Slide 1 of 14
Slide 1 - Autonomous Smart Farming Robot
Autonomous Smart Farming Robot
Revolutionizing Agriculture through Robotics and Artificial Intelligence
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Photo by ThisisEngineering on Unsplash
Autonomous Smart Farming Robot
Generated from prompt:
Create a modern, visually appealing university-level presentation titled "Autonomous Smart Farming Robot". Include slides: 1) Title slide, 2) Introduction & problem (agriculture challenges in Sri Lanka), 3) Objectives, 4) System overview diagram, 5) Hardware components, 6) Software architecture, 7) Working process (step-by-step), 8) AI & computer vision module, 9) Smart irrigation & spraying system (relay control), 10) Team member responsibilities (5 roles), 11) Cost estimation (LKR), 12) Advantages & impact, 13) Future improvements, 14) Conclusion. Use icons, clean layout, minimal text, and engineering-style diagrams.
This deck presents an autonomous smart farming robot project addressing Sri Lanka's agriculture challenges. It details objectives, system overview, hardware (Raspberry Pi, Arduino), software architecture with AI/CV (TensorFlow Lite, OpenCV), working,
April 3, 202614 slides
Slide 2 of 14
Slide 2 - Agriculture Challenges in Sri Lanka
- Agriculture contributes 7.4% of Sri Lanka's GDP (2020)
- 27.1% of the population engaged in agricultural activities
- Challenges: Labor shortages, inefficient resource management, and rising costs
- Need for technology integration to improve productivity and sustainability
Slide 3 of 14
Slide 3 - Project Objectives
- Automate critical farming tasks (irrigation, spraying)
- Reduce human intervention and operational costs
- Implement real-time precision farming using Computer Vision
- Enhance crop yield through data-driven resource application
Slide 4 of 14
Slide 4 - System Overview
- Central Microcontroller: Processes inputs and manages tasks
- Sensors: Detect soil moisture and crop health status
- Actuators: Control irrigation relays and spraying mechanisms
- Vision Module: Real-time analysis for decision making
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Photo by Daniel Beckemeier on Unsplash
Slide 5 of 14
Slide 5 - Hardware Components
- Processing: Raspberry Pi 4 (AI and Vision processing)
- Controller: Arduino Mega for low-level actuator control
- Vision: Pi Camera Module for crop detection
- Sensors: Soil moisture sensors, ultrasonic distance sensors
- Power: 12V Li-ion battery pack with power management system
Slide 6 of 14
Slide 6 - Software Architecture
| Module | Technology/Methodology | ||
|---|---|---|---|
| Vision Engine,OpenCV and TensorFlow Lite],[ | ],[ | ],[ | ]]},order_index:5,style: |
Slide 7 of 14
Slide 7 - Working Process
- 1. Initialize sensors and vision system.
- 2. Navigate field using ultrasonic sensors.
- 3. Capture image and detect target crops/weeds.
- 4. If disease/weed detected: Trigger spray.
- 5. Check soil moisture: Trigger irrigation if low.
- 6. Log data to cloud via Wi-Fi.
Slide 8 of 14
Slide 8 - AI & Computer Vision Module
- Model: Convolutional Neural Network (CNN) trained on TensorFlow Lite.
- Processing: Raspberry Pi optimized for real-time inference.
- Task: Real-time detection and classification of crop disease or weed presence.
- Outcome: Accurate localized decision-making for spraying application.
Slide 9 of 14
Slide 9 - Smart Irrigation & Spraying
- Controller: Arduino Mega triggers relays.
- Irrigation: Solenoid valve control based on soil moisture data.
- Spraying: Relay-controlled pump for precision pesticide delivery.
- Optimization: Minimizes waste and reduces environmental chemical impact.
Slide 10 of 14
Slide 10 - Team Member Responsibilities
- Project Lead: Overall system integration & management.
- Hardware Engineer: Component selection and circuit assembly.
- Software Developer: AI and computer vision implementation.
- Embedded Systems: Arduino and sensor driver integration.
- QA/Field Testing: Validation and field performance testing.
Slide 11 of 14
Slide 11 - Cost Estimation (LKR)
| Item | Estimated Cost (LKR) | |||
|---|---|---|---|---|
| Raspberry Pi 4 Board, 25,000],[ | ],[ | ],[ | ],[ | ]]},order_index:10,style: |
Slide 12 of 14
Slide 12 - Advantages & Impact
- Increases crop yield by 20% through precision care.
- Reduces chemical usage for spraying by 40%.
- Minimizes labor requirement in field maintenance.
- Promotes sustainable farming practices in Sri Lanka.
Slide 13 of 14
Slide 13 - Future Improvements
- Integration of advanced LIDAR for better navigation.
- Long-range 5G/LoRa connectivity for remote monitoring.
- Solar-powered charging station for 24/7 autonomy.
- Expansion to diverse crop types.
Slide 14 of 14
Slide 14 - Conclusion
Conclusion: Building a Smarter Future for Farming
Transforming Sri Lanka's Agricultural Sector with Advanced Robotics
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