The Utilization of The Grey Level Co-Occurrence Matrix (GLCM) Method for Classifying Grape Texture Based on Pesticide Residue Levels in Post Harvest Sorting
DOI:
https://doi.org/10.33795/jartel.v16i2.9092Keywords:
GLCM, Image processing, Non-destructive testing, Pesticide residue, Post-harvest sortingAbstract
This study presents the implementation of the Grey Level Co-occurrence Matrix (GLCM) method for non-destructive classification of grape surface textures based on pesticide residue levels during post-harvest sorting. The background of this research lies in the growing concern over excessive pesticide use that affects fruit safety and quality. The objective is to develop a smart classification system capable of identifying texture variations caused by pesticide residues and estimating fruit quality efficiently. The method integrates digital image processing to extract GLCM features—contrast, correlation, dissimilarity, homogeneity, angular second moment, energy, and entropy—with spectral reflectance data obtained from the AS7263 Near-Infrared (NIR) sensor operating at 610–860 nm wavelengths. The data were processed using a Random Forest classification algorithm implemented on a Raspberry Pi 4B controller. The experimental results show that GLCM features, particularly contrast and homogeneity, effectively distinguish residue categories with an accuracy rate of 92.7%. The combination of texture analysis and NIR spectroscopy provides a reliable, efficient, and non-destructive approach for intelligent post-harvest grape sorting.
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