Enabling wide area persistent remote sensing for agriculture applications by developing and coordinating multiple heterogeneous platforms

Lead Research Organisation: Loughborough University
Department Name: Aeronautical and Automotive Engineering

Abstract

This project is to develop wide area, persistent remote sensing capability for agriculture applications by developing and coordinating a number of sensing platforms such as satellites, unmanned aerial vehicles, airships, and even ground unmanned vehicles. It is aimed to provide an unprecedented high density of spatial and temporal information required in sustainable agriculture. Agriculture is currently facing serious challenges in securing food supply to the world population. Global population will continue to grow in the near future with an increasing aging-population structure. Thus the demand for food is expected to continue to rise as global population grows and a rising middle class desires more meat and dairy products in rapidly developing countries like China. Similarly, the total demand for energy and fresh water will increase as a result of economic growth in China. The increased frequency of extreme weather events occurring will seriously hamper food production. Sustainable intensive agriculture is widely perceived to be the answer to the challenge, which aims to increase food production without adversely damaging natural resources and environment. This increased food production is achieved through breeding cultivars with increased resource efficiency and yield potential, better deployment of these cultivars, and better crop husbandry to reduce crop losses due to adverse factors (e.g. pests, diseases, flooding, drought). Remote sensing plays a key role in developing sustainable agriculture for China and other countries. Remote sensing provides timely, synoptic, cost-effective and repetitive information about crops, their growth environments and other key relevant elements such as migratory insects and diseases. The observed data from the remote sensing can find a wide range of applications; for example, not only providing timely information for farm management or early detection of diseases but also for understanding the biological science involved in agriculture and developing statistical crop modelling for future predictions. Despite all the advances in remote sensing platforms particularly unmanned aerial vehicles, they are still not able to provide required wide area persistent remote sensing capability; for example, both macroscopic and microscopic data are required in understanding outbreak and the propagation of some diseases and pests. This project is to advance the current remote sensing capability by two approaches: 1) further improving the current sensing platforms particularly airships and small scale unmanned aircraft including both pointing systems and vehicles; 2) more importantly coordinating different types of existing sensing platforms (i.e. satellites, unmanned aircraft, or airship) based on their performance and characteristics. With the aim of reducing operation cost and the reliance on the operator's experience/skills while fulfilling the remote sensing requirements for a specific application, both strategical and tactical decision making, planning and coordination tools for the deployment of the platforms will be developed to automate most of the remote sensing tasks for agriculture using autonomous system technologies.

Planned Impact

The direct impact of this work would be on agriculture and environment of China. This project will develop technologies enabling wide area persistent remote sensing with a high density of spatial and temporal observation information as required by sustainable intensive farming. This would enable a wide range of applications of remoting sensing technologies such as better crop health monitoring, early detection of pest and disease, and provision of information for better farm management including targeted intervention/treatment.

The outcome of this project will also support a better understanding in a number of biological science subjects including plant science, while providing new understanding in pest and disease. For example, it is quite challenging to understand the influence of nutrition and other environment factors on the migration of locusts, and the relationship between group collective behaviour and individual ones. Large amounts of observation data at different spatial and temporal scales are required continuously to track a large swarm of locusts and some specific individual movement (day and night). Observation data collected at different scales are also important to build the links between microscopic and macroscopic studies of biological science and crop growth. Various modelling tools and methods have been proposed in agriculture but, at each level, it is imperative to collect adequate and sufficient data to calibrate models. Thus, this project will benefit UK, China and other countries in biological science and other subjects.

The wide area persistent remote sensing capability can find a wide range of applications in addition to sustainable intensive agriculture. For example, it can be directly applied to forest protection and monitoring including fire detection and monitoring and pest/disease monitoring. It could also be applied to wild animal protection and tracking (e.g. migratory animals), pollution monitoring and tracking, flooding prediction, etc., where wide area and persistent remote sensing capability is required.
 
Description Agri-Tech China Network+
Amount £40,000 (GBP)
Funding ID Proof-of-Concept-Round 1: PC003 
Organisation Rothamsted Research 
Sector Academic/University
Country United Kingdom of Great Britain & Northern Ireland (UK)
Start 04/2017 
End 08/2017
 
Description Agri-Tech China Network+ (ATCNN) Large Project Award: Remote Sensing in Agriculture in China: Applying remote sensing to improve nitrogen use efficiency for potato breeding and commercial production
Amount £88,000 (GBP)
Organisation Science and Technologies Facilities Council (STFC) 
Sector Academic/University
Country United Kingdom of Great Britain & Northern Ireland (UK)
Start 04/2018 
End 03/2019
 
Description Agri-Tech in China Newton Network+ (ATCNN) Small Project Award: Assessing the reliability of RS data to predict crop disease
Amount £12,700 (GBP)
Organisation Rothamsted Research 
Sector Academic/University
Country United Kingdom of Great Britain & Northern Ireland (UK)
Start 04/2018 
End 06/2018
 
Description Autonomous safe driving system for agriculture spray machines; Agri-Tech in China: Newton Network+
Amount £30,000 (GBP)
Funding ID SM013 
Organisation Rothamsted Research 
Sector Academic/University
Country United Kingdom of Great Britain & Northern Ireland (UK)
Start 02/2018 
End 06/2018
 
Description COSMIC P&D: Pest and Disease Emergence Prediction and Control for Sustainable Agriculture (PADEPSA)
Amount £192,000 (GBP)
Organisation Science and Technologies Facilities Council (STFC) 
Sector Academic/University
Country United Kingdom of Great Britain & Northern Ireland (UK)
Start 02/2018 
End 03/2019
 
Description Innovate UK: Jiangsu-UK Industrial Challenge Programme; AgriRobot: Autonomous agricultural robot system for precision spraying
Amount £150,000 (GBP)
Funding ID Project Number 104016 
Organisation Innovate UK 
Sector Public
Country United Kingdom of Great Britain & Northern Ireland (UK)
Start 03/2018 
End 02/2020
 
Description NeWMap: Enhanced farm-specific NutriEnt and Water stress Maps).
Amount £10,000 (GBP)
Organisation Rothamsted Research 
Sector Academic/University
Country United Kingdom of Great Britain & Northern Ireland (UK)
Start 03/2018 
End 07/2018
 
Description BUAA 
Organisation Beihang University
Country China, People's Republic of 
Sector Academic/University 
PI Contribution We are working very closely with Beihang University (BUAA) on this project. This project is about remote sensing for agriculture application in China, particularly supporting precision agriculture. We develop and provides data processing and fusion algorithms for early detection of disease/pest for crops and assess the level of damages.
Collaborator Contribution BUAA collect remote sensing data from the selected test fields in various seasons by operating UAV and corresponding payload. It also helps to talk with farmers and local agriculture organisations to get the ground truth and local data. BUAA has managed to secure £3M RMB from the National Science Foundation of China for supporting this project.
Impact This is a typical multi-disciplinary project which covers satellite application, UAVs, agriculture, and data processing with optical sensors. Some initial data sets have been been provided by BUAA.
Start Year 2016
 
Description JU 
Organisation Jiangsu University
PI Contribution We work together on an intelligent irrigation for crop management with the support of remote sensing data. We process data from satellites and unmanned aerial vehicles, the crop water response models for assessing and predicting the water needs.
Collaborator Contribution Jiangsu University has a national centre for irrigation and fluid machines which specialises in the research and development of irrigation machines, pumps and sprinklers. Their contributions include the development of energy save and more efficient sprinklers and irrigation machines, the computer controlled irrigation equipment and the integration of the decision making tools on the irrigation machines for variable rate irrigation.
Impact The feasibility of the concept has been investigated by the researchers at Loughborough and Jiangsu University. We visited each sides for a number of times and conducted joint research. This leads to develop a bid for major UK-China Agri-Tech Innovation Challenges. This is a multidisciplinary collaboration with the researchers from unmanned aerial vehicles, water engineering, agriculture, computer science and fluid mechanics.
Start Year 2016
 
Description NAFU 
Organisation North West Agriculture and Forestry University
Country China, People's Republic of 
Sector Academic/University 
PI Contribution We developed remote sensing technology using unmanned aerial vehicles for yellow rust detection. Our contribution including planning for remote sensing using UAVs, defining and customise the multispectral cameras for aerial imaging and post data processing methods and tolls for automatic detection of yellow rust disease for wheat.
Collaborator Contribution Northwest Agriculture and Forestry University (NAFU) rents a piece of land about 4 Ma from a local farmer, plant the winter wheat and take all the responsibilities for crop management including irrigation and weeding. They also divide the land into 6 areas and inoculate the yellow rust disease at different levels for each area. The researchers at NAFU also take the ground truth data manually for the comparison with remote sensing results.
Impact The collaboration is still active and we are expected to see first outcome of the remote sensing for yellow rust disease with UAV in May,
Start Year 2017
 
Description Agri-TechQuZhou 
Form Of Engagement Activity Participation in an activity, workshop or similar
Part Of Official Scheme? No
Geographic Reach International
Primary Audience Other audiences
Results and Impact I attended a workshop in Beijing and visited local farmers and a number villages in the Hebai Province organised by Newton Agri-Tech Network in 2017. The main purpose of the workshop was to generate more impact from our Newton funded Agri-Tech project and build up a better understanding of the needs of smallholder farmers in China . For this four days workshop and visit, I have gave a talk about our research, listened the presentations from Chinese side, and had opportunities to discuss with local farmers, and agriculture extension services. We visited agriculture field station and STB (Scientific Technology Backyard) in Quzhou County of Hebai Province.
Year(s) Of Engagement Activity 2017
 
Description ChinaDaily article 
Form Of Engagement Activity A magazine, newsletter or online publication
Part Of Official Scheme? No
Geographic Reach International
Primary Audience Media (as a channel to the public)
Results and Impact The journalist of ChinaDaily Cecily Liu have telephone interviewed Prof wen-Hua Chen for several times and published an article on Chinadaily on 6th August 2016 about the Agri-Tech project joint sponsored by UK Newton Fund and the Natural Science Foundation of China. It shall reach a wide range of audience, not only explaining how the latest technology such as UAVs could support the change of the practices in farming but also improving the awareness of the UK's support to developing countries including China
Year(s) Of Engagement Activity 2016
 
Description Interview by BBC radio Leicester 
Form Of Engagement Activity A broadcast e.g. TV/radio/film/podcast (other than news/press)
Part Of Official Scheme? No
Geographic Reach Regional
Primary Audience Media (as a channel to the public)
Results and Impact Dr Matthew Coombes was interviewed by BBC Radio Leicester about how remote sensing by Unmanned Aerial Vehicles (UAV) and satellites could help to realise intelligent irrigation for saving water for Chinese farmers and outside China. This would improve the awareness of newly available technologies for precision agriculture for a wide range of audience.
Year(s) Of Engagement Activity 2017