Offline-First App Uses On-Device AI to Detect Crop Diseases in Central America

Smallholder farmers in Costa Rica and Central America often work in areas with little or no cellular coverage, making cloud-based disease diagnosis impractical. To address this, developers built HealthyCropping, an open-source, non-commercial Android app that runs its core functions, including photo capture, inference, and severity assessment, without an internet connection. The app uses specialist MobileNetV3 models for coffee leaves, cacao pods, and banana leaves, quantized to under 10 MB each and executed through native TFLite runtimes in under 50 milliseconds. To prevent accuracy loss from differences between Python training and mobile preprocessing, the team built a shared fixture test suite that checks for numerical parity between the two environments. Results are stored locally and synchronized to a cloud backend once connectivity returns.