Projects from FAVORIOT Community

1. From Student Project to Smart City Solution: How This IoT Environmental System Is Turning Real-Time Data into Action
2. Inside a Smart Environment Monitoring System: A Student’s ESP32 IoT Project That Combines 5 Sensors and Cloud Analytics
3. A Student Innovation in Smart Kitchen Safety Using IoT
4. Edge AI Meets Cloud Intelligence: Evaluating Favoriot for Lightweight Telemetry and Rapid Visualisation
5. From Embedded Logic to Intelligent Infrastructure: A Smart Parking IoT Journey
6. From Embedded Project to Scalable IoT Architecture: A First-Year Student’s Growth Story
7. Learning IoT by Doing: Ts. Dyg Khayrunsalihaty Bariyyah Abang Othman’s Troubleshooting Journey
8. From Classroom to Gold Medal: A Student’s Real-World IoT Journey with Favoriot at ITEX
9. Hands-On IoT Exploration: Lessons from Adekunle Joshua
10. We Are Looking for Builders, Not Just Users – Why Naveen’s Story Can Become an Inspiration
11. Check for new and latest Projects from the Community HERE.

IoT R&D Project Challenges Using Favoriot Insight Framework

• Daily Project Challenges listed HERE.

Project Proposals

1. [Tutorial]: Real-Time Tire Pressure and Temperature Monitoring Using FAVORIOT
2. [Tutorial]: Predictive Vehicle Maintenance Using Sensors and FAVORIOT
3. [Tutorial]: IoT-Based Smart Vehicle Monitoring System using FAVORIOT
4. [Tutorial]: Smart Temperature Monitoring in Cold Transportation Using Favoriot
5. [Tutorial]: Smart Water Quality Monitoring for Cities using Favoriot IoT Platform.
6. [Tutorial]: Smart Street Lighting System Utilizing IoT and FAVORIOT
7. [Tutorial]: Automatic Sorting Trash Bin with Favoriot IoT Platform
8. [Tutorial]: Smart Agriculture Robot using the Favoriot platform
9. [Tutorial]: Automated Parking System with IoT using Favoriot
10. [Tutorial]: Automatic Bottle Filling & Capping System Using FAVORIOT Platform
11. [Tutorial]: Smart Conveyor System with Automatic Object Sorting using Favoriot IoT Platform
12. [Tutorial]: FAVORIOT Project: Smart Cooling System for Manufacturing Machines Using Favoriot
13. [Tutorial]: Robotic Arm for Automated Material Handling using Favoriot IoT Platform and Telegram Integration
14. [Tutorial]: Building a Smart Inventory System with Telegram Alerts Using Favoriot
15. [How-To] Send Data to the FAVORIOT Platform Using REST, MQTT and CoAP
16. [Tutorial]: CNC Machine Automation dengan IoT Monitoring Using FAVORIOT
17. [Tutorial]: Automated Quality Inspection System Using AI & Favoriot
18. [Tutorial]: Smart Conveyor System with IoT Monitoring
19. Check for the latest Tutorials HERE.

FAVORIOT Resources

• General
  • FAVORIOT Website
  • Try and Register for FREE
  • Favoriot Full Documentation
  • What is the Favoriot Platform Edge Gateway?
  • What Is Favoriot Edge Gateway and How Does It Work?
  • App Review: IoT MQTT Panel as a Practical Companion for FAVORIOT Users
• Pricing
  • How to Choose the Right Favoriot Plan for Your IoT Project
  • Favoriot Ecosystem Plan
  • Why Universities Need an IoT Ecosystem, Not Fragmented IoT Accounts
  • When IoT Builders Outgrow Dashboards: Why the Favoriot Platform Developer Plan Exists
  • Favoriot Launches Lite Plan to Support Students, Beginners, and Early IoT Builders
• Faybee AI – IoT Copilot
  • FAVORIOT’s Faybee: The Little Helper (IoT Copilot) That Makes IoT Feel Less Lonely
  • Beyond the Code: How Faybee is Making IoT Development Less Lonely and More Human
• Favoriot Intelligence
  • Favoriot Insight Framework
  • What is Favoriot Insight Framework (FIF)?
  • Favoriot Machine Learning
  • Why Favoriot’s ML Infrastructure Reduces Costs
  • Why Favoriot’s Built-in Machine Learning Matters for AI Researchers and IoT Developers
  • Favoriot’s Rule Engine 2.0: A Structured Approach to IoT Automation
  • The Key Differences: Favoriot’s Rule Engine 2.0 and AI Agents
• IoT & AIoT Labs
  • [Infographics] Building Future IoT Talent with a Ready-to-Deploy FAVORIOT IoT Lab
  • [Infographics] Building Tomorrow’s Innovators: Why the Favoriot AIoT Lab Ecosystem Matters Now More Than Ever
• Trainings
  • FAVORIOT AIoT Fundamentals & Decision Intelligence
  • Mastering AIoT with FAVORIOT: Turning Engineers, Builders, and Thinkers into Practitioners
  • FAVORIOT AIoT Fundamentals & Decision Intelligence
• Favoriot Partner Network
  • [Infographics] Favoriot AIoT Platform: A 5-Layer Architectural Framework for Scalable, Secure, and Intelligent IoT Deployment
  • Join Favoriot Partner Network
• Videos (Playlist & Highlights)
  • How-To Use Favoriot Platform Playlist
  • Favoriot IoT World Playlist
  • IoT Deep Dive Playlist
  • Favoriot Sembang Santai Playlist
  • IoT Deep Dive – Episode 7 (FAVORIOT Insight Framework)
  • IoT Deep Dive – Episode 4 (Favoriot Partner Network Solves IoT Fragmentation)
  • IoT Deep Dive – Episode 5 (Building IoT Solutions With Favoriot Middleware)
  • Favoriot IoT World – Episode 3 (Unboxing the AIoT Lab)
  • Favoriot IoT World – Episode 6 (Favoriot AIoT Architecture – Data to Decision)
  • Favoriot IoT World – Episode 4 (Favoriot’s IoT Pricing)
• FULL FAVORIOT RESOURCES
• Others
  • List of Dr Mazlan Abbas – Books

Disclaimer

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

• General
  • FAVORIOT Website
  • Try to register for FREE
  • Favoriot Full Documentation
  • What is the Favoriot Platform Edge Gateway?
  • What Is Favoriot Edge Gateway and How Does It Work?
  • App Review: IoT MQTT Panel as a Practical Companion for FAVORIOT Users
• Pricing
  • How to Choose the Right Favoriot Plan for Your IoT Project
  • Favoriot Ecosystem Plan
  • Why Universities Need an IoT Ecosystem, Not Fragmented IoT Accounts
  • When IoT Builders Outgrow Dashboards: Why the Favoriot Platform Developer Plan Exists
  • Favoriot Launches Lite Plan to Support Students, Beginners, and Early IoT Builders
• Faybee AI – IoT Copilot
  • FAVORIOT’s Faybee: The Little Helper (IoT Copilot) That Makes IoT Feel Less Lonely
  • Beyond the Code: How Faybee is Making IoT Development Less Lonely and More Human
• Favoriot Intelligence
  • Favoriot Insight Framework
  • What is Favoriot Insight Framework (FIF)?
  • Favoriot Machine Learning
  • Why Favoriot’s ML Infrastructure Reduces Costs
  • Why Favoriot’s Built-in Machine Learning Matters for AI Researchers and IoT Developers
  • Favoriot’s Rule Engine 2.0: A Structured Approach to IoT Automation
  • The Key Differences: Favoriot’s Rule Engine 2.0 and AI Agents
• IoT & AIoT Labs
  • [Infographics] Building Future IoT Talent with a Ready-to-Deploy FAVORIOT IoT Lab
  • [Infographics] Building Tomorrow’s Innovators: Why the Favoriot AIoT Lab Ecosystem Matters Now More Than Ever
• Trainings
  • FAVORIOT AIoT Fundamentals & Decision Intelligence
  • Mastering AIoT with FAVORIOT: Turning Engineers, Builders, and Thinkers into Practitioners
  • FAVORIOT AIoT Fundamentals & Decision Intelligence
• Favoriot Partner Network
  • [Infographics] Favoriot AIoT Platform: A 5-Layer Architectural Framework for Scalable, Secure, and Intelligent IoT Deployment
  • Join Favoriot Partner Network
• Videos (Playlist & Highlights)
  • How-To Use Favoriot Platform Playlist
  • Favoriot IoT World Playlist
  • IoT Deep Dive Playlist
  • Favoriot Sembang Santai Playlist
  • IoT Deep Dive – Episode 7 (FAVORIOT Insight Framework)
  • IoT Deep Dive – Episode 4 (Favoriot Partner Network Solves IoT Fragmentation)
  • IoT Deep Dive – Episode 5 (Building IoT Solutions With Favoriot Middleware)
  • Favoriot IoT World – Episode 3 (Unboxing the AIoT Lab)
  • Favoriot IoT World – Episode 6 (Favoriot AIoT Architecture – Data to Decision)
  • Favoriot IoT World – Episode 4 (Favoriot’s IoT Pricing)
• FAVORIOT Projects
  • DAILY IoT (or AIoT) Project Challenges
  • IoT Projects Using Favoriot
• FULL FAVORIOT RESOURCES
• Others
  • List of Dr Mazlan Abbas – Books
• More
  • Favoriot Resources (Always Updated)

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.