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The food industry relies heavily on the visual appeal and physical integrity of fruits to maintain quality standards and meet consumer expectations. Traditionally, fruit inspection for damages such as bruises, blemishes, and rot has been carried out manually, which is not only time-consuming but also prone to human error and inconsistency. With increasing demand for automation and efficiency, manual inspection methods no longer suffice for large-scale operations, leading to the need for smart, scalable solutions. This project introduces an automated fruit damage detection system leveraging image processing and deep learning techniques. By analyzing fruit images through a Convolutional Neural Network (CNN), the system accurately classifies fruits into categories like "Healthy," "Bruised," or "Rotten." The methodology involves image collection, preprocessing, labeling, model training, and deployment through a user-friendly interface, enabling real-time feedback and integration into industrial quality control systems. The prototype demonstrates the potential to significantly improve inspection accuracy, reduce operational costs, and enhance overall efficiency in sorting and packaging. Although the initial focus is on apples, the system is scalable and adaptable to various fruit types. This approach represents a practical and impactful step toward modernizing quality assurance practices in the food industry.