The quality grading of mangoes is a crucial task for mango growers as it vastly affects their profit. However, until today, this process still relies on laborious efforts of humans, who are prone to fatigue and errors. In smart agriculture, the automatic quality assessment and grading application is essential to post-harvest processing. Methods to generate higher quality fruit sorting, quality maintenance, production, and cut back labor concentration. This paper aims to develop a comprehensive solution for non-destructive mango quality assessment by integrating computer vision and electromagnetic techniques. The computer vision tasks, executed in Google Colab, involve training a deep learning model to detect and classify external defects and characteristics of mangoes. Concurrently, the microwave imaging system employs a Nano Vector Network Analyzer (Nano-VNA) and the 85,070 Dielectric Probe Software E07.02.29 to measure the dielectric properties of the mangoes. This dual approach provides insights into both the external and internal quality of the fruit. The methodology emphasizes rigorous calibration and validation processes to ensure data accuracy. Initial results indicate that integrating computer vision with microwave imaging can significantly improve the reliability of mango quality assessments.