Home / Projects / Smart Rice Guard - AI Bird Detector
Featured Project
Smart Rice Guard - AI Bird Detector
Top 10 national Samsung Innovation Campus IoT system that detects birds in rice fields with ESP32-CAM, YOLO AI, Ubidots, and automated speaker response.
Tech Stack
ESP32-CAM · YOLO · Ubidots · Flask · Streamlit · Python
Architecture Diagram
Smart Rice Guard is an agricultural IoT system from Samsung Innovation Campus 2024/2025. It detects birds in rice fields with ESP32-CAM and YOLO-based AI, then triggers an automated speaker response through a dashboard-connected flow.
Problem
Area pertanian membutuhkan cara yang lebih responsif untuk mendeteksi burung di lahan. Solusi manual seperti pengawasan langsung atau alat statis tidak selalu cukup ketika respons harus terjadi cepat di lapangan.
Solution
Membangun IoT system dengan ESP32-CAM, YOLO AI, dashboard Ubidots, dan speaker otomatis supaya deteksi burung bisa langsung berubah menjadi aksi di lapangan.
Architecture Decisions
- Used ESP32-CAM as the field camera module for direct image capture in the rice field context
- Used YOLO-based detection to identify birds from camera input
- Connected monitoring to Ubidots so the prototype had a dashboard layer, not just device output
- Kept the speaker trigger flow simple so detection could become an immediate field response
Trade-offs
- Limited camera resolution (640x480) for bandwidth efficiency on rural networks
- Server-side inference was easier to iterate than forcing the AI model onto constrained hardware
- Simple speaker activation was prioritized over complex behavioral patterns
Lessons Learned
- Start with minimal viable IoT setup, add complexity only when needed
- Field testing is mandatory — lab conditions are nothing like real rice fields
Features
- Real-time bird detection with YOLO
- Speaker activation on detection
- ESP32-CAM field device
- Ubidots dashboard integration
- Configurable speaker and camera settings
- Rice disease analysis from uploaded images
- Automatic image capture and logging
Achievement
- Top 10 National, University Category, Samsung Innovation Campus 2024/2025
Challenges Overcome
- Deploying custom AI models on limited IoT hardware
- Maintaining stable ESP32-CAM live stream to dashboard
- Synchronizing detection events with hardware output (speaker)
- Building responsive dashboard UI using Streamlit