Fruits Classification and Shelf-Life Prediction using CNN

Thisproject tackles the need for smarter ways to check and manage the quality of fruits in farms and stores. We propose a system that uses advanced computer technology, specifically Convolutional Neural Networks (CNN), to do two important things: accurately identify different types of fruits and predict how long they will stay fresh based on their appearance. To make this happen, we collect pictures of all sorts of fruits- ripe and not-so-ripe. Then, we create a special computer program (CNN) that learns from these pictures to accurately tell one fruit from another. Next, we take it a step further. We teach our computer program to look at a fruit and predict how much longer it will stay fresh. This prediction is based on the fruit’s appearance, like changes in color and texture. Adding this prediction feature helps farmers and store owners make better decisions about when to use or sell them. We’re also looking into making our smart fruit-checking system work on small devices that can be used in real time, like in a farm or store. This way, it’s not just something fancy for big companies but a practical tool for everyday use. The goal is to have a system that can accurately identify fruits and predict how fresh they are. If successful, this system could make a big difference in the fruit industry by helping with quality control, managing supplies better, and reducing wasted food. This research is a step forward in making technology work for a smarter and more efficient agricultural and food industry.