An Image Processing Study: Image Enhancement, Image Segmentation, and Image Classification using Milkfish Freshness Images

Nur Ryan Dwi Cahyo, Maulana Malik Ibrahim Al-Ghiffary

Abstract


This Research uses sophisticated image processing techniques to handle the important problem of assessing the freshness of milkfish. Researcher presenting a CNN classification method that is trained by ResNet-101, is put through a rigorous evaluation process that includes GLCM feature extraction, parameter setup, and thorough confusion matrix assessments. The effects of both Image Enhancement and Image Segmentation techniques on texture characteristics during GLCM extraction are carefully analyzed at various freshness levels, measuring Contrast, Homogeneity, Correlation, and Energy values. With training using Adam optimization, a batch size of 16, a learning rate of 0.0001, and 30 epochs, the suggested CNN is configured to produce a balanced convergence period of 23 minutes and 36 seconds. The suggested approach performs exceptionally well in evaluation, reaching a remarkable accuracy of 99.72% throughout training. Additional testing during the testing phase confirms its effectiveness, since all metrics (recall, precision, and F1-score) remain at 100%. These outcomes highlight the resilience of the ResNet-101-based CNN in this particular image processing job and demonstrate its effectiveness in correctly categorizing milkfish freshness levels.

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References


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