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Use Inspired Science

ICICLE will build and prove its system around three application domains: Smart Foodsheds, Digital Agriculture, and Animal Ecology. The team will use edge devices such as drones and remote sensing platforms to map, monitor, and design the underlying topology, species composition, and function of a landscape to co-design an AI-enabled Cyberinfrastructure which, in turn, will enhance food security, allow superior digital management of agricultural production, and help us better understand the movement and behavior of animals in the context of habitats.

Smart Foodsheds

Smart Foodsheds integrate the many functions in our complex food systems including production, processing, distribution, preparation, and diet-related health in ways that simultaneously improve environmental, social and economic outcomes for everyone from producers to consumers. Cyberinfrastructure and data from throughout a foodshed, including production, supply chain networks, waste streams, public health and healthcare systems, and more will allow AI approaches for predicting practices that increase resilience and assessing tradeoffs among policy scenarios.

Digital Agriculture

Digital Agriculture aims to develop prescriptive and trustworthy models for crop care and water management. Data for both in-field and regional decisions would come from a variety of in situ (e.g., IoT field sensors), land-based (e.g., agricultural field machinery), and remote (e.g., UAV and satellite) sensors designed to assess biotic and abiotic crop stressors. Real-time sensor data will be combined with historical weather and cropping patterns to generate actionable management information at multiple scales.

US-India Collaboration on Digital Agriculture

This research collaboration will contribute novel design paradigms for context-adaptive CI and aims to develop next-generation CI for Digital Agriculture including AI and machine learning methods targeting 3 core areas.
  • Crop Health Modeling 
    • Sense crop health and level context to predict crop yield
    • Detect stressors and diseases for geographically diverse crops
    • Apply remedies with little human intervention via Internet of Things (IoT) and sensor systems
  • Aerial Crop Scouting
    • CI for fully autonomous aerial systems
    • Simplify deployment of UAV in real fields to capture common crop health conditions
    • Provide accurate maps that yield valuable insights for crop management
  • Privacy-Preserving Data Exchange
    • Create secure, trustworthy, and privacy-preserving platforms that connect farmers and allow them to share information and resources safely
Building upon the existing ICICLE infrastructure, CI and AI capabilities, researchers will leverage contextual conditions in India for Digital Agriculture that differ from the United States to (1) expose brittle CI components, (2) make AI4CI more robust and expansive in the long-term, (3) devise principles that yield context-aware CI

Animal Ecology

An Animal Ecologist may study animal social behavior and movement across landscapes, working at scales ranging from individuals to entire groups, while examining changes due to habitat context and conditions. To derive data-enriched scientific inferences, the ecologist must combine data from a variety of sources including images from satellites, drones, camera traps, and hand-held cameras, point cloud scans, and GPS tracks from on-body animal sensors, working in highly distributed asynchronous iterative settings.