Project highlights

This project will support global efforts to investigate and monitor declining pollinator
population by fabricating bio-inspired ‘synthetic’ flower attractants.
This interdisciplinary project will use cutting edge techniques in design including 3D printing,
CAD and visualisation, in design and microfabrication to replicate flower shape, colours
(including UV), smells and function (i.e. providing micro-capillary sugar solution)
‘Synthetic’ flowers will be tested in controlled settings using captive insects, as well as being
deployed and tested in real world settings as attractants for machine vision AI-assisted
insect monitoring systems.


Pollinators contribute to healthy and resilient ecosystems, being responsible for helping 90% of the
world’s flowering plants reproduce (EPA, 2020). This is key in maintaining natural ecosystem
services, in maintaining sufficient seeds and fruits for dispersal and propagation and in maintaining
genetic diversity (USFC, 2020). Society is entirely dependent on ecosystem services, such as
pollination, arising from the interactions between biodiversity and the physical and chemical
environment. Modern agricultural systems are dependent on pollinators to ensure crop
performance, whose outcome collectively contributes to national food security. Pollinating insect
population decline has been linked to several drivers and challenges, including widespread use of
pesticides (Woodcock et al, 2017) and changing climatic patterns (Settele et al, 2016). Given the
importance of pollinators, improved knowledge is needed on their abundance and distribution
worldwide. However, despite the UK supporting a world-leading scheme for systematic monitoring
of insect pollinators (, led by Carvell and partners) and other countries
developing similar approaches, this relies heavily on the limited capacity of both field surveyors and
taxonomists, and there are significant trade-offs between sampling intensity, capacity and cost
(Breeze et al, 2020). Automated monitoring tools are showing promise for tracking changes in
pollinators by using recently developed tools in machine learning (Hoye et al, 2021), potentially
opening the door to large scale monitoring, especially in areas that are remote or lack taxonomic
expertise (easy RIDER, co-led by August). However, one of the remaining challenges for autonomous
monitoring of pollinators is the creation of standardised attractants. Existing systems use either live
flowers, which can be highly variable in space and time (Hoye et al, 2021), or they use coloured
sheets, which are a poor attractant as compared to flowers (Diopsis, 2020).

This research project aims to develop and evaluate bioinspired insect attractants to enhance and
standardise insect monitoring systems. This aim will be supported by the following provisional
research objectives:
1. Identify the attributes of a flower important for attracting insects (olfactory, visual,
chemical), the forms that are most effective (e.g. colour preference), and the relative
importance of features for different species groups (e.g. bees vs flies). This will be conducted
by a literature review.
2. Design and manufacture of bioinspired insect attractants that can be standardised. This will
make use of microfabrication methods such as 3D printing, CAD and visualisation.
3. Deploy bioinspired attractants with autonomous AI monitoring systems in experimental and
real-world conditions to quantify their ability to increase the number of insects drawn to the camera, and to standardise protocols. This will include undertaking comparative testing in
biodiverse regions of the world through collaboration with Operation Wallacea, including
engagement with school groups (see LoS).

CENTA Flagship

This is a CENTA Flagship Project

Case funding

This project is suitable for CASE funding


UK Centre for Ecology & Hydrology


  • Organisms and Ecosystems


Project investigator


How to apply


To address the project aim, a three-stage research project methodology is proposed. In the first
instance a systematic review will be undertaken of prior work identifying how insects are attracted
to the particular facets of flowering plants in the pollination process. The project will consider colour
(visible and UV), shape, and function of component parts. The student will use advanced
spectrometric and morphological scanning techniques to assess natural flower variation (figure 1b)
and insect pollinator behaviours. Subsequent visualisation and modelling will be conducted and
evaluated using virtual reality techniques.
A second stage will be to develop and construct ‘synthetic’ flowers and flower components using
precision 3D printing technology and advanced micro-fabrication methods. Designs will be put
forward for testing to establish pollinators’ preferences.
Finally, the designs will be tested in both natural and controlled conditions. Automated AI-based
classification systems will be used to assess the outcomes of each experiment (Figure 1a), utilising
machine-vision techniques to identify and track visiting insects. The project will draw conclusions
and recommendations, considering the implications for biodiversity, agricultural production, and
citizen science.

Training and skills

The research will include guidance and training in the specialist skills required in the project. This will
include skills in design, microfabrication, research methods, and insect surveys.
Design – The student will be trained in sample handling and scanning, CAD, virtual reality presentation
and analysis. These skills will be crucialto enable the student to work up designs for ‘synthetic’ flowers.
Fabrication – The student will be trained in precision micro-fabrication techniques including 3D
printing and other CNC tools. They will also learn CAD and general workshop skills, independent and
team working skills, and effective communication skills which will all be key for the fabrication phase
of the project.
Analysis – To support the assessment of the designs created the student will learn experimental
design and analysis, traditional methods for surveying pollinators, and machine vision techniques.
These will be used to assess the effectiveness of the ‘synthetic’ flowers created.

Partners and collaboration

The project will be led by CENTA partner UKCEH in conjunction with Cranfield University. UKCEH is a
leading provider of excellent environmental science that is relevant to society’s needs. The proposed
work is directly linked to the UKCEH ‘Biodiversity’ science challenge and is further relevant to a)
‘ASSIST’ national capability programme achieving sustainable agricultural systems, b) ‘PMRP’ the
Pollinator Monitoring and Research Partnership (PMRP), and c) ‘Easy-Rider’ which brings together
experts across European and North American to develop automated sensors using computer vision
and deep learning to monitor insects.
The project is proposed in partnership with Operation Wallacea (Opwall) which facilitates research
around the world at its field sites. Opwall has extensive experience of running projects in expedition
settings and of engaging students in research and will support and enable the student in testing their
‘synthetic’ flowers in a tropical setting, and to engage school groups in their research.
The project has also gained the support of Natural England who will engage with the research as it
progresses, attending meetings with the research team and providing focussed advice and guidance.

Further details

Dr Tom August
Computational Ecologist
Email: [email protected]
Tel: +44 (0)1491 692536

Dr Claire Carvell
Pollinator Ecologist
Email: [email protected]
Tel: +44 (0)1491 692540

Professor Stephen Hallett
Chair of Applied Environmental Informatics
Cranfield University
Email: [email protected]
Tel: +44 (0)786 7500697

Professor Leon Williams
Chair of Design-Led Innovation and Head of Centre for Competitive Creative Design C4D
School of Water, Energy and Environment, Building 82 G05
Email: [email protected]
Tel: +44 (0) 1234 75 8531

To apply, please visit:

Possible timeline

Year 1

Conduct of a systematic review of pollination research, identification of selected pollinator
insect and plant species for the research and development of a theoretical model and ethical
framework development as required. Springtime work will include planting of selected flowering
plant species in glasshouses and natural beds to ensure a supply of suitable flowers for analysis.
Summer work will involve spectrometric and morphological scanning of natural flower specimens
and subsequent analysis in Cranfield’s ‘Virtual Reality’ facility. The year will conclude with an
investigation of the capabilities and potential of micro-fabrication techniques, and development of
plans as to which characteristics of flowers and flower components to replicate as well as the
experimental design approaches needed. Training in all required techniques will be provided

Year 2

The early part of the year will involve fabrication of the flower parts using specialised 3D
printing and micro-fabrication techniques, and development of the monitoring and classification
techniques. As Summer progresses, the planned experiments will be conducted, and data gathered
and analysed.

Year 3

Further analysis and presentation of results. As Summer progresses a series of further ‘live’
experiments may be conducted and monitored under natural conditions, this may include an
expedition to test the designs in tropical settings and to engage with school groups. The latter part
of the year will involve writing up the thesis, which it is intended will be presented in ‘manuscript’
form, with suitable conferences and workshops being attended by the researcher.

Further reading

Breeze, T.D., Bailey, A.P., Balcombe, K.G., Brereton, T., Comont, R., Edwards, M., Garratt, M.P., Harvey,M., Hawes, C., Isaac, N., Jitlal, M., Jones, C.M., Kunin, W.E., Lee, P., Morris, R.K.A., Musgrove, A., O’Connor, R.S., Peyton, J., Potts, S.G., Roberts, S.P.M., Roy, D.B., Roy, H.E., Tang, C.Q., Vanbergen, A.J., Carvell, C., 2021. Pollinator monitoring more than pays for itself. Journal of Applied Ecology 58, 44–57.

Høye, T.T., Ärje, J., Bjerge, K., Hansen, O.L.P., Iosifidis, A., Leese, F., Mann, H.M.R., Meissner, K., Melvad, C., Raitoharju, J., 2021. Deep learning and computer vision will transform entomology. PNAS 118.

Settele, J., Bishop, J., Potts, S.G., 2016. Climate change impacts on pollination. Nature Plants 2, 1–3.

Woodcock, B.A., Bullock, J.M., Shore, R.F., Heard, M.S., Pereira, M.G., Redhead, J., Ridding, L., Dean, H.,Sleep, D., Henrys, P., Peyton, J., Hulmes, S., Hulmes, L., Sárospataki, M., Saure, C., Edwards, M.,Genersch, E., Knäbe, S., Pywell, R.F., 2017. Country-specific effects of neonicotinoid pesticides on honey bees and wild bees. Science 356, 1393–1395.


The research anticipated will be conducted both at facilities in UKCEH and in Cranfield University. The
researcher being based principally at UKCEH within the Wallingford Biodiversity Monitoring and
Analysis group. The researcher will work in an environment controlled to reduce risk of COVID-19
( The work at Wallingford will primarily be ‘desk-based’.
Practical and specialist work will also be undertaken in Cranfield University in various laboratory and
glasshouse facilities, with similarly established COVID-19 safe working policies (see It is not forseen that COVID-19 restrictions will place
an unduly significant burden on the research planned. However, the experimental phases of the work
will be time-dependent and seasonal and will therefore be planned carefully.