AI-Assisted Monitoring of Dairy Manufacturing Conditions - Seeed XIAO ESP32C3

// Connections
// XIAO ESP32C3 :
//                                Grove - Temperature & Humidity Sensor
// A4   --------------------------- SDA
// A5   --------------------------- SCL
//                                Grove - Integrated Pressure Sensor
// A0   --------------------------- S
//                                Gravity: I2C 1Kg Weight Sensor Kit - HX711
// A4   --------------------------- SDA
// A5   --------------------------- SCL
//                                DS18B20 Waterproof Temperature Sensor
// D6   --------------------------- Data
//                                SSD1306 OLED Display (128x64)
// A4   --------------------------- SDA
// A5   --------------------------- SCL
//                                MicroSD Card Module (Built-in on the XIAO Expansion board)
// D10  --------------------------- MOSI
// D9   --------------------------- MISO
// D8   --------------------------- CLK (SCK)
// D2   --------------------------- CS (SS)
//                                Button (Built-in on the XIAO Expansion board)
// D1   --------------------------- +

static const unsigned char PROGMEM sd [] = {
0x0F, 0xFF, 0xFF, 0xFE, 0x1F, 0xFF, 0xFF, 0xFF, 0x1F, 0xFE, 0x7C, 0xFF, 0x1B, 0x36, 0x6C, 0x9B,
0x19, 0x26, 0x4C, 0x93, 0x19, 0x26, 0x4C, 0x93, 0x19, 0x26, 0x4C, 0x93, 0x19, 0x26, 0x4C, 0x93,
0x19, 0x26, 0x4C, 0x93, 0x19, 0x26, 0x4C, 0x93, 0x19, 0x26, 0x4C, 0x93, 0x1F, 0xFF, 0xFF, 0xFF,
0x1F, 0xFF, 0xFF, 0xFF, 0x1F, 0xFF, 0xFF, 0xFF, 0x1F, 0xFF, 0xFF, 0xFF, 0x1F, 0xFF, 0xFF, 0xFF,
0x3F, 0xFF, 0xFF, 0xFF, 0x7F, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF,
0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFC, 0xC7, 0xFF, 0xFF, 0xF9, 0x41, 0xFF, 0x1F, 0xF9, 0xDD, 0xFF,
0x1F, 0xFC, 0xDD, 0xFF, 0x1F, 0xFE, 0x5D, 0xFF, 0x1F, 0xF8, 0x43, 0xFF, 0x1F, 0xFD, 0xFF, 0xFF,
0x3F, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF,
0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF,
0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF,
0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFE
};

...

display.clearDisplay();
display.drawBitmap(48, 0, sd, 32, 44, SSD1306_WHITE);
display.display();

#include <FS.h>
#include <SPI.h>
#include <SD.h>
#include <Adafruit_GFX.h>
#include <Adafruit_SSD1306.h>
#include <SensirionI2CSht4x.h>
#include <DFRobot_HX711_I2C.h>
#include <OneWire.h>
#include <DallasTemperature.h>

File myFile;
// Define the CSV file name:
const char* data_file = "/yogurt_data.csv";

#define SCREEN_WIDTH 128 // OLED display width, in pixels
#define SCREEN_HEIGHT 64 // OLED display height, in pixels
#define OLED_RESET    -1 // Reset pin # (or -1 if sharing Arduino reset pin)

Adafruit_SSD1306 display(SCREEN_WIDTH, SCREEN_HEIGHT, &Wire, OLED_RESET);

SensirionI2CSht4x sht4x;

// Define the HX711 weight sensor.
DFRobot_HX711_I2C MyScale;

// Define the DS18B20 waterproof temperature sensor settings:
#define ONE_WIRE_BUS D6
OneWire oneWire(ONE_WIRE_BUS);
DallasTemperature DS18B20(&oneWire);

#define button D1
// Define the button state and the duration to utilize the integrated button in two different modes: long press and short press.
int button_state = 0;
#define DURATION 5000

  display.begin(SSD1306_SWITCHCAPVCC, 0x3C);
  display.display();
  delay(1000);

  DS18B20.begin();

  sht4x.begin(Wire);
  uint32_t serialNumber;
  error = sht4x.serialNumber(serialNumber);

void err_msg(){
  // Show the error message on the SSD1306 screen.
  display.clearDisplay();
  display.drawBitmap(48, 0, _error, 32, 32, SSD1306_WHITE);
  display.setTextSize(1);
  display.setTextColor(SSD1306_WHITE);
  display.setCursor(0,40);
  display.println("Check the serial monitor to see the error!");
  display.display();
}

  if(error){
    Serial.print("Error: Grove - Temperature & Humidity Sensor not initialized!\n");
    errorToString(error, errorMessage, 256);
    Serial.println(errorMessage);
    err_msg();
  }else{
    Serial.print("Grove - Temperature & Humidity Sensor successfully initialized: "); Serial.println(serialNumber);
  }

  while (!MyScale.begin()) {
    Serial.println("Error: HX711 initialization is failed!");
    err_msg();
    delay(1000);
  }
  Serial.println("HX711 initialization is successful!");

  MyScale.setCalWeight(100);
  // Set the calibration threshold (g).
  MyScale.setThreshold(30);
  // Display the current calibration value.
  Serial.print("\nCalibration Value: "); Serial.println(MyScale.getCalibration());
  MyScale.setCalibration(MyScale.getCalibration());
  delay(1000);

  if(!SD.begin()){
    Serial.println("Error: SD card initialization failed!\n");
    err_msg();
    while (1);
  }
  Serial.println("SD card is detected successfully!\n");

void get_temperature_and_humidity(){
  // Obtain the measurements generated by the Grove - Temperature & Humidity Sensor.
  error = sht4x.measureHighPrecision(temperature, humidity);
  if(error){
    Serial.print("Error trying to execute measureHighPrecision(): ");
    errorToString(error, errorMessage, 256);
    Serial.println(errorMessage);
  }else{
    Serial.print("\nTemperature : "); Serial.print(temperature); Serial.println("°C");
    Serial.print("Humidity : "); Serial.print(humidity); Serial.println("%");
  }
  delay(500);
}

void get_pressure(){
  // Obtain the measurements generated by the Grove - Integrated Pressure Sensor.
  rawValue = 0;
  // Convert the accumulation of raw data to the pressure estimation.
  for (int x = 0; x < 10; x++) rawValue = rawValue + analogRead(pressure_s_pin);
  pressure = (rawValue - offset) * 700.0 / (fullScale - offset);
  Serial.print("\nPressure : "); Serial.print(pressure); Serial.println(" kPa");
}

void get_weight(int calibration){
  weight = MyScale.readWeight();
  weight = weight - calibration;
  if(weight < 0.5) weight = 0;
  Serial.print("\nWeight: "); Serial.print(weight); Serial.println(" g");
  delay(500);
}

void get_milk_temperature(){
  // Obtain the temperature measurement generated by the DS18B20 Waterproof Temperature Sensor.
  DS18B20.requestTemperatures();
  m_temperature = DS18B20.getTempCByIndex(0);
  Serial.print("\nMilk Temperature: "); Serial.print(m_temperature); Serial.println("°C");
}

void home_screen(){
  display.clearDisplay();
  display.setTextSize(1);
  display.setTextColor(SSD1306_WHITE);
  display.setCursor(0,0);
  display.println("Temp => " + String(temperature) + " *C");
  display.println("Humidity => " + String(humidity) + " %");
  display.println("Pres. => " + String(pressure) + " kPa");
  display.println();
  display.println("M_Temp => " + String(m_temperature) + " *C");
  display.println("Weight => " + String(weight) + " g");
  display.println();
  display.println("Selected Class => " + String(class_number));
  display.display();
}

void save_data_to_SD_Card(fs::FS &fs, int consistency_level){
  // Open the given CSV file on the SD card in the APPEND file mode.
  // FILE MODES: WRITE, READ, APPEND
  myFile = fs.open(data_file, FILE_APPEND);
  delay(1000);
  // If the given file is opened successfully:
  if(myFile){
    Serial.print("\n\nWriting to "); Serial.print(data_file); Serial.println("...");
    // Create the data record to be inserted as a new row:
    String data_record = String(temperature) + "," + String(humidity) + "," + String(pressure) + "," + String(m_temperature) + "," + String(weight) + ',' + String(consistency_level);
    // Append the data record:
    myFile.println(data_record);
    // Close the CSV file:
    myFile.close();
    Serial.println("Data saved successfully!\n");
    // Notify the user after appending the given data record successfully.
    display.clearDisplay();
    display.drawBitmap(48, 0, sd, 32, 44, SSD1306_WHITE);
    display.setTextSize(1);
    display.setTextColor(SSD1306_WHITE);
    display.setCursor(0,48);
    display.println("Data saved to the SD card!");
    display.display();
  }else{
    // If XIAO ESP32C3 cannot open the given CSV file successfully:
    Serial.println("\nXIAO ESP32C3 cannot open the given CSV file successfully!\n");
    err_msg();
  }
  // Exit and clear:
  delay(4000);
}

  button_state = 0;
  if(!digitalRead(button)){
    timer = millis();
    button_state = 1;
    while((millis()-timer) <= DURATION){
      if(digitalRead(button)){
        button_state = 2;
        break;
      }
    }
  }

  if(button_state == 1){
    // Save the given data record to the given CSV file on the SD card when long-pressed.
    save_data_to_SD_Card(SD, class_number);
  }else if(button_state == 2){
    // Change the class number when short-pressed.
    class_number++;
    if(class_number > 2) class_number = 0;
    Serial.println("\n\nSelected Class: " + String(class_number) + "\n");
  }

import numpy as np
import pandas as pd
from csv import writer

    def __init__(self, csv_path):
        # Read the data set from the given CSV file.
        self.df = pd.read_csv(csv_path)
        # Define the class (label) names.
        self.class_names = ["Thinner", "Optimum", "Curdling"]

    def scale_data_elements(self):
        self.df["scaled_temperature"] = self.df["temperature"] / 100
        self.df["scaled_humidity"] = self.df["humidity"] / 100
        self.df["scaled_pressure"] = self.df["pressure"] / 1000
        self.df["scaled_milk_temperature"] = self.df["milk_temperature"] / 100
        self.df["scaled_starter_weight"] = self.df["starter_weight"] / 10
        print("Data Elements Scaled Successfully!")

    def split_dataset_by_labels(self, class_number):
        l = len(self.df)
        sample_number = 0
        # Split the data set according to the yogurt consistency levels (classes):
        for i in range(l):
            # Add the header as the first row:
            processed_data = [["temperature", "humidity", "pressure", "milk_temperature", "starter_weight"]]
            if(self.df["consistency_level"][i] == class_number):
                row = [self.df["scaled_temperature"][i], self.df["scaled_humidity"][i], self.df["scaled_pressure"][i], self.df["scaled_milk_temperature"][i], self.df["scaled_starter_weight"][i]]
                processed_data.append(row)
                # Increment the sample number:
                sample_number+=1
                # Create a CSV file for each data record, identified with the sample number.
                filename = "data/{}.sample_{}.csv".format(self.class_names[class_number], sample_number)
                with open(filename, "a", newline="") as f:
                    for r in range(len(processed_data)):
                        writer(f).writerow(processed_data[r])
                    f.close()
                print("CSV File Successfully Created: " + filename)

dataset.scale_data_elements()
for c in range(len(dataset.class_names)):
    dataset.split_dataset_by_labels(c)

#include <IoT_AI-driven_Yogurt_Processing_inferencing.h>

#define BLYNK_TEMPLATE_ID "<_TEMPLATE_ID_>"
#define BLYNK_DEVICE_NAME "<_DEVICE_NAME_>"
#define BLYNK_AUTH_TOKEN "<_AUTH_TOKEN_>"

#include <WiFi.h>
#include <WiFiClient.h>
#include <BlynkSimpleEsp32.h>
#include <SPI.h>
#include <Adafruit_GFX.h>
#include <Adafruit_SSD1306.h>
#include <SensirionI2CSht4x.h>
#include <DFRobot_HX711_I2C.h>
#include <OneWire.h>
#include <DallasTemperature.h>

char auth[] = BLYNK_AUTH_TOKEN;
#define TEMP_WIDGET     V4
#define HUMD_WIDGET     V12
#define PRES_WIDGET     V6
#define M_TEMP_WIDGET   V7
#define WEIGHT_WIDGET   V8
#define BUTTON_WIDGET   V9
#define LABEL_WIDGET    V10

#define FREQUENCY_HZ        EI_CLASSIFIER_FREQUENCY
#define INTERVAL_MS         (1000 / (FREQUENCY_HZ + 1))

// Define the features array to classify one frame of data.
float features[EI_CLASSIFIER_DSP_INPUT_FRAME_SIZE];
size_t feature_ix = 0;

float threshold = 0.60;

// Define the yogurt consistency level (class) names:
String classes[] = {"Thinner", "Optimum", "Curdling"};

static const unsigned char PROGMEM *class_icons[] = {thinner, optimum, curdling};

Blynk.begin(auth, ssid, pass);

  Blynk.run();

void update_Blynk_parameters(){
  // Transfer the collected yogurt processing information to the Blynk dashboard.
  Blynk.virtualWrite(TEMP_WIDGET, temperature);
  Blynk.virtualWrite(HUMD_WIDGET, humidity);
  Blynk.virtualWrite(PRES_WIDGET, pressure);
  Blynk.virtualWrite(M_TEMP_WIDGET, m_temperature);
  Blynk.virtualWrite(WEIGHT_WIDGET, weight);
}

BLYNK_WRITE(BUTTON_WIDGET){
  int buttonValue = param.asInt();
  if(buttonValue){ model_running = true; }
  else{ Blynk.virtualWrite(LABEL_WIDGET, "Waiting..."); }
}

void run_inference_to_make_predictions(int multiply){
  // Scale (normalize) data items depending on the given model:
  float scaled_temperature = temperature / 100;
  float scaled_humidity = humidity / 100;
  float scaled_pressure = pressure / 1000;
  float scaled_milk_temperature = m_temperature / 100;
  float scaled_starter_weight = weight / 10;

  // Copy the scaled data items to the features buffer.
  // If required, multiply the scaled data items while copying them to the features buffer.
  for(int i=0; i<multiply; i++){
    features[feature_ix++] = scaled_temperature;
    features[feature_ix++] = scaled_humidity;
    features[feature_ix++] = scaled_pressure;
    features[feature_ix++] = scaled_milk_temperature;
    features[feature_ix++] = scaled_starter_weight;
  }

  // Display the progress of copying data to the features buffer.
  Serial.print("Features Buffer Progress: "); Serial.print(feature_ix); Serial.print(" / "); Serial.println(EI_CLASSIFIER_DSP_INPUT_FRAME_SIZE);

  // Run inference:
  if(feature_ix == EI_CLASSIFIER_DSP_INPUT_FRAME_SIZE){
    ei_impulse_result_t result;
    // Create a signal object from the features buffer (frame).
    signal_t signal;
    numpy::signal_from_buffer(features, EI_CLASSIFIER_DSP_INPUT_FRAME_SIZE, &signal);
    // Run the classifier:
    EI_IMPULSE_ERROR res = run_classifier(&signal, &result, false);
    ei_printf("\nrun_classifier returned: %d\n", res);
    if(res != 0) return;

    // Print the inference timings on the serial monitor.
    ei_printf("Predictions (DSP: %d ms., Classification: %d ms., Anomaly: %d ms.): \n",
        result.timing.dsp, result.timing.classification, result.timing.anomaly);

    // Obtain the prediction results for each label (class).
    for(size_t ix = 0; ix < EI_CLASSIFIER_LABEL_COUNT; ix++){
      // Print the prediction results on the serial monitor.
      ei_printf("%s:\t%.5f\n", result.classification[ix].label, result.classification[ix].value);
      // Get the predicted label (class).
      if(result.classification[ix].value >= threshold) predicted_class = ix;
    }
    Serial.print("\nPredicted Class: "); Serial.println(predicted_class);

    // Detect anomalies, if any:
    #if EI_CLASSIFIER_HAS_ANOMALY == 1
      ei_printf("Anomaly : \t%.3f\n", result.anomaly);
    #endif

    // Clear the features buffer (frame):
    feature_ix = 0;
  }
}

if(model_running){ run_inference_to_make_predictions(1); model_running = false; }

  if(predicted_class != -1){
      // Transfer the predicted label (class) to the Blynk application (dashboard).
      Blynk.virtualWrite(LABEL_WIDGET, classes[predicted_class]);
      // Print the predicted label (class) on the built-in screen.
      display.clearDisplay();
      display.drawBitmap(48, 0, class_icons[predicted_class], 32, 32, SSD1306_WHITE);
      display.setTextSize(1);
      display.setTextColor(SSD1306_WHITE);
      display.setCursor(0,40);
      display.println("Transferred to Blynk");
      String c = "Class: " + classes[predicted_class];
      int str_x = c.length() * 6;
      display.setCursor((SCREEN_WIDTH - str_x) / 2, 56);
      display.println(c);
      display.display();
      // Clear the predicted label (class).
      predicted_class = -1;
      delay(1000);
  }

  if(millis() - timer >= 30*1000){ update_Blynk_parameters(); Serial.println("\n\nBlynk Dashboard: Data Transferred Successfully!\n"); timer = millis(); }