GJ_Poultry is an open-source IoT-based poultry monitoring system designed to track and visualize key environmental parameters in a chicken coop.
It integrates low-cost sensors (temperature, humidity, gas, and camera modules) with a microcontroller and web app to continuously collect and analyze data.

By maintaining optimal coop conditions, the system helps:

• Reduce mortality and growth delays
• Improve feed efficiency
• Enable data-driven farm management

The project aligns with precision livestock farming principles — leveraging IoT, control systems, and physics-based modeling to enhance animal welfare and productivity.

Typical wiring setup: DHT22 → Digital pin 3 MQ135 → A0 DS18B20 → Digital pin 2 Buzzer → Digital pin 4 Wi-Fi → ESP8266 built-in

The firmware is written in Arduino (C/C++).

1. Install Arduino IDE and libraries:
   • Adafruit DHT
   • OneWire
   • DallasTemperature
2. Configure pins & Wi-Fi credentials in GJ_Poultry.ino.
3. Upload code to ESP8266 or ESP32 board.
4. Monitor serial output (baud rate: 115200).

#include <DHT.h>
#include <OneWire.h>
#include <DallasTemperature.h>

#define DHTPIN 3
#define DHTTYPE DHT22
DHT dht(DHTPIN, DHTTYPE);

#define ONE_WIRE_BUS 2
OneWire oneWire(ONE_WIRE_BUS);
DallasTemperature ds18(&oneWire);

const int MQ135_PIN = A0;

void setup() {
  Serial.begin(115200);
  dht.begin();
  ds18.begin();
}

void loop() {
  float humidity = dht.readHumidity();
  float temp1 = dht.readTemperature();
  ds18.requestTemperatures();
  float temp2 = ds18.getTempCByIndex(0);
  int mqRaw = analogRead(MQ135_PIN);
  float co2ppm = (mqRaw - 33.0) * (1000.0 / (4095.0 - 33.0));

  Serial.printf("Temp1=%.2fC Temp2=%.2fC Humidity=%.1f%% CO2=%.1fppm\n",
                 temp1, temp2, humidity, co2ppm);
  delay(5000);
}

Web Dashboard Options:

• Blynk IoT App
• Add widgets for gauges and plots.
• Link virtual pins to ESP8266. -View real-time updates on mobile.

Run locally:

$ pip install -r requirements.txt
$ python server.py
# or
$ npm install && npm start
Visit: http://localhost:5000

-Real-time sensor charts -Alerts for threshold breaches -CSV data export

Dashboard with adjustable time ranges

Collected data are stored in /src/data.csv as:

timestamp,temperature_C,humidity_%,co2_ppm
2025-11-01 06:00:00,24.2,45.7,350
2025-11-01 06:05:00,24.5,46.0,355
2025-11-01 06:10:00,24.3,45.5,348
Data Fields
Column	Unit	Description
timestamp	ISO time	Reading time
temperature_C	°C	DHT22 / DS18B20 temperature
humidity_%	%RH	Ambient humidity
co2_ppm	ppm	MQ-135 gas reading

• Time-Series Graphs: Temperature, humidity, and gas concentration over time.
• Threshold Bands: Safe/unsafe zones highlighted on charts.
• Historical Comparison: Weekly or daily parameter trends.
• KPI Overlay: Mortality, productivity, and feed efficiency vs. environment.

import pandas as pd
import matplotlib.pyplot as plt

data = pd.read_csv('data.csv', parse_dates=['timestamp'])
plt.plot(data['timestamp'], data['temperature_C'], label='Temp (°C)')
plt.plot(data['timestamp'], data['humidity_%'], label='Humidity (%)')
plt.xlabel('Time'); plt.ylabel('Value')
plt.legend(); plt.show()

🎯 Target:
Maintain >95% uptime, CO₂ < 1000 ppm, and temperature between 18–24 °C for optimal flock health.

This system contributes to smart poultry research by:

• Demonstrating IoT integration with real-time environmental feedback
• Applying open-source, low-cost hardware for small farms
• Supporting data analytics for mortality reduction and performance optimization

Supporting Studies:

• Syafar et al. – IoT-based poultry coop monitoring with DHT22 and MQ-135
• Phiri et al. – Arduino-ZigBee-GSM smart farming model
• Liu et al., 2024 – Integrated environmental & performance dashboards for smart poultry houses

Maintaining coop temperature uses the convective heat transfer equation:

Q = m_dot * c_p * ΔT

Where:

m_dot = mass flow rate of air (kg/s)

c_p = specific heat capacity of air (kJ/kg·K)

ΔT = temperature difference (°C)

💨 Gas Law Relation For MQ-135 readings, gas concentration depends on the ideal gas law:

p * V = n * R * T

Where:

p = pressure (Pa)

V = volume (m³)

n = number of moles

R = universal gas constant (8.314 J/mol·K)

T = temperature (K)

💧 Humidity Sensing The DHT22 measures relative humidity (RH) using a capacitive sensor:

C = ε * A / d

Where:

C = capacitance (F)

ε = permittivity of the dielectric

A = area of the capacitor plates

d = distance between plates

🌡️ Comfort Index (Enthalpy Approximation)

h = 1.006*T + RH * (2501 + 1.86*T)

Where:

T = temperature in °C

RH = relative humidity (fraction)

h = enthalpy (kJ/kg)

📈 Production Equations

Copy code
# Mortality Rate
CMR = (D / N) * 100  # D = number of deaths, N = initial population

# Egg Production Rate
EPR = E / C           # E = eggs produced, C = number of chickens

Data Flow:

Arduino → CSV → Web App → Visualization
Sensors → ESP8266 via GPIO/ADC
Data → Logged as data.csv
Web App → Displays charts (real-time & historical)
Analysis → Performed using Python/Pandas

• Improved environmental control via sensor feedback
• Enhanced animal welfare
• Scalable and reproducible open-source design
• Potential to reduce mortality by 10–15% through environmental optimization

• Syafar et al. – IoT-based poultry coop monitoring with DHT22 and MQ-135
• Phiri et al. – Arduino-ZigBee-GSM smart farming model
• Liu et al., 2024 – Integrated environmental & performance dashboards for smart poultry houses

• Integrate PID-based temperature control (using fans/heaters)
• Add cloud synchronization (Firebase/MQTT)
• Implement predictive analytics using Machine Learning models

This project is released under the MIT License.
Feel free to use, modify, and extend it for research and educational purposes.