## 1 Introduction
The majority of current research in the field of maize crop detection is concentrated on the identification of defects and the classification of maize varieties. There is a paucity of studies addressing the detection of maize mildew. [PERSON] et al. proposed a new method using a deep CNN as a general feature extractor. Compared with the model based only on simple features, the model trained by extracting features using a CNN had a higher classification accuracy for maize seed varieties. A CNN-artificial neural network (ANN) classifier performed the best [10]. [PERSON] et al. combined a dual-path CNN model to establish a defect detection method based on a watershed algorithm. The results proved that the model was effective in identifying defective seeds and defect-free seeds [11]. [PERSON] et al. proposed a method for segmenting and classifying maize kernels, which solved the problem of inspecting the quality of maize seeds [12]. [PERSON] et al. proposed an improved seed quality evaluation method based on maize seeds with different qualities based on the Inception-Residuals Network (ResNet). The results showed that the proposed method had good comprehensive performance in the detection of maize seed appearance quality [13]. [PERSON] et al. proposed an improved CST-YOLOv5s (YOLOv5s + CBAM + SPPCPCSC + Transformer) algorithm, which was capable of detecting damaged and mildewed corn grains with high _Precision_ and _mAP50_ value. However, the size of the model was increased to 1.93 times that of the original model [14]. In order to develop a portable device for the accurate detection of corn mildew, a new detection algorithm for corn mildew is urgently needed. A review of the current research status at home and abroad reveals a plethora of studies on the quality of maize appearance varieties. However, there is a paucity of experiments on the detection of maize mildew. The focus of this study is to use DL technology to detect maize seed surface mildew. DL frameworks such as YOLOv5 were used to detect the surface defects of maize seeds, especially to identify healthy, mildly moldy, and severely moldy maize grains using self-created maize seed data sets. In order to achieve the above main goals, three aspects of work need to be completed: (1) The construction of a maize seed surface mildew detection hardware system. In accordance with the specifications for the detection of maize seed surface mildew, an image acquisition apparatus for maize seed surface mildew was devised, and a hardware system for maize seed surface mildew was assembled. The image data of healthy, mildly infected, and severely infected maize kernels were collected. (2) The process of data cleaning and image preprocessing. In order to meet the requirements of the DL algorithm with respect to both the image and data set, OpenCV was employed for the purpose of image preprocessing. Furthermore, the data set was augmented through a series of operations, including image transformation, enhancement, segmentation, filtering, and morphological processing, with the objective of enhancing the robustness of the trained model. (3) A DL algorithm for analyzing and detecting the collected data. According to the characteristics of maize seed surface mildew, a YOLOv5s model was used for training. On this basis, the YOLOv5s-Shufflenet model was proposed to replace the YOLOv5s backbone network with a ShuffleNet CNN. Then, a CBAM was integrated into the improved YOLOv5s-ShuffleNet neck network, resulting in the proposed YOLOv5s-ShuffleNet-CBAM model. Finally, a healthy maize kernel, slightly mildewed maize kernel, and severely mildewed maize kernel were identified, and the model volume was simplified [15].