The seedling emergence rate is a crucial indicator for evaluating the growth status of crops in agricultural production and can provide valuable recommendations for subsequent crop planting and field management strategies. Currently, the determination of the emergence rate relies on manual seedling counting, which is not only labour-intensive and time-consuming, but also prone to human errors. Therefore, we utilize drone-captured images of peanut seedlings and employs deep learning networks to estimate seedling numbers. Specifically, we incorporate the BIFPN (Bidirectional Feature Pyramid Network) feature fusion module into the original Centernet model, which would combine multi-scale feature information. This modification not only enhances the accuracy of identification but also improves the localization of seedlings. To address the issue of false positives caused by complex field backgrounds in seedling recognition, we integrate the Contrastive Loss module to increase the discrepancy between positive and negative samples. The results demonstrate that the proposed method significantly enhances both precision and recall rates for peanut seedling recognition under three different scenes, compared to the original model. Furthermore, the proposed method is also applied in real peanut breading field, fulfilling the practical requirements for emergence rate calculation.