This project presents a machine learning-driven fruit profiling system utilizing advanced deep learning and computer vision techniques to analyze fruit images. The system comprises two main components: fruit type identification with caloric estimation, and fruit ripeness classification. The first component detects various categories of fruits and vegetables, providing estimated nutritional information based on recognized types. The second component assesses the ripeness stage, distinguishing different maturity and spoilage levels across multiple fruit varieties. Both components employ the YOLO V9 algorithm for accurate and efficient detection. By integrating static nutritional data with dynamic quality assessment, the system offers a comprehensive tool for evaluating produce through image analysis. This approach enables quick, automated classification and quality estimation, facilitating applications in nutrition tracking, agricultural management, and supply chain monitoring.

Keywords: Fruit profiling, deep learning, computer vision, YOLO V9, fruit classification, caloric estimation, ripeness detection, image analysis, nutritional assessment, produce quality, machine learning, object detection.