We implemented a dual-stage inference workflow. Edge-based classification runs directly on the device using a minimized local model, falling back to secure cloud deep analysis only when prediction confidence drops below a specified threshold.
Custom camera UI coupled with client-side WebAssembly to execute instant local crop image preprocessing and model execution without network delays.
Secondary analysis API running heavy classification models. Features compressed image payload ingestion pipelines to minimize cellular data consumption.
Scaled backend instances hosted on AWS GPU nodes. Static model asset weights are cached on Cloudflare Edge locations to enable rapid offline app client updates.
Local on-device machine learning execution latency without requiring cellular coverage.
Verified success rate in detecting target crop leaf diseases in early development stages.
98.5% Target active adoption rate among field agents (Architecture Benchmark).