Inspired by food security, local livelihood, and sustainable development we created a bioassay for detecting plant pathogens as our topic for the iGEM 2022 competition. Our project, CropFold, faces a problem that is globally significant and increasing by the effects of climate change and globalisation for instance. We designed a novel detection system which is affordable and easy to use, and can be modified to detect several different pathogens. To have the most impact, we also wanted to address the UN sustainable development goals with our project.


Each year 20-40 % of crops are reduced by the effects of various plant pests and diseases, and the amount of losses is estimated to rise by 10-25 % per degree of average global warming (Food and Agriculture Organisation of the United Nations). With global warming happening quite fast, we might find ourselves losing more than half of the crops we produce to pests in just a couple of decades. Not to mention other challenges climate change brings to food production. Areas available for cultivation will be severely reduced when the climate becomes too hot and dry. With the increasing population, the need for food as well as space for living will increase which makes the equation even more problematic. And, in the time of globalisation, pests spreading to new areas seems almost inevitable as they have multiple pathways such as seeds, planting and packaging materials, cargo, and specific insects. (IPPC Secretariat, 2021.)

Also, different crises, for example, the crisis in Ukraine and the Covid-19 pandemic have demonstrated how vulnerable our economic system is to changes in supply. Especially for us in Finland located next to Russia, the current conflict has emphasised the importance of self-sufficiency in food production, and security of supply. As new threats are constantly emerging we need to do all we can in order to secure the crops we are cultivating.

A field.

When the first symptoms of a plant disease occur, the yield might already be lost, since treatment often does not exist. The lack of treatment and sufficient amount of detection methods for the pathogens might lead to excessive pesticide use as a precaution when any insects, harmful or not, are seen. The over-dosing of pesticides has adverse effects on pollinators and might even lead to human health problems resulting from pesticide residues in food. (Umapathi et al, 2022). Therefore, detection of plant diseases before the symptoms appear and intervention is crucial for saving the crop yield and gives an alternative approach to dealing with plant pathogens which might decrease the need for excessive pesticide use.

Many plant pathogens are host specific and infect only one or few different plants (Roossinck, 2010). That is why we need to tackle as many different pathogens as we can. Current solutions include for instance deep learning and laboratory techniques (Abade et al, 2021). The laboratory techniques that can currently be used to detect pathogens, RT-PCR and ELISA, are laborious and require specific equipment and professionals to do the testing (Martinelli et al, 2015). This creates inequality between farms that have access to test laboratory facilities and those that have not. For example, in remote areas, there might not be a laboratory near for testing. Thus, there is a need for affordable, fast, and easy-to-use, on-site performed testing that would make all the needed tools and information accessible for every farmer. Also, the large genetic diversity found in many viral species can impose severe constraints on the design and performance of some detection systems (Glasa et al, 2021).


To answer these challenges we designed CropFold, a system that can detect various plant pathogens to help farmers determine possible infections before any visible signs of infection occur. This can help them to stop the infection from spreading by, for instance, isolating or destroying the infected plants. Our system is a cell-free modular detection system for plant pathogens that uses toehold mechanism to recognise the cause of the infection. The final implementation will be a paper based test presided by a target sequence amplification from the plant sample with NASBA (Nucleic Acid Sequence Based Amplification) (Deiman et al, 2002). A positive reaction on the paper would be seen after a short incubation as an appearing colour. To the user CropFold is affordable and a relatively fast way to check for plant pathogens, and doesn’t require any specific laboratory equipment or expertise. Therefore, it can be performed on-site. Since globally there are countless different plant pathogens, we wanted to make our system to detect not only one but as many pathogens as possible. That means our system is modular, so with only small modifications, it can be utilised to detect different pathogens.

A CropFold logo.
Bottles and pipettes in a laminar hood.

Our system is a cell-free transcription and translation reaction which is regulated with a toehold switch. The reaction is composed of E. coli lysates which provide translation and transcription machinery, a toehold plasmid and a trigger sequence which are the on/off switch, and other necessary components such as amino acids and energy reagents. When the toehold switch is triggered with a target, it unfolds, which will start the reaction and the expression of a reporter gene. As reporter genes, we tested the colorimetric reporter β-galactosidase and the fluorescent reporters mScarlet and mScarlet-I (Bindels et al, 2017; Miller, 1972). For a more detailed description of the system visit the design page.

We tested our system with Barley yellow dwarf virus (BYDV) and used Zika Virus as a control (Domier, 2008; Pardee et al, 2016). In general, only the toehold switch and the target sequence is designed according to the pathogen, so by modifying the toehold switch the whole reaction can be changed to detect a different pathogen. Having this feature also with a library of other plant pathogens, our system reaches its potential.

Our inspiration

When starting as a new team in January, we immediately started researching different topics to help us find our idea for this year. Our initial ideas ranged from making bioplastics from coffee grounds to detecting P. aeruginosa from contact lens solution, but the longer we pondered the more clear it became that we wanted to concentrate on something that has to do with the environment surrounding us here in Turku. Also, we had difficulties finding novelty or engineering success from these first project ideas we came up with.

When facing trouble finding the right idea for our project, we had a conversation with our PI Asst. Prof. Pauli Kallio, and decided to change our approach and start brainstorming from a synthetic biology point of view rather than the problem itself. After considering multiple different tools of synthetic biology, we decided to build a detection system, and after that, we needed to come up with the problem we could solve. But even with the system in mind, we were not quite sure what pathogen we should settle on. One of the most prominent ideas was to detect Lyme disease from ticks, as Lyme disease carried by ticks is a remarkable health hazard in the coastal areas of Finland, and currently spreading even wider due to climate change (Gray et al, 2009).

The other project idea we had was to focus on plant pathogens infecting Finnish crops, mainly potatoes. We found a Finnish research group doing research on potato virus A (PVA) and consulted the head of the group Kristiina Mäkinen (Helsinki University). As we found out that at the moment the newest and most prominent of the potato viruses is potato virus Y (PVY) that’s currently spreading across the world without quick detection methods, we started to pitch an easy, on-site detection system for it. When reading more about the topic we were delighted to come across a research paper by Arce et al (2021) focusing on the same topic. From this, we found assurance that our project could be achievable and beneficial to a wider community.

Potatoes in a bucket.
A person with a lab coat on holding a bottle.

While doing research on our methods we also came upon the Barley Yellow Dwarf Virus (BYDV), a globally remarkable plant virus that infects all the major cereal crops here in Finland (Domier, 2008). As the toehold system was already proven to work on PVY, we decided to use BYDV as our proof of concept, and simultaneously show that toehold switches can feasibly be used to detect several plant viruses (Arce et al, 2021). We wanted to make a cell-free system to ensure a safe usage in field conditions and compliance with EU GMO legislation, so we found great inspiration from EPFL 2019 team’s experimentation with their own PURE (Protein Synthesis Using Recombinant Elements) system to provide the necessary proteins for cell-free reactions. Unfortunately, we didn’t have the resources to test our toeholds with their OnePot PURE protocol this time, but we tested our system also with a commercial PURExpress kit (Lavickova & Maerkl, 2019). But regardless, we created an effective and affordable design for our detection system, CropFold.

Please see our Integrated Human Practices page for more details about our topic decision and sources of inspiration.

Sustainability of our project

When brainstorming our project we wanted it to be impactful not only locally but also globally. Thus, we looked at the United Nations Sustainable Development Goals (SDGs) to get a better understanding of global-scale concerns that we could solve with our project. In 2015 all United Nations member countries adopted 17 sustainable development goals. They are a call for action to make our world a better place, end poverty and protect the planet. (United Nations) To find out which SDGs our project addresses, please read our Sustainable Development Goals page.

A picture with the logos of 17 SDGs.


  • Abade, A., Ferreira, P. A. & de Barros Vidal, F. (2021). Plant diseases recognition on images using convolutional neural networks: A systematic review. Computers and Electronics in Agriculture, 185:106125.

  • Arce, A., Guzman Chavez, F., Gandini, C., Puig, J., Matute, T., Haseloff, J., Dalchau, N., Molloy, J., Pardee, K., Federici, F. (2021). Decentralizing Cell-Free RNA Sensing With the Use of Low-Cost Cell Extracts. Frontiers in Bioengineering and Biotechnology, 9.

  • Bindels, D. S., Haarbosch, L., van Weeren, L., Postma, M., Wiese, K. E., Mastop, M., Aumonier, S., Gotthard, G., Royant, A., Hink, M. A., Gadella Jr, T. W. J. (2017). mScarlet: a bright monomeric red fluorescent protein for cellular imaging. Nature Methods, 14, 53-56.

  • Deiman, B., van Aarle, P., & Sillekens, P. (2002). Characteristics and applications of nucleic acid sequence-based amplification (NASBA). Molecular biotechnology, 20(2), 163–179.

  • Domier, L. L. (2008). Barley Yellow Dwarf Viruses. In B. W. J. Mahy & M. H. V. Van Regenmortel (Ed.). Encyclopedia of Virology (3rd ed. pp. 279–286). Oxford: Academic Press

  • EPFL 2019 iGEM team. Retrieved September 24, 2022 from

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  • Glasa, M., Hančinský, R., Šoltys, K., Predajňa, L., Tomašechová, J., Hauptvogel, P., Mrkvová, M., Mihálik, D., Candresse, T. (2021). Molecular Characterization of Potato Virus Y (PVY) Using High-Throughput Sequencing: Constraints on Full Genome Reconstructions Imposed by Mixed Infection Involving Recombinant PVY Strains. Plants (Basel), 10:753.

  • Gray, J. S., Dautel, H., Estrada-Peña, A., Kahl, O. & Lindgren, E. (2009). Effects of Climate Change on Ticks and Tick-Borne Diseases in Europe. Interdisciplinary Perspectives on Infectious Diseases, 2009:593232.

  • IPPC Secretariat. (2021). Scientific review of the impact of climate change on plant pests: A global challenge to prevent and mitigate plant-pest risks in agriculture, forestry and ecosystems. FAO on behalf of the IPPC Secretariat.

  • Lavickova, B. & Maerkl, S. (2019). A Simple, Robust, and Low-Cost Method To Produce the PURE Cell-Free System. ACS Synthetic Biology, 8(2), 455–462.

  • Martinelli, F., Scalenghe, R., Davino, S., Panno, S., Scuderi, G., Ruisi, P., Villa, P., Stroppiana, D., Boschetti, M., Goulart, L. R., Davis, C. E., Dandekar, A. M. (2015). Advanced methods of plant disease detection: A review. Agronomy for Sustainable Development, 35:1–25.

  • Miller, J. (1972). Experiments in Molecular Genetics. Cold Spring Harbor NY: Cold Spring Harbor Laboratory

  • Pardee, K., Green, A. A., Takahashi, M. K., Braff, D., Lambert, G., Lee, J. W., Ferrante, T., Ma, D., Donghia, N., Fan, M., Daringer, N. M., Bosch, I., Dudley, D. M., O’Connor, D. H., Gehrke, L., Collins, J. J. (2016). Rapid, Low-Cost Detection of Zika Virus Using Programmable Biomolecular Components. Cell, 165:1255–1266.

  • Roossinck, M. J. (2010). Lifestyles of plant viruses. Philosophical Transactions of the Royal Society B: Biological Sciences, 365:1899–1905.

  • Umapathi, R., Park, B., Sonwal, S., Rani, G. M., Cho, Y. & Huh, Y. S. (2022). Advances in optical-sensing strategies for the on-site detection of pesticides in agricultural foods. Trends in Food Science & Technology, 119:69–89.

  • United Nations: Department of Economic and Social Affairs. The 17 goals - Sustainable Development. Retrieved September 24, 2022 from