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ouyale/Early-Detection-Crop-Diseases-Kenya

Domaine:

agriculture

Type de record:

projectmodel
Créateur:
ouy
Hôte:
AI-powered crop disease detection for Kenyan smallholder farms — Omdena Kenya Chapter (Product Owner) # Early Detection of Crop Diseases in Kenyan Smallholder Farms 🌿 > **Led a 52-member Omdena team as Product Owner to build an AI-powered mobile application for real-time crop disease detection — achieving up to 100% classification accuracy across 4 crops and 20+ disease classes for Kenyan smallholder farmers.**          ## Table of Contents - My Role & Contributions - Problem Statement - Dataset - Methodology - Model Performance - Deployment - Technologies Used - Team & Acknowledgments - Links & Resources - Author ## My Role & Contributions As **Product Owner** for the Omdena Kenya Chapter, I led the end-to-end delivery of this AI-powered crop disease detection system. My responsibilities included: - **Team Leadership** — Coordinated **52+ collaborators** across data collection, model development, and deployment task groups - **Product Strategy** — Defined the project vision, success criteria, and delivery roadmap - **Sprint Management** — Led sprint planning, daily standups, task prioritization, and retrospectives - **Stakeholder Communication** — Served as the primary liaison between the technical team, Omdena leadership, and agricultural domain experts - **Technical Oversight** — Oversaw the complete ML pipeline from raw image data collection through model evaluation and mobile app deployment - **Model Selection** — Drove the decision to deploy YOLO based on its superior accuracy-speed tradeoff across all four crop categories - **Quality Assurance** — Established evaluation benchmarks and ensured models met accuracy thresholds before deployment approval ## Problem Statement Kenya's **7.5 million smallholder farmers** produce approximately **80% of the country's agricultural output**, yet they face a critical challenge: **limited access to crop disease diagnostic resources**. When diseases go undetected, farmers can experience **30–40% yield losses**, directly threatening food security and rural li …

Visit

github.com

Tasks

computer visionimage classification