Published Paper Details:

The Intelligent Tomato Sorting Machine helps sort tomatoes accurately and efficiently, reducing human effort and improving productivity in farming and food industries. A Raspberry Pi camera captures images of tomatoes as they slide down a smooth surface. A Convolutional Neural Network (CNN) is trained to analyse these images and classify tomatoes as ripe or unripe based only on their colour. The model is developed and trained using Google Colab which provides powerful computers for faster learning. Once trained, the model is loaded onto a Raspberry Pi, which processes images in real time. Based on the classification, the Raspberry Pi activates servomotors to push each tomato into the correct bin. The project has two main steps and the first step is Training the Model in this step we will be Collecting tomato images, training a CNN model in Google Colab and preparing it to classify tomatoes by colour and the second step is Building the Sorting System in this we will Setup a simple sliding surface and a sorting mechanism to move and separate tomatoes based on their classification. This system makes tomato sorting faster, easier, and more reliable, helping industries improve efficiency and quality control.