Case Study · 09 / 10
Computer Vision for Agriculture & Health
Enabled real-time rural diagnostics without internet dependency and delivered affordable AI access for underserved users.
Computer VisionMobile Computer Vision
Offline
Runs
Real-time
Latency
Low
Cost
Challenge
No real-time mobile tools existed for rural disease or posture detection, and cloud-based CV required constant connectivity and expensive infra.
Solution
Trained MobileNetV3 and quantized YOLO-Nano classifiers, converted them to TensorFlow Lite with INT8 quantization, and deployed to low-cost Android devices for on-device inference.
Impact
Enabled real-time rural diagnostics without internet dependency and delivered affordable AI access for underserved users.
Tech stack
TensorFlow LitePyTorch MobileYOLO-NanoAndroidOpenCV
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