An Arduino UNO serves as the main controller of the system. A microphone sensor is used to capture ambient insect sounds in the field. The captured sound signals are analyzed based on frequency characteristics to differentiate and classify insect species. Once identified, the corresponding insect name is displayed on a web page through the ESP8266 Wi-Fi module.

Additionally, four IR sensors are placed at the four corners of the field to monitor pest movement and presence. The status of these sensors is continuously updated and displayed on the web interface. When harmful insect activity exceeds a predefined threshold, the system automatically activates a relay circuit to control a pesticide spraying mechanism or AC motor. The relay ON/OFF status is also monitored and displayed on the web page for real-time tracking.

The ESP8266 Wi-Fi module enables IoT-based remote monitoring, allowing farmers to observe insect detection data, sensor status, and relay control through a web page from anywhere. This smart system reduces manual monitoring effort, ensures timely pest control, minimizes pesticide overuse, and improves overall agricultural productivity.

Main Objectives in this project:

  To detect insect activity in agricultural fields using a microphone sensor.

  To identify and classify insects based on sound frequency analysis.

  To monitor pest movement using four IR sensors placed at the four corners of the field.

  To display identified insect names and sensor status on a web page using IoT.

  To automatically control pesticide spraying using a relay when harmful insects are detected.

  To provide real-time remote monitoring through the ESP8266 Wi-Fi module.

  To reduce crop damage and minimize excessive pesticide usage.

Components used:

• Arduino UNO Microcontroller.
• Regulated Power supply.
• Esp8266 Wi-Fi module.
• Relay with AC motor.
• Four IR sensor.
• Microphone.
• LCD display.

Software used:

1. Embedded C programming.
2. Arduino IDE programmer for dumping code into Micro controller.
3. Express SCH for Circuit design.

Regulated power supply:

video:

The system is built around a Raspberry Pi 4, which acts as the central processing unit. Voice commands are captured through a microphone module and processed locally using TinyML-based deep learning models, allowing efficient and low-latency voice recognition without continuous cloud dependency. Advanced noise filtering techniques are implemented to ensure high accuracy even in noisy environments. Recognized commands are matched with predefined keywords such as “Open Door” and “Close Door”.

Upon successful recognition, the Raspberry Pi drives a servo motor to perform precise door movement operations. The current system status, including command recognition and door position, is displayed on an LCD display for real-time user feedback. An SD card is used for data storage, including trained models and system logs. The entire system operates on a 5V DC power supply, making it suitable for embedded and IoT applications.

This project demonstrates an efficient, low-power, and cost-effective voice-controlled smart door system using TinyML and deep learning, with potential applications in smart homes, assistive technology, and secure access control systems.

The main objectives of this project are:

• To design and develop a voice recognition–based door control system using TinyML and deep learning techniques.
• To implement offline voice command recognition on an IoT device for improved privacy and low latency.
• To efficiently filter background noise and improve voice recognition accuracy using TinyML models.
• To control a servo motor for door opening and closing based on recognized voice commands.
• To display the system status and door operation (Open/Close) on an LCD display.
• To utilize Raspberry Pi 4 as the main controller for processing voice commands and device control.
• To create a low-power, cost-effective, and user-friendly smart door automation system.
• To demonstrate the applicability of TinyML in real-time IoT applications such as smart homes and access control systems.

The major building blocks of this project are:

1. Adapter power supply.
2. Raspberry pi 4.
3. SD card.
4. LCD display.
5. Micro phone.
6. Servo Motor.

Software’s used:

• Raspbian OS.
• TinyML and deep learning techniques.

video: