Periodic Reporting for period 1 - SAAT2020 (Scoring crops with Agricultural Artificial intelligence Technologies)
Reporting period: 2019-12-01 to 2020-05-31
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 the world population grows, the demand for agriculture is increasing. This puts pressure on farmers, growers, agricultural companies and food producers to supply more, and to supply faster. The pressure is aggravated by the global effects of a changing climate on food production worldwide.
Modern-day plant breeding uses only a fraction of the available information about plant 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 technology platform xSeedScore® provides bioinformatics solutions, powered by artificial intelligence algorithms. Our solution, xSeedScore, helps medium-sized family-owned seed companies and food and beverage companies to accelerate their breeding program up to 5 times, to reduce the area necessary for breeding crops by up to 67%, and to double the predictive accuracy of current statistical methods.
The main objective of this project was to obtain an in-depth understanding of the market, its conditions, potential partners and define a go-to-market strategy for marketing and sales.
Computomics is ready to scale its breeding performance prediction services and expand it across multiple markets. Our goal is to scale xSeedScore to an easy-to-distribute and widely-used technology, which can be sold independently of other Computomics services.
Modern-day plant breeding uses only a fraction of the available information about plant 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 technology platform xSeedScore® provides bioinformatics solutions, powered by artificial intelligence algorithms. Our solution, xSeedScore, helps medium-sized family-owned seed companies and food and beverage companies to accelerate their breeding program up to 5 times, to reduce the area necessary for breeding crops by up to 67%, and to double the predictive accuracy of current statistical methods.
The main objective of this project was to obtain an in-depth understanding of the market, its conditions, potential partners and define a go-to-market strategy for marketing and sales.
Computomics is ready to scale its breeding performance prediction services and expand it across multiple markets. Our goal is to scale xSeedScore to an easy-to-distribute and widely-used technology, which can be sold independently of other Computomics services.
In order to gain an in-depth understanding of the plant breeding market we performed a four-phased approach.
1. We obtained the latest Plant Breeding Market Report from Markets and Markets.
2. We received SMEI Coaching from an EIC consultant with experience in fruit and vegetable breeding and the agricultural industry in Europe.
3. We organized a two-day workshop with an industry consultant.
4. We performed our own secondary research.
Based on the insights gathered throughout these approaches, we first defined our target regions and identified target customers. Second, we used the relationship of our Business Coach and our industry consultant to connect with some of these companies to explore the potential use of our product within their current structure. Third, we are working on additional marketing material like a reference customer database and language-specific versions of our information material to better address our Latin American market and customers.
1. We obtained the latest Plant Breeding Market Report from Markets and Markets.
2. We received SMEI Coaching from an EIC consultant with experience in fruit and vegetable breeding and the agricultural industry in Europe.
3. We organized a two-day workshop with an industry consultant.
4. We performed our own secondary research.
Based on the insights gathered throughout these approaches, we first defined our target regions and identified target customers. Second, we used the relationship of our Business Coach and our industry consultant to connect with some of these companies to explore the potential use of our product within their current structure. Third, we are working on additional marketing material like a reference customer database and language-specific versions of our information material to better address our Latin American market and customers.
Our product is unique in combining very heterogeneous data sources using our novel machine learning-based technology. We work towards meeting the EU’s sustainability challenges of zero hunger, responsible consumption and production, climate action, and sustainable use of land. Computomics and xSeedScore contribute to 9 of the 17 UN Sustainability Goals.