gabrielmahia/shamba

Swahili-native crop disease detection using Gemini Vision — first in East Africa # 🌿 Shamba Scan AI Swahili-native crop disease detection — upload a leaf photo, get a diagnosis and treatment steps in Swahili, grounded in the PlantVillage dataset. Upload a photo of a diseased plant leaf → AI diagnosis in Swahili → treatment guidance → prevention. ## Research Basis - Mohanty et al. (2016) — *"Using Deep Learning for Image-Based Plant Disease Detection"* arXiv:1604.03169 - PlantVillage dataset: 54,306 images, 14 crops, 26 diseases. 99.35% accuracy on held-out test set. - Dolatabadian (2025) — Image-based crop disease detection using machine learning. *Plant Pathology*, Wiley. - Springer Nature (2026) — Comprehensive AI plant disease review including African crop systems. ## Why East Africa Kenya loses an estimated **14.1% of annual crop yield** to disease — matching the global average but hitting harder because extension officers are scarce (1 per 3,000+ farmers in some counties). Smartphone penetration crossed 50% in Kenya in 2024. This closes the gap. ## Novelty No prior deployable Swahili-language crop disease detection app exists in Kenya or East Africa. Existing tools (PlantVillage app, Nuru app) are English-only and require app installation. This is Swahili-first, mobile-optimized, and zero-install. ## Stack - Gemini Vision (gemini-2.0-flash) — multimodal image analysis - Streamlit — zero-install mobile-first deployment - MIT License · DEMO data clearly labeled ## Disclaimer Educational demonstration. Not a substitute for professional agricultural extension advice. For certified diagnosis: contact your local KALRO office. --- *© 2026 Gabriel Mahia / AI Kung Fu LLC · gabrielmahia.github.io* ## IP & Collaboration MIT licensed. Feedback via GitHub Issues only — pull requests are not accepted. Full policy: docs/architecture/IP_POLICY.md. Security reports: see SECURITY.md.