'Throughout the project, significant advancements were made, furthering the development and capabilities of the xSeedScore® technology. The work performed has been instrumental in addressing key challenges in plant breeding, specifically in automating data processing, improving accuracy, and enhancing scalability for global deployment.
In WP2, which focused on creating collaboration agreements for breeders and distributors, substantial progress was made in establishing legal frameworks for data sharing, ensuring compliance with data privacy and intellectual property laws. This included successful development and negotiation of legal agreements such as NDAs and MTAs. Tailored data-sharing interfaces are now being developed to meet specific client needs. These agreements ensure secure and efficient sharing of data between Computomics and breeders, as well as distributors. Additionally, collaboration with distributors like BayWa and Beiselen helped strengthen the understanding of market requirements for the genetic-based crop placement model, positioning Computomics for future partnerships.
WP3 saw significant advancements in line breeding and hybrid breeding activities. Data collection efforts from diverse sources led to the development of comprehensive datasets for multiple European crop species, such as barley, wheat, and rice. Computational modeling using these datasets is ongoing, with notable progress in creating high-quality models for European varieties. This was complemented by successful simulations of breeding lines and hybrids, with advanced crossings generated for pilot clients. The development of automated workflows for data processing and simulations has significantly improved the efficiency of generating breeding recommendations. The creation of the Line Breeding Dashboard and Hybrid Breeding Dashboard further enhanced decision-making by providing breeders with intuitive and interactive tools to analyze large datasets and make data-driven decisions.
WP4, dedicated to scaling up and automating processes, made substantial strides in increasing cost-effectiveness through automation. This included development of a modular software tool for automated error detection and data processing to streamline data integration into xSeedScore®´and customer-specfici process routines and automated dashboards that allow breeders to directly interact with results. A scalable, efficient process was created reducing manual intervention and accelerating data analysis. In addition, field tests were evaluated with an automated interface developed for downloading genotyping and field test data, improving model creation speed. Also, API was developed to allow location-based crop placement recommentations, bridging the gap between farmers, seed companies and traders.
