Quality Classification of Harumanis Mango Based on External Multi-Parameter and Machine Learning Techniques

Grading Harumanis mangoes is traditionally done through manual visual inspection, which is subjective, inconsistent, and labor-intensive. Industry practices report only 70–80% consistency among human graders, with accuracy further declining under fatigue or high volumes. These limitations hinder uniform quality assurance, especially for export markets. To address this, an image-based, non-destructive grading system was developed, focusing on external features such as surface defect severity, ripeness index, shape uniformity, and size. A dataset of 1,018 mango samples was collected and analyzed using a machine vision system. Features were extracted through image segmentation and color–shape analysis, then classified using a Fuzzy Inference System (FIS) and Machine Learning (ML) models including SVM, MLPNN, and ANFIS. Enhanced SVM variants were also implemented to assess performance gains. Results showed strong performance across all parameters: ripeness index accuracy reached 93.5%, shape uniformity 91.6%, and size classification over 96%. The enhanced SVM+ achieved the best overall accuracy at 95.1% with the lowest error rates. The proposed system demonstrated clear improvements over manual grading and effectively classified mangoes into PREMIUM, GRADE 1, GRADE 2, and REJECT categories, supporting its potential for reliable real-world deployment.