DevNex Mobile Client
Plant Pulse helps farmers and agricultural professionals identify rice diseases and plant species using AI, mobile image capture, cloud synchronization, and real-time agricultural insights.
Project Objective
The goal was to create an accessible agricultural assistant that detects rice diseases early and identifies plant species accurately, reducing dependency on manual diagnosis and unavailable expert consultation.
Traditional diagnosis can be slow, expensive, and difficult to access in remote areas. Delays can spread disease, reduce yield, and leave farmers without reliable treatment or prevention guidance.
DevNex built a complete mobile platform combining AI inference, treatment recommendations, scan history, reminders, cloud storage, location tracking, weather context, and offline support.
Product Screens
The mobile interface keeps diagnosis, recommendations, reminders, and crop history clear for field use.
Key Features
AI-powered Rice Disease Detection
Plant Species Identification
Disease Treatment Recommendations
Prevention and Management Guidelines
User Authentication & Profile Management
Offline Scan History Support
Cloud Data Synchronization
Weather Information Integration
Treatment Reminder System
Agricultural Analytics Dashboard
Disease Mapping & Location Tracking
Secure Cloud Storage
Multi-language Agricultural Information
Mobile-Friendly User Experience
Technology Stack
The project uses modular Flutter screens, FastAPI services, optimized AI models, Supabase infrastructure, and SQLite local storage to keep the app useful even when connectivity is limited.
Frontend
Flutter, Dart, Provider State Management
Backend
FastAPI, Python
AI Models
TensorFlow Lite, ConvNeXt-Tiny
Database & Storage
Supabase PostgreSQL, Supabase Storage, SQLite
Cloud & Deployment
Hugging Face Spaces
External APIs
OpenWeather API
Final Outcome
Plant Pulse gives farmers instant diagnostic support, treatment recommendations, weather insights, cloud synchronization, and offline accessibility, combining AI, mobile computing, and cloud technology for improved agricultural productivity.
Real-time rice disease detection from mobile images
Accurate plant identification with AI model support
Treatment guidance, prevention steps, and reminders
Offline-first scan history with cloud synchronization