This guide will show you how to build an AI-powered quality inspection system using a camera and send inspection results to the FAVORIOT IoT platform in real time.

🔧 Step 1: What You Need

Hardware:

• Raspberry Pi (or any computer with a camera)
• Camera (USB or Pi Camera)
• Internet connection

Software:

• Python 3
• Libraries: opencv-python, tensorflow, numpy, requests

🛠️ Step 2: Install the Required Software

Open Terminal and run:

sudo apt update && sudo apt upgrade -y
sudo apt install python3-pip -y
pip3 install opencv-python numpy requests tensorflow

🧠 Step 3: Train an AI Model to Detect Defects

Create a folder called dataset_defects with 2 subfolders: defect and normal.

Now, use this Python code to train the model:

import tensorflow as tf
from tensorflow.keras.preprocessing.image import ImageDataGenerator

dataset_path = "dataset_defects"
batch_size = 32
img_size = (224, 224)

datagen = ImageDataGenerator(rescale=1./255, validation_split=0.2)

train_data = datagen.flow_from_directory(
    dataset_path,
    target_size=img_size,
    batch_size=batch_size,
    class_mode="binary",
    subset="training"
)

val_data = datagen.flow_from_directory(
    dataset_path,
    target_size=img_size,
    batch_size=batch_size,
    class_mode="binary",
    subset="validation"
)

base_model = tf.keras.applications.MobileNetV2(input_shape=(224, 224, 3), include_top=False, weights="imagenet")
base_model.trainable = False

model = tf.keras.Sequential([
    base_model,
    tf.keras.layers.GlobalAveragePooling2D(),
    tf.keras.layers.Dense(1, activation="sigmoid")
])

model.compile(optimizer="adam", loss="binary_crossentropy", metrics=["accuracy"])
model.fit(train_data, validation_data=val_data, epochs=10)

model.save("defect_detection_model.h5")

🎥 Step 4: Real-Time Defect Detection Using Camera

Once the model is trained and saved, run this script:

import cv2
import numpy as np
import tensorflow as tf

model = tf.keras.models.load_model("defect_detection_model.h5")
cap = cv2.VideoCapture(0)

while True:
    ret, frame = cap.read()
    if not ret:
        break

    img = cv2.resize(frame, (224, 224))
    img = np.expand_dims(img, axis=0) / 255.0
    prediction = model.predict(img)[0][0]

    label = "Defect Detected!" if prediction > 0.5 else "Product OK"
    color = (0, 0, 255) if prediction > 0.5 else (0, 255, 0)

    cv2.putText(frame, label, (50, 50), cv2.FONT_HERSHEY_SIMPLEX, 1, color, 2)
    cv2.imshow("Quality Inspection", frame)

    if cv2.waitKey(1) & 0xFF == ord('q'):
        break

cap.release()
cv2.destroyAllWindows()

🌐 Step 5: Send Defect Results to FAVORIOT

✅ 1. Set Up a Device in FAVORIOT

1. Log in to Favoriot Platform
2. Go to Devices → Add Device
3. Note down your Device Developer ID and API Key

✅ 2. Add Code to Send Data

Below is the function to send results:

import requests
import json

DEVICE_ID = "YOUR_DEVICE_ID"
API_KEY = "YOUR_FAVORIOT_API_KEY"
URL = "https://apiv2.favoriot.com/v2/streams"

def send_data_to_favoriot(status):
    payload = {
        "device_developer_id": DEVICE_ID,
        "data": {
            "status": status
        }
    }

    headers = {
        "Content-Type": "application/json",
        "Apikey": API_KEY
    }

    response = requests.post(URL, data=json.dumps(payload), headers=headers)
    print("Response from Favoriot:", response.json())

✅ 3. Combine with Real-Time Detection

Add this snippet inside your prediction logic:

if prediction > 0.5:
    send_data_to_favoriot("Defect Detected!")
else:
    send_data_to_favoriot("Product OK")

📊 Step 6: View Data on FAVORIOT Dashboard

• Go to your device on the FAVORIOT Dashboard
• Click on Streams to view defect data
• You can also create graphs or alert rules for monitoring

🚀 Bonus Tips

• Add Telegram Alerts using Telegram Bot API
• Add Dashboard Charts using Favoriot’s visualization
• Improve accuracy with better dataset or model tuning

✅ Summary

With this project, you have:

✅ Built a real-time defect detection system
✅ Displayed results on screen
✅ Sent reports to FAVORIOT cloud platform

References

• Favoriot Platform Registration (FREE and Paid)
• Favoriot Full Documentation (Official)
• Favoriot Github
• How-To Use Favoriot Platform Video Playlist

Disclaimer

This article provides a step-by-step guide and only serves as a guideline. The source code may need adjustments to fit the final project design.

Smart Conveyor System with IoT Monitoring

This project shows how to build a smart conveyor system that:

• Sorts products using IR sensors and servo motors
• Monitors speed and status in real-time
• Sends and receives data via the FAVORIOT IoT platform

1. Project Goal

✅ Automatically detect products, sort them, and adjust conveyor speed
✅ Send data to FAVORIOT Cloud for real-time monitoring
✅ Remotely control speed using the FAVORIOT Dashboard

2. Main Components

3. System Design

🏗️ Hardware:

• Conveyor with IR sensor and servo motor
• Controlled by Arduino + ESP8266

☁️ IoT Setup:

• Data sent to FAVORIOT cloud via WiFi
• IoT Dashboard displays:
  • Speed
  • Status (Running/Stopped)
  • Product Detected

4. Wiring Diagram

• IR Sensor → Arduino digital pin
• Servo Motor → PWM pin
• ESP8266 → TX/RX (for WiFi communication)

5. Sending Data to FAVORIOT

Step 1: Sign up at FAVORIOT Platform

Create a new device called Smart_Conveyor_001 to get your API key.

Step 2: Arduino Code (ESP8266)

#include <ESP8266WiFi.h>
#include <ESP8266HTTPClient.h>

const char* ssid = "Your_WiFi_Name";
const char* password = "Your_WiFi_Password";
const char* server = "http://apiv2.favoriot.com/v2/streams";
const char* api_key = "YOUR_FAVORIOT_API_KEY";

void setup() {
  Serial.begin(115200);
  WiFi.begin(ssid, password);
  while (WiFi.status() != WL_CONNECTED) {
    delay(1000);
    Serial.println("Connecting to WiFi...");
  }
}

void loop() {
  if (WiFi.status() == WL_CONNECTED) {
    HTTPClient http;
    http.begin(server);
    http.addHeader("Content-Type", "application/json");
    http.addHeader("Apikey", api_key);

    String jsonData = "{\"device_developer_id\":\"Smart_Conveyor_001\",\"data\":{\"speed\":100,\"status\":\"Running\",\"product_detected\":1}}";

    int httpResponseCode = http.POST(jsonData);
    Serial.println(httpResponseCode);
    http.end();
  }
  delay(5000); // Send every 5 seconds
}

6. IoT Dashboard on FAVORIOT

Use the built-in dashboard or tools like Node-RED to:

• View real-time speed and status
• Count products
• Display alerts when needed

7. Receiving Control Commands (Remote Control)

Use FAVORIOT’s API to send speed commands to your device.

Example JSON Payload:

{
  "device_developer_id": "Smart_Conveyor_001",
  "data": {
    "speed": 50
  }
}

Arduino Code to Read Command:

String speed_value = received_data["data"]["speed"];
int motorSpeed = speed_value.toInt();
analogWrite(motorPin, motorSpeed);

8. Testing & Simulation Checklist

✅ IR Sensor Test – Detect object presence
✅ Speed Test – Change speed via dashboard
✅ Data Transmission – View updates on dashboard
✅ Sorting Automation – Simulate sorting flow

9. Summary & What’s Next

✅ Project Highlights:

• Remote monitoring & control
• Real-time speed and object status updates
• Simple integration with FAVORIOT

📈 Future Enhancements:

• Add AI Camera for advanced object detection
• Use Machine Learning to adjust speed automatically
• Switch to MQTT for faster data transmission

This project proves that IoT + automation can enhance productivity and reduce human error in smart manufacturing. With the FAVORIOT Platform, developers can scale up easily and integrate analytics, alerts, and AI features.