No Internet? No Problem. How I Built a Crop Disease Detector for Rural India

This is a submission for the Gemma 4 Challenge: Build with Gemma 4

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How I Built an Offline AI Crop Doctor for Indian Farmers Using Gemma 4

Every year, Indian farmers lose 20–30% of their harvest to crop diseases they cannot identify in time. Agricultural experts are concentrated in cities. Internet is unreliable in villages. And most existing tools are in English - a language most farmers don't read or speak.

I built KhetAI to change that.

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What I Built

KhetAI is a fully offline AI crop diagnostic tool for Indian smallholder farmers. A farmer:

1. Takes a photo of their affected crop
2. Asks a question in their native language (Hindi, Kannada, Tamil, Telugu, and more)
3. Gets an instant structured diagnosis with treatment steps, local remedies, and an escalation flag

No internet needed. No cloud. No data ever leaves the device.

Tech Stack:

Layer	Technology
AI Model	Gemma 4 (gemma4:e4b) via Ollama
Backend	FastAPI (Python)
Frontend	HTML + Tailwind CSS (single file, no build step)
Inference	Ollama - fully on-device

The result the farmer sees includes:

• Diagnosis with confidence level (High / Medium / Low)
• Numbered treatment steps
• Affordable local remedies like neem oil and copper sulfate
• Prevention tips
• A red escalation warning if they need to see a real expert

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Demo

▶️ Demo Video: Watch on YouTube

Real output from KhetAI - tested with a flower photo asking "What is it's name?":

{
  "diagnosis": "Healthy crop - Black-eyed Susan (Rudbeckia family)",
  "confidence": "Medium",
  "treatment": [
    "Ensure soil remains consistently moist but not waterlogged",
    "Feed with balanced slow-release fertilizer at start of growing season"
  ],
  "local_remedies": [
    "Add compost around the base to improve soil nutrients",
    "Light mulch layer of wood chips conserves moisture"
  ],
  "prevention": [
    "Avoid overhead watering - water the base instead",
    "Deadhead spent flowers regularly to encourage new blooms"
  ],
  "escalate": false,
  "summary": "Your crop is healthy - maintain consistent watering and add compost for best yields"
}

Gemma 4 correctly identified the plant, gave practical advice, and suggested affordable local remedies - all in one response, in the farmer's chosen language.

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Code

🔗 GitHub Repository: https://github.com/Tech-Psycho95/KhetAI

Run it yourself:

# 1. Pull the model
ollama pull gemma4:e4b

# 2. Install dependencies
pip install fastapi uvicorn python-multipart ollama

# 3. Start the backend
uvicorn main:app --reload --port 8000

# 4. Open frontend
# Just double-click index.html in your browser

The backend /analyze endpoint accepts a crop photo + question, converts the image to base64, and sends both to Gemma 4 via Ollama:

response = ollama.chat(
    model="gemma4:e4b",
    messages=[
        {
            "role": "user",
            "content": f"[Language: {language}]\nFarmer's question: {question}",
            "images": [image_b64],
        }
    ],
    system=SYSTEM_PROMPT,
    options={"temperature": 0.2},
)

The system prompt instructs Gemma 4 to always return a clean structured JSON object with diagnosis, treatment, remedies, prevention, and an escalation flag. Low temperature (0.2) ensures consistent, reliable output every time.

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How I Used Gemma 4

I chose Gemma 4 E4B (gemma4:e4b) - here's why it was the right fit:

Multimodal out of the box
Gemma 4 processes both a crop photo and a text question together in a single API call. No separate vision pipeline, no extra model. The farmer uploads an image and asks their question — Gemma 4 analyzes both simultaneously.

Runs fully offline via Ollama
gemma4:e4b runs locally on a consumer GPU with no API key, no internet, and no subscription. This is non-negotiable for rural India deployment where connectivity is absent.

Multilingual natively
Gemma 4 handles Hindi, Kannada, Tamil, Telugu, Marathi, Bengali, Gujarati, and Punjabi without any translation layer. I simply pass the farmer's preferred language in the system prompt — the model responds in that language automatically.

Why E4B over E2B or 31B Dense?

• E2B is too small for reliable multimodal diagnosis accuracy
• 31B Dense requires expensive hardware beyond a typical village kiosk
• E4B hits the sweet spot — strong diagnostic accuracy, runs on a mid-range GPU, 9.6GB model size

Before Gemma 4, this project would have required a separate vision model, a separate language model, a translation layer, and a cloud API — meaning internet dependency. Gemma 4 collapses all four into a single local model call.

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What's Next

KhetAI today runs on a single device for a single farmer. The roadmap:

• Phase 2 - Shared village kiosk over local WiFi, no internet needed
• Phase 3 - Fine-tune on Indian crop disease datasets using Unsloth + LoRA
• Phase 4 - Voice input in local languages + government scheme alerts
• Phase 5 - Federated learning across villages, models improve from anonymised farm data

The infrastructure cost to serve 1,000 farmers? Zero. Because everything runs locally.

Comments

[Zanne]

This is beautiful, high-impact engineering. While many are using AI for desk jobs, you’re using Gemma 4 to protect someone's actual livelihood and food source where the internet can't reach. 🙌

[Shivam Singh]

@zanne thank you for your kind words