Technology is changing every industry, and agriculture is no exception. The Internet of Things (IoT) and edge computing provide powerful tools to make traditional farming practices more efficient, sustainable, and data-driven. One affordable and versatile platform that can form the basis for such a smart agriculture system is the Raspberry Pi.

In this blog post, we will build a smart agriculture system using IoT devices to monitor soil moisture, temperature, and humidity levels across a farm. The goal is to optimize irrigation and ensure optimal growing conditions for crops. We’ll use a Raspberry Pi running Raspbian OS, Actian Zen Edge for database management, Zen Enterprise to handle the detected anomalies on the remote server database, and Python with the Zen ODBC interface for data handling. Additionally, we’ll leverage AWS SNS (Simple Notification Service) to send alerts for detected anomalies in real-time for immediate action.

Prerequisites

Before we start, ensure you have the following:

• A Raspberry Pi running Raspbian OS.
• Python installed on your Raspberry Pi.
• Actian Zen Edge database installed.
• PyODBC library installed.
• AWS SNS set up with an appropriate topic and access credentials.

Step 1: Setting Up the Raspberry Pi

First, update your Raspberry Pi and install the necessary libraries:

sudo apt-get update
sudo apt-get upgrade
sudo apt-get install python3-pip
pip3 install pyodbc boto3

Step 2: Install Actian Zen Edge

Follow the instructions on the Actian Zen Edge download page to download and install Actian Zen Edge on your Raspberry Pi.

Step 3: Create Tables in the Database

We need to create tables to store sensor data and anomalies. Connect to your Actian Zen Edge database and create the following table:

CREATE TABLE sensor_data (
    id identity PRIMARY KEY,
    timestamp DATETIME,
    soil_moisture FLOAT,
    temperature FLOAT,
    humidity FLOAT
);

Install Zen Enterprise, connect to the central database, and create the following table:

 CREATE TABLE anomalies (
    id identity PRIMARY KEY ,
    timestamp DATETIME,
    soil_moisture FLOAT,
    temperature FLOAT,
    humidity FLOAT,
    description longvarchar
);

Step 4: Define the Python Script

Now, let’s write the Python script to handle sensor data insertion, anomaly detection, and alerting via AWS SNS.

Anomaly Detection Logic

Define a function to check for anomalies based on predefined thresholds:

def check_for_anomalies(data):
    threshold = {'soil_moisture': 30.0, 'temperature': 35.0, 'humidity': 70.0}
    anomalies = []
    if data['soil_moisture'] < threshold['soil_moisture']:
        anomalies.append('Low soil moisture detected')
    if data['temperature'] > threshold['temperature']:
        anomalies.append('High temperature detected')
    if data['humidity'] > threshold['humidity']:
        anomalies.append('High humidity detected')
    return anomalies

Insert Sensor Data

Define a function to insert sensor data into the database:

import pyodbc

def insert_sensor_data(data):
    conn = pyodbc.connect('Driver={Pervasive ODBC 
Interface};servername=localhost;Port=1583;serverdsn=demodata;')
    cursor = conn.cursor()
    cursor.execute("INSERT INTO sensor_data (timestamp, soil_moisture, temperature, humidity) VALUES (?, ?, ?, ?)",
                   (data['timestamp'], data['soil_moisture'], data['temperature'], data['humidity']))
    conn.commit()
    cursor.close()
    conn.close()

Send Anomalies to the Remote Database

Define a function to send detected anomalies to the database:

def send_anomalies_to_server(anomaly_data):
    conn = pyodbc.connect('Driver={Pervasive ODBC Interface};servername=<remote server>;Port=1583;serverdsn=demodata;')
    cursor = conn.cursor()
    cursor.execute("INSERT INTO anomalies (timestamp, soil_moisture, temperature, humidity, description) VALUES (?, ?, ?, ?, ?)",
                   (anomaly_data['timestamp'], anomaly_data['soil_moisture'], anomaly_data['temperature'], anomaly_data['humidity'], anomaly_data['description']))
    conn.commit()
    cursor.close()
    conn.close()

Send Alerts Using AWS SNS

Define a function to send alerts using AWS SNS:

def send_alert(message):
    sns_client = boto3.client('sns', aws_access_key_id='Your key ID',
    aws_secret_access_key ='Your Access key’, region_name='your-region')
    topic_arn = 'arn:aws:sns:your-region:your-account-id:your-topic-name'
    response = sns_client.publish(
        TopicArn=topic_arn,
        Message=message,
        Subject='Anomaly Alert'
    )
    return response

Replace your-region, your-account-id, and your-topic-name with your actual AWS SNS topic details.

Step 5: Generate Sensor Data

Define a function to simulate real-world sensor data:

import random
import datetime

def generate_sensor_data():
    return {
        'timestamp': datetime.datetime.now().strftime('%Y-%m-%d %H:%M:%S'),
        'soil_moisture': random.uniform(20.0, 40.0),
        'temperature': random.uniform(15.0, 45.0),
        'humidity': random.uniform(30.0, 80.0)
    }

Step 6: Main Function to Simulate Data Collection and Processing

Finally, put everything together in a main function:

def main():
    for _ in range(100):
        sensor_data = generate_sensor_data()
        insert_sensor_data(sensor_data)
        anomalies = check_for_anomalies(sensor_data)
        if anomalies:
            anomaly_data = {
                'timestamp': sensor_data['timestamp'],
                'soil_moisture': sensor_data['soil_moisture'],
                'temperature': sensor_data['temperature'],
                'humidity': sensor_data['humidity'],
                'description': ', '.join(anomalies)
            }
            send_anomalies_to_server(anomaly_data)
            send_alert(anomaly_data['description'])
if __name__ == "__main__":
    main()

Conclusion

And there you have it! By following these steps, you’ve successfully set up a basic smart agriculture system on a Raspberry Pi using Actian Zen Edge and Python. This system, which monitors soil moisture, temperature, and humidity levels, detects anomalies, stores data in databases, and sends notifications via AWS SNS, is a scalable solution for optimizing irrigation and ensuring optimal growing conditions for crops. Now, it’s your turn to apply this knowledge and contribute to the future of smart agriculture.

Remember to replace placeholders with your actual AWS SNS topic details and database connection details. Happy farming!

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