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Detection Method for Tomato Leaf Mildew Based on Hyperspectral Fusion Terahertz Technology

Leaf mildew is a common disease of tomato leaves. Its detection is an important means to reduce yield loss from the disease and improve tomato quality. In this study, a new method was developed for the multi-source detection of tomato leaf mildew by THz hyperspectral imaging through combining intern...

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Autores principales: Zhang, Xiaodong, Wang, Yafei, Zhou, Zhankun, Zhang, Yixue, Wang, Xinzhong
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9914460/
https://www.ncbi.nlm.nih.gov/pubmed/36766063
http://dx.doi.org/10.3390/foods12030535
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author Zhang, Xiaodong
Wang, Yafei
Zhou, Zhankun
Zhang, Yixue
Wang, Xinzhong
author_facet Zhang, Xiaodong
Wang, Yafei
Zhou, Zhankun
Zhang, Yixue
Wang, Xinzhong
author_sort Zhang, Xiaodong
collection PubMed
description Leaf mildew is a common disease of tomato leaves. Its detection is an important means to reduce yield loss from the disease and improve tomato quality. In this study, a new method was developed for the multi-source detection of tomato leaf mildew by THz hyperspectral imaging through combining internal and external leaf features. First, multi-source information obtained from tomato leaves of different disease grades was extracted by near-infrared hyperspectral imaging and THz time-domain spectroscopy, while the influence of low-frequency noise was removed by the Savitzky Golay (SG) smoothing algorithm. A genetic algorithm (GA) was used to optimize the selection of the characteristic near-infrared hyperspectral band. Principal component analysis (PCA) was employed to optimize the THz characteristic absorption spectra and power spectrum dimensions. Recognition models were developed for different grades of tomato leaf mildew infestation by incorporating near-infrared hyperspectral imaging, THz absorbance, and power spectra using the backpropagation neural network (BPNN), and the models had recognition rates of 95%, 96.67%, and 95%, respectively. Based on the near-infrared hyperspectral features, THz time-domain spectrum features, and classification model, the probability density of the posterior distribution of tomato leaf health parameter variables was recalculated by a Bayesian network model. Finally, a fusion diagnosis and health evaluation model of tomato leaf mildew with hyperspectral fusion THz was established, and the recognition rate of tomato leaf mildew samples reached 97.12%, which improved the recognition accuracy by 0.45% when compared with the single detection method, thereby achieving the accurate detection of facility diseases.
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spelling pubmed-99144602023-02-11 Detection Method for Tomato Leaf Mildew Based on Hyperspectral Fusion Terahertz Technology Zhang, Xiaodong Wang, Yafei Zhou, Zhankun Zhang, Yixue Wang, Xinzhong Foods Article Leaf mildew is a common disease of tomato leaves. Its detection is an important means to reduce yield loss from the disease and improve tomato quality. In this study, a new method was developed for the multi-source detection of tomato leaf mildew by THz hyperspectral imaging through combining internal and external leaf features. First, multi-source information obtained from tomato leaves of different disease grades was extracted by near-infrared hyperspectral imaging and THz time-domain spectroscopy, while the influence of low-frequency noise was removed by the Savitzky Golay (SG) smoothing algorithm. A genetic algorithm (GA) was used to optimize the selection of the characteristic near-infrared hyperspectral band. Principal component analysis (PCA) was employed to optimize the THz characteristic absorption spectra and power spectrum dimensions. Recognition models were developed for different grades of tomato leaf mildew infestation by incorporating near-infrared hyperspectral imaging, THz absorbance, and power spectra using the backpropagation neural network (BPNN), and the models had recognition rates of 95%, 96.67%, and 95%, respectively. Based on the near-infrared hyperspectral features, THz time-domain spectrum features, and classification model, the probability density of the posterior distribution of tomato leaf health parameter variables was recalculated by a Bayesian network model. Finally, a fusion diagnosis and health evaluation model of tomato leaf mildew with hyperspectral fusion THz was established, and the recognition rate of tomato leaf mildew samples reached 97.12%, which improved the recognition accuracy by 0.45% when compared with the single detection method, thereby achieving the accurate detection of facility diseases. MDPI 2023-01-25 /pmc/articles/PMC9914460/ /pubmed/36766063 http://dx.doi.org/10.3390/foods12030535 Text en © 2023 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Zhang, Xiaodong
Wang, Yafei
Zhou, Zhankun
Zhang, Yixue
Wang, Xinzhong
Detection Method for Tomato Leaf Mildew Based on Hyperspectral Fusion Terahertz Technology
title Detection Method for Tomato Leaf Mildew Based on Hyperspectral Fusion Terahertz Technology
title_full Detection Method for Tomato Leaf Mildew Based on Hyperspectral Fusion Terahertz Technology
title_fullStr Detection Method for Tomato Leaf Mildew Based on Hyperspectral Fusion Terahertz Technology
title_full_unstemmed Detection Method for Tomato Leaf Mildew Based on Hyperspectral Fusion Terahertz Technology
title_short Detection Method for Tomato Leaf Mildew Based on Hyperspectral Fusion Terahertz Technology
title_sort detection method for tomato leaf mildew based on hyperspectral fusion terahertz technology
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9914460/
https://www.ncbi.nlm.nih.gov/pubmed/36766063
http://dx.doi.org/10.3390/foods12030535
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