Fused-Deep-Features Based Grape Leaf Disease Diagnosis

Rapid and accurate grape leaf disease diagnosis is of great significance to its yield and quality of grape. In this paper, aiming at the identification of grape leaf diseases, a fast and accurate detection method based on fused deep features, extracted from a convolutional neural network (CNN), plus...

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Autores principales:	Yun Peng, Shengyi Zhao, Jizhan Liu
Formato:	article
Lenguaje:	EN
Publicado:	MDPI AG 2021
Materias:	grape leaf disease SVM convolutional neural network (CNN) deep feature fusion Agriculture S
Acceso en línea:	https://doaj.org/article/9152ca6bd7af4f79b7368866d3756459
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spelling	oai:doaj.org-article:9152ca6bd7af4f79b7368866d37564592021-11-25T16:07:52ZFused-Deep-Features Based Grape Leaf Disease Diagnosis10.3390/agronomy111122342073-4395https://doaj.org/article/9152ca6bd7af4f79b7368866d37564592021-11-01T00:00:00Zhttps://www.mdpi.com/2073-4395/11/11/2234https://doaj.org/toc/2073-4395Rapid and accurate grape leaf disease diagnosis is of great significance to its yield and quality of grape. In this paper, aiming at the identification of grape leaf diseases, a fast and accurate detection method based on fused deep features, extracted from a convolutional neural network (CNN), plus a support vector machine (SVM) is proposed. In the research, based on an open dataset, three types of state-of-the-art CNN networks, three kinds of deep feature fusion methods, seven species of deep feature layers, and a multi-class SVM classifier were studied. Firstly, images were resized to meet the input requirements of the CNN network; then, the deep features of the input images were extracted via the specific deep feature layer of the CNN network. Two kinds of deep features from different networks were then fused using different fusion methods to increase the effective classification feature information. Finally, a multi-class SVM classifier was trained with the fused deep features. The experimental results on the open dataset show that the fused deep features with any kind of fusion method can obtain a better classification performance than using a single type of deep feature. The direct concatenation of the Fc1000 deep feature extracted from ResNet50 and ResNet101 can achieve the best classification result compared with the other two fusion methods, and its F1 score is 99.81%. Furthermore, the SVM classifier trained using the proposed method can achieve a classification performance comparable to that of using the CNN model directly, but the training time is less than 1 s, which has an advantage over spending tens of minutes training a CNN model. The experimental results indicate that the method proposed in this paper can achieve fast and accurate identification of grape leaf diseases and meet the needs of actual agricultural production.Yun PengShengyi ZhaoJizhan LiuMDPI AGarticlegrape leaf diseaseSVMconvolutional neural network (CNN)deep feature fusionAgricultureSENAgronomy, Vol 11, Iss 2234, p 2234 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	grape leaf disease SVM convolutional neural network (CNN) deep feature fusion Agriculture S
spellingShingle	grape leaf disease SVM convolutional neural network (CNN) deep feature fusion Agriculture S Yun Peng Shengyi Zhao Jizhan Liu Fused-Deep-Features Based Grape Leaf Disease Diagnosis
description	Rapid and accurate grape leaf disease diagnosis is of great significance to its yield and quality of grape. In this paper, aiming at the identification of grape leaf diseases, a fast and accurate detection method based on fused deep features, extracted from a convolutional neural network (CNN), plus a support vector machine (SVM) is proposed. In the research, based on an open dataset, three types of state-of-the-art CNN networks, three kinds of deep feature fusion methods, seven species of deep feature layers, and a multi-class SVM classifier were studied. Firstly, images were resized to meet the input requirements of the CNN network; then, the deep features of the input images were extracted via the specific deep feature layer of the CNN network. Two kinds of deep features from different networks were then fused using different fusion methods to increase the effective classification feature information. Finally, a multi-class SVM classifier was trained with the fused deep features. The experimental results on the open dataset show that the fused deep features with any kind of fusion method can obtain a better classification performance than using a single type of deep feature. The direct concatenation of the Fc1000 deep feature extracted from ResNet50 and ResNet101 can achieve the best classification result compared with the other two fusion methods, and its F1 score is 99.81%. Furthermore, the SVM classifier trained using the proposed method can achieve a classification performance comparable to that of using the CNN model directly, but the training time is less than 1 s, which has an advantage over spending tens of minutes training a CNN model. The experimental results indicate that the method proposed in this paper can achieve fast and accurate identification of grape leaf diseases and meet the needs of actual agricultural production.
format	article
author	Yun Peng Shengyi Zhao Jizhan Liu
author_facet	Yun Peng Shengyi Zhao Jizhan Liu
author_sort	Yun Peng
title	Fused-Deep-Features Based Grape Leaf Disease Diagnosis
title_short	Fused-Deep-Features Based Grape Leaf Disease Diagnosis
title_full	Fused-Deep-Features Based Grape Leaf Disease Diagnosis
title_fullStr	Fused-Deep-Features Based Grape Leaf Disease Diagnosis
title_full_unstemmed	Fused-Deep-Features Based Grape Leaf Disease Diagnosis
title_sort	fused-deep-features based grape leaf disease diagnosis
publisher	MDPI AG
publishDate	2021
url	https://doaj.org/article/9152ca6bd7af4f79b7368866d3756459
work_keys_str_mv	AT yunpeng fuseddeepfeaturesbasedgrapeleafdiseasediagnosis AT shengyizhao fuseddeepfeaturesbasedgrapeleafdiseasediagnosis AT jizhanliu fuseddeepfeaturesbasedgrapeleafdiseasediagnosis
_version_	1718413329918066688

Fused-Deep-Features Based Grape Leaf Disease Diagnosis

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