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The subsequent maturity classification phase reaches a training accuracy of 99% and a testing accuracy of 91%, demonstrating strong generalization capabilities. A key strength of the proposed method lies in its resilience to suboptimal image quality, making it suitable for deployment in uncontrolled, real-world environments. 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