Identification Method of Wheat Cultivars by Using a Convolutional Neural Network Combined with Images of Multiple Growth Periods of Wheat

Wheat is a very important food crop for mankind. Many new varieties are bred every year. The accurate judgment of wheat varieties can promote the development of the wheat industry and the protection of breeding property rights. Although gene analysis technology can be used to accurately determine wh...

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Autores principales:	Jiameng Gao, Chengzhong Liu, Junying Han, Qinglin Lu, Hengxing Wang, Jianhua Zhang, Xuguang Bai, Jiake Luo
Formato:	article
Lenguaje:	EN
Publicado:	MDPI AG 2021
Materias:	wheat deep learning convolutional neural networks bagging-based ensemble estimator algorithm cultivars identification multiple growth periods Mathematics QA1-939
Acceso en línea:	https://doaj.org/article/25186ea147374a95a46b104bad31d0f6
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spelling	oai:doaj.org-article:25186ea147374a95a46b104bad31d0f62021-11-25T19:06:01ZIdentification Method of Wheat Cultivars by Using a Convolutional Neural Network Combined with Images of Multiple Growth Periods of Wheat10.3390/sym131120122073-8994https://doaj.org/article/25186ea147374a95a46b104bad31d0f62021-10-01T00:00:00Zhttps://www.mdpi.com/2073-8994/13/11/2012https://doaj.org/toc/2073-8994Wheat is a very important food crop for mankind. Many new varieties are bred every year. The accurate judgment of wheat varieties can promote the development of the wheat industry and the protection of breeding property rights. Although gene analysis technology can be used to accurately determine wheat varieties, it is costly, time-consuming, and inconvenient. Traditional machine learning methods can significantly reduce the cost and time of wheat cultivars identification, but the accuracy is not high. In recent years, the relatively popular deep learning methods have further improved the accuracy on the basis of traditional machine learning, whereas it is quite difficult to continue to improve the identification accuracy after the convergence of the deep learning model. Based on the ResNet and SENet models, this paper draws on the idea of the bagging-based ensemble estimator algorithm, and proposes a deep learning model for wheat classification, CMPNet, which is coupled with the tillering period, flowering period, and seed image. This convolutional neural network (CNN) model has a symmetrical structure along the direction of the tensor flow. The model uses collected images of different types of wheat in multiple growth periods. First, it uses the transfer learning method of the ResNet-50, SE-ResNet, and SE-ResNeXt models, and then trains the collected images of 30 kinds of wheat in different growth periods. It then uses the concat layer to connect the output layers of the three models, and finally obtains the wheat classification results through the softmax function. The accuracy of wheat variety identification increased from 92.07% at the seed stage, 95.16% at the tillering stage, and 97.38% at the flowering stage to 99.51%. The model’s single inference time was only 0.0212 s. The model not only significantly improves the classification accuracy of wheat varieties, but also achieves low cost and high efficiency, which makes it a novel and important technology reference for wheat producers, managers, and law enforcement supervisors in the practice of wheat production.Jiameng GaoChengzhong LiuJunying HanQinglin LuHengxing WangJianhua ZhangXuguang BaiJiake LuoMDPI AGarticlewheatdeep learningconvolutional neural networksbagging-based ensemble estimator algorithmcultivars identificationmultiple growth periodsMathematicsQA1-939ENSymmetry, Vol 13, Iss 2012, p 2012 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	wheat deep learning convolutional neural networks bagging-based ensemble estimator algorithm cultivars identification multiple growth periods Mathematics QA1-939
spellingShingle	wheat deep learning convolutional neural networks bagging-based ensemble estimator algorithm cultivars identification multiple growth periods Mathematics QA1-939 Jiameng Gao Chengzhong Liu Junying Han Qinglin Lu Hengxing Wang Jianhua Zhang Xuguang Bai Jiake Luo Identification Method of Wheat Cultivars by Using a Convolutional Neural Network Combined with Images of Multiple Growth Periods of Wheat
description	Wheat is a very important food crop for mankind. Many new varieties are bred every year. The accurate judgment of wheat varieties can promote the development of the wheat industry and the protection of breeding property rights. Although gene analysis technology can be used to accurately determine wheat varieties, it is costly, time-consuming, and inconvenient. Traditional machine learning methods can significantly reduce the cost and time of wheat cultivars identification, but the accuracy is not high. In recent years, the relatively popular deep learning methods have further improved the accuracy on the basis of traditional machine learning, whereas it is quite difficult to continue to improve the identification accuracy after the convergence of the deep learning model. Based on the ResNet and SENet models, this paper draws on the idea of the bagging-based ensemble estimator algorithm, and proposes a deep learning model for wheat classification, CMPNet, which is coupled with the tillering period, flowering period, and seed image. This convolutional neural network (CNN) model has a symmetrical structure along the direction of the tensor flow. The model uses collected images of different types of wheat in multiple growth periods. First, it uses the transfer learning method of the ResNet-50, SE-ResNet, and SE-ResNeXt models, and then trains the collected images of 30 kinds of wheat in different growth periods. It then uses the concat layer to connect the output layers of the three models, and finally obtains the wheat classification results through the softmax function. The accuracy of wheat variety identification increased from 92.07% at the seed stage, 95.16% at the tillering stage, and 97.38% at the flowering stage to 99.51%. The model’s single inference time was only 0.0212 s. The model not only significantly improves the classification accuracy of wheat varieties, but also achieves low cost and high efficiency, which makes it a novel and important technology reference for wheat producers, managers, and law enforcement supervisors in the practice of wheat production.
format	article
author	Jiameng Gao Chengzhong Liu Junying Han Qinglin Lu Hengxing Wang Jianhua Zhang Xuguang Bai Jiake Luo
author_facet	Jiameng Gao Chengzhong Liu Junying Han Qinglin Lu Hengxing Wang Jianhua Zhang Xuguang Bai Jiake Luo
author_sort	Jiameng Gao
title	Identification Method of Wheat Cultivars by Using a Convolutional Neural Network Combined with Images of Multiple Growth Periods of Wheat
title_short	Identification Method of Wheat Cultivars by Using a Convolutional Neural Network Combined with Images of Multiple Growth Periods of Wheat
title_full	Identification Method of Wheat Cultivars by Using a Convolutional Neural Network Combined with Images of Multiple Growth Periods of Wheat
title_fullStr	Identification Method of Wheat Cultivars by Using a Convolutional Neural Network Combined with Images of Multiple Growth Periods of Wheat
title_full_unstemmed	Identification Method of Wheat Cultivars by Using a Convolutional Neural Network Combined with Images of Multiple Growth Periods of Wheat
title_sort	identification method of wheat cultivars by using a convolutional neural network combined with images of multiple growth periods of wheat
publisher	MDPI AG
publishDate	2021
url	https://doaj.org/article/25186ea147374a95a46b104bad31d0f6
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_version_	1718410266600800256

Identification Method of Wheat Cultivars by Using a Convolutional Neural Network Combined with Images of Multiple Growth Periods of Wheat

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