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Abstract Accurate tomato maturity assessment based on visual appearance is essential for quality control in agricultural production, yet remains challenging under complex visual conditions due to background clutter and ambiguous maturity cues. Many existing methods can suppress background interference to some extent, but lack explicit mechanisms to ensure that classification decisions are formed within maturity-relevant fruit regions.
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