## 1 Introduction
These methods rely on the sensory system of operators, including the assessment of attributes such as touch, sight, and smell, in conjunction with personal experience, to evaluate quality. However, this method has many limitations, such as the insufficient consistency of the results, low efficiency, potential damage, and susceptibility to subjective bias, which means that it cannot fully and objectively reflect the true state of seeds. Especially in the detection of large quantities of samples, it is difficult for manual methods to meet the needs of modern agricultural production. In addition, while chemical sampling is an accurate method, it is both time-consuming and costly, making it unsuitable for large-scale commercial seed detection. The advancement of science and technology has led to the gradual integration of non-destructive testing techniques, including electronic nose technology, spectral imaging technology, image  
Figure 1: Annual maize production of China from 1978 to 2023.  
processing technology, and machine vision, into the domain of agricultural product testing. These methodologies offer robust technical support for the advancement of agricultural automation, reduce labor costs, and enhance detection efficiency and accuracy. The rise of computer vision technology has further promoted the development of image processing and gradually replaced manual operation. However, traditional computer vision relies on a lot of engineering experience and prior knowledge and analyzes image features by manually extracting them, which has limitations when dealing with complex problems. In contrast, machine learning (ML) techniques, particularly deep learning (DL), demonstrate enhanced adaptability and accuracy through the automatic learning of features within data sets. DL has demonstrated considerable success in a range of tasks, including image classification, localization, detection, segmentation, and tracking. The model multilayered architecture facilitates the learning of intricate feature representations, thereby enhancing the efficiency and precision of data representation learning. Applying DL to machine vision for processing and analyzing images of seeds can quickly and effectively detect mildew, improving seed quality, reducing the introduction of mold, ensuring food safety, and contributing to the modernization and automation of agricultural production. Although DL has demonstrated great potential in many fields, its application in agricultural crop detection still needs further research and exploration. This study aims to utilize DL algorithms to achieve the automatic detection of surface defects in maize seeds, enhancing seed quality, increasing germination rates, boosting yield and income, reducing labor costs, and providing strong support for agricultural production. DL has been employed extensively in agricultural contexts. [PERSON] et al. proposed a novel type of point-centered convolutional neural network (CNN) that demonstrated superior performance compared to full-band-based CNNs and holds significant promise for the non-destructive identification of moldy peanuts [5]. [PERSON] et al. integrated a near-infrared (NIR) snapshot hyperspectral sensor with DL to develop a multi-modal, real-time coffee bean defect detection algorithm for sorting defective green coffee beans [6]. [PERSON] et al. proposed a Partial Least Squares Discriminant Analysis (PLS-DA) model that uses spectral features for early diagnosis, which can effectively detect whether wheat leaves are infected with powdery mildew [7]. [PERSON] et al. proposed an improved YOLOv5 algorithm and proposed a Bidirectional Feature Pyramid Network (BiFPN)-S structure, called the YOLOv5-B algorithm, which can accurately identify apple fruit growth in real time [8]. [PERSON] et al. combined the DL algorithm with edge detection threshold processing based on the optimized S2 Anet (Single-shot Alignment Network) model, and the optimized algorithm was able to identify cracked and non-cracked seeds effectively [9].