UK-China Agritech Challenge: CropDoc - Precision Crop Disease Management for Farm Productivity and Food Security

Lead Research Organisation: Manchester Metropolitan University
Department Name: Sch of Computing, Maths and Digital Tech

Abstract

CropDoc seeks to exploit existing research on Potato disease identification and outbreak management in the domain of precision agriculture, agriculture digitisation & decision management support. It will harness cutting-edge technologies (i.e. IoT, mobile devices, crowd sourced data, big data analytics and cloud computing). It will build a decision support system that generates insight from multiple data collected from remote sensing above the fields and IoT ground sensing within the fields for monitoring & prediction of disease in real time. CropDoc will base its data service and analytics platform on open standards and will allow interoperability through open APIs. This will ensure an end-platform ecosystem can emerge that consumes the data & analytics service, allowing farmers to use their platform of choice, while allowing central authorities to identify and manage sector outbreaks.

The initial focus will be on potato late blight disease, one of the most devastating crop diseases in China. In a typical blight pressure season crop protection chemicals cost the industry an estimated $10-20bn per annum. Late blight has been referred to as a 'community disease', due to its ability to spread rapidly from field to field under the right weather conditions. Asexual spores travel easily on the wind when the weather is cool and moist, and can rapidly infect neighbouring fields. As such, understanding the symptoms of the disease and what to do when it is detected are essential to preventing an outbreak from rapidly turning into an epidemic.

Planned Impact

The potential beneficiaries can be broadly classified into two groups including specific users and wider users.

1.1 Specific users
1) Our collaborators from both China and UK, Plant disease control and management agencies, pathologists, and farmers/homegrowers. They will benefit from the project by using our decision support system to assist diagnosis of plant diseases and outbreak management.
2) Researchers from Bioscience, Remote Sensing, and Computer Science. This project will advance new practices for such researchers by delivering an ICT-based solution using data-driven approaches (new algorithms and software). These new algorithms can be applied and adapted to other bioscience related problems (e.g. not just crop diseases but also other plant diseases). This project will complement and strengthen the current research activities in the area of Food Security and Traceability in both host and the partner institution and will contribute to the international standing of UK research in this area and beyond. Most importantly, the output of this project will provide a solid work to attract further funds from different sources (e.g. Horizon 2020).
3) Skilled researchers, fluent in crop disease detection, image processing, remote sensing, machine learning, big data processing and analytics will be trained by the unique interdisciplinary nature of this project which brings together researchers from Computer Science and Bioscience.
4) Research Councils and policy makers, who will be able to draw on the outcomes of the project to advise policy development and decision makers on appropriate strategies for future investment on enhancing plant disease management and improving food safety and traceability.
5) Industry practitioners/farm companies who provide solutions for plant disease control and prevention. Our system could be potentially commercialised and transformed to new products for disease diagnosis and prevention. Additionally, our platform is a standard, open platform, which can be easily plugged into existing plant disease control and management systems.
6) The users from education sector. Students and lecturers in Bioscience, Remote Sensing, and Computer Science will be able to utilize the algorithms and the user-friendly tool for learning and teaching in relation to plant health science and image pattern recognition related topics.

1.2 Wider users
We anticipate that the project will generate wider user interest, in particular from the general public. Our ICT-enabled intelligent solution will increase operability, measurability, visibility for the general public who have little knowledge about the crop diseases. One potential impact of this research will be to educate broader communities and raise public awareness and understanding of plant pathology, and to support the future of plant pathology.

We will adopt different impact activities to ensure all the potential beneficiaries have the opportunity to benefit from this research including:
1) The dissemination of project deliverables and software through the project website hosted at MMU, research publications in prestigious journals and appropriate conferences (e.g. IEEE Transactions on Automation Science and Engineering, Precision Agriculture, The British Society for Plant Pathology, etc.).
2) Seminars and workshops for researchers across multi-disciplines, end users and industry partners.
3) Public engagement activities for general audience by distributing flyers, posters and involving in outreach activities (Science Festival, etc.).
4) Dissemination through both internal publications (e.g.ManMetLife) and external publications to gain wider coverage of the project.
 
Description we have developed deep learning methods based on UAV imagery which could be used for potato disease detection in this project
Exploitation Route The project is still ongoing
Sectors Agriculture, Food and Drink,Digital/Communication/Information Technologies (including Software),Environment

 
Description UK-China Agritech Challenge: CropDoc - Precision Crop Disease Management for Farm Productivity and Food Security
Amount £449,193 (GBP)
Funding ID BB/S020969/1 
Organisation Biotechnology and Biological Sciences Research Council (BBSRC) 
Sector Public
Country United Kingdom
Start 02/2019 
End 02/2022
 
Description Academic partnership 
Organisation University of Chinese Academy of Sciences
Department Institute of Remote Sensing and Digital Earth
Country China 
Sector Charity/Non Profit 
PI Contribution we have worked together to get a research collaborative project.
Collaborator Contribution we have worked together to get a research collaborative project.
Impact still active
Start Year 2019