Instant crop diagnosis for the 2G world.
Edge-first AI powered by MobileNetV2, built for real farms—not fast internet.
Validated across 15 different crop disease classes (Tomato, Potato, Pepper) using a custom MobileNetV2 architecture.
Most farmers rely on delayed expert visits or guesswork, often losing days and yields. Existing AI tools assume reliable internet and high-end devices—luxuries that rural agriculture rarely has.
Sanjivani brings Expert AI to the edge. Farmers capture a leaf image and get instant actionable insights within seconds, even in offline or low-bandwidth conditions.
Custom MobileNetV2 model covering 15+ disease classes for Tomato, Potato, and Pepper.
Sub-2-second diagnosis replaces multi-day consultation delays.
Functional without active internet. Syncs data automatically when connectivity returns.
Designed for low-end Android devices with minimal memory and compute footprint.
IndexedDB ensures zero data loss during network failures or outages.
Provides immediate treatment recommendations alongside disease identification.
The core challenge wasn't just accuracy—it was accessibility. Rural farmers have zero latency tolerance and spotty connection.
We utilized a "Store-and-Forward" architecture. Images are cached locally in IndexedDB and predictions are served immediately from the device's model or a lightweight local server, syncing only when possible.
MobileNetV2 (Fine-tuned) + PyTorch.
Flask API + OpenCV Pre-processing.
React PWA + Service Workers.
@app.route('/predict', methods=['POST'])
def predict():
# 1. Validate Input
if 'file' not in request.files:
return jsonify({'error': 'No file'})
# 2. Pre-process for MobileNet
img_bytes = request.files['file'].read()
tensor = transform_image(img_bytes)
# 3. Inference Time
prediction = model(tensor)
confidence = torch.softmax(prediction)
return jsonify({
'class': class_names[prediction.argmax()],
'confidence': confidence.item()
})