Linked ontology platform for data-driven crop variety performance recommendations

Overview

This solution proposes a linked ontology platform that synthesizes disparate agronomic datasets through quality labeling and controlled vocabularies to generate crop variety performance predictions. By integrating a corporate knowledge graph with public crop, trait, environment, and agronomy ontologies, the platform enables discovery and analysis of labeled data across crops and species. The approach addresses a persistent challenge in agricultural data science: combining heterogeneous datasets from different sources, formats, and quality levels to produce actionable variety recommendations for specific target environments and management practices.

The platform offers value to breeding programs, agritech companies, and agricultural research organizations seeking to leverage existing data assets for improved decision-making. By applying the Plackett-Luce model to ranked heterogeneous data, the system can identify environmental and management factors that consistently influence variety performance across seasons and locations.

Technical specifications

Key components:

• Linked ontology integration: Combines Crop Ontology, Trait Ontology, Planteome, Wheat trait, Environment and Agronomy Ontologies, and EPPO with a corporate wheat ontology
• Quality labeling workflow: Applies controlled vocabulary labels to public and internal datasets, building on proven approaches from Integrated Breeding Platforms, Breeding API, and EMPHASIS
• Graph database visualization: Enables real-time semantic queries and interactive visualization of trait stacks linked to underlying data
• Plackett-Luce analytical model: Processes heterogeneous labeled data by converting absolute values to ranks, enabling analysis of diverse datasets including on-farm participatory trial data
• Minimum Viable Product scope: Initial delivery focused on wheat, with labeled data from CGIAR repositories

Validated foundations:

• Platforms such as GEMS and GARDIAN demonstrate successful integration of agrifood datasets using linked public ontologies
• A graph database visualization prototype of Crop Ontology has shown strong potential for trait data queries
• The Plackett-Luce model has been validated in analyzing on-farm participatory trials and identifying environmental covariates influencing variety performance

Technology readiness level

The underlying technologies are at a moderate readiness level. The ontology linking approach and Plackett-Luce model have been validated through published research and existing platforms like GEMS and GARDIAN. The proposed Minimum Viable Product represents the next development stage, focusing on wheat as the initial crop. The MVP will integrate public ontologies with a corporate knowledge graph, label public CGIAR data, and demonstrate interactive visualization through five semantic queries. Following MVP development, the heterogeneous labeled data will be analyzed using the Plackett-Luce model to predict variety performance, with potential expansion to additional crops beyond wheat.