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A method for detecting the quality of cotton seeds based on an improved ResNet50 model

The accurate and rapid detection of cotton seed quality is crucial for safeguarding cotton cultivation. To increase the accuracy and efficiency of cotton seed detection, a deep learning model, which was called the improved ResNet50 (Impro-ResNet50), was used to detect cotton seed quality. First, the...

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Detalles Bibliográficos
Autores principales: Du, Xinwu, Si, Laiqiang, Li, Pengfei, Yun, Zhihao
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Public Library of Science 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9931132/
https://www.ncbi.nlm.nih.gov/pubmed/36791128
http://dx.doi.org/10.1371/journal.pone.0273057
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author Du, Xinwu
Si, Laiqiang
Li, Pengfei
Yun, Zhihao
author_facet Du, Xinwu
Si, Laiqiang
Li, Pengfei
Yun, Zhihao
author_sort Du, Xinwu
collection PubMed
description The accurate and rapid detection of cotton seed quality is crucial for safeguarding cotton cultivation. To increase the accuracy and efficiency of cotton seed detection, a deep learning model, which was called the improved ResNet50 (Impro-ResNet50), was used to detect cotton seed quality. First, the convolutional block attention module (CBAM) was embedded into the ResNet50 model to allow the model to learn both the vital channel information and spatial location information of the image, thereby enhancing the model’s feature extraction capability and robustness. The model’s fully connected layer was then modified to accommodate the cotton seed quality detection task. An improved LRelu-Softplus activation function was implemented to facilitate the rapid and straightforward quantification of the model training procedure. Transfer learning and the Adam optimization algorithm were used to train the model to reduce the number of parameters and accelerate the model’s convergence. Finally, 4419 images of cotton seeds were collected for training models under controlled conditions. Experimental results demonstrated that the Impro-ResNet50 model could achieve an average detection accuracy of 97.23% and process a single image in 0.11s. Compared with Squeeze-and-Excitation Networks (SE) and Coordination Attention (CA), the model’s feature extraction capability was superior. At the same time, compared with classical models such as AlexNet, VGG16, GoogLeNet, EfficientNet, and ResNet18, this model had superior detection accuracy and complexity balances. The results indicate that the Impro-ResNet50 model has a high detection accuracy and a short recognition time, which meet the requirements for accurate and rapid detection of cotton seed quality.
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spelling pubmed-99311322023-02-16 A method for detecting the quality of cotton seeds based on an improved ResNet50 model Du, Xinwu Si, Laiqiang Li, Pengfei Yun, Zhihao PLoS One Research Article The accurate and rapid detection of cotton seed quality is crucial for safeguarding cotton cultivation. To increase the accuracy and efficiency of cotton seed detection, a deep learning model, which was called the improved ResNet50 (Impro-ResNet50), was used to detect cotton seed quality. First, the convolutional block attention module (CBAM) was embedded into the ResNet50 model to allow the model to learn both the vital channel information and spatial location information of the image, thereby enhancing the model’s feature extraction capability and robustness. The model’s fully connected layer was then modified to accommodate the cotton seed quality detection task. An improved LRelu-Softplus activation function was implemented to facilitate the rapid and straightforward quantification of the model training procedure. Transfer learning and the Adam optimization algorithm were used to train the model to reduce the number of parameters and accelerate the model’s convergence. Finally, 4419 images of cotton seeds were collected for training models under controlled conditions. Experimental results demonstrated that the Impro-ResNet50 model could achieve an average detection accuracy of 97.23% and process a single image in 0.11s. Compared with Squeeze-and-Excitation Networks (SE) and Coordination Attention (CA), the model’s feature extraction capability was superior. At the same time, compared with classical models such as AlexNet, VGG16, GoogLeNet, EfficientNet, and ResNet18, this model had superior detection accuracy and complexity balances. The results indicate that the Impro-ResNet50 model has a high detection accuracy and a short recognition time, which meet the requirements for accurate and rapid detection of cotton seed quality. Public Library of Science 2023-02-15 /pmc/articles/PMC9931132/ /pubmed/36791128 http://dx.doi.org/10.1371/journal.pone.0273057 Text en © 2023 Du et al https://creativecommons.org/licenses/by/4.0/This is an open access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
spellingShingle Research Article
Du, Xinwu
Si, Laiqiang
Li, Pengfei
Yun, Zhihao
A method for detecting the quality of cotton seeds based on an improved ResNet50 model
title A method for detecting the quality of cotton seeds based on an improved ResNet50 model
title_full A method for detecting the quality of cotton seeds based on an improved ResNet50 model
title_fullStr A method for detecting the quality of cotton seeds based on an improved ResNet50 model
title_full_unstemmed A method for detecting the quality of cotton seeds based on an improved ResNet50 model
title_short A method for detecting the quality of cotton seeds based on an improved ResNet50 model
title_sort method for detecting the quality of cotton seeds based on an improved resnet50 model
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9931132/
https://www.ncbi.nlm.nih.gov/pubmed/36791128
http://dx.doi.org/10.1371/journal.pone.0273057
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