Integrating advanced earth observation and environmental information for sustainable management of crop pests and diseases

Lead Research Organisation: CAB International
Department Name: International Development (UK)

Abstract

The project aims to bring together and produce cutting edge research to provide pest and disease monitoring and forecast information, integrating multi-source (Earth Observation (EO), meteorological and vertical looking radar) to support decision making in the sustainable management of insect pests and diseases. The project will explore the integration and fusion of new data sources from recently launched satellites with existing data products. This will overcome spatial and temporal differences to produce new data solutions and algorithms which are suitable for pest and disease monitoring and prediction, intervention efficacy forecasting and estimation of yield losses. The new data products and algorithms will be tested using two candidate systems: a fungal disease of wheat (stripe rust) and a serious insect pest (migratory locust). The corresponding efficacy of a biopesticide used to control the locust will also be explored, with the aim to investigate whether the same data inputs produced during this project can be utilised under a wide range of systems, leading to a greater impact of data assimilation in the future. Models will be validated in the laboratory and in the field to give a measure of certainty of predictions. Additionally, risk and loss estimation will be investigated using cutting edge EO techniques, and monitoring of locusts will be explored using Vertical Looking Radar, a technology which is capable of identifying the size and species of insect flying through a radar beam. In addition to building monitoring and forecasting systems with data assimilated during this project, routes to extend this information to appropriate end users will be explored to ensure maximum impact of technologies developed during the project. The project consortium will work closely with NATESC in China to ensure the system is built in a way that is compatible with existing methods of information dissemination. The project consortium is a strong multidisciplinary team with expertise in EO, vertical-looking radar technology and agricultural research and extension.

Planned Impact

This project will have an impact on a wide range of audiences. The wider academic audience will benefit through the production of new algorithms and data products which may be used in further predictive modelling, monitoring and risk forecasting work. For example improved models for estimating insect body temperature, derived from EO data sources, could be applicable for a wide range of targets not explored within this project and could pave the way for improved pest and disease prediction models. The project consortium will benefit through the exchange of knowledge and ideas between organisations. The nature of the project team will ensure that impact of the work is optimal as the team brings together leading researchers in the field of EO from both China and the UK (RADI-CAS, ZJU, Assimila LTD and Kings College London), and integrates them with leading agricultural research and extension institutions from UK and China (CABI, Rothamsted Research, IPP-CAAS). The synergy of the consortium ensures that developments within the project are directly applicable to the agricultural end user. NATESC (Ministry of Agriculture's National Agro-Tech Extension and Service Centre) will act as an external consultant to ensure that all outputs of the project are applicable to its extension messaging and the final work package of the project aims to identify how messages produced during the project could actually integrate into NATESC's advisory systems in the future, ensuring the maximum impact for this work. The overall impact of the research will lead to pathways for better information messages available to both NATESC as an end user of the information and for farmers who are end users of this information. i.e. when and where a biopesticide application will be optimal; where wheat rust is developing and needs spot application of pesticide. The wider goal of this work is to assist NATESC to work towards a reduction in chemical pesticide use in line with their objectives to have 0% increase in pesticide use by 2020.

Publications

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Huang L (2018) New Triangle Vegetation Indices for Estimating Leaf Area Index on Maize in Journal of the Indian Society of Remote Sensing

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Huiqin, M. (2017) Remote sensing monitoring of wheat powdery mildew based on AdaBoost model combining mRMR algorithm in Transactions of the Chinese Society of Agricultural Engineering

 
Description ADDITIONAL FUNDING: Research Grant, Newton Fund
Amount £606,000 (GBP)
Funding ID ST/N006712/1 
Organisation Science and Technologies Facilities Council (STFC) 
Sector Academic/University
Country United Kingdom
Start 03/2018 
End 03/2019
 
Title Data logger dataset 
Description Field data of temperature and humidity in a wheat experimental site 
Type Of Material Database/Collection of data 
Year Produced 2018 
Provided To Others? No  
Impact Validation of wheat yellow rust development model. 
 
Title Intra canopy temperature related to solar radiation 
Description Field collected data of in canopy temperature related to changes in solar radiation. 
Type Of Material Database/Collection of data 
Year Produced 2018 
Provided To Others? No  
Impact Data used to determine the derivation of link between observed and surface temperature and camopy temperature 
 
Title Locust internal body temperature 
Description Internal body temperature of locusts over two field seasons required for modeling pathogen development 
Type Of Material Database/Collection of data 
Year Produced 2018 
Provided To Others? No  
Impact Use to develop a model predicting how long it will take for a fungal biopesticide to kill 90 % of a locust population. 
 
Title Locust surrogate and weather poles data 
Description Collection of field data of temperature at 4 different levels in the canopy and surrogates of locusts 
Type Of Material Database/Collection of data 
Year Produced 2018 
Provided To Others? No  
Impact Data used to validate locust and pathogen development models 
 
Description Wheat rust modelling partnership 
Organisation Assimila Ltd
PI Contribution Working with RADI and Assimila to develop a model for predicting wheat rust spread. CABI and IPP carried out a literature review on the current state of wheat rust modelling. For the locust and biopesticide development models ZJU and IPP, CAAS worked to collect ground truthing data and developed models. CABI assisted with the ground truthing data collection and model development with Assimila.
Collaborator Contribution IPP and RADI carried out field work to ground truth data. RADI and Assimila worked on developing models for wheat rust prediction and spread. KCL are working on Land Surface Temperature (LST) and how that data fits into the various models while Rothamsted Research are determining the biodiversity of insects affected by locust control insecticides. Assimila are also developing models to predict locust development and time for the biopesticide to kill locusts. IPP, CAAS are also working on biopesticide survival in the environment in Inner Mongolia and ZJU are looking at soil moisture effects in Inner Mongolia.
Impact Data Product and Land Surface Temperature Algorithm Report delivered by KCL; Desk study for preparing baseline of current locust and wheat rust expectations of relevant players delivered jointly by CABI and NATESC
Start Year 2016
 
Description Wheat rust modelling partnership 
Organisation Chinese Academy of Agricultural Sciences
Department Institute of Plant Protection
PI Contribution Working with RADI and Assimila to develop a model for predicting wheat rust spread. CABI and IPP carried out a literature review on the current state of wheat rust modelling. For the locust and biopesticide development models ZJU and IPP, CAAS worked to collect ground truthing data and developed models. CABI assisted with the ground truthing data collection and model development with Assimila.
Collaborator Contribution IPP and RADI carried out field work to ground truth data. RADI and Assimila worked on developing models for wheat rust prediction and spread. KCL are working on Land Surface Temperature (LST) and how that data fits into the various models while Rothamsted Research are determining the biodiversity of insects affected by locust control insecticides. Assimila are also developing models to predict locust development and time for the biopesticide to kill locusts. IPP, CAAS are also working on biopesticide survival in the environment in Inner Mongolia and ZJU are looking at soil moisture effects in Inner Mongolia.
Impact Data Product and Land Surface Temperature Algorithm Report delivered by KCL; Desk study for preparing baseline of current locust and wheat rust expectations of relevant players delivered jointly by CABI and NATESC
Start Year 2016
 
Description Wheat rust modelling partnership 
Organisation King's College London
Country United Kingdom 
Sector Academic/University 
PI Contribution Working with RADI and Assimila to develop a model for predicting wheat rust spread. CABI and IPP carried out a literature review on the current state of wheat rust modelling. For the locust and biopesticide development models ZJU and IPP, CAAS worked to collect ground truthing data and developed models. CABI assisted with the ground truthing data collection and model development with Assimila.
Collaborator Contribution IPP and RADI carried out field work to ground truth data. RADI and Assimila worked on developing models for wheat rust prediction and spread. KCL are working on Land Surface Temperature (LST) and how that data fits into the various models while Rothamsted Research are determining the biodiversity of insects affected by locust control insecticides. Assimila are also developing models to predict locust development and time for the biopesticide to kill locusts. IPP, CAAS are also working on biopesticide survival in the environment in Inner Mongolia and ZJU are looking at soil moisture effects in Inner Mongolia.
Impact Data Product and Land Surface Temperature Algorithm Report delivered by KCL; Desk study for preparing baseline of current locust and wheat rust expectations of relevant players delivered jointly by CABI and NATESC
Start Year 2016
 
Description Wheat rust modelling partnership 
Organisation Rothamsted Research
Country United Kingdom 
Sector Academic/University 
PI Contribution Working with RADI and Assimila to develop a model for predicting wheat rust spread. CABI and IPP carried out a literature review on the current state of wheat rust modelling. For the locust and biopesticide development models ZJU and IPP, CAAS worked to collect ground truthing data and developed models. CABI assisted with the ground truthing data collection and model development with Assimila.
Collaborator Contribution IPP and RADI carried out field work to ground truth data. RADI and Assimila worked on developing models for wheat rust prediction and spread. KCL are working on Land Surface Temperature (LST) and how that data fits into the various models while Rothamsted Research are determining the biodiversity of insects affected by locust control insecticides. Assimila are also developing models to predict locust development and time for the biopesticide to kill locusts. IPP, CAAS are also working on biopesticide survival in the environment in Inner Mongolia and ZJU are looking at soil moisture effects in Inner Mongolia.
Impact Data Product and Land Surface Temperature Algorithm Report delivered by KCL; Desk study for preparing baseline of current locust and wheat rust expectations of relevant players delivered jointly by CABI and NATESC
Start Year 2016
 
Description Wheat rust modelling partnership 
Organisation University of Chinese Academy of Sciences
Department Institute of Remote Sensing and Digital Earth
PI Contribution Working with RADI and Assimila to develop a model for predicting wheat rust spread. CABI and IPP carried out a literature review on the current state of wheat rust modelling. For the locust and biopesticide development models ZJU and IPP, CAAS worked to collect ground truthing data and developed models. CABI assisted with the ground truthing data collection and model development with Assimila.
Collaborator Contribution IPP and RADI carried out field work to ground truth data. RADI and Assimila worked on developing models for wheat rust prediction and spread. KCL are working on Land Surface Temperature (LST) and how that data fits into the various models while Rothamsted Research are determining the biodiversity of insects affected by locust control insecticides. Assimila are also developing models to predict locust development and time for the biopesticide to kill locusts. IPP, CAAS are also working on biopesticide survival in the environment in Inner Mongolia and ZJU are looking at soil moisture effects in Inner Mongolia.
Impact Data Product and Land Surface Temperature Algorithm Report delivered by KCL; Desk study for preparing baseline of current locust and wheat rust expectations of relevant players delivered jointly by CABI and NATESC
Start Year 2016
 
Description Wheat rust modelling partnership 
Organisation Zhejiang University
Country China 
Sector Academic/University 
PI Contribution Working with RADI and Assimila to develop a model for predicting wheat rust spread. CABI and IPP carried out a literature review on the current state of wheat rust modelling. For the locust and biopesticide development models ZJU and IPP, CAAS worked to collect ground truthing data and developed models. CABI assisted with the ground truthing data collection and model development with Assimila.
Collaborator Contribution IPP and RADI carried out field work to ground truth data. RADI and Assimila worked on developing models for wheat rust prediction and spread. KCL are working on Land Surface Temperature (LST) and how that data fits into the various models while Rothamsted Research are determining the biodiversity of insects affected by locust control insecticides. Assimila are also developing models to predict locust development and time for the biopesticide to kill locusts. IPP, CAAS are also working on biopesticide survival in the environment in Inner Mongolia and ZJU are looking at soil moisture effects in Inner Mongolia.
Impact Data Product and Land Surface Temperature Algorithm Report delivered by KCL; Desk study for preparing baseline of current locust and wheat rust expectations of relevant players delivered jointly by CABI and NATESC
Start Year 2016
 
Title A Remote sensing quantitative inversion method and system for crop biophysical and biochemical parameters 
Description The invention comprehensively considers the size and directional characteristics of vegetation canopy reflectance data, and realizes reducing the computational complexity while improving the numerical precision of the physical parameter inversion. 
IP Reference ZL201610077364.9 
Protection Patent granted
Year Protection Granted 2018
Licensed No
Impact The invention helps improve retrieval accuracy
 
Title Detecting specific primers for the endogenous nature of Metarhizium in plant roots 
Description The invention proposed a method to detect specific primers for the endogenous nature of Metarhizium in plant roots. 
IP Reference CN201610395664.1 
Protection Patent granted
Year Protection Granted 2016
Licensed No
Impact The invention helps detect Metarhizium to reduce crop loss
 
Title Green fluorescent protein marker gene egfp specific primer, kit and method for detecting endophyticity of Metarhizium plant 
Description The invention proposed a Green fluorescent protein marker gene egfp specific primer, kit and method for detecting endophyticity of Metarhizium plant. 
IP Reference CN201810648106.0 
Protection Patent granted
Year Protection Granted 2018
Licensed No
Impact The invention makes it convenient in field measurement of Metarhizium
 
Title Leaf area index spatial scale conversion method and device 
Description The device transforms spatial scale of leaf area index from first spatial resolution obtained by leaf area index remote sensing inversion model 
IP Reference CN201610158240.3 
Protection Patent granted
Year Protection Granted 2016
Licensed No
Impact The invention propose an innovative method for LAI retrieval at different scales
 
Title Method for constructing remote sensing and reporting system for crop pests and diseases and remote sensing measuring and reporting system 
Description The invention fully utilizes remote sensing technology, geographic information resources, network sharing technology to realize crop parameter inversion, pest and disease monitoring, pest and disease prediction and other models of network real-time operation 
IP Reference CN201610154069.9 
Protection Patent granted
Year Protection Granted 2016
Licensed No
Impact The system provides data and decision-making support in precision agriculture and plant protection.
 
Title Portable manual multi-angle observation device 
Description The device can quickly adjust the angle of the observation zenith angle to achieve rapid acquisition of crop canopy reflectance under different observation zenith angles 
IP Reference CN201711285282.4 
Protection Patent granted
Year Protection Granted 2017
Licensed No
Impact The device improves efficiency in field measurements
 
Title Surface soil moisture inversion method based on passive microwave remote sensing data 
Description The invention proposed a surface soil moisture inversion method based on passive microwave remote sensing data, and the calculated soil water content data value has higher inversion precision under watery surface conditions. 
IP Reference CN201810801987.5 
Protection Patent granted
Year Protection Granted 2018
Licensed No
Impact The invention improves soil moisture accuracy under watery surface
 
Title Surface soil water reduction method based on multi-source remote sensing satellite fusion data 
Description Using optical remote sensing data to spatially downscale the surface soil inversion by passive microwave to obtain remote sensing images of surface soil moisture with higher spatial resolution. 
IP Reference CN201810083706.7 
Protection Patent granted
Year Protection Granted 2018
Licensed No
Impact The integration of optical and microwave data improves soil water inversion accuracy.
 
Description Baseline study with Stakeholders 
Form Of Engagement Activity Participation in an activity, workshop or similar
Part Of Official Scheme? No
Geographic Reach National
Primary Audience Professional Practitioners
Results and Impact CABI met with a dozen representatives of agencies managing locusts and rust at national, provincial, city, county and farm level. CABI learned about the participants aims and objectives, and the way they like to work and was made aware of the boundaries of responsibility for agencies such as NATESC. A discussion on preferred channels of communication that support people at all the levels best - a highlight was realising just how important channels such as WeChat are to Chinese people, and how a solution to knowledge dissemination should definitely incorporate that alongside existing channels.
Year(s) Of Engagement Activity 2017