Published July 25, 2025 © GPL3+

Environmental Trend Tracker with M5StickC PLUS 2

Monitor and forecast environmental trends using temperature, humidity, and pressure sensors powered by M5StickC PLUS 2 and Streamlit. Ideal

IntermediateFull instructions provided24 hours162

Environmental Trend Tracker with M5StickC PLUS 2

Things used in this project

Hardware components

M5Stack M5StickC ESP32-PICO Mini IoT Development Board

M5Stack ENV III Unit with Temperature Humidity Air Pressure Sensor (SHT30+QMP6988)

Software apps and online services

Arduino IDE

Story

Project Overview

This project transforms the compact **M5StickC PLUS 2** with an **ENV III sensor module** into a real-time environmental monitoring station that displays temperature, humidity, and air pressure — and even predicts the weather based on sensor data.

---

Why This Project?

Weather affects our lives every day, but having a personal weather station can be expensive or too bulky for daily use.

This lightweight, low-power solution fits in your hand, displays real-time readings on screen, and pushes data to a web dashboard for visualization and prediction.

It’s an excellent entry point for:

Learning embedded systems and Python
Practicing data visualization with Streamlit
Exploring weather prediction from a data science perspective
--

How It Works

1. **Sensor Collection**

The M5StickC PLUS 2 reads temperature (°C), humidity (%), and air pressure (hPa) from the ENV III sensor every 2 seconds.

2. **Serial Communication**

The data is printed via serial in CSV format and received on a host computer using Python (`serial_to_csv.py`).

3. **Data Logging and Visualization**

A Python Streamlit web dashboard (`app.py`) loads the CSV file and plots time-series trends for all three environmental variables with prediction logic.

4. **Weather Forecasting**

Based on recent changes in pressure, temperature, and humidity, the system predicts possible weather conditions (e.g., sunny, rainy, or stable).

---

What Makes It Special?

1 / 2

Code

#include <Wire.h>
#include <M5StickCPlus2.h>
#include <Adafruit_SHT31.h>
#include <DFRobot_QMP6988.h>

// 
Adafruit_SHT31 sht30 = Adafruit_SHT31();
DFRobot_QMP6988 qmp;

void setup() {
  M5.begin();
  Wire.begin();  //  SDA/SCL

  Serial.begin(115200);
  delay(1000);
  Serial.println("Initializing sensors...");

  // 
  if (!sht30.begin(0x44)) {
    Serial.println("Couldn't find SHT30 sensor!");
    while (1);
  }

  // 
  if (!qmp.begin()) {
    Serial.println("Couldn't find QMP6988 sensor!");
    while (1);
  }

  M5.Lcd.setRotation(3);
  M5.Lcd.fillScreen(BLACK);
  M5.Lcd.setTextColor(WHITE, BLACK);
  M5.Lcd.setTextSize(2);
}

void loop() {
  float temperature = sht30.readTemperature();  // 
  float humidity = sht30.readHumidity();        // 
  float pressure = qmp.readPressure();          //  Pa

  M5.Lcd.fillScreen(BLACK);
  M5.Lcd.setCursor(10, 10);
  if (!isnan(temperature)) {
    M5.Lcd.printf("Temp: %.2f C", temperature);
  } else {
    M5.Lcd.println("Temp error");
  }

  M5.Lcd.setCursor(10, 40);
  if (!isnan(humidity)) {
    M5.Lcd.printf("Humi: %.2f %%", humidity);
  } else {
    M5.Lcd.println("Humi error");
  }

  M5.Lcd.setCursor(10, 70);
  if (!isnan(pressure)) {
    M5.Lcd.printf("Pres: %.2f hPa", pressure / 100.0);  //  hPa
  } else {
    M5.Lcd.println("Pres error");
  }

  //   serial_to_csv.py 
  Serial.printf("T: %.2f, H: %.2f, P: %.2f\n", temperature, humidity, pressure / 100.0);

  delay(2000);
}

import serial
import pandas as pd
from datetime import datetime

# 
port = 'COM5'
baudrate = 115200

ser = serial.Serial(port, baudrate, timeout=1)
data = []
print(" Receiving data...")

try:
    while len(data) < 100:  # 100
        line = ser.readline().decode(errors='ignore').strip()
        if "T:" in line and "H:" in line and "P:" in line:
            try:
                parts = line.split(",")
                temp = float(parts[0].split(":")[1])
                humi = float(parts[1].split(":")[1])
                pres = float(parts[2].split(":")[1])
                timestamp = datetime.now().strftime("%H:%M:%S")
                data.append([timestamp, temp, humi, pres])
                print(f"{timestamp} | Temp: {temp}C | Humi: {humi}% | Press: {pres}hPa")
            except:
                continue
finally:
    ser.close()
    df = pd.DataFrame(data, columns=["time", "temperature", "humidity", "pressure"])
    df.to_csv("data.csv", index=False, encoding='utf-8')
    print(" Data saved to data.csv")

import streamlit as st
import pandas as pd
import matplotlib.pyplot as plt
import matplotlib.dates as mdates  #  

st.set_page_config(page_title="Environmental Monitoring Dashboard", layout="centered")
st.title(" M5StickC PLUS 2 Environmental Monitor")

# Load CSV data
df = pd.read_csv("data.csv")
df['time'] = pd.to_datetime(df['time'])  #   time  datetime 

# Show latest data preview
st.subheader(" Latest Data Preview")
st.dataframe(df.tail(10))

# Temperature trend
st.subheader(" Temperature Trend")
fig1, ax1 = plt.subplots(figsize=(6, 3))
ax1.plot(df['time'], df['temperature'], color='orange', label='Temperature (C)')
ax1.set_xlabel("Time")
ax1.set_ylabel("Temperature (C)")
ax1.xaxis.set_major_locator(mdates.AutoDateLocator())  #  
ax1.xaxis.set_major_formatter(mdates.DateFormatter('%H:%M:%S'))  #  
ax1.tick_params(axis='x', rotation=45)
ax1.legend()
st.pyplot(fig1)

# Humidity trend
st.subheader(" Humidity Trend")
fig2, ax2 = plt.subplots(figsize=(6, 3))
ax2.plot(df['time'], df['humidity'], color='blue', label='Humidity (%)')
ax2.set_xlabel("Time")
ax2.set_ylabel("Humidity (%)")
ax2.xaxis.set_major_locator(mdates.AutoDateLocator())
ax2.xaxis.set_major_formatter(mdates.DateFormatter('%H:%M:%S'))
ax2.tick_params(axis='x', rotation=45)
ax2.legend()
st.pyplot(fig2)

# Pressure trend
st.subheader(" Pressure Trend")
fig3, ax3 = plt.subplots(figsize=(6, 3))
ax3.plot(df['time'], df['pressure'], color='green', label='Pressure (hPa)')
ax3.set_xlabel("Time")
ax3.set_ylabel("Pressure (hPa)")
ax3.xaxis.set_major_locator(mdates.AutoDateLocator())
ax3.xaxis.set_major_formatter(mdates.DateFormatter('%H:%M:%S'))
ax3.tick_params(axis='x', rotation=45)
ax3.legend()
st.pyplot(fig3)

# Weather prediction
st.subheader(" Weather Forecast")
if len(df) >= 2:
    # 
    t_now, h_now, p_now = df[['temperature', 'humidity', 'pressure']].iloc[-1]
    t_prev, h_prev, p_prev = df[['temperature', 'humidity', 'pressure']].iloc[-2]

    dt = t_now - t_prev
    dh = h_now - h_prev
    dp = p_now - p_prev

    # 
    st.markdown(f"**Pressure :** `{dp:.2f} hPa`**Humidity :** `{dh:.2f} %`**Temp :** `{dt:.2f} C`")

    # 
    if dp < -1.0 and dh > 1.0 and dt < -0.3:
        forecast = " Likely rain or overcast  pressure dropping, humidity rising"
    elif dp > 1.0 and dh < -1.0 and dt > 0.3:
        forecast = " Clear or improving  pressure rising and drier"
    elif abs(dp) < 0.5 and abs(dh) < 2 and abs(dt) < 0.5:
        forecast = " Stable weather  no major changes"
    else:
        forecast = " Fluctuating  unclear, possibly humid or cloudy"

    st.markdown(f"**Forecast:** {forecast}")
else:
    st.write("Not enough data to predict weather.")


st.markdown("""
---
 This dashboard visualizes real-time environmental data (temperature, humidity, and pressure) collected by the M5StickC PLUS 2 + ENV III module.  
Data is sampled every 2 seconds and can support basic weather trend predictions.
""")

time,temperature,humidity,pressure
18:38:25,24.69,69.83,10.13
18:38:27,24.69,69.71,10.13
18:38:29,24.69,69.61,10.13
18:38:31,24.7,69.86,10.13
18:38:33,24.73,69.99,10.13
18:38:35,24.73,70.14,10.13
18:38:37,24.77,70.09,10.13
18:38:39,24.74,69.96,10.13
18:38:41,24.74,69.92,10.13
18:38:43,24.76,69.88,10.13
18:38:45,24.76,69.81,10.13
18:38:47,24.79,69.74,10.13
18:38:49,24.77,69.7,10.13
18:38:51,24.77,69.66,10.13
18:38:54,24.79,69.62,10.13
18:38:56,24.79,69.57,10.13
18:38:58,24.79,69.57,10.13
18:39:00,24.83,69.56,10.13
18:39:02,24.81,69.63,10.13
18:39:04,24.8,69.76,10.13
18:39:06,24.8,69.86,10.13
18:39:08,24.83,69.94,10.13
18:39:10,24.81,69.94,10.13
18:39:12,24.84,69.96,10.13
18:39:14,24.83,69.92,10.13
18:39:16,24.81,69.9,10.13
18:39:18,24.83,69.96,10.13
18:39:20,24.84,70.21,10.13
18:39:22,24.86,70.41,10.13
18:39:25,24.83,70.51,10.13
18:39:27,24.87,70.65,10.13
18:39:29,24.84,70.74,10.13
18:39:31,24.86,70.72,10.13
18:39:33,24.87,70.89,10.13
18:39:35,24.87,71.08,10.13
18:39:37,24.87,71.24,10.13
18:39:39,24.88,71.33,10.13
18:39:41,24.87,71.51,10.13
18:39:43,24.87,71.67,10.13
18:39:45,24.88,71.86,10.13
18:39:47,24.87,71.98,10.13
18:39:49,24.91,72.07,10.13
18:39:51,24.88,72.12,10.13
18:39:53,24.9,72.2,10.13
18:39:56,24.88,72.35,10.13
18:39:58,24.9,72.45,10.13
18:40:00,24.91,72.52,10.13
18:40:02,24.9,72.55,10.13
18:40:04,24.9,72.58,10.13
18:40:06,24.9,72.6,10.13
18:40:08,24.91,72.58,10.13
18:40:10,24.92,72.57,10.13
18:40:12,24.91,72.53,10.13
18:40:14,24.91,72.39,10.13
18:40:16,24.91,72.19,10.13
18:40:18,24.92,72.05,10.13
18:40:20,24.91,71.95,10.13
18:40:22,24.92,71.81,10.13
18:40:24,24.95,71.74,10.13
18:40:26,24.92,71.63,10.13
18:40:29,24.92,71.61,10.13
18:40:31,24.92,71.59,10.13
18:40:33,24.92,71.57,10.13
18:40:35,24.95,71.68,10.13
18:40:37,24.92,71.85,10.13
18:40:39,24.97,71.96,10.13
18:40:41,24.97,72.11,10.13
18:40:43,24.95,72.34,10.13
18:40:45,24.97,72.52,10.13
18:40:47,24.95,72.62,10.13
18:40:49,24.95,72.65,10.13
18:40:51,24.97,72.68,10.13
18:40:53,24.97,72.74,10.13
18:40:55,24.95,72.86,10.13
18:40:57,24.97,72.89,10.13
18:41:00,24.98,72.95,10.13
18:41:02,24.95,72.98,10.13
18:41:04,24.97,73.01,10.13
18:41:06,24.97,72.97,10.13
18:41:08,24.97,72.95,10.13
18:41:10,24.97,72.93,10.13
18:41:12,24.98,72.94,10.13
18:41:14,24.99,72.92,10.13
18:41:16,24.99,72.91,10.13
18:41:18,24.98,72.88,10.13
18:41:20,24.98,72.81,10.13
18:41:22,24.97,72.78,10.13
18:41:24,24.98,72.68,10.13
18:41:26,24.98,72.58,10.13
18:41:28,24.99,72.54,10.13
18:41:31,24.98,72.45,10.13
18:41:33,24.99,72.44,10.13
18:41:35,24.98,72.36,10.13
18:41:37,24.97,72.29,10.13
18:41:39,25.01,72.27,10.13
18:41:41,24.99,72.27,10.13
18:41:43,24.99,72.31,10.13
18:41:45,24.99,72.33,10.13
18:41:47,25.01,72.32,10.13
18:41:49,25.01,72.32,10.13