By 2050, our planet will be home to more than ten billion people, resulting in an estimated 50% increase in the amount of food, feed, and biofuel required. This year, farmers around the world will produce 30 million tons less grain than required to feed everyone. As a society, we need to move towards smarter data-driven crop production systems that are able to better utilize soil, time, and resources. Modern-day plant breeding uses only a fraction of the available information about genetics and the environment to select new crops. Computomics is a bioinformatics company founded in 2012 that tackles these challenges. Our software-as-a-service (SaaS) technology platform xSeedScore® provides bioinformatics solutions, powered by artificial intelligence algorithms. We work with clients worldwide to solve their most complex crop-related challenges. With over 100 projects, € 1.1m in revenue (2018), and a client retention rate of 80%, our goal is to have our data analysis pipelines used globally. Our solution, xSeedScore, helps medium-sized family-owned seed companies, and large agricultural companies alike to accelerate their breeding program by reducing the time and area necessary for breeding crops by 50%-67% and doubling the predictive value of current statistical methods. Our 2022 revenues are projected at € 5.5m. This growth is fueled by our vision of a more equitable, healthy, and sustainable food system in which EU agriculture can stay globally competitive while still upholding its values on crop protection and genetic regulation. Our team is unique in its combination of innovation and expertise, bringing together bioinformatics, plant genetics, machine learning in biology, and artificial intelligence. Computomics is ready to scale its breeding performance prediction services and expand it across multiple markets. With the right understanding of potential partners and market conditions, access to Phase I funding will give us the ability to reach large clients.

Global food security challenges, due to exposure to more frequent and intense climate extremes, are threatening to erode and reverse gains made in ending hunger and malnutrition globally, and to shake the foundations of crop production locally. Therefore, plant breeders are under pressure to tackle climate resiliency and resource efficiency. Machine learning-based technologies have the potential to address these challenges and enable breeders to produce stable, value-added products, which contribute to resource-efficient agriculture that can feed the world, but are so far still under development and not commercially available. Computomics has developed xSeedScore, the first disruptive machine learning-based technology enabling more climate-resilient varieties, decreased land and water use, reduced time-to-market and a competitive alternative to genetic modification. With help of the EIC Accelerator, Computomics expects to reach by 2026 a turnover of €18M and 50 employees.

SAFFI targets food for EU’s 15 million and China’s 45 million children under the age of three. It aims at developing an integrated approach to enhance the identification, assessment, detection and mitigation of safety risks raised by microbial and chemical hazards all along EU and China infant food chains. SAFFI will benchmark the main safety risks through an extensive hazard identification system based on multiple data sources and a risk ranking procedure. It will also develop procedures to enhance top-down and bottom-up hazard control by combining management options with a panel of technologies for the detection and mitigation of priority hazards. SAFFI will discover unexpected contaminants by predictive toxicology and improve risk-based food safety management of biohazards by omics and predictive microbiology. SAFFI will co-develop with and deliver to stakeholders a decision-support system (DSS) to enhance safety control all along the food chain. This DSS will integrate the databases, procedures and methods described above and will be a framework for a generic DSS dedicated to other food. This overall methodology will be implemented in two complementary European and Chinese mirror projects and exemplified for each, with four case studies that were selected to cover priority hazards, main ingredients, processes and control steps of the infant food chain. Resulting databases, tools and procedures will be shared, cross-validated, concatenated, benchmarked and finally harmonized for further use in the EU and China. SAFFI will also set up training and knowledge transfer activities to foster EU-China harmonization of good practices, regulations, standards and technologies, and will cluster with other projects under the EU-China FAB Flagship initiative for continuous upgrade of food safety control. This EU-China multi-actor consortium of 20 partners involves academia, food safety authorities, infant food companies, paediatrics and technological and data-science SMEs.