Traditional agricultural extension services face significant challenges in reaching remote farmers and providing timely advice. This study presents an innovative approach using large vision and language models to provide real-time agricultural guidance to cocoa farmers in Ivory Coast.
Our system combines computer vision for disease detection with multilingual natural language processing to deliver personalized advice through accessible channels like WhatsApp, achieving remarkable improvements in crop yield and farmer engagement.
Farmer Reach
Active farmer participants
Yield Impact
Average yield increase
Response Time
Average response time
Methodology
System Architecture
Phase 1: Data Collection
Gathering historical agricultural data and establishing baseline metrics
Phase 2: Model Development
Training vision and language models on region-specific agricultural data
Phase 3: Pilot Implementation
Initial deployment with 100 farmers for system validation
Phase 4: Full Scale Deployment
Expansion to over 10,000 farmers with continuous monitoring
Results & Impact
Yield Improvement Over Time
Disease Detection Accuracy
Accuracy in identifying common cocoa diseases
Farmer Engagement
Monthly active usage rate
Environmental Impact
Reduction in pesticide use through targeted application
Future Directions
Model Enhancement
Integration of satellite imagery for broader environmental context
Geographic Expansion
Adaptation for other West African cocoa-growing regions
Feature Development
Implementation of predictive pest outbreak alerts
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