AgriSahayak
AI-Powered Crop Disease Detection Agent
WhatsApp-based farming assistant using Gemini Vision AI for real-time crop disease detection, treatment plans, and weather alerts. Bilingual Urdu/English.
Why it mattered
Small farmers need fast crop disease diagnosis, localized treatment, and supplier guidance, but expert help can take days to reach them.
What shipped
A multi-agent farming assistant that accepts crop images, diagnoses likely disease, explains treatment in simple language, and connects the farmer to nearby support.
Proof signal
60% projected crop loss reduction
System flow
Image upload enters a vision agent, the diagnosis is passed into a treatment planner, weather and supplier context are layered in, and the response is returned through a farmer-friendly interface.
Honest maturity snapshot
Manual questions, scattered context, and slow follow-up.
An agent workflow that routes intent, calls tools, and returns useful next actions.
Features
- Gemini Vision diagnosis for crop leaf images.
- Localized treatment plan with practical next actions.
- Bilingual Urdu/English communication flow.
- Supplier discovery designed around a local radius.
- Weather-aware recommendation layer.
- Hackathon-ready demo flow with clear farmer value.
Engineering challenges
- Balancing confident diagnosis with safe uncertainty language.
- Keeping the UX simple for users who may not be technical.
- Turning a broad agricultural problem into a focused hackathon MVP.
Results
- Won Innovista Agentic AI Hackathon.
- Projected crop loss reduction story made the business value easy to understand.
- Became the strongest flagship case study for agentic AI plus social impact.
What I learned
- Tradeoff learned: Balancing confident diagnosis with safe uncertainty language.
- Proof learned: Won Innovista Agentic AI Hackathon.
- Next iteration: Add agronomist review workflow for high-risk diagnoses.