果蔬成熟度检测---基于AS7341颜色传感器 简单

# Check the incoming data packet:
if(isset($_GET["F1"]) && isset($_GET["F2"]) && isset($_GET["F3"]) && isset($_GET["F4"]) && isset($_GET["F5"]) && isset($_GET["F6"]) && isset($_GET["F7"]) && isset($_GET["F8"]) && isset($_GET["nir_1"]) && isset($_GET["nir_2"]) && isset($_GET["class"])){
    # Create the data array.
    $data = array($_GET["F1"], $_GET["F2"], $_GET["F3"], $_GET["F4"], $_GET["F5"], $_GET["F6"], $_GET["F7"], $_GET["F8"], $_GET["nir_1"], $_GET["nir_2"], $_GET["class"], date("m/d"));
    # Insert the recently generated data array into the CSV file as a new row.        
    $file = fopen("spectral_color_database.csv", "a");
    fputcsv($file, $data);
    fclose($file);
    // Print result:
    echo "Data Inserted Successfully!";
}else{
    echo "Waiting for data from the AS7341 sensor to insert...";
}

sudo mv /home/pi/Downloads/Vegetables_and_Fruits_Data_Logger/
/var/www/html/

char ssid[] = "SSID";        // your network SSID (name)
char pass[] = "PASSWORD";    // your network password (use for WPA, or use as key for WEP)
int keyIndex = 0;            // your network key Index number (needed only for WEP)
int status = WL_IDLE_STATUS;

// Enter the IPAddress of your Raspberry Pi.
IPAddress server(192, 168, 1, 20);

// Initialize the Ethernet client library
WiFiClient client; /* WiFiSSLClient client; */

// Define the AS7341 object.
DFRobot_AS7341 as7341;
// Define AS7341 data objects:
DFRobot_AS7341::sModeOneData_t data1;
DFRobot_AS7341::sModeTwoData_t data2;

// Detect if I2C can communicate properly 
  while (as7341.begin() != 0) {
    Serial.println("I2C init failed, please check if the wire connection is correct");
    delay(1000);
  }

  // Enable the built-in LED on the AS7341 sensor.
  as7341.enableLed(true);

if (WiFi.status() == WL_NO_MODULE) { Serial.println("Connection Failed!"); while (true); }
  // Attempt to connect to the WiFi network:
  while (status != WL_CONNECTED) {
    Serial.println("Attempting to connect to WiFi !!!");
    // Connect to WPA/WPA2 network. Change this line if using open or WEP network:
    status = WiFi.begin(ssid, pass);
    // Wait 10 seconds for connection:
    delay(10000);
  }

void read_controls(){
  // Potentiometer:
  pot_val = analogRead(pot);
  // Class Buttons:
  class_1_val = digitalRead(class_1);
  class_2_val = digitalRead(class_2);
  class_3_val = digitalRead(class_3);
  class_4_val = digitalRead(class_4);
}

// Start spectrum measurement:
  // Channel mapping mode: 1.eF1F4ClearNIR
  as7341.startMeasure(as7341.eF1F4ClearNIR);
  // Read the value of sensor data channel 0~5, under eF1F4ClearNIR
  data1 = as7341.readSpectralDataOne();
  // Channel mapping mode: 2.eF5F8ClearNIR
  as7341.startMeasure(as7341.eF5F8ClearNIR);
  // Read the value of sensor data channel 0~5, under eF5F8ClearNIR
  data2 = as7341.readSpectralDataTwo();
  // Print data:
  Serial.print("F1(405-425nm): "); Serial.println(data1.ADF1);
  Serial.print("F2(435-455nm): "); Serial.println(data1.ADF2);
  Serial.print("F3(470-490nm): "); Serial.println(data1.ADF3);
  Serial.print("F4(505-525nm): "); Serial.println(data1.ADF4);
  Serial.print("F5(545-565nm): "); Serial.println(data2.ADF5);
  Serial.print("F6(580-600nm): "); Serial.println(data2.ADF6);
  Serial.print("F7(620-640nm): "); Serial.println(data2.ADF7);
  Serial.print("F8(670-690nm): "); Serial.println(data2.ADF8);
  // CLEAR and NIR:
  Serial.print("Clear_1: "); Serial.println(data1.ADCLEAR);
  Serial.print("NIR_1: "); Serial.println(data1.ADNIR);
  Serial.print("Clear_2: "); Serial.println(data2.ADCLEAR);
  Serial.print("NIR_2: "); Serial.println(data2.ADNIR);
  Serial.print("\n------------------------------\n");
  delay(1000);

if(!class_1_val) make_a_get_request("/Vegetables_and_Fruits_Data_Logger/", "0");
  if(!class_2_val) make_a_get_request("/Vegetables_and_Fruits_Data_Logger/", "1");
  if(!class_3_val) make_a_get_request("/Vegetables_and_Fruits_Data_Logger/", "2");
  if(!class_4_val) make_a_get_request("/Vegetables_and_Fruits_Data_Logger/", "3");

void make_a_get_request(String application, String _class){
  // Connect to the web application named Vegetables_and_Fruits_Data_Logger. Change '80' with '443' if you are using SSL connection.
  if(client.connect(server, 80)){
    // If successful:
    Serial.println("\n\nConnected to the server!");
    // Create the query string:
    String query = application + "?F1="+data1.ADF1+"&F2="+data1.ADF2+"&F3="+data1.ADF3+"&F4="+data1.ADF4+"&F5="+data2.ADF5+"&F6="+data2.ADF6+"&F7="+data2.ADF7+"&F8="+data2.ADF8+"&nir_1="+data1.ADNIR+"&nir_2="+data2.ADNIR+"&class="+_class;
    // Make an HTTP Get request:
    client.println("GET " + query + " HTTP/1.1");
    client.println("Host: 192.168.1.20");
    client.println("Connection: close");
    client.println();
  }else{
    Serial.println("Server Error!");
    digitalWrite(red, HIGH);
  }
  delay(2000); // Wait 2 seconds after connection...
  // If there are incoming bytes available, get the response from the web application.
  String response = "";
  while (client.available()) { char c = client.read(); response += c; }
  if(response != ""){
    Serial.println(response);
    Serial.println("\n");
    // Check whether the transferred data is inserted successfully or not:
    if(response.indexOf("Data Inserted Successfully!") > 0){
      digitalWrite(green, HIGH);
    }else{
      digitalWrite(red, HIGH);
    }
  }
  // Turn off LEDs:
  delay(3000);
  digitalWrite(green, LOW);
  digitalWrite(red, LOW);
}

import tensorflow as tf
from tensorflow import keras
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd

csv_path = "E:\PYTHON\Vegetables_and_Fruits_Ripeness_Detection\spectral_color_database.csv"
df = pd.read_csv(csv_path)

def graphics(self, column_1, column_2, x_label, y_label):
        # Show requested columns from the data set:
        plt.style.use("dark_background")
        plt.gcf().canvas.set_window_title('Vegetables and Fruits Ripeness Detection by Color')
        plt.hist2d(self.df[column_1], self.df[column_2], cmap='RdBu')
        plt.colorbar()
        plt.xlabel(x_label)
        plt.ylabel(y_label)
        plt.title(x_label)
        plt.show()

def graphics(self, column_1, column_2, x_label, y_label):
        # Show requested columns from the data set:
        plt.style.use("dark_background")
        plt.gcf().canvas.set_window_title('Vegetables and Fruits Ripeness Detection by Color')
        plt.hist2d(self.df[column_1], self.df[column_2], cmap='RdBu')
        plt.colorbar()
        plt.xlabel(x_label)
        plt.ylabel(y_label)
        plt.title(x_label)
        plt.show()

def scale_data_and_define_inputs(self):
        self.df["scaled_F1"] = self.df.pop("F1") / 1000
        self.df["scaled_F2"] = self.df.pop("F2") / 1000
        self.df["scaled_F3"] = self.df.pop("F3") / 1000
        self.df["scaled_F4"] = self.df.pop("F4") / 1000
        self.df["scaled_F5"] = self.df.pop("F5") / 1000
        self.df["scaled_F6"] = self.df.pop("F6") / 1000
        self.df["scaled_F7"] = self.df.pop("F7") / 1000
        self.df["scaled_F8"] = self.df.pop("F8") / 1000
        self.df["scaled_NIR_1"] = self.df.pop("NIR_1") / 1000
        self.df["scaled_NIR_2"] = self.df.pop("NIR_2") / 1000
        # Create the inputs array using the scaled variables:
        for i in range(len(self.df)):
            self.inputs.append(np.array([self.df["scaled_F1"][i], self.df["scaled_F2"][i], self.df["scaled_F3"][i], self.df["scaled_F4"][i], self.df["scaled_F5"][i], self.df["scaled_F6"][i], self.df["scaled_F7"][i], self.df["scaled_F8"][i], self.df["scaled_NIR_1"][i], self.df["scaled_NIR_2"][i]]))
        self.inputs = np.asarray(self.inputs)

def split_data(self):
        l = len(self.df)
        # (95%, 5%) - (training, test)
        self.train_inputs = self.inputs[0:int(l*0.95)]
        self.test_inputs = self.inputs[int(l*0.95):]
        self.train_labels = self.labels[0:int(l*0.95)]
        self.test_labels = self.labels[int(l*0.95):]

def build_and_train_model(self):
        # Build the neural network:
        self.model = keras.Sequential([
            keras.Input(shape=(10,)),
            keras.layers.Dense(16),
            keras.layers.Dense(32),
            keras.layers.Dense(64),
            keras.layers.Dense(128),
            keras.layers.Dense(256),
            keras.layers.Dense(4, activation='softmax')
        ])
        # Compile:
        self.model.compile(optimizer='adam', loss="sparse_categorical_crossentropy", metrics=['accuracy'])
        # Train:
        self.model.fit(self.train_inputs, self.train_labels, epochs=19)

    ...

        ...

    # Test the accuracy:
        print("\n\nModel Evaluation:")
        test_loss, test_acc = self.model.evaluate(self.test_inputs, self.test_labels) 
        print("Evaluated Accuracy: ", test_acc)

def save_model(self):
        self.model.save("E:\PYTHON\Vegetables_and_Fruits_Ripeness_Detection\model\ANN_Ripeness_Detection.h5")

def make_predictions(self):
        saved_model = keras.models.load_model("E:\PYTHON\Vegetables_and_Fruits_Ripeness_Detection\model\ANN_Ripeness_Detection.h5")
        prediction_array = np.array([
            [0.131,0.148,0.184,0.765,1,1,1,0.809,0.276,0.273],
            [0.143,0.122,0.157,0.228,1,1,1,1,0.335,0.334],
            [0.038,0.032,0.032,0.030,0.042,0.055,0.467,0.880,0.100,0.102],
            [0.002,0.001,0.001,0.002,0.003,0.003,0.002,0.007,0.010,0.012]
        ])
        predictions = saved_model.predict(prediction_array)
        print("\n\nModel Predictions:\n")
        for i in range(len(prediction_array)):
            print("INPUT[" + str(i+1) + "] => ", self.ripeness_class_names[np.argmax(predictions[i])])

// Connections
// Arduino Nano 33 IoT :           
//                                AS7341 11-Channel Spectral Color Sensor
// 3.3V --------------------------- +
// GND  --------------------------- -
// A5   --------------------------- C
// A4   --------------------------- D
//                                10K Potentiometer
// A0   --------------------------- S
//                                Class Button_1 (6x6)
// D2   ---------------------------
//                                Class Button_2 (6x6)
// D3   ---------------------------
//                                Class Button_3 (6x6)
// D4   ---------------------------
//                                Class Button_4 (6x6)
// D5   ---------------------------
//                                5mm Green LED
// D6   ---------------------------
//                                5mm Red LED
// D7   ---------------------------