Objectives
Objectives
AgriScienceFM builds the foundational AI infrastructure for agricultural science, open, explainable, and reusable
Project Mission
Foundational AI infrastructure for agricultural science
AgriScienceFM aims to create the foundational AI infrastructure for agricultural science by developing open, explainable, and reusable agricultural foundation models
The project addresses the challenges of conventional AI approaches in agriculture by using foundation models as jump-off points, enabling the fine-tuning of AI models for agricultural science applications in two distinct ways: first, through performance improvements in accuracy and less uncertain decision-making; and second, by lowering the data barriers for developing performant models in new conditions or tasks
Vision
A future where agricultural scientists, farmers, policymakers, and innovators can access open, trustworthy, and reusable AI tools that improve food security, sustainability, and climate resilience
Goal
To develop, benchmark, and openly share three agriculture-specific AI foundation models that help researchers and practitioners better understand complex agricultural systems and support data-driven solutions for food security, sustainability, and climate resilience
Core Objectives
Six objectives shaping the project
01
Develop Agricultural Foundation Models
Create three complementary AI foundation models focused on environmental systems, agricultural management, and plant structure & growth, supporting a wide range of downstream applications
02
Enable Data-Efficient AI
Reduce dependency on large labeled datasets using self-supervised learning and large-scale multimodal pretraining
03
Build Open and FAIR Resources
Develop reusable, openly accessible datasets, pipelines, benchmarks, evaluation protocols and models, findable, accessible, interoperable and reusable by design
04
Advance Explainable & Trustworthy AI
Integrate transparency, uncertainty quantification and responsible AI practices for safe, reliable systems
05
Create Standardized Benchmarks
Establish comprehensive evaluation benchmarks for agricultural AI across diverse scientific and real-world challenges
06
Support Climate-Resilient Agriculture
Accelerate innovation for sustainable farming, climate adaptation, efficient resource management and food security
Key Deliverables
What the project delivers
Beyond the three AgriFM foundation models, detailed on the Foundation Models page, the project delivers open, reusable resources for the agricultural research community
01
Open Benchmarking Suite
A comprehensive evaluation framework for agricultural AI systems
02
FAIR-Compliant Datasets
Curated multimodal datasets for the research community
03
Reusable AI Pipelines
Open-source tools and pipelines across the model lifecycle
04
Publications & Dissemination
Scientific contributions and community engagement
05
Roadmap for Agricultural AI
Guidelines and best practices for future AI development in agriculture
Long-Term Impact
A major reference point for future AI research in agricultural sciences, enabling collaborative, open, and interdisciplinary innovation
Committed to open science, interdisciplinary collaboration, and responsible AI innovation for sustainable agriculture
FUNDING
Funded under the Horizon Europe programme
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