Tomato Quality Classification

The "Tomato Quality Classification" project aims to enhance the accuracy of tomato quality assessment using advanced machine learning techniques. Leveraging Convolutional Neural Networks (CNN) and MobileNet, the project addresses the binary classification of tomatoes into two categories: Healthy and Rejected. Utilizing a dataset from Kaggle, which contains images of tomatoes, this project demonstrates the application of deep learning in agricultural quality control. By implementing CNN and MobileNet, the model effectively learns and distinguishes between healthy and rejected tomatoes, providing an automated solution for quality classification. The results exhibit high accuracy and efficiency, contributing significantly to quality assurance processes in the agricultural industry. This project not only improves quality control but also demonstrates the potential of AI in practical applications.

KEYWORDS: Tomato Quality, Classification, CNN, MobileNet, Binary Classification, Machine Learning, Deep Learning, Image Classification, Kaggle Dataset, Quality Assurance.