The team led by Wenjiang Huang at the Aerospace Information Research Institute, Chinese Academy of Sciences has integrated WebGIS technology and remote sensing technology to develop a vegetation pest and disease remote sensing monitoring and forecasting system. This system includes a front-end display system and a back-end management system, enabling online model calculations, product production, and the formulation of control decision schemes. Through this system, the team regularly produces and publishes monitoring and forecasting maps and scientific reports in both Chinese and English for 19 major crop pests and diseases across 38 countries globally. These results have been continuously adopted by domestic and international government departments, organizations, and industry enterprises, including the General Office of the Communist Party of China, the General Office of the State Council, the Ministry of Agriculture and Rural Affairs, the Ministry of Science and Technology, the National Forestry and Grassland Administration, the Food and Agriculture Organization of the United Nations (FAO), the Group on Earth Observations (GEO), and the Global Biodiversity Information Facility (GBIF). The system has significantly enhanced the efficiency of pest and disease monitoring and forecasting and the effectiveness of service delivery.

①Data Query Function: This function allows users to query vegetation pest and disease remote sensing monitoring and forecasting results as needed. When a user submits a request, the server searches the database for the required data. If the requested data is not available in the database, the system calls the data and the corresponding vegetation pest and disease monitoring and forecasting models in the database for online computation. The results are then rendered, published, and finally displayed in the user's browser.

②Information Feedback Function: If the vegetation pest and disease types or time periods provided by the system do not meet the user's needs, users can submit their requirements through the contact information provided by the system. Users need to provide their contact details and specify the types and time periods of vegetation pests and diseases they wish to monitor and forecast. Once the request is submitted, the administrator will review it in the backend. If the request is feasible, the administrator will call the relevant data and models to conduct the monitoring and forecasting. The corresponding results will then be provided to the user.

Academic paper

Dong Yingying,Xu Fang,Liu Linyi,Du Xiaoping,Ren Binyuan, Guo Anting,Geng Yun,Ruan Chao,Ye Huichun,Huang Wenjing,Zhu Yining. Automatic System for Crop Pest and Disease Dynamic Monitoring and Early Forecasting[J]. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2020, 13: 4410-4418.

Software Copyright

[1] Crop Pest and Disease Remote Sensing Monitoring and Prediction System V1.0 (Registration Number: 2016SR179540)

[2] Crop Pest and Disease Reporting Software V1.0 (Registration Number: 2016SR258366)

[3] National Locust Remote Sensing Dynamic Monitoring and Early Warning System [Abbreviation: Locust Remote Sensing Dynamic Monitoring and Early Warning System] V1.0 (Registration Number: 2021SR1961717)

[4] Asia-Africa Desert Locust Disaster Remote Sensing Monitoring System V1.0 (Registration Number: 2022SR0406834)

Data Access Link

https://data.apps.fao.org/map/catalog/srv/chi/catalog.search#/metadata/c3916b80-ec82-4576-87ab-63d608e75c1d

https://www.gbif.org/dataset/dfa36691-529b-43b6-9e61-b76275d94ffc

https://www.gbif.org/dataset

http://map.rscrop.com/

Product Manager

Yingying Dong, Ph.D., is an Associate Researcher and Master's Supervisor at the Aerospace Information Research Institute, Chinese Academy of Sciences. She has been selected for various talent programs, including Beijing's Science and Technology Star and the Youth Innovation Promotion Association of the Chinese Academy of Sciences. Her primary research areas include remote sensing inversion mechanisms and models for crop physical and chemical parameters, monitoring and forecasting mechanisms for pest and disease, and the development of intelligent platforms. Dr. Dong has led 13 projects, including the National Key R&D Program for International Scientific and Technological Innovation Cooperation, the National Natural Science Foundation projects, and tasks under the Strategic Priority Research Program of the Chinese Academy of Sciences (Category A). She has participated in 7 national and provincial scientific research projects and has published 126 academic papers, with 20 as the first author. She has been granted or has applied for 32 invention patents and registered 16 software copyrights. Dr. Dong has received awards such as the Liang Xi Forestry Science and Technology Award and the Da Bei Nong Science and Technology Award. She serves as an editor for international journals including Frontiers in Plant Science, Remote Sensing, and Agronomy.

Related Links: http://www.xinhuanet.com/2022-08/27/c_1128953425.htm

https://www.gov.cn/xinwen/2020-08/31/content_5538693.htm