## 1 Introduction  
Rice, as the largest in terms of planting area, highest in yield per unit area, and most abundant cereal crop in China, accounted for 34.58% of the total grain production in 2022, holding a dominant position in both grain consumption and production [1]. However, factors such as uneven field surfaces, insufficient sedimentation time in paddy fields, deep water layers in the field, exposed and hardened soil, harsh operating environments, and driver errors often lead to issues such as missed planting and floating seedlings during the operation of rice transplanting machines [2]. Currently, the assessment of transplanting machine operation quality heavily relies on manual judgment, resulting in low efficiency, poor working conditions, and increased labor costs [3]. In order to enhance rice yield and the efficiency of transplanting machine operations, it is crucial to identify the locations of abnormal seedling states and alert manual intervention. Timely alarms should be triggered in case of large-scale missed planting or floating seedlings, serving as reminders for improper driver operation or the need for machine maintenance. This approach aims to reduce the labor costs associated with subsequent reseeding efforts [4]. For the rapid and accurate assessment of transplanting machine operation quality, achieving precise recognition of seedling morphology is of paramount importance. Currently, scholars both domestically and internationally utilize image processing techniques to perform image segmentation and classification of seedlings based on crop features, including color and geometric morphological characteristics. [PERSON] et al. [5], for instance, leverage color information from G-R and G-B components of seedlings against the water field background. They select the ExG factor suitable for most green crops and use the Otsu automatic threshold method to achieve optimal thresholding, effectively separating seedlings from the background. [PERSON] et al. [6] establish an HSV color model based on a rice seedling dataset and train it for the recognition of rice seedlings. [PERSON] [7] employs both RGB cameras and TOF depth cameras to capture rice images. The author combines these two types of images, fusing pixel information related to color, texture, and shape features. A random forest model is constructed, utilizing multiple features for rice seedling recognition and achieving a 5% increase in accuracy. While these methods successfully segment seedlings from the water field background, the diverse morphologies of seedlings and the complex and variable paddy environments pose challenges. Furthermore, the effectiveness of these approaches is significantly influenced by the interaction between the target and background, leading to poor generalization ability and robustness of the models. In recent years, as the application of machine vision in agriculture has continued to deepen, its technology has demonstrated the capability to handle complex patterns and large-scale data in agricultural production. With adaptability and real-time performance, methods for seedling object detection based on deep learning have become widespread [8]. [PERSON] et al. [9], by improving deep learning algorithm models, proposed a Faster RCNN with VGG19 [10] model using transfer learning. This model achieved recognition and detection of corn seedlings under various conditions, including the full growth cycle, multiple weather conditions, and various angles. The recognition accuracy and recall rate were both above 95%. They also utilized the MobileNetV3-SSD with a pruning [11] model for distinguishing between seedlings and weeds, showing good detection results. [PERSON] et al. [12], focusing on detecting missing seedlings in rice seedlings, optimized the hyperparameters of the YOLOv5 model using the cosine annealing algorithm. By filtering out low-quality detection boxes based on the confidence and category of each output candidate region, the model accurately located the detection image containing the missing seedling position.