<p>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.</p>