Design of an Artificial Vision Algorithm for the Detection and Classification of the Ripeness Degree of Kent Mangoes in the Packaging Area

This research aims to develop an artificial vision algorithm to enhance the precision and accuracy in detecting and classifying different ripeness levels of Kent mangoes. The Python programming language was utilized as the primary tool for software development, following a non-experimental, applied, and quantitative methodology that incorporates rigorous error testing procedures. The outcome of this investigation resulted in the creation of a sophisticated software system capable of facilitating the detection and classification of Kent mangoes with an impressive 98% efficiency rate, accompanied by a minimal 2% margin of error. It is concluded that this research underscores the pivotal role of artificial intelligence technology in strengthening the industrial sector, particularly in the context of exportation. Additionally, it is worth noting that this research exclusively focuses on mango type-based classification, a previously unexplored aspect in existing studies, offering an innovative approach to classification that promises to substantially improve efficiency and precision in the identification and categorization of Kent mangoes intended for export purposes.