Urban Agriculture through IoT-Based Resilient Hydroponic Farming — A Machine Learning Approach
Objective
The objective is to design an IoT-based hydroponic farming system for urban agriculture that uses machine learning to monitor, predict, and optimize plant growth conditions. The system aims to improve crop yield, resource efficiency, and sustainability by intelligently controlling environmental and nutrient parameters. The objective is to design an IoT-based hydroponic farming system for urban agriculture that uses machine learning to monitor, predict, and optimize plant growth conditions. The system aims to improve crop yield, resource efficiency, and sustainability by intelligently controlling environmental and nutrient parameters. The objective is to design an IoT-based hydroponic farming system for urban agriculture that uses machine learning to monitor, predict, and optimize plant growth conditions. The system aims to improve crop yield, resource efficiency, and sustainability by intelligently controlling environmental and nutrient parameters.
Abstract
Urban Agriculture through IoT-Based Resilient Hydroponic Farming is an intelligent system designed to enable efficient crop cultivation in urban environments using automated monitoring and machine learning techniques. The system utilizes Arduino as the main controller along with sensors such as soil moisture, pH, DHT11, and ultrasonic sensors to continuously monitor hydroponic conditions like water level, humidity, temperature, and nutrient balance. A relay-controlled DC water pump is used to automatically maintain optimal growing conditions when deviations are detected. NodeMCU is used for IoT-based data uploading to cloud platforms for remote monitoring. Python is used for data processing and machine learning, where a Random Forest algorithm predicts abnormal conditions and helps in decision-making for system control. LCD displays real-time environmental parameters, ensuring easy monitoring. This system improves crop yield, reduces water usage, and enables smart urban farming through automation and predictive analytics.
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:
- Arduino Uno
- NodeMCU
- LCD Display
- Soil Moisture Sensor
- pH Sensor
- DHT11 Sensor
- Ultrasonic Sensor
- Relay Module
- DC Water Pump
- Power Supply
- 12V Adapter
- Connectors – 30
Software components:
- Arduino IDE
- Embedded C
- Python
Learning Outcomes
- Arduino pin diagram and architecture
- How to install Arduino IDE and required software
- Setting up and installation procedure for Arduino IDE
- Introduction to Arduino development environment
- Basics of Embedded C / Python programming
- Basics of IoT platforms
- 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
- Thesis writing skills
Paper Publishing
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