low cost, multi sensor non destructive banana ripeness estimation using machine learning

The main objectives of this project are to estimate banana ripeness using a low-cost, multi-sensor setup combined with machine learning techniques. It collects data from non-destructive sensors like color, gas, and temperature to classify ripeness stages accurately. This system aims to assist farmers and vendors in making informed decisions without damaging the fruit during testing