Grass cutting issue in farms
Farmers should be autonomously able to cut grass
Pune, Maharashtra, India
Solution
| Component | Description |
|---|---|
| Technical Components | ### **Comprehensive Analysis & Technical Specifications for Autonomous Farm Robot** #### **1. Technical Components Needed** | **Component** | **Purpose** | **Key Specifications** | |------------------------------|------------|------------------------| | **Robotic Base** | Mobility & Navigation | - Differential-drive or 4-wheel electric motor | | | - Weight capacity ≥ 50 kg (for battery & cutter) | **Cutting Mechanism** | Grass Cutting | - Electric-powered rotary blade (adjustable height) | **Onboard Computer** | Processing & Control | - Raspberry Pi 5 / Jetson Nano (AI edge computing) | **GNSS (GPS+RTK)** | Precision Navigation | - RTK-GPS (±2cm accuracy) + IMU for dead reckoning | **Cameras (Stereo/Single)** | Reconnaissance & Obstacle Avoidance | - 4K Day/Night camera + LiDAR/Depth sensor | **Wireless Communication** | Remote Control & Data Transmission | - 4G/5G modem + Wi-Fi (for local farm network) | **Weather Sensors** | Environmental Monitoring | - Rain sensor + Wind sensor (avoid storms) | **Solar Panel + Battery** | Autonomous Charging | - 200W Solar panel + 48V LiFePO4 battery (7 kWh) | **Fence/Boundary Sensors** | Geofencing | - RFID/magnetic tape sensors (prevent straying) | **Microcontroller (MCU)** | Low-level Motor Control | - Arduino / STM32-based PWM motor controllers --- #### **2. Recommended Tech Stack** | **Layer** | **Technology** | **Justification** | |----------------------------|---------------|-------------------| | **Edge AI Processing** | Python + TensorFlow Lite (YOLOv5) | Optimized for real-time grass detection | **Navigation & Pathfinding** | ROS2 + SLAM (Cartographer) | Robust autonomous path tracking | **Communication Protocol** | MQTT (farm network) + HTTPS (remote) | Secure & low-latency data transfer | **Cloud Backend** | AWS IoT Core (remote access) | Scalable data logging & telemetry | **Mobile App** | Flutter (Android/iOS) | Cross-platform GPS path drawing --- ### **3. Detailed Implementation Steps** **Phase 1: Hardware Assembly & Sensor Integration** - Assemble robotic platform (motor drivers, wheels, chassis). - Install solar panels + charging circuitry (MPPT controller). - Mount cameras & LiDAR for obstacle detection. - Integrate GNSS module with RTK correction (U-Blox ZED-F9P). **Phase 2: Software Configuration** - Flash ROS2 on onboard computer (path planning + SLAM). - Train custom ML model (grass detection with OpenCV+DNN). - Set up geofencing logic (RFID fences/GNSS boundaries). **Phase 3: Remote Connectivity** - Deploy AWS IoT Core for command/control. - Develop mobile app (path drawing + live video feed). - Configure weather-triggered return-to-base logic. **Phase 4: Testing & Optimization** - Test autonomous mowing in dry/wet conditions. - Validate battery life (>8 hrs runtime). - Stress-test communication failsafes. --- ### **4. Required Technical Learning** - **Robotics:** ROS2, SLAM, PID motor control - **AI/ML:** Custom dataset (grass vs weeds), TensorFlow Lite - **Embedded Systems:** Solar charge controllers, motor PWM control - **Cloud:** AWS IoT Core, MQTT protocols --- ### **5. Budget Calculation** | **Category** | **Item** | **Estimated Cost (USD)** | |--------------------------|------------------------------|--------------------------| | **Hardware** | Robotic Chassis + Motors | $1,500 | | | Solar Panel + LiFePO4 Battery | $1,200 | | | GNSS (RTK) + IMU | $800 | | | Cameras + LiDAR | $1,000 | | **Software** | ROS2 Licenses (optional) | $200 | | | AWS IoT Core (annual) | $300 | | **Maintenance (Year 1)** | Spare Parts + Cloud Fees | $500 | | **Total Estimated Cost** | | **$5,500** | *(Note: Costs may vary based on vendor/scale.)* --- ### **Conclusion** This autonomous farm robot meets all stated behaviors while avoiding prohibited actions (slow speed, geofencing, no pesticides). The architecture leverages edge AI for grass detection and ROS2 for reliable navigation. Solar power ensures self-sufficiency, while 4G/RTK enables remote control. Further optimizations could include swarm robotics for larger farms. Would you like refinement in any specific area (e.g., alternative motor types, ML model choices)? |
| Key Features |
Feature: Autonomous roving
Format: Software based routing Usage: THe user draws a route for the robot and the robot follows it
Feature: Identification of waste using camera
Format: GSM Connection using GNSS Usage: THe information will go to microcontroller |
| Implementation Steps | The implementation should happen linearly as each feature |