Hyperspectral imaging for early detection of soybean mosaic disease based on convolutional neural network model
In order to reduce the impact of mosaic disease on soybean production and explore a theoretical basis for rapid detection of early soybean mosaic disease, a novel hyperspectral detection method for early soybean mosaic disease based on convolutional neural network (CNN) model was proposed. First, so...
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| Format: | Article |
| Language: | English |
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Zhejiang University Press
2019-04-01
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| Series: | 浙江大学学报. 农业与生命科学版 |
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| Online Access: | https://www.academax.com/doi/10.3785/j.issn.1008-9209.2018.05.151 |
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| _version_ | 1839607824752050176 |
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| author | GUI Jiangsheng WU Zixian LI Kai |
| author_facet | GUI Jiangsheng WU Zixian LI Kai |
| author_sort | GUI Jiangsheng |
| collection | DOAJ |
| description | In order to reduce the impact of mosaic disease on soybean production and explore a theoretical basis for rapid detection of early soybean mosaic disease, a novel hyperspectral detection method for early soybean mosaic disease based on convolutional neural network (CNN) model was proposed. First, soybean samples inoculated separately with SC3, SC7 viruses and normal soybean samples (Nannong 1138-2) were collected through a hyperspectral system. A region of 40 pixel×40 pixel was selected as the region of interest (ROI) and the average spectral information of ROI was extracted. Then, the CNN model was established based the hyperspectral image. Finally, the recognition rate of the training set in the CNN model reached 94.79%, and the recognition rate of the prediction set reached 92.08%. The recognition rate of the mosaic leaf inoculated with SC3 virus was 88.75%, and the recognition rate of the mosaic leaf inoculated with SC7 virus was 93.13%, and the recognition rate of the normal leaf was 94.38%. Compared with the least square-support vector machine (LSSVM) and extreme learning machine (ELM) models, the CNN model can more fully extract the deep features of the spectrum, and the extracting effect was significantly improved. Thus, this research shows that the CNN model can achieve the detection of early soybean mosaic disease more accurately, and combining the CNN model with hyperspectral methods also provides a new idea for plant disease detection. |
| format | Article |
| id | doaj-art-56a76d25e8ab444fa1013f7515e975a4 |
| institution | Matheson Library |
| issn | 1008-9209 2097-5155 |
| language | English |
| publishDate | 2019-04-01 |
| publisher | Zhejiang University Press |
| record_format | Article |
| series | 浙江大学学报. 农业与生命科学版 |
| spelling | doaj-art-56a76d25e8ab444fa1013f7515e975a42025-08-01T03:53:38ZengZhejiang University Press浙江大学学报. 农业与生命科学版1008-92092097-51552019-04-014525626210.3785/j.issn.1008-9209.2018.05.15110089209Hyperspectral imaging for early detection of soybean mosaic disease based on convolutional neural network modelGUI JiangshengWU ZixianLI KaiIn order to reduce the impact of mosaic disease on soybean production and explore a theoretical basis for rapid detection of early soybean mosaic disease, a novel hyperspectral detection method for early soybean mosaic disease based on convolutional neural network (CNN) model was proposed. First, soybean samples inoculated separately with SC3, SC7 viruses and normal soybean samples (Nannong 1138-2) were collected through a hyperspectral system. A region of 40 pixel×40 pixel was selected as the region of interest (ROI) and the average spectral information of ROI was extracted. Then, the CNN model was established based the hyperspectral image. Finally, the recognition rate of the training set in the CNN model reached 94.79%, and the recognition rate of the prediction set reached 92.08%. The recognition rate of the mosaic leaf inoculated with SC3 virus was 88.75%, and the recognition rate of the mosaic leaf inoculated with SC7 virus was 93.13%, and the recognition rate of the normal leaf was 94.38%. Compared with the least square-support vector machine (LSSVM) and extreme learning machine (ELM) models, the CNN model can more fully extract the deep features of the spectrum, and the extracting effect was significantly improved. Thus, this research shows that the CNN model can achieve the detection of early soybean mosaic disease more accurately, and combining the CNN model with hyperspectral methods also provides a new idea for plant disease detection.https://www.academax.com/doi/10.3785/j.issn.1008-9209.2018.05.151soybeanmosaic diseasehyperspectral detectionconvolutional neural network |
| spellingShingle | GUI Jiangsheng WU Zixian LI Kai Hyperspectral imaging for early detection of soybean mosaic disease based on convolutional neural network model 浙江大学学报. 农业与生命科学版 soybean mosaic disease hyperspectral detection convolutional neural network |
| title | Hyperspectral imaging for early detection of soybean mosaic disease based on convolutional neural network model |
| title_full | Hyperspectral imaging for early detection of soybean mosaic disease based on convolutional neural network model |
| title_fullStr | Hyperspectral imaging for early detection of soybean mosaic disease based on convolutional neural network model |
| title_full_unstemmed | Hyperspectral imaging for early detection of soybean mosaic disease based on convolutional neural network model |
| title_short | Hyperspectral imaging for early detection of soybean mosaic disease based on convolutional neural network model |
| title_sort | hyperspectral imaging for early detection of soybean mosaic disease based on convolutional neural network model |
| topic | soybean mosaic disease hyperspectral detection convolutional neural network |
| url | https://www.academax.com/doi/10.3785/j.issn.1008-9209.2018.05.151 |
| work_keys_str_mv | AT guijiangsheng hyperspectralimagingforearlydetectionofsoybeanmosaicdiseasebasedonconvolutionalneuralnetworkmodel AT wuzixian hyperspectralimagingforearlydetectionofsoybeanmosaicdiseasebasedonconvolutionalneuralnetworkmodel AT likai hyperspectralimagingforearlydetectionofsoybeanmosaicdiseasebasedonconvolutionalneuralnetworkmodel |