#SSP2526 - Smart_Agriculture_Monitoring_System

Team Members 1. Sai Bhavith Reddy Bokka - 16704000 2. Sri Tarun Desigan Gnana Desiga Raja - 16392748 3. Praveena Boddu - 16462269

Smart Agriculture Monitoring System

1- Embedded System Proposal

 The main motto of this project is to design and implement a Smart Agriculture Monitoring System that monitors the environmental parameters such as soil moisture, temperature, humidity and light intensity. This system provides efficient irrigration and optimal crop growth by automating the  actuation of a motor/pump using the real-time sensor data. The main idea is to use the STM32F446RE as the main controller for sensing and decision-making and a Raspberry Pi 3(running a custom Yocto-built image) for data visualization and data logging.

Aim of the Project:

 • Real-time sensing of data from multiple sensors.

 • COntrol and actuation of hardware based on environmental parameters.

 • Integration of STM32 and Raspberry Pi(with custom Yocto image).

2- Hardware Design

The hardware components of the system are as follows:

 • STM32F446RE Microcontroller - used as interface for the sensors and control.

 • Raspberry Pi 3 - used as a secondary controller to display data in the monitor using custom Yocto image.

 • SHT31 Temperature and Humidity Sensor - digital sensing element connected with STM32 using i2c.

 • LDR (Light Dependent Resistor) - used to sense the light intensity.

 • Soil moisture Sensor - Analog sensor used to sense the soil moisture.
 
 • DC motor - An actuator which is powered on when the soil moisture sensed falls below the fixed value(i.e.,30).

3- Embedded Code Implementation and Architechture

 Visual Studio code with platformIO extension is used for the development of the firmware of the project.

STM32 Working:

  • Read analog data from Soil Moisture and LDR sensors

  • Read digital data from SHT31 Temperature and Humidity sensor via I2C

  • Compare the soil moisture level with a threshold value(30%)

  • Turn ON/OFF the DC motor using L298N driver

  • Send all sensor readings to Raspberry Pi through UART.

Raspberry Pi (Yocto Image) Working:

  • Using UART portocol sensor data is received from STM32.

  • Received data is displayed in monitor and logged.

System Flow:

  • The required environmental parameters are sensed by the sensors.

  • Data is read by the STM32 and processed.

  • Data is transmitted to Raspberry Pi via UART

  • Raspberry Pi displays data using a Yocto-based Python application

This modular structure allows independent testing and degging of both microcontroller firmware and Linux-side application.

4- Raspberry Pi Setup and Yocto Customization:

The Raspberry Pi 3 runs a custom embedded Linux image built using the Yocto Project the Yocto build environment was configured with following layers:

  • Poky(Base Yocto distribution).

  • meta-openembedded(For Python and hardware libraries)

  • meta-raspberrypi(Hardwarre BSP for Raspberry Pi 3)

  • meta-monitor(custom layer) was created for this project to include a custom C applicaton.

Custom Layer (meta-monitor):

A new layer named meta-monitor was created and added to the file inside this layer, a BitBake recipe monitor_1.0.bb was written to:

 • Complile our C source code that receives sensor data from the STM32 using UART communication protocol.

 • Install the resulting binary into the root filesystem.

 • Register a system service to launch the binary automatically at boot.

C Application Functionality:

The C program on the Raspberry Pi 3 performs the following tasks:

1. Initializes the UART communication at the rate of 115200 baud.

2. Continuosly reads the sensor data sent by the STM32F446re.

3. Parses the incoming temperature, humidity, light, and soil-moisture values.

4. The data is displayed for continuos monitoring.

5- Evaluation:

The system was successfully developed and tested with all components functioning.

Observation:

 • The STM32F446re sensed environmental data and controlled the motor automatically based on soil moisture readings.

 • The SHT31 sensor provided accurate temperature and humidity data.

 • The Raspberry Pi 3 with custom built Yocto image, successfully displayed live sensor data.

 • When the soil moisture value fell below the fixed value, the DC motor actuatedd automatically.

Results

 • Real-time monitoring of data was achieved.

 • Automated actuation of motor was done.

 • Communication through UART between STM32 and Raspberry Pi was reliable.

 • Custom image using Yocto was deployed.

Conclusion:

 The Smart Agriculture Monitoring System was successfully implemented using the STM32 F446RE and Raspberry Pi 3. Sensors like SHT31, LDR and soil moisture sensor accuratelly measured environmental conditions, while the STM32 controlled the water pump based on soil moisture levels. A custom Yocto-based Linux image on the Raspberry Pi enabled local data monitoring through UART communication. The system operated efficiently without internet dependency, achieving full automation for basic agricultural needs.

7024ENG-2526SEPNOV/SSP2526-Smart_Agriculture_Monitoring_System

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