International Journal of Science, Engineering and TechnologyAuthors: Samruddhi Prashant Raut, Bhumika Ramesh Sabale, Khushi Tuffailahmed Shaikh, Nikita Pradip Sabale, Assistant Professor Vaishali Khandave
Abstract: Fruit quality plays a vital role in ensuring consumer satisfaction and nutritional integrity. Manual fruit inspection is often subjective inconsistent and time consuming leading to inaccurate grading and post harvest losses. Advances in artificial intelligence (AI) machine learning (ML) and computer vision have been enable to Intelligent Systems Capable of automated quality assessment through image analysis. This paper explores the concept and architecture of SNAP TO BUY- AI based fruit quality detection and recommen- dation system inspired by research on intelligent visual inspection models. The proposed system is designed to capture fruit images via a mobile camera perform preprocessing to enhance clarity extract deep visual features using convolutional neural network (CNNs) and classify fruits based on ripeness levels ripe, unripe or over ripe Based on the analysis the system provides actionable recommendations such as buy, do not buy or store for two to three days. This study reviews rel- evant literature and presents the system architecture, dataset development strategy and modern evaluation approach. The objective is to provide a comprehensive understanding of how AL and ML can revolutionize fruit quality detection to enhance consumer decision making through intelligent automation.
