A Modular CNN Framework for Hierarchical Mango Grading and Quality Assessment

Mango 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.