A generic intelligent tomato classification system for practical applications using DenseNet-201 with transfer learning

Abstract A generic intelligent tomato classification system based on DenseNet-201 with transfer learning was proposed and the augmented training sets obtained by data augmentation methods were employed to train the model. The trained model achieved high classification accuracy on the images of diffe...

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Autores principales:	Tao Lu, Baokun Han, Lipin Chen, Fanqianhui Yu, Changhu Xue
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2021
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/9dd87356d6194258bcebc8b227504420
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spelling	oai:doaj.org-article:9dd87356d6194258bcebc8b2275044202021-12-02T18:49:22ZA generic intelligent tomato classification system for practical applications using DenseNet-201 with transfer learning10.1038/s41598-021-95218-w2045-2322https://doaj.org/article/9dd87356d6194258bcebc8b2275044202021-08-01T00:00:00Zhttps://doi.org/10.1038/s41598-021-95218-whttps://doaj.org/toc/2045-2322Abstract A generic intelligent tomato classification system based on DenseNet-201 with transfer learning was proposed and the augmented training sets obtained by data augmentation methods were employed to train the model. The trained model achieved high classification accuracy on the images of different quality, even those containing high levels of noise. Also, the trained model could accurately and efficiently identify and classify a single tomato image with only 29 ms, indicating that the proposed model has great potential value in real-world applications. The feature visualization of the trained models shows their understanding of tomato images, i.e., the learned common and high-level features. The strongest activations of the trained models show that the correct or incorrect target recognition areas by a model during the classification process will affect its final classification accuracy. Based on this, the results obtained in this study could provide guidance and new ideas to improve the development of intelligent agriculture.Tao LuBaokun HanLipin ChenFanqianhui YuChanghu XueNature PortfolioarticleMedicineRScienceQENScientific Reports, Vol 11, Iss 1, Pp 1-8 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Tao Lu Baokun Han Lipin Chen Fanqianhui Yu Changhu Xue A generic intelligent tomato classification system for practical applications using DenseNet-201 with transfer learning
description	Abstract A generic intelligent tomato classification system based on DenseNet-201 with transfer learning was proposed and the augmented training sets obtained by data augmentation methods were employed to train the model. The trained model achieved high classification accuracy on the images of different quality, even those containing high levels of noise. Also, the trained model could accurately and efficiently identify and classify a single tomato image with only 29 ms, indicating that the proposed model has great potential value in real-world applications. The feature visualization of the trained models shows their understanding of tomato images, i.e., the learned common and high-level features. The strongest activations of the trained models show that the correct or incorrect target recognition areas by a model during the classification process will affect its final classification accuracy. Based on this, the results obtained in this study could provide guidance and new ideas to improve the development of intelligent agriculture.
format	article
author	Tao Lu Baokun Han Lipin Chen Fanqianhui Yu Changhu Xue
author_facet	Tao Lu Baokun Han Lipin Chen Fanqianhui Yu Changhu Xue
author_sort	Tao Lu
title	A generic intelligent tomato classification system for practical applications using DenseNet-201 with transfer learning
title_short	A generic intelligent tomato classification system for practical applications using DenseNet-201 with transfer learning
title_full	A generic intelligent tomato classification system for practical applications using DenseNet-201 with transfer learning
title_fullStr	A generic intelligent tomato classification system for practical applications using DenseNet-201 with transfer learning
title_full_unstemmed	A generic intelligent tomato classification system for practical applications using DenseNet-201 with transfer learning
title_sort	generic intelligent tomato classification system for practical applications using densenet-201 with transfer learning
publisher	Nature Portfolio
publishDate	2021
url	https://doaj.org/article/9dd87356d6194258bcebc8b227504420
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A generic intelligent tomato classification system for practical applications using DenseNet-201 with transfer learning

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