Use Cases | AgriScienceFM

Use Cases

Four major scientific and societal challenges where AgriScienceFM develops and evaluates agricultural foundation models

UC1, Use Case

Crop & Water Monitoring from Space

Monitoring crops, water availability, and agricultural land using Earth Observation and remote sensing

OBJECTIVES

◆Improve crop classification

◆Detect drought and water stress

◆Forecast crop yields

◆Monitor agricultural landscapes

◆Support sustainable water management

POTENTIAL IMPACT

◆Better food security monitoring

◆Early warning systems

◆Improved climate resilience

◆Support for policymakers & farmers

UC1

Satellite imageryWeather dataGeospatial AISpatio-temporal FMs

UC2

Large language modelsAgronomy knowledge graphsDocument understandingAdvisory data analysis

UC2, Use Case

Farmer Advice on Soil Health

AI systems that understand agronomic knowledge and provide soil-health recommendations

OBJECTIVES

◆Improve soil management

◆Support nutrient recommendations

◆Enable localized advisory systems

◆Assist sustainable farming

POTENTIAL IMPACT

◆Healthier soils

◆Reduced fertilizer misuse

◆Better farm management

◆Accessible agricultural knowledge

UC3, Use Case

Phenotyping & Breeding for Climate-Resilient Crops

Plant phenotyping and breeding support using advanced imaging and AI

OBJECTIVES

◆Analyze plant growth

◆Detect stress and diseases

◆Support breeding decisions

◆Accelerate resilient crop development

POTENTIAL IMPACT

◆Faster breeding cycles

◆More resilient crops

◆Improved food production

◆Better adaptation to climate extremes

UC3

Computer visionMultimodal plant imagingAI phenotypingSynthetic biological data

UC4

Sensor fusionAI imaging systemsPredictive analyticsDecision-support systems

UC4, Use Case

Precision Farming for Pest & Disease Management

Precision agriculture through AI-driven monitoring and decision support for pests and diseases

OBJECTIVES

◆Early disease detection

◆Precision intervention recommendations

◆Monitor livestock and crops

◆Reduce unnecessary chemical usage

POTENTIAL IMPACT

◆Lower crop losses

◆Reduced environmental impact

◆More efficient operations

◆Improved sustainability

Cross-Modal AI Integration

How agricultural foundation models collaborate

Beyond the four use cases, AgriScienceFM explores how models work together, creating a roadmap for future agricultural AI ecosystems

Cross-modal alignmentShared embeddingsAgentic AI workflowsExplainable reasoningDynamic AI orchestration

Committed to open science, interdisciplinary collaboration, and responsible AI innovation for sustainable agriculture

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Funded under the Horizon Europe programme

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