dataset are accurate at 93.2%, 96.1%, 93.6%, 94.3%, and 96.9%, respectively. In comparison, the accuracy of the proposed methods has been improved by 1.5% and 2.7%, respectively. The proposed ResNet-50-SVM and the VGG16-SVM weed classification approaches are effective and can achieve high recognition accuracy. MDPI 2023-08-13 /pmc/articles/PMC10458607/ /pubmed/37631689 http://dx.doi.org/10.3390/s23167153 Text en © 2023 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). |
spellingShingle | Article Wu, Yanjuan He, Yuzhe Wang, Yunliang Multi-Class Weed Recognition Using Hybrid CNN-SVM Classifier |
title | Multi-Class Weed Recognition Using Hybrid CNN-SVM Classifier |
title_full | Multi-Class Weed Recognition Using Hybrid CNN-SVM Classifier |
title_fullStr | Multi-Class Weed Recognition Using Hybrid CNN-SVM Classifier |
title_full_unstemmed | Multi-Class Weed Recognition Using Hybrid CNN-SVM Classifier |
title_short | Multi-Class Weed Recognition Using Hybrid CNN-SVM Classifier |
title_sort | multi-class weed recognition using hybrid cnn-svm classifier |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10458607/ https://www.ncbi.nlm.nih.gov/pubmed/37631689 http://dx.doi.org/10.3390/s23167153 |
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