ArticleScientific reports2025
SSM-based detection of rice seedling deficiency.
Article in Scientific reports, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Abstract
In large-scale rice cultivation, seedling deficiency is a common issue that significantly impacts timely replanting decisions. Traditional manual inspection methods are inefficient and labor-intensive, highlighting the need for an automated and accurate detection approach. This study proposes a rice seedling deficiency detection method based on a state space model, aiming to improve detection precision for small seedlings. With a dual-branch feature extraction module built upon the State Space Model (Mamba), and the wavelet convolution transform, enhances the detection accuracy on the self-constructed rice seedling deficiency dataset. Experiments show that the proposed optimized model achieves a mAP50 of 78%, outperforming other baseline models. The results indicate the effectiveness and practicality of the approach, offering a novel and efficient solution for detecting missing seedlings in rice fields.
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