Augmented Evaluation of Mango Quality: MLP Model for Five-Class Severity Grading

Quality evaluation of mangoes in the realm of agriculture contributes to successful competition on the market and customer delight. Nonetheless, conventional human-based grading techniques usually are not reliable in terms of accuracy and standardization which requires high technology intervention. Through this research, a new level of automation which would be able to make accurate estimations on the classification of various degrees or severity is introduced and this is bound to bring revolutions in fruit grading methods. In this pioneering study, they develop an innovative technology for the accurate categorization of severity stages of mangoes leading to advancement in the manner fruit evaluations are performed. A modern Multi-layer Perceptron (MLP) model with sophisticated machine learning technologies provides the highest possible classification accuracy of 97.62%. This new approach involves scrutiny and evaluation of attributes like color, size, texture, and exterior faults. The process ensures that the assessment is precise while guaranteeing uniform evaluation important in competing in the local market and exporting to other countries. Efficiency gains are only a part of the bigger picture - they will result in increased production on par with international standards, thus providing economic development for companies and consumers' happiness. These studies demonstrate a paradigm shift focusing on standardized, reliable, and efficient mango grading procedures, set to change traditional agricultural trade and enhance global food safety standards.