Machine Learning model developed for NEC-Food hackathon

This model predicts the approximate expiry time of perishable goods by taking real-time environmental parameters like

• Temperature (Farenheit)
• Humidity (relative humidity - %)
• Ice (0/1)
• Water Sprinkled (0/1)
• Carbon Dioxide Levels (ppm)
• Oxygen Levels (ppm)
• Ethylene Concentration (ppm)

• Most of the food products, especially fruits, produce an organic compound called Ethylene
• It helps in the ripening of the fruit
• The level of ethylene and rate of ripening is a variety-dependent process
• It is very hard to control during logistics and storage.

The model takes real-time environmental parameters like

• Temperature (Farenheit)
• Humidity (relative humidity - %)
• Ice (0/1)
• Water Sprinkled (0/1)
• Carbon Dioxide Levels (ppm)
• Oxygen Levels (ppm)

The recommended Ethylene level (which is predicted) and the real-time data from the sensor is compared. If the conditions is unfavorable, signals are sent to ventilation systems until the level of Ethylene is reduced.

• Install Jupyter Notebooks from here
• Else, use Google Colab

• Open Jupyter
• Run the notebooks