Automatic System for Crop Pest and Disease Dynamic Monitoring and Early Forecasting
Infected areas and damage levels due to crop pest and disease have been growing seriously according to the climate change. We aim to develop an automatic system to provide national pest and disease dynamic monitoring and early forecasting products, by integrating multisource information (Earth Obser...
| Main Authors: | , , , , , , , , , , |
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| Format: | Article |
| Language: | English |
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IEEE
2020-01-01
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| Series: | IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/9153907/ |
| _version_ | 1811266237720690688 |
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| author | Yingying Dong Fang Xu Linyi Liu Xiaoping Du Binyuan Ren Anting Guo Yun Geng Chao Ruan Huichun Ye Wenjing Huang Yining Zhu |
| author_facet | Yingying Dong Fang Xu Linyi Liu Xiaoping Du Binyuan Ren Anting Guo Yun Geng Chao Ruan Huichun Ye Wenjing Huang Yining Zhu |
| author_sort | Yingying Dong |
| collection | DOAJ |
| description | Infected areas and damage levels due to crop pest and disease have been growing seriously according to the climate change. We aim to develop an automatic system to provide national pest and disease dynamic monitoring and early forecasting products, by integrating multisource information (Earth Observation, meteorological, ecological, entomological, and plant pathological, etc.) and cutting edge research on pest and disease modeling to support decision making in the sustainable management of pest and disease. First, we selected the sensitive indexes for pest and disease habitat monitoring and early forecasting, and then optimized the forecasting model's parameters to enhance its applicability in national level. Second, we developed an automatic system based on web GIS platform to efficiently realize the national pest and disease dynamic habitat monitoring and early forecasting. Finally, we released the pest and disease forecasting thematic maps. China's national disease wheat yellow rust (<italic>Puccinia striiformis</italic>) and national pest oriental migratory locust (<italic>Locusta migratoria manilensis (Meyen)</italic>) are taking as the experimental objects. Based on the developed system, we forecasted the infected areas of rust and locust in China, in 2019, with these R-square values are higher than 0.87. This system would not only promote the efficacy of pest and disease management and prevention by improving accuracy of monitoring and forecasting, but also help to reduce the amount of chemical pesticides, which could thus guarantee food security and agriculture sustainable development in China. |
| first_indexed | 2024-04-12T20:39:35Z |
| format | Article |
| id | doaj.art-a4878e0b7df6463fbdf1c6437e6387c5 |
| institution | Directory Open Access Journal |
| issn | 2151-1535 |
| language | English |
| last_indexed | 2024-04-12T20:39:35Z |
| publishDate | 2020-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing |
| spelling | doaj.art-a4878e0b7df6463fbdf1c6437e6387c52022-12-22T03:17:28ZengIEEEIEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing2151-15352020-01-01134410441810.1109/JSTARS.2020.30133409153907Automatic System for Crop Pest and Disease Dynamic Monitoring and Early ForecastingYingying Dong0https://orcid.org/0000-0002-2865-5020Fang Xu1Linyi Liu2https://orcid.org/0000-0003-4587-2489Xiaoping Du3https://orcid.org/0000-0002-0618-0984Binyuan Ren4Anting Guo5Yun Geng6Chao Ruan7Huichun Ye8Wenjing Huang9Yining Zhu10https://orcid.org/0000-0003-4498-477XKey Laboratory of Digital Earth Science, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing, ChinaBeijing Advanced Innovation Center for Imaging Technology, School of Mathematics, Capital Normal University, Beijing, ChinaKey Laboratory of Digital Earth Science, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing, ChinaKey Laboratory of Digital Earth Science, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing, ChinaNational Agricultural Technology Extension and Service Center, Beijing, ChinaKey Laboratory of Digital Earth Science, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing, ChinaKey Laboratory of Digital Earth Science, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing, ChinaKey Laboratory of Digital Earth Science, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing, ChinaKey Laboratory of Digital Earth Science, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing, ChinaKey Laboratory of Digital Earth Science, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing, ChinaBeijing Advanced Innovation Center for Imaging Technology, School of Mathematics, Capital Normal University, Beijing, ChinaInfected areas and damage levels due to crop pest and disease have been growing seriously according to the climate change. We aim to develop an automatic system to provide national pest and disease dynamic monitoring and early forecasting products, by integrating multisource information (Earth Observation, meteorological, ecological, entomological, and plant pathological, etc.) and cutting edge research on pest and disease modeling to support decision making in the sustainable management of pest and disease. First, we selected the sensitive indexes for pest and disease habitat monitoring and early forecasting, and then optimized the forecasting model's parameters to enhance its applicability in national level. Second, we developed an automatic system based on web GIS platform to efficiently realize the national pest and disease dynamic habitat monitoring and early forecasting. Finally, we released the pest and disease forecasting thematic maps. China's national disease wheat yellow rust (<italic>Puccinia striiformis</italic>) and national pest oriental migratory locust (<italic>Locusta migratoria manilensis (Meyen)</italic>) are taking as the experimental objects. Based on the developed system, we forecasted the infected areas of rust and locust in China, in 2019, with these R-square values are higher than 0.87. This system would not only promote the efficacy of pest and disease management and prevention by improving accuracy of monitoring and forecasting, but also help to reduce the amount of chemical pesticides, which could thus guarantee food security and agriculture sustainable development in China.https://ieeexplore.ieee.org/document/9153907/Pestdiseasemonitoringforecastingsystem |
| spellingShingle | Yingying Dong Fang Xu Linyi Liu Xiaoping Du Binyuan Ren Anting Guo Yun Geng Chao Ruan Huichun Ye Wenjing Huang Yining Zhu Automatic System for Crop Pest and Disease Dynamic Monitoring and Early Forecasting IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing Pest disease monitoring forecasting system |
| title | Automatic System for Crop Pest and Disease Dynamic Monitoring and Early Forecasting |
| title_full | Automatic System for Crop Pest and Disease Dynamic Monitoring and Early Forecasting |
| title_fullStr | Automatic System for Crop Pest and Disease Dynamic Monitoring and Early Forecasting |
| title_full_unstemmed | Automatic System for Crop Pest and Disease Dynamic Monitoring and Early Forecasting |
| title_short | Automatic System for Crop Pest and Disease Dynamic Monitoring and Early Forecasting |
| title_sort | automatic system for crop pest and disease dynamic monitoring and early forecasting |
| topic | Pest disease monitoring forecasting system |
| url | https://ieeexplore.ieee.org/document/9153907/ |
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