Agrobot: Agricultural Robot using IoT and Machine Learning (ML)
Objective
The main objective of this project is to create an Agrobot, an agricultural robot, utilizing IoT and Machine Learning (ML) technologies to revolutionize farming practices by automating tasks, optimizing resource utilization, and improving crop management
Abstract
The "Agribot" project introduces an innovative agricultural robot designed to revolutionize farming practices by harnessing the capabilities of IoT (Internet of Things) and Machine Learning (ML). This autonomous robot incorporates a range of components, including a Raspberry Pi as the core controller, Bluetooth for remote control of forward and backward movement, a DHT11 sensor for monitoring temperature and humidity, soil moisture sensors for moisture level assessment, and a TCS3200 color sensor to evaluate the health of plants. The motor driver with motors enables the robot to navigate across fields efficiently. Machine Learning is employed for serial communication with a Python-enabled PC, providing the ability to process and analyze data collected by the Agrobot. This integrated approach promises to enhance farming efficiency, optimize resource usage, and promote sustainable agriculture practices by enabling intelligent decision-making based on environmental and plant health data.
Keywords: Raspberry PI, Bluettoth , IOT, Machine learning, DHT11 sensor.
NOTE: Without the concern of our team, please don't submit to the college. This Abstract varies based on student requirements.
Block Diagram
Specifications
Hardware Components:
- Raspberry PI
- DHT11 Sensor
- Soil moisture Sensor
- Bluetooth
- TCS3200 Sensor
- Motor driver
- Motors
Software Components:
- Raspbian OS
- Python
Learning Outcomes
- Raspberry pin diagram and architecture
- How to install Raspbian OS
- Setting up and installation procedure for Raspberry
- Introduction to Raspberry
- Basic coding in Raspberry
- Working of power supply
- About Project Development Life Cycle:
- Planning and Requirement Gathering (software’s, Tools, Hardware components, etc.,)
- Schematic preparation
- Code development and debugging
- Hardware development and debugging
- Development of the Project and Output testing
- Practical exposure to:
- Hardware and software tools.
- Solution providing for real time problems.
- Working with team/ individual.
- Work on Creative ideas.
- Project development Skills
- Problem analyzing skills
- Problem solving skills
- Creativity and imaginary skills
- Programming skills
- Deployment
- Testing skills
- Debugging skills
- Project presentation skills
Paper Publishing
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Disclaimer - Takeoff Edu Group Projects are not associated or affiliated with IEEE in any way. The IEEE Projects mentioned here are mentioned in the context of student projects, whose ideas are derived from IEEE publications, not projects of or by IEEE.
