PashuSwasthya
Offline-First Multilingual Mobile App for Cattle Breed & Disease Diagnosis
1. THE PROBLEM
Rural cattle farmers face severe economic losses due to delayed veterinary diagnosis for cattle diseases. Existing diagnostic platforms require cloud servers and high-speed internet, which are unavailable in remote agricultural zones.
2. TECHNICAL APPROACH & DECISIONS
1On-Device Quantized TFLite Inference
Trained a custom MobileNetV3 Convolutional Neural Network (CNN) in TensorFlow, then applied INT8 post-training quantization to export a 14MB TensorFlow Lite model deployed directly on the mobile app bundle.
Cloud-hosted Inference API (PyTorch on AWS)
Cloud inference is impossible without internet connectivity. Quantizing MobileNetV3 reduced model size from 65MB to 14MB while preserving 91.2% classification accuracy on mobile CPUs.
2Multilingual Voice Guidance UI
Implemented localized audio playback in Kannada and Malayalam for non-tech-literate agricultural workers.
Text-only multi-language translation strings
Field interviews revealed many elderly farmers struggled with dense technical text on small screens; voice output provided immediate accessibility.
3. TRADE-OFFS & HONEST REFLECTION
Quantizing the CNN model to INT8 reduced precision slightly on rare edge-case cattle breeds (a 2.8% drop), but was necessary to guarantee real-time < 50ms inference on legacy Android chips without overheating the device.
4. CONCRETE OUTCOME & METRICS
Shipped a 100% offline-first veterinary diagnostic tool operating with sub-50ms inference latency and zero cloud dependency.