An Offline, Voice-Enabled Mobile System for Leaf Disease Detection in Low-Resource Agricultural Settings of Bangladesh

The main contribution is a complete offline, voice-first pipeline for crop leaf disease diagnosis, not just a compressed classifier but full Bangla voice guidance built for low-literacy users, running entirely on entry-level Android hardware with no internet dependency. The quantized MobileNetV2 model (3.8 MB, 92.8% accuracy) delivers a full capture-to-spoken-diagnosis cycle in under 400ms, addressing a gap in prior mobile plant-disease systems, which are typically cloud-dependent, text-based or English-only. The system was field-validated with 10 real smallholder farmers of mixed literacy, achieving 100% unassisted task completion and 4.7/5 satisfaction — early evidence that offline, voice-first agricultural AI is both technically and practically viable in rural Bangladesh.