Adhikary, RahulRahulAdhikaryPine, SandipanSandipanPineChoudhury, Subhra JyotiSubhra JyotiChoudhurySungheetha, AkeyAkeySungheethaRajesh Sharma, RRRajesh SharmaPradeep Ghantasala, G SG SPradeep Ghantasala2026-02-112026-02-1120249798350354379http://dx.doi.org/10.1109/ODICON62106.2024.10797503https://www.scopus.com/inward/record.uri?eid=2-s2.0-105003892421&doi=10.1109%2fODICON62106.2024.10797503&partnerID=40&md5=ac6226118160ece807034af56ae92091https://gnanaganga.alliance.edu.in/handle/123456789/9607Mango fruits are widely recognized for their diverse applications in the food, beverage, and various industries, making them a global commodity of significance. the escalating demand for premium-quality mangoes highlights the necessity for dependable and efficient grading techniques. Traditional mango grading heavily relies on human expertise, often susceptible to subjectivity, errors, and time-intensive procedures. In this study, we introduce a CNN-based mango grading model to automate and enhance the accuracy ofmango quality assessment. Our proposed model employs CNNs in a modular, sequential approach to extract relevant features from mango images and classify them into distinct quality grades. We utilize a comprehensive dataset of mango images with corresponding quality labels for training and validation. Experimental outcomes underscore the effectiveness of the CNN-based method in accurately grading mangoes, presenting a promising solution to improve efficiency and objectivity in mango grading. In conclusion, the CNN-based mango grading model represents a significant step forward in modernizing and optimizing mango quality assessment processes. the combination of machine learning techniques with image analysis offers a robust solution to address longstanding challenges associated with traditional grading methods. As this technology evolves, it has the potential to revolutionize quality control practices in the food industry. This CNN-based approach outperforms other classifiers for this specific mango grading problem. © 2024 IEEE.enCnn Modular OrganizationDeep LearningFood ProductionMango Health DetectionConference paper