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In response to these issues, this paper proposes a lightweight model for tobacco leaf drying stage recognition, designed to leverage limited computational resources to enhance the models ability to extract multi-scale features from images of drying stages while simultaneously reducing model complexity and parameter count to improve generalization capability

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