Use Cases

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 imagery
  • Weather data
  • Geospatial AI
  • Spatio-temporal FMs

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

UC2

  • Large language models
  • Agronomy knowledge graphs
  • Document understanding
  • Advisory data analysis

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 vision
  • Multimodal plant imaging
  • AI phenotyping
  • Synthetic biological data

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

UC4

  • Sensor fusion
  • AI imaging systems
  • Predictive analytics
  • Decision-support systems

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 alignment
  • Shared embeddings
  • Agentic AI workflows
  • Explainable reasoning
  • Dynamic AI orchestration