🌾 Process — Farm to Market

The SmartHarvest process represents the end-to-end flow of agricultural intelligence — from farm-level data collection to local retail and global export. Every phase is powered by integrated AI, IoT, and blockchain technologies working together to build a transparent, resilient, and sustainable agricultural supply chain.

1. Harvest & Data Acquisition

The process begins at the farm level, where data fuels every intelligent decision. Smart IoT devices and field sensors capture soil moisture, temperature, humidity, and crop maturity in real time. Farmers use mobile dashboards to log harvest data, including batch IDs, timestamps, and geolocation.

The AI Forecasting Engine analyzes yield volumes, local demand, and historical market behavior to generate post-harvest guidance. Farmers receive instant insights on when and where to sell — ensuring equitable pricing and market alignment.

For export-bound batches , compliance data (pesticide use, harvest date, certification) is securely recorded on a blockchain ledger for traceability.

2. Post-Harvest Handling & Quality Inspection

After harvesting, produce enters the AI Quality Control pipeline. Computer vision systems powered by CNN models (YOLOv8, ResNet-50, and custom lightweight architectures) assess produce for ripeness, defects, and freshness with confidence scoring for validation.

Products are categorized as fresh, unripe, or rejected with digital precision.
Accepted batches proceed to packaging stations for local or export allocation.
Quality results are logged into the digital chain-of-custody ledger to ensure transparency.

This phase guarantees consistency, fairness, and compliance, reducing export rejection rates and ensuring credibility with buyers.

3. Smart Storage & Real-Time Environmental Control

The Real-Time Decision Support System (RTDSS) governs storage and transport environments. IoT sensors continuously track temperature, humidity, CO₂, and light intensity, while ML algorithms detect deviations.

Automated controls adjust cooling and ventilation dynamically.
Alerts are sent to managers via dashboards for instant corrective action.
Historical data feeds back into AI models to improve predictive accuracy.

This feedback-driven storage system preserves freshness, quality, and compliance with both local and international standards.

IT21804274 — Logistics Optimization

4. AI-Driven Logistics & Distribution

Once products are ready for dispatch, AI Logistics Optimization modules plan routes and fleet allocation using Reinforcement Learning (RL) and predictive analytics.

Local routes prioritize freshness and minimal handling to nearby distributors.
Export consignments are directed to ports and airports under cold-chain conditions.
Dynamic routing minimizes fuel use, travel time, and carbon emissions.

Logistics evolves from static scheduling to an adaptive, data-optimized operation, ensuring reliable, on-time deliveries across all markets.

IT21839160 — AI-Based Quality Control

5. Retail & Export Distribution

On arrival, traceability and transparency remain intact. Retailers and exporters verify product origin, quality scores, and environmental history through blockchain-based digital records.

Export buyers validate authenticity and safety standards through digital certificates.
Local markets use AI-powered dynamic pricing to balance demand and freshness.
Every stakeholder gains confidence through verifiable, end-to-end traceability.

This phase completes the AI-SCM lifecycle, where technology reinforces trust and efficiency at the last mile.

6. Feedback & Continuous Optimization

In the final phase, data collected from all stages — harvest, inspection, logistics, and distribution — is fed into a central data lake for continuous learning.

ML models retrain using real-world performance data to enhance accuracy.
System feedback improves forecasting, routing, and grading algorithms.
Each cycle becomes more efficient, predictive, and sustainable over time.

This self-learning architecture turns SmartHarvest into a living, evolving ecosystem that continuously improves agricultural productivity and sustainability.

Conclusion

From farm to market — and from local shelves to global exports — SmartHarvest defines a new standard for intelligent, traceable, and responsible agriculture. By uniting AI, IoT, and blockchain technologies, the platform positions Sri Lanka’s agricultural sector at the forefront of transparent, resilient, and globally competitive food supply chains.