Dynamic Remote Sensing Prediction for Wheat Fusarium Head Blight by Combining Host and Habitat Conditions
Remote sensing technology provides a feasible option for early prediction for wheat Fusarium head blight (FHB). This study presents a methodology for the dynamic prediction of this classic meteorological crop disease. Host and habitat conditions were comprehensively considered as inputs of the FHB p...
| Main Authors: | , , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2020-09-01
|
| Series: | Remote Sensing |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2072-4292/12/18/3046 |
| _version_ | 1797553344057704448 |
|---|---|
| author | Yingxin Xiao Yingying Dong Wenjiang Huang Linyi Liu Huiqin Ma Huichun Ye Kun Wang |
| author_facet | Yingxin Xiao Yingying Dong Wenjiang Huang Linyi Liu Huiqin Ma Huichun Ye Kun Wang |
| author_sort | Yingxin Xiao |
| collection | DOAJ |
| description | Remote sensing technology provides a feasible option for early prediction for wheat Fusarium head blight (FHB). This study presents a methodology for the dynamic prediction of this classic meteorological crop disease. Host and habitat conditions were comprehensively considered as inputs of the FHB prediction model, and the advantages, accuracy, and generalization ability of the model were evaluated. Firstly, multi-source satellite images were used to predict growth stages and to obtain remote sensing features, then weather features around the predicted stages were extracted. Then, with changes in the inputting features, the severity of FHB was dynamically predicted on February 18, March 6, April 23, and May 9, 2017. Compared to the results obtained by the Logistic model, the prediction with the Relevance Vector Machine performed better, with the overall accuracy on these four dates as 0.71, 0.78, 0.85, and 0.93, and with the area under the receiver operating characteristic curve as 0.66, 0.67, 0.72, and 0.75. Additionally, compared with the prediction with only one factor, the integration of multiple factors was more accurate. The results showed that when the date of the remote sensing features was closer to the heading or flowering stage, the prediction was more accurate, especially in severe areas. Though the habitat conditions were suitable for FHB, the infection can be inhibited when the host’s growth meets certain requirements. |
| first_indexed | 2024-03-10T16:15:01Z |
| format | Article |
| id | doaj.art-e0b158f0b8864ed093eea7e14bd9a679 |
| institution | Directory Open Access Journal |
| issn | 2072-4292 |
| language | English |
| last_indexed | 2024-03-10T16:15:01Z |
| publishDate | 2020-09-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Remote Sensing |
| spelling | doaj.art-e0b158f0b8864ed093eea7e14bd9a6792023-11-20T14:12:28ZengMDPI AGRemote Sensing2072-42922020-09-011218304610.3390/rs12183046Dynamic Remote Sensing Prediction for Wheat Fusarium Head Blight by Combining Host and Habitat ConditionsYingxin Xiao0Yingying Dong1Wenjiang Huang2Linyi Liu3Huiqin Ma4Huichun Ye5Kun Wang6Key Laboratory of Digital Earth Science, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, ChinaKey Laboratory of Digital Earth Science, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, ChinaKey Laboratory of Digital Earth Science, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, ChinaKey Laboratory of Digital Earth Science, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, ChinaKey Laboratory of Digital Earth Science, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, ChinaKey Laboratory of Digital Earth Science, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, ChinaKey Laboratory of Digital Earth Science, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, ChinaRemote sensing technology provides a feasible option for early prediction for wheat Fusarium head blight (FHB). This study presents a methodology for the dynamic prediction of this classic meteorological crop disease. Host and habitat conditions were comprehensively considered as inputs of the FHB prediction model, and the advantages, accuracy, and generalization ability of the model were evaluated. Firstly, multi-source satellite images were used to predict growth stages and to obtain remote sensing features, then weather features around the predicted stages were extracted. Then, with changes in the inputting features, the severity of FHB was dynamically predicted on February 18, March 6, April 23, and May 9, 2017. Compared to the results obtained by the Logistic model, the prediction with the Relevance Vector Machine performed better, with the overall accuracy on these four dates as 0.71, 0.78, 0.85, and 0.93, and with the area under the receiver operating characteristic curve as 0.66, 0.67, 0.72, and 0.75. Additionally, compared with the prediction with only one factor, the integration of multiple factors was more accurate. The results showed that when the date of the remote sensing features was closer to the heading or flowering stage, the prediction was more accurate, especially in severe areas. Though the habitat conditions were suitable for FHB, the infection can be inhibited when the host’s growth meets certain requirements.https://www.mdpi.com/2072-4292/12/18/3046wheatfusarium head blightdynamic predictionremote sensingmultiple factors |
| spellingShingle | Yingxin Xiao Yingying Dong Wenjiang Huang Linyi Liu Huiqin Ma Huichun Ye Kun Wang Dynamic Remote Sensing Prediction for Wheat Fusarium Head Blight by Combining Host and Habitat Conditions Remote Sensing wheat fusarium head blight dynamic prediction remote sensing multiple factors |
| title | Dynamic Remote Sensing Prediction for Wheat Fusarium Head Blight by Combining Host and Habitat Conditions |
| title_full | Dynamic Remote Sensing Prediction for Wheat Fusarium Head Blight by Combining Host and Habitat Conditions |
| title_fullStr | Dynamic Remote Sensing Prediction for Wheat Fusarium Head Blight by Combining Host and Habitat Conditions |
| title_full_unstemmed | Dynamic Remote Sensing Prediction for Wheat Fusarium Head Blight by Combining Host and Habitat Conditions |
| title_short | Dynamic Remote Sensing Prediction for Wheat Fusarium Head Blight by Combining Host and Habitat Conditions |
| title_sort | dynamic remote sensing prediction for wheat fusarium head blight by combining host and habitat conditions |
| topic | wheat fusarium head blight dynamic prediction remote sensing multiple factors |
| url | https://www.mdpi.com/2072-4292/12/18/3046 |
| work_keys_str_mv | AT yingxinxiao dynamicremotesensingpredictionforwheatfusariumheadblightbycombininghostandhabitatconditions AT yingyingdong dynamicremotesensingpredictionforwheatfusariumheadblightbycombininghostandhabitatconditions AT wenjianghuang dynamicremotesensingpredictionforwheatfusariumheadblightbycombininghostandhabitatconditions AT linyiliu dynamicremotesensingpredictionforwheatfusariumheadblightbycombininghostandhabitatconditions AT huiqinma dynamicremotesensingpredictionforwheatfusariumheadblightbycombininghostandhabitatconditions AT huichunye dynamicremotesensingpredictionforwheatfusariumheadblightbycombininghostandhabitatconditions AT kunwang dynamicremotesensingpredictionforwheatfusariumheadblightbycombininghostandhabitatconditions |