Regional prediction of Fusarium head blight occurrence in wheat with remote sensing based Susceptible-Exposed-Infectious-Removed model
Fusarium head blight (FHB) is one of the major fungal diseases affecting wheat production worldwide, influencing kernel development and producing poisonous mycotoxins. Mechanistic models have been extensively used for plant disease simulation; however, regional disease prediction using these models...
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| Format: | Article |
| Language: | English |
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Elsevier
2022-11-01
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| Series: | International Journal of Applied Earth Observations and Geoinformation |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S156984322200231X |
| _version_ | 1811334276241686528 |
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| author | Yingxin Xiao Yingying Dong Wenjiang Huang Linyi Liu |
| author_facet | Yingxin Xiao Yingying Dong Wenjiang Huang Linyi Liu |
| author_sort | Yingxin Xiao |
| collection | DOAJ |
| description | Fusarium head blight (FHB) is one of the major fungal diseases affecting wheat production worldwide, influencing kernel development and producing poisonous mycotoxins. Mechanistic models have been extensively used for plant disease simulation; however, regional disease prediction using these models is difficult because they simplify the heterogeneous plant growth conditions. Herein, we present a remote sensing based Susceptible-Exposed-Infectious-Removed (SEIR) model for regional prediction of FHB occurrence in wheat. Plant properties that are key to the development of FHB are extracted from remote sensing data or data products to initialize or drive the model. Fractional vegetation cover products, time-series curves from satellite images, and vegetation indices were used to indicate plant density, phenology, and vegetation vigor. We applied our model to a plain region in China that suffers greatly from FHB annually. The SEIR model was parameterized by incorporating remote sensing data products, and then calibrated and verified for regional FHB prediction. The model was trained and evaluated by comparing the results of its prediction of FHB incidence to field observations during the susceptible period for wheat; satisfactory results were observed with a correlation coefficient of 0.804, root mean-square error of 0.131, classification accuracy of 0.860, and missed detection rate of 0.035 when the model was initialized with the Optimized Soil Adjusted Vegetation Index (OSAVI). The disease progress curves furnished by our model display an S-shape—a characteristic of polycyclic diseases—which matches the wheat FHB epidemiology. These results indicate that our remote sensing-based SEIR model is promising for the regional prediction of FHB occurrence in wheat. |
| first_indexed | 2024-04-13T17:05:21Z |
| format | Article |
| id | doaj.art-a676bc919e5343b293edea687706dda7 |
| institution | Directory Open Access Journal |
| issn | 1569-8432 |
| language | English |
| last_indexed | 2024-04-13T17:05:21Z |
| publishDate | 2022-11-01 |
| publisher | Elsevier |
| record_format | Article |
| series | International Journal of Applied Earth Observations and Geoinformation |
| spelling | doaj.art-a676bc919e5343b293edea687706dda72022-12-22T02:38:30ZengElsevierInternational Journal of Applied Earth Observations and Geoinformation1569-84322022-11-01114103043Regional prediction of Fusarium head blight occurrence in wheat with remote sensing based Susceptible-Exposed-Infectious-Removed modelYingxin Xiao0Yingying Dong1Wenjiang Huang2Linyi Liu3State Key Laboratory of Remote Sensing Science, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, China; University of Chinese Academy of Sciences, Beijing 100049, ChinaState Key Laboratory of Remote Sensing Science, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, China; University of Chinese Academy of Sciences, Beijing 100049, China; Corresponding author at: State Key Laboratory of Remote Sensing Science, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, China.State Key Laboratory of Remote Sensing Science, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, China; University of Chinese Academy of Sciences, Beijing 100049, ChinaState Key Laboratory of Remote Sensing Science, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, ChinaFusarium head blight (FHB) is one of the major fungal diseases affecting wheat production worldwide, influencing kernel development and producing poisonous mycotoxins. Mechanistic models have been extensively used for plant disease simulation; however, regional disease prediction using these models is difficult because they simplify the heterogeneous plant growth conditions. Herein, we present a remote sensing based Susceptible-Exposed-Infectious-Removed (SEIR) model for regional prediction of FHB occurrence in wheat. Plant properties that are key to the development of FHB are extracted from remote sensing data or data products to initialize or drive the model. Fractional vegetation cover products, time-series curves from satellite images, and vegetation indices were used to indicate plant density, phenology, and vegetation vigor. We applied our model to a plain region in China that suffers greatly from FHB annually. The SEIR model was parameterized by incorporating remote sensing data products, and then calibrated and verified for regional FHB prediction. The model was trained and evaluated by comparing the results of its prediction of FHB incidence to field observations during the susceptible period for wheat; satisfactory results were observed with a correlation coefficient of 0.804, root mean-square error of 0.131, classification accuracy of 0.860, and missed detection rate of 0.035 when the model was initialized with the Optimized Soil Adjusted Vegetation Index (OSAVI). The disease progress curves furnished by our model display an S-shape—a characteristic of polycyclic diseases—which matches the wheat FHB epidemiology. These results indicate that our remote sensing-based SEIR model is promising for the regional prediction of FHB occurrence in wheat.http://www.sciencedirect.com/science/article/pii/S156984322200231XFusarium head blight in wheatRegional predictionRemote sensingSusceptible-Exposed-Infectious-Removed modelFHB epidemiologyPolycyclic diseases |
| spellingShingle | Yingxin Xiao Yingying Dong Wenjiang Huang Linyi Liu Regional prediction of Fusarium head blight occurrence in wheat with remote sensing based Susceptible-Exposed-Infectious-Removed model International Journal of Applied Earth Observations and Geoinformation Fusarium head blight in wheat Regional prediction Remote sensing Susceptible-Exposed-Infectious-Removed model FHB epidemiology Polycyclic diseases |
| title | Regional prediction of Fusarium head blight occurrence in wheat with remote sensing based Susceptible-Exposed-Infectious-Removed model |
| title_full | Regional prediction of Fusarium head blight occurrence in wheat with remote sensing based Susceptible-Exposed-Infectious-Removed model |
| title_fullStr | Regional prediction of Fusarium head blight occurrence in wheat with remote sensing based Susceptible-Exposed-Infectious-Removed model |
| title_full_unstemmed | Regional prediction of Fusarium head blight occurrence in wheat with remote sensing based Susceptible-Exposed-Infectious-Removed model |
| title_short | Regional prediction of Fusarium head blight occurrence in wheat with remote sensing based Susceptible-Exposed-Infectious-Removed model |
| title_sort | regional prediction of fusarium head blight occurrence in wheat with remote sensing based susceptible exposed infectious removed model |
| topic | Fusarium head blight in wheat Regional prediction Remote sensing Susceptible-Exposed-Infectious-Removed model FHB epidemiology Polycyclic diseases |
| url | http://www.sciencedirect.com/science/article/pii/S156984322200231X |
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