Detection of wheat Fusarium head blight using UAV-based spectral and image feature fusion
Infection caused by Fusarium head blight (FHB) has severely damaged the quality and yield of wheat in China and threatened the health of humans and livestock. Inaccurate disease detection increases the use cost of pesticide and pollutes farmland, highlighting the need for FHB detection in wheat fiel...
| Main Authors: | , , , , , , , |
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| Format: | Article |
| Language: | English |
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Frontiers Media S.A.
2022-09-01
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| Series: | Frontiers in Plant Science |
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| Online Access: | https://www.frontiersin.org/articles/10.3389/fpls.2022.1004427/full |
| _version_ | 1828110729915400192 |
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| author | Hansu Zhang Linsheng Huang Wenjiang Huang Wenjiang Huang Wenjiang Huang Yingying Dong Yingying Dong Shizhuang Weng Jinling Zhao Huiqin Ma Linyi Liu |
| author_facet | Hansu Zhang Linsheng Huang Wenjiang Huang Wenjiang Huang Wenjiang Huang Yingying Dong Yingying Dong Shizhuang Weng Jinling Zhao Huiqin Ma Linyi Liu |
| author_sort | Hansu Zhang |
| collection | DOAJ |
| description | Infection caused by Fusarium head blight (FHB) has severely damaged the quality and yield of wheat in China and threatened the health of humans and livestock. Inaccurate disease detection increases the use cost of pesticide and pollutes farmland, highlighting the need for FHB detection in wheat fields. The combination of spectral and spatial information provided by image analysis facilitates the detection of infection-related damage in crops. In this study, an effective detection method for wheat FHB based on unmanned aerial vehicle (UAV) hyperspectral images was explored by fusing spectral features and image features. Spectral features mainly refer to band features, and image features mainly include texture and color features. Our aim was to explain all aspects of wheat infection through multi-class feature fusion and to find the best FHB detection method for field wheat combining current advanced algorithms. We first evaluated the quality of the two acquired UAV images and eliminated the excessively noisy bands in the images. Then, the spectral features, texture features, and color features in the images were extracted. The random forest (RF) algorithm was used to optimize features, and the importance value of the features determined whether the features were retained. Feature combinations included spectral features, spectral and texture features fusion, and the fusion of spectral, texture, and color features to combine support vector machine, RF, and back propagation neural network in constructing wheat FHB detection models. The results showed that the model based on the fusion of spectral, texture, and color features using the RF algorithm achieved the best performance, with a prediction accuracy of 85%. The method proposed in this study may provide an effective way of FHB detection in field wheat. |
| first_indexed | 2024-04-11T11:22:49Z |
| format | Article |
| id | doaj.art-8794263ff0d541a5b9d6a524b36ccd2e |
| institution | Directory Open Access Journal |
| issn | 1664-462X |
| language | English |
| last_indexed | 2024-04-11T11:22:49Z |
| publishDate | 2022-09-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Plant Science |
| spelling | doaj.art-8794263ff0d541a5b9d6a524b36ccd2e2022-12-22T04:26:37ZengFrontiers Media S.A.Frontiers in Plant Science1664-462X2022-09-011310.3389/fpls.2022.10044271004427Detection of wheat Fusarium head blight using UAV-based spectral and image feature fusionHansu Zhang0Linsheng Huang1Wenjiang Huang2Wenjiang Huang3Wenjiang Huang4Yingying Dong5Yingying Dong6Shizhuang Weng7Jinling Zhao8Huiqin Ma9Linyi Liu10National Engineering Research Center for Agro-Ecological Big Data Analysis and Application, Anhui University, Hefei, ChinaNational Engineering Research Center for Agro-Ecological Big Data Analysis and Application, Anhui University, Hefei, ChinaState Key Laboratory of Remote Sensing Science, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing, ChinaUniversity of Chinese Academy of Sciences, Beijing, ChinaKey Laboratory for Earth Observation of Hainan Province, Sanya, ChinaState Key Laboratory of Remote Sensing Science, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing, ChinaUniversity of Chinese Academy of Sciences, Beijing, ChinaNational Engineering Research Center for Agro-Ecological Big Data Analysis and Application, Anhui University, Hefei, ChinaNational Engineering Research Center for Agro-Ecological Big Data Analysis and Application, Anhui University, Hefei, ChinaState Key Laboratory of Remote Sensing Science, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing, ChinaState Key Laboratory of Remote Sensing Science, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing, ChinaInfection caused by Fusarium head blight (FHB) has severely damaged the quality and yield of wheat in China and threatened the health of humans and livestock. Inaccurate disease detection increases the use cost of pesticide and pollutes farmland, highlighting the need for FHB detection in wheat fields. The combination of spectral and spatial information provided by image analysis facilitates the detection of infection-related damage in crops. In this study, an effective detection method for wheat FHB based on unmanned aerial vehicle (UAV) hyperspectral images was explored by fusing spectral features and image features. Spectral features mainly refer to band features, and image features mainly include texture and color features. Our aim was to explain all aspects of wheat infection through multi-class feature fusion and to find the best FHB detection method for field wheat combining current advanced algorithms. We first evaluated the quality of the two acquired UAV images and eliminated the excessively noisy bands in the images. Then, the spectral features, texture features, and color features in the images were extracted. The random forest (RF) algorithm was used to optimize features, and the importance value of the features determined whether the features were retained. Feature combinations included spectral features, spectral and texture features fusion, and the fusion of spectral, texture, and color features to combine support vector machine, RF, and back propagation neural network in constructing wheat FHB detection models. The results showed that the model based on the fusion of spectral, texture, and color features using the RF algorithm achieved the best performance, with a prediction accuracy of 85%. The method proposed in this study may provide an effective way of FHB detection in field wheat.https://www.frontiersin.org/articles/10.3389/fpls.2022.1004427/fullhyperspectral imagesUAVcrop stressfeature fusionclassification models |
| spellingShingle | Hansu Zhang Linsheng Huang Wenjiang Huang Wenjiang Huang Wenjiang Huang Yingying Dong Yingying Dong Shizhuang Weng Jinling Zhao Huiqin Ma Linyi Liu Detection of wheat Fusarium head blight using UAV-based spectral and image feature fusion Frontiers in Plant Science hyperspectral images UAV crop stress feature fusion classification models |
| title | Detection of wheat Fusarium head blight using UAV-based spectral and image feature fusion |
| title_full | Detection of wheat Fusarium head blight using UAV-based spectral and image feature fusion |
| title_fullStr | Detection of wheat Fusarium head blight using UAV-based spectral and image feature fusion |
| title_full_unstemmed | Detection of wheat Fusarium head blight using UAV-based spectral and image feature fusion |
| title_short | Detection of wheat Fusarium head blight using UAV-based spectral and image feature fusion |
| title_sort | detection of wheat fusarium head blight using uav based spectral and image feature fusion |
| topic | hyperspectral images UAV crop stress feature fusion classification models |
| url | https://www.frontiersin.org/articles/10.3389/fpls.2022.1004427/full |
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