The increasing health risks associated with chemical-based fruit ripening methods have created a strong demand for safe and intelligent non-chemical alternatives. This project proposes a Smart Non-Chemical Fruit Ripening System that employs high electric fields and alternating magnetic fields to achieve controlled and uniform fruit ripening without the use of harmful chemicals. An IR sensor is used to detect the presence of fruit inside the ripening chamber. Once detected, the system evaluates the condition of the fruit using multiple environmental and gas sensors, including a methane gas sensor, MQ-135 air quality sensor, DHT22 temperature and humidity sensor, and moisture sensor. The collected data is processed by an ESP32-based TinyML model to classify the fruit condition as either good or bad. Based on the analysis, the Arduino Nano activates relay-controlled copper coils and silver plates that generate controlled high electric and alternating magnetic fields inside the ripening chamber, stimulating biochemical changes in fruits similar to natural ripening. If the fruit is identified as being in bad condition, the system prevents the ripening process by keeping the relay-controlled copper coils and silver plates in the OFF state. If the fruit is classified as good, the relays are activated to initiate the ripening process using controlled high electric and alternating magnetic fields. The ripening progress is continuously monitored and displayed as a percentage from 1% to 100% on an LCD display as well as on a web-based monitoring page. Upon reaching 100% completion, the system automatically turns OFF the relays and activates a buzzer alert to indicate the completion of the ripening process. The real-time system status and all sensor data are continuously displayed on the LCD and uploaded to the web page for remote monitoring and analysis. This automated, intelligent, and non-chemical ripening solution enhances fruit safety, quality, and operational efficiency, making it suitable for modern smart agriculture and post-harvest management systems.  

The main objectives of the project are:

• To detect the presence of fruit using an IR sensor
• To analyze fruit condition using gas, temperature, humidity, and moisture sensors
• To classify fruit as good or bad using TinyML
• To activate ripening only for good quality fruits
• To display ripening progress (1–100%) on LCD and web page
• To provide alerts after completion using a buzzer
• To upload all sensor data to a web interface.

  The main building blocks of the project are:  

1. SMPS.
2. ESP32.
3. ARDUINO NANO
4. Gas Methane sensor.
5. Mq-135 Air quality sensor.
6. DHT22.
7. LCD display.
8. IR sensor.
9. Buzzer.
10. Moisture sensor.
11. Relays.
12. Copper coils and silver-plates.

  Software’s used:  

1. ARDUINO IDE compiler for Embedded C programming.
2. Express SCH for Circuit design.
3. WEB technology.
4. Python Language.
5. TinyML.

    Block Diagram of the Project:  

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