Jennifer Natnat

https://doi.org/10.64823/ijeee.2601005

This study presents an automated cocoa bean classification and defect detection system developed in accordance with the ASEAN Standard for Cocoa Bean (ASEAN Stan 34:2014). Implemented in MATLAB, the system utilizes digital image processing and computer vision techniques to segment individual cocoa beans, extract geometric, color, and texture features, and classify them into quality categories: Extra Class, Class I, Class II, and Non-Compliant. The image processing workflow integrates color space transformations (RGB to HSV), adaptive thresholding, and morphological filtering to ensure accurate segmentation. Feature extraction targets parameters such as area, length, aspect ratio, texture entropy, and color uniformity to identify specific defects including moldy, slaty, insect-damaged, and germinated beans. Validation against independent expert grading achieved an overall reliability rate of 96.2%, with high precision (0.93) and recall (0.91), and an average processing speed of 1.3 seconds per image. The developed analyzer provides an objective, rapid, and repeatable tool to support standardization and postharvest cocoa quality control across the ASEAN region.

The current study introduces an automated cocoa bean classification and defect detection system developed in accordance with the ASEAN Standard for Cocoa Bean (ASEAN Stan 34:2014). The system, which has been developed in MATLAB, employs digital image processing and computer vision methods to identify and segment individual cocoa beans, extract their geometric, color, and texture-based features, and classify the beans into quality categories as defined by ASEAN: Extra Class, Class I, Class II, and Non Compliant. An image processing workflow that incorporates color space transformations, morphological filtering, and adaptive thresholding enhances segmentation accuracy when identifying the crop under review. Feature extraction identifies characteristics, such as area, aspect ratio, texture entropy, and color uniformity. It is additionally capable of identifying defects within the beans, including moldy, slaty, insect-damaged, and germinated beans. Validation against independent expert grading achieved a reliability rate of 96.2% indicating the system's potential to effectively assess quality and limit human bias as part of an objective quality control approach. The analyzer also has the potential to carry out rapid quality assessment and produce detailed Excel and PDF reports to support standardization and efficiency in postharvest cocoa quality control throughout the ASEAN region.