Rapid nondestructive detection of peanut varieties and peanut mildew based on hyperspectral imaging and stacked machine learning models

Moldy peanut seeds are damaged by mold, which seriously affects the germination rate of peanut seeds. At the same time, the quality and variety purity of peanut seeds profoundly affect the final yield of peanuts and the economic benefits of farmers. In this study, hyperspectral imaging technology wa...

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Published in:	Frontiers in plant science Vol. 13; p. 1047479
Main Authors:	Wu, Qingsong, Xu, Lijia, Zou, Zhiyong, Wang, Jian, Zeng, Qifeng, Wang, Qianlong, Zhen, Jiangbo, Wang, Yuchao, Zhao, Yongpeng, Zhou, Man
Format:	Journal Article
Language:	English
Published:	Switzerland Frontiers Media SA 10.11.2022 Frontiers Media S.A
Subjects:	Accuracy Airborne microorganisms Algorithms Cameras Chromatography Classification Crop yield Ensemble learning Food Food quality Germination Humidity Hyperspectral imaging Imaging techniques Legumes Machine learning Mildew mildew detection Mold nondestructive testing Nuts peanut seeds Peanuts Plant Science Real time Seeds Spectrum analysis stacked ensemble learning model variety classification Vision systems Wavelengths nondestructive testing variety classification peanut seeds stacked ensemble learning model mildew detection
ISSN:	1664-462X, 1664-462X
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Abstract	Moldy peanut seeds are damaged by mold, which seriously affects the germination rate of peanut seeds. At the same time, the quality and variety purity of peanut seeds profoundly affect the final yield of peanuts and the economic benefits of farmers. In this study, hyperspectral imaging technology was used to achieve variety classification and mold detection of peanut seeds. In addition, this paper proposed to use median filtering (MF) to preprocess hyperspectral data, use four variable selection methods to obtain characteristic wavelengths, and ensemble learning models (SEL) as a stable classification model. This paper compared the model performance of SEL and extreme gradient boosting algorithm (XGBoost), light gradient boosting algorithm (LightGBM), and type boosting algorithm (CatBoost). The results showed that the MF-LightGBM-SEL model based on hyperspectral data achieves the best performance. Its prediction accuracy on the data training and data testing reach 98.63% and 98.03%, respectively, and the modeling time was only 0.37s, which proved that the potential of the model to be used in practice. The approach of SEL combined with hyperspectral imaging techniques facilitates the development of a real-time detection system. It could perform fast and non-destructive high-precision classification of peanut seed varieties and moldy peanuts, which was of great significance for improving crop yields.
AbstractList	Moldy peanut seeds are damaged by mold, which seriously affects the germination rate of peanut seeds. At the same time, the quality and variety purity of peanut seeds profoundly affect the final yield of peanuts and the economic benefits of farmers. In this study, hyperspectral imaging technology was used to achieve variety classification and mold detection of peanut seeds. In addition, this paper proposed to use median filtering (MF) to preprocess hyperspectral data, use four variable selection methods to obtain characteristic wavelengths, and ensemble learning models (SEL) as a stable classification model. This paper compared the model performance of SEL and extreme gradient boosting algorithm (XGBoost), light gradient boosting algorithm (LightGBM), and type boosting algorithm (CatBoost). The results showed that the MF-LightGBM-SEL model based on hyperspectral data achieves the best performance. Its prediction accuracy on the data training and data testing reach 98.63% and 98.03%, respectively, and the modeling time was only 0.37s, which proved that the potential of the model to be used in practice. The approach of SEL combined with hyperspectral imaging techniques facilitates the development of a real-time detection system. It could perform fast and non-destructive high-precision classification of peanut seed varieties and moldy peanuts, which was of great significance for improving crop yields. Moldy peanut seeds are damaged by mold, which seriously affects the germination rate of peanut seeds. At the same time, the quality and variety purity of peanut seeds profoundly affect the final yield of peanuts and the economic benefits of farmers. In this study, hyperspectral imaging technology was used to achieve variety classification and mold detection of peanut seeds. In addition, this paper proposed to use median filtering (MF) to preprocess hyperspectral data, use four variable selection methods to obtain characteristic wavelengths, and ensemble learning models (SEL) as a stable classification model. This paper compared the model performance of SEL and extreme gradient boosting algorithm (XGBoost), light gradient boosting algorithm (LightGBM), and type boosting algorithm (CatBoost). The results showed that the MF-LightGBM-SEL model based on hyperspectral data achieves the best performance. Its prediction accuracy on the data training and data testing reach 98.63% and 98.03%, respectively, and the modeling time was only 0.37s, which proved that the potential of the model to be used in practice. The approach of SEL combined with hyperspectral imaging techniques facilitates the development of a real-time detection system. It could perform fast and non-destructive high-precision classification of peanut seed varieties and moldy peanuts, which was of great significance for improving crop yields. Moldy peanut seeds are damaged by mold, which seriously affects the germination rate of peanut seeds. At the same time, the quality and variety purity of peanut seeds profoundly affect the final yield of peanuts and the economic benefits of farmers. In this study, hyperspectral imaging technology was used to achieve variety classification and mold detection of peanut seeds. In addition, this paper proposed to use median filtering (MF) to preprocess hyperspectral data, use four variable selection methods to obtain characteristic wavelengths, and ensemble learning models (SEL) as a stable classification model. This paper compared the model performance of SEL and extreme gradient boosting algorithm (XGBoost), light gradient boosting algorithm (LightGBM), and type boosting algorithm (CatBoost). The results showed that the MF-LightGBM-SEL model based on hyperspectral data achieves the best performance. Its prediction accuracy on the data training and data testing reach 98.63% and 98.03%, respectively, and the modeling time was only 0.37s, which proved that the potential of the model to be used in practice. The approach of SEL combined with hyperspectral imaging techniques facilitates the development of a real-time detection system. It could perform fast and non-destructive high-precision classification of peanut seed varieties and moldy peanuts, which was of great significance for improving crop yields.Moldy peanut seeds are damaged by mold, which seriously affects the germination rate of peanut seeds. At the same time, the quality and variety purity of peanut seeds profoundly affect the final yield of peanuts and the economic benefits of farmers. In this study, hyperspectral imaging technology was used to achieve variety classification and mold detection of peanut seeds. In addition, this paper proposed to use median filtering (MF) to preprocess hyperspectral data, use four variable selection methods to obtain characteristic wavelengths, and ensemble learning models (SEL) as a stable classification model. This paper compared the model performance of SEL and extreme gradient boosting algorithm (XGBoost), light gradient boosting algorithm (LightGBM), and type boosting algorithm (CatBoost). The results showed that the MF-LightGBM-SEL model based on hyperspectral data achieves the best performance. Its prediction accuracy on the data training and data testing reach 98.63% and 98.03%, respectively, and the modeling time was only 0.37s, which proved that the potential of the model to be used in practice. The approach of SEL combined with hyperspectral imaging techniques facilitates the development of a real-time detection system. It could perform fast and non-destructive high-precision classification of peanut seed varieties and moldy peanuts, which was of great significance for improving crop yields.
Author	Wang, Jian Wang, Yuchao Zou, Zhiyong Zhao, Yongpeng Wang, Qianlong Xu, Lijia Zeng, Qifeng Zhen, Jiangbo Zhou, Man Wu, Qingsong
AuthorAffiliation	1 College of Mechanical and Electrical Engineering, Sichuan Agricultural University , Yaan , China 2 College of Food Sciences, Sichuan Agricultural University , Yaan , China
AuthorAffiliation_xml	– name: 2 College of Food Sciences, Sichuan Agricultural University , Yaan , China – name: 1 College of Mechanical and Electrical Engineering, Sichuan Agricultural University , Yaan , China
Author_xml	– sequence: 1 givenname: Qingsong surname: Wu fullname: Wu, Qingsong – sequence: 2 givenname: Lijia surname: Xu fullname: Xu, Lijia – sequence: 3 givenname: Zhiyong surname: Zou fullname: Zou, Zhiyong – sequence: 4 givenname: Jian surname: Wang fullname: Wang, Jian – sequence: 5 givenname: Qifeng surname: Zeng fullname: Zeng, Qifeng – sequence: 6 givenname: Qianlong surname: Wang fullname: Wang, Qianlong – sequence: 7 givenname: Jiangbo surname: Zhen fullname: Zhen, Jiangbo – sequence: 8 givenname: Yuchao surname: Wang fullname: Wang, Yuchao – sequence: 9 givenname: Yongpeng surname: Zhao fullname: Zhao, Yongpeng – sequence: 10 givenname: Man surname: Zhou fullname: Zhou, Man
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Keywords	nondestructive testing variety classification peanut seeds stacked ensemble learning model mildew detection
Language	English
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Notes	ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 14 content type line 23 This article was submitted to Sustainable and Intelligent Phytoprotection, a section of the journal Frontiers in Plant Science These authors have contributed equally to this work Edited by: Ning Yang, Jiangsu University, China Reviewed by: Yongcheng Jiang, Tianjin Agricultural University, China; Yongming Chen, Hubei Normal University, China
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Snippet	Moldy peanut seeds are damaged by mold, which seriously affects the germination rate of peanut seeds. At the same time, the quality and variety purity of...
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SubjectTerms	Accuracy Airborne microorganisms Algorithms Cameras Chromatography Classification Crop yield Ensemble learning Food Food quality Germination Humidity Hyperspectral imaging Imaging techniques Legumes Machine learning Mildew mildew detection Mold nondestructive testing Nuts peanut seeds Peanuts Plant Science Real time Seeds Spectrum analysis stacked ensemble learning model variety classification Vision systems Wavelengths
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Title	Rapid nondestructive detection of peanut varieties and peanut mildew based on hyperspectral imaging and stacked machine learning models
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