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Detection of Rice Fungal Spores Based on Micro- Hyperspectral and Microfluidic Techniques
As rice is one of the world’s most important food crops, protecting it from fungal diseases is very important for agricultural production. At present, it is difficult to diagnose rice fungal diseases at an early stage using relevant technologies, and there are a lack of rapid detection methods. This...
Autores principales: | , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2023
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9954447/ https://www.ncbi.nlm.nih.gov/pubmed/36832044 http://dx.doi.org/10.3390/bios13020278 |
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author | Zhang, Xiaodong Song, Houjian Wang, Yafei Hu, Lian Wang, Pei Mao, Hanping |
author_facet | Zhang, Xiaodong Song, Houjian Wang, Yafei Hu, Lian Wang, Pei Mao, Hanping |
author_sort | Zhang, Xiaodong |
collection | PubMed |
description | As rice is one of the world’s most important food crops, protecting it from fungal diseases is very important for agricultural production. At present, it is difficult to diagnose rice fungal diseases at an early stage using relevant technologies, and there are a lack of rapid detection methods. This study proposes a microfluidic chip-based method combined with microscopic hyperspectral detection of rice fungal disease spores. First, a microfluidic chip with a dual inlet and three-stage structure was designed to separate and enrich Magnaporthe grisea spores and Ustilaginoidea virens spores in air. Then, the microscopic hyperspectral instrument was used to collect the hyperspectral data of the fungal disease spores in the enrichment area, and the competitive adaptive reweighting algorithm (CARS) was used to screen the characteristic bands of the spectral data collected from the spores of the two fungal diseases. Finally, the support vector machine (SVM) and convolutional neural network (CNN) were used to build the full-band classification model and the CARS filtered characteristic wavelength classification model, respectively. The results showed that the actual enrichment efficiency of the microfluidic chip designed in this study on Magnaporthe grisea spores and Ustilaginoidea virens spores was 82.67% and 80.70%, respectively. In the established model, the CARS-CNN classification model is the best for the classification of Magnaporthe grisea spores and Ustilaginoidea virens spores, and its F1-core index can reach 0.960 and 0.949, respectively. This study can effectively isolate and enrich Magnaporthe grisea spores and Ustilaginoidea virens spores, providing new methods and ideas for early detection of rice fungal disease spores. |
format | Online Article Text |
id | pubmed-9954447 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-99544472023-02-25 Detection of Rice Fungal Spores Based on Micro- Hyperspectral and Microfluidic Techniques Zhang, Xiaodong Song, Houjian Wang, Yafei Hu, Lian Wang, Pei Mao, Hanping Biosensors (Basel) Article As rice is one of the world’s most important food crops, protecting it from fungal diseases is very important for agricultural production. At present, it is difficult to diagnose rice fungal diseases at an early stage using relevant technologies, and there are a lack of rapid detection methods. This study proposes a microfluidic chip-based method combined with microscopic hyperspectral detection of rice fungal disease spores. First, a microfluidic chip with a dual inlet and three-stage structure was designed to separate and enrich Magnaporthe grisea spores and Ustilaginoidea virens spores in air. Then, the microscopic hyperspectral instrument was used to collect the hyperspectral data of the fungal disease spores in the enrichment area, and the competitive adaptive reweighting algorithm (CARS) was used to screen the characteristic bands of the spectral data collected from the spores of the two fungal diseases. Finally, the support vector machine (SVM) and convolutional neural network (CNN) were used to build the full-band classification model and the CARS filtered characteristic wavelength classification model, respectively. The results showed that the actual enrichment efficiency of the microfluidic chip designed in this study on Magnaporthe grisea spores and Ustilaginoidea virens spores was 82.67% and 80.70%, respectively. In the established model, the CARS-CNN classification model is the best for the classification of Magnaporthe grisea spores and Ustilaginoidea virens spores, and its F1-core index can reach 0.960 and 0.949, respectively. This study can effectively isolate and enrich Magnaporthe grisea spores and Ustilaginoidea virens spores, providing new methods and ideas for early detection of rice fungal disease spores. MDPI 2023-02-15 /pmc/articles/PMC9954447/ /pubmed/36832044 http://dx.doi.org/10.3390/bios13020278 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 Song, Houjian Wang, Yafei Hu, Lian Wang, Pei Mao, Hanping Detection of Rice Fungal Spores Based on Micro- Hyperspectral and Microfluidic Techniques |
title | Detection of Rice Fungal Spores Based on Micro- Hyperspectral and Microfluidic Techniques |
title_full | Detection of Rice Fungal Spores Based on Micro- Hyperspectral and Microfluidic Techniques |
title_fullStr | Detection of Rice Fungal Spores Based on Micro- Hyperspectral and Microfluidic Techniques |
title_full_unstemmed | Detection of Rice Fungal Spores Based on Micro- Hyperspectral and Microfluidic Techniques |
title_short | Detection of Rice Fungal Spores Based on Micro- Hyperspectral and Microfluidic Techniques |
title_sort | detection of rice fungal spores based on micro- hyperspectral and microfluidic techniques |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9954447/ https://www.ncbi.nlm.nih.gov/pubmed/36832044 http://dx.doi.org/10.3390/bios13020278 |
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