“Automated Banana Maturity Detection and Quality Assessment Using MATLAB”

This research presented an automated fruit quality and maturity evaluation system based on image processing and sensor-based analysis. The proposed system integrates image acquisition, preprocessing, segmentation, feature extraction, and classification techniques to assess fruit quality in a non-destructive manner. Visual characteristics such as color, size, shape, and texture are extracted from fruit images, while additional information from gas and sound sensors is utilized to improve the accuracy of maturity and quality assessment.   The developed system successfully classifies fruits into different maturity stages, including unripe, ripe, and overripe categories. The application of the K-Nearest Neighbor (KNN) algorithm enables efficient and accurate classification, achieving an overall accuracy of approximately 96%. The integration of multiple sensing technologies enhances system reliability and provides a more comprehensive evaluation compared to conventional manual inspection methods.   The proposed approach offers several advantages, including reduced human intervention, improved consistency, faster processing, and preservation of fruit integrity through non-destructive testing. These features make the system suitable for applications in precision agriculture, post-harvest management, food processing industries, and automated quality control systems. Overall, the research demonstrates that image processing combined with machine learning and sensor technologies can provide an effective, accurate, and cost-efficient solution for automated fruit quality evaluation. The developed system contributes to the advancement of smart agricultural practices and has significant potential for future enhancements through the integration of advanced artificial intelligence techniques, cloud-based monitoring, and real-time IoT applications.

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