Deep Convolutional Neural Networks for Tea Tree Pest Recognition and Diagnosis

Due to the benefits of convolutional neural networks (CNNs) in image classification, they have been extensively used in the computerized classification and focus of crop pests. The intention of the current find out about is to advance a deep convolutional neural network to mechanically identify 14 s...

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Autores principales:	Jing Chen, Qi Liu, Lingwang Gao
Formato:	article
Lenguaje:	EN
Publicado:	MDPI AG 2021
Materias:	CNN fine-tune SVM MLP tea pests classification Mathematics QA1-939
Acceso en línea:	https://doaj.org/article/adbe67f04624406684882f9b9d6d5b25
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spelling	oai:doaj.org-article:adbe67f04624406684882f9b9d6d5b252021-11-25T19:07:03ZDeep Convolutional Neural Networks for Tea Tree Pest Recognition and Diagnosis10.3390/sym131121402073-8994https://doaj.org/article/adbe67f04624406684882f9b9d6d5b252021-11-01T00:00:00Zhttps://www.mdpi.com/2073-8994/13/11/2140https://doaj.org/toc/2073-8994Due to the benefits of convolutional neural networks (CNNs) in image classification, they have been extensively used in the computerized classification and focus of crop pests. The intention of the current find out about is to advance a deep convolutional neural network to mechanically identify 14 species of tea pests that possess symmetry properties. (1) As there are not enough tea pests images in the network to train the deep convolutional neural network, we proposes to classify tea pests images by fine-tuning the VGGNET-16 deep convolutional neural network. (2) Through comparison with traditional machine learning algorithms Support Vector Machine (SVM) and Multi-Layer Perceptron (MLP), the performance of our method is evaluated (3) The three methods can identify tea tree pests well: the proposed convolutional neural network classification has accuracy up to 97.75%, while MLP and SVM have accuracies of 76.07% and 68.81%, respectively. Our proposed method performs the best of the assessed recognition algorithms. The experimental results also show that the fine-tuning method is a very powerful and efficient tool for small datasets in practical problems.Jing ChenQi LiuLingwang GaoMDPI AGarticleCNNfine-tuneSVMMLPtea pests classificationMathematicsQA1-939ENSymmetry, Vol 13, Iss 2140, p 2140 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	CNN fine-tune SVM MLP tea pests classification Mathematics QA1-939
spellingShingle	CNN fine-tune SVM MLP tea pests classification Mathematics QA1-939 Jing Chen Qi Liu Lingwang Gao Deep Convolutional Neural Networks for Tea Tree Pest Recognition and Diagnosis
description	Due to the benefits of convolutional neural networks (CNNs) in image classification, they have been extensively used in the computerized classification and focus of crop pests. The intention of the current find out about is to advance a deep convolutional neural network to mechanically identify 14 species of tea pests that possess symmetry properties. (1) As there are not enough tea pests images in the network to train the deep convolutional neural network, we proposes to classify tea pests images by fine-tuning the VGGNET-16 deep convolutional neural network. (2) Through comparison with traditional machine learning algorithms Support Vector Machine (SVM) and Multi-Layer Perceptron (MLP), the performance of our method is evaluated (3) The three methods can identify tea tree pests well: the proposed convolutional neural network classification has accuracy up to 97.75%, while MLP and SVM have accuracies of 76.07% and 68.81%, respectively. Our proposed method performs the best of the assessed recognition algorithms. The experimental results also show that the fine-tuning method is a very powerful and efficient tool for small datasets in practical problems.
format	article
author	Jing Chen Qi Liu Lingwang Gao
author_facet	Jing Chen Qi Liu Lingwang Gao
author_sort	Jing Chen
title	Deep Convolutional Neural Networks for Tea Tree Pest Recognition and Diagnosis
title_short	Deep Convolutional Neural Networks for Tea Tree Pest Recognition and Diagnosis
title_full	Deep Convolutional Neural Networks for Tea Tree Pest Recognition and Diagnosis
title_fullStr	Deep Convolutional Neural Networks for Tea Tree Pest Recognition and Diagnosis
title_full_unstemmed	Deep Convolutional Neural Networks for Tea Tree Pest Recognition and Diagnosis
title_sort	deep convolutional neural networks for tea tree pest recognition and diagnosis
publisher	MDPI AG
publishDate	2021
url	https://doaj.org/article/adbe67f04624406684882f9b9d6d5b25
work_keys_str_mv	AT jingchen deepconvolutionalneuralnetworksforteatreepestrecognitionanddiagnosis AT qiliu deepconvolutionalneuralnetworksforteatreepestrecognitionanddiagnosis AT lingwanggao deepconvolutionalneuralnetworksforteatreepestrecognitionanddiagnosis
_version_	1718410299180056576

Deep Convolutional Neural Networks for Tea Tree Pest Recognition and Diagnosis

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