About the Project: Scope
What We Do
We design, build, and evaluate an integrated AI supply-chain framework for agriculture. The system focuses on the post-harvest journey of perishables (fruits & vegetables) and unifies four key capabilities:
- Demand Forecasting — LSTM time-series models for market prediction.
- Real-Time Decision Support — IoT-driven monitoring during storage and transport.
- AI Quality Control — Computer vision grading and classification.
- Logistics Optimization — AI-assisted routing and vehicle allocation.
Reference: R25-033 – AI-Enhanced Supply Chain Management in Agriculture
Where It Applies
Our scope covers post-harvest operations — from farm gate to warehouse and market — prioritizing perishable supply chains. While validated in Sri Lanka, the framework is generalizable to regions facing similar inefficiencies.
R25-033 – AI-Enhanced Supply Chain Management in Agriculture
How It Works (At a Glance)
- Forecasting Layer: LSTM models fuse historical sales, crop yields, weather, and market signals to produce region-aware demand projections. IT21817212 – Predictive Analytics
- Monitoring & Control Layer: IoT sensors stream temperature, humidity, CO₂, and light data; ML models trigger alerts and adjustments to protect freshness in transit. R25-033
- Quality Layer: Computer-vision grading standardizes quality for fair pricing and export compliance. R25-033
- Logistics Layer: AI plans routes and allocates vehicles to minimize time, spoilage, and cost. R25-033
Who Benefits
Better planning, less waste, and fairer pricing through data transparency.
Smarter routing, storage, and real-time monitoring to reduce losses.
Analytical insights for food-security and waste-reduction programs.
Modular AI components that can be commercialized as SaaS or API solutions.