Fruit quality assessment plays a crucial role in ensuring that only top-quality produce reaches the consumer. Traditional methods of manual inspection are labor-intensive and often subjective, leading to inconsistencies in the grading process. This project presents an automated system for fruit quality assessment using Convolutional Neural Networks (CNNs), aimed at overcoming the limitations of traditional manual inspection methods, which are labor-intensive, time-consuming, and inconsistent. The model is trained on a dataset consisting of six fruit types: apple, banana, guava, lime, orange, and pomegranate, with a primary focus on apples, bananas, and pomegranates. By leveraging non-invasive, image-based techniques, the CNN model analyzes visual features like color, texture, and size to classify fruits by quality, including ripeness and defects. A structured methodology, including data collection, preprocessing, and data augmentation, ensures the model’s robustness against variations in lighting and orientation. Evaluated on metrics such as accuracy, precision, and recall, the CNN model achieves high accuracy, offering a reliable alternative to manual grading. This automated system significantly improves consistency and speed, making it suitable for both small- and large-scale agricultural operations. The project highlights the potential of deep learning to enhance quality control in the agricultural sector, reducing human dependency and improving efficiency in fruit grading processes.