Method for Identifying the Number of Corn Seedlings Based on MS-YOLOv7 Model

In response to the problems such as the inconsistent size, dense distribution and high difficulty in identification of corn seedlings in the field environment, a study on the identification of the number of corn seedlings based on the MS-YOLOv7 model was carried out. In the efficient aggregation network ELAN of the original model YOLOv7, a depthwise separable convolution module was introduced to enhance the model's ability to extract fine features; by adding the CBAM attention mechanism module in the feature layer output by the main network, the recognition accuracy of the model for small targets was strengthened; the loss function CIOU was replaced with WIOU to improve the quality of anchor boxes; to expand the receptive field of the model for the target, a SPPF spatial pyramid structure was constructed. The experimental results show that the average precision mean of the improved MS-YOLOv7 model is 95.1%, which can provide technical support for the precise counting of corn seedlings in the field environment.