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Convolutional neural network in rice disease recognition: accuracy, speed and lightweight

There are many rice diseases, which have very serious negative effects on rice growth and final yield. It is very important to identify the categories of rice diseases and control them. In the past, the identification of rice disease types was completely dependent on manual work, which required a hi...

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Autores principales: Ning, Hongwei, Liu, Sheng, Zhu, Qifei, Zhou, Teng
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10646333/
https://www.ncbi.nlm.nih.gov/pubmed/38023901
http://dx.doi.org/10.3389/fpls.2023.1269371
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author Ning, Hongwei
Liu, Sheng
Zhu, Qifei
Zhou, Teng
author_facet Ning, Hongwei
Liu, Sheng
Zhu, Qifei
Zhou, Teng
author_sort Ning, Hongwei
collection PubMed
description There are many rice diseases, which have very serious negative effects on rice growth and final yield. It is very important to identify the categories of rice diseases and control them. In the past, the identification of rice disease types was completely dependent on manual work, which required a high level of human experience. But the method often could not achieve the desired effect, and was difficult to popularize on a large scale. Convolutional neural networks are good at extracting localized features from input data, converting low-level shape and texture features into high-level semantic features. Models trained by convolutional neural network technology based on existing data can extract common features of data and make the framework have generalization ability. Applying ensemble learning or transfer learning techniques to convolutional neural network can further improve the performance of the model. In recent years, convolutional neural network technology has been applied to the automatic recognition of rice diseases, which reduces the manpower burden and ensures the accuracy of recognition. In this paper, the applications of convolutional neural network technology in rice disease recognition are summarized, and the fruitful achievements in rice disease recognition accuracy, speed, and mobile device deployment are described. This paper also elaborates on the lightweighting of convolutional neural networks for real-time applications as well as mobile deployments, and the various improvements in the dataset and model structure to enhance the model recognition performance.
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spelling pubmed-106463332023-01-01 Convolutional neural network in rice disease recognition: accuracy, speed and lightweight Ning, Hongwei Liu, Sheng Zhu, Qifei Zhou, Teng Front Plant Sci Plant Science There are many rice diseases, which have very serious negative effects on rice growth and final yield. It is very important to identify the categories of rice diseases and control them. In the past, the identification of rice disease types was completely dependent on manual work, which required a high level of human experience. But the method often could not achieve the desired effect, and was difficult to popularize on a large scale. Convolutional neural networks are good at extracting localized features from input data, converting low-level shape and texture features into high-level semantic features. Models trained by convolutional neural network technology based on existing data can extract common features of data and make the framework have generalization ability. Applying ensemble learning or transfer learning techniques to convolutional neural network can further improve the performance of the model. In recent years, convolutional neural network technology has been applied to the automatic recognition of rice diseases, which reduces the manpower burden and ensures the accuracy of recognition. In this paper, the applications of convolutional neural network technology in rice disease recognition are summarized, and the fruitful achievements in rice disease recognition accuracy, speed, and mobile device deployment are described. This paper also elaborates on the lightweighting of convolutional neural networks for real-time applications as well as mobile deployments, and the various improvements in the dataset and model structure to enhance the model recognition performance. Frontiers Media S.A. 2023-11-01 /pmc/articles/PMC10646333/ /pubmed/38023901 http://dx.doi.org/10.3389/fpls.2023.1269371 Text en Copyright © 2023 Ning, Liu, Zhu and Zhou https://creativecommons.org/licenses/by/4.0/This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author (s) and the copyright owner (s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
spellingShingle Plant Science
Ning, Hongwei
Liu, Sheng
Zhu, Qifei
Zhou, Teng
Convolutional neural network in rice disease recognition: accuracy, speed and lightweight
title Convolutional neural network in rice disease recognition: accuracy, speed and lightweight
title_full Convolutional neural network in rice disease recognition: accuracy, speed and lightweight
title_fullStr Convolutional neural network in rice disease recognition: accuracy, speed and lightweight
title_full_unstemmed Convolutional neural network in rice disease recognition: accuracy, speed and lightweight
title_short Convolutional neural network in rice disease recognition: accuracy, speed and lightweight
title_sort convolutional neural network in rice disease recognition: accuracy, speed and lightweight
topic Plant Science
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10646333/
https://www.ncbi.nlm.nih.gov/pubmed/38023901
http://dx.doi.org/10.3389/fpls.2023.1269371
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