The AI Crop Doctor. Hybrid CNN + GenAI architecture that understands your crops, speaks your language, and works offline.
Deterministic vision for 95% accuracy in <100ms, combined with Generative AI for contextual advice and native language explanations.
From a prototype to a production-grade platform.
We moved beyond simple classification. Sanjivani 2.0 implements a **Hybrid AI Pipeline**. The MobileNetV2 model runs first for speed and safety. Only if a disease is detected, the context (weather, location, crop stage) is injected into **Gemini 1.5 Flash**.
This ensures that the advice isn't just "Early Blight"—it's "Early Blight detected in Pune during high humidity; skip watering and use Copper Oxychloride."
async def analyze_crop_health(image, context):
# 1. Fast Edge Inference (10ms)
visual_pred = mobilenet.predict(image)
# 2. Safety Guardrails
if visual_pred.confidence < 0.6:
return Security.flag_uncertainty()
# 3. GenAI Contextualization (1.2s)
advice = await gemini.generate(
role="agronomist",
prompt=f"Diagnosed {visual_pred.disease} in {context.weather}..."
)
return Response(diagnosis=visual_pred, advice=advice)
Real-time wind & rain tracking to advise the perfect time to spray.
Dynamic timeline tracking growth stages from sowing to harvest.
Local-first storage of all diagnoses for long-term farm tracking.