Objective To address the issues of imbalanced data samples and low detection accuracy in maize leaf disease detection under complex environments.

Method An improved object detection network, SF_YOLOv5 was proposed. First, based on the multi-scale pyramid structure of YOLOv5, a novel spatial-feature pyramid structure (SPD-FPN) was designed to enhance the network’s ability to recognize small-target disease features at high-resolution levels while retaining large-target information at low-resolution levels, thereby improving overall detection accuracy and robustness. Second, the Focal Loss function was introduced to increase the weight of hard-to-classify samples and reduce the influence of easily classified samples, ensuring that the model focused more on the minority samples often overlooked in imbalanced datasets. Additionally, transfer learning was applied to the design of SF_YOLOv5, where pre-trained YOLOv5 model parameters were transferred to the improved SF_YOLOv5 network for training. This leveraged knowledge from large-scale datasets to enhance the model’s generalization capability for maize disease detection.

Result Experimental validation on the constructed maize disease dataset showed that SF_YOLOv5 achieved a mean average precision (mAP) of 93.3% and a recall of 89.6%, significantly outperforming the original YOLOv5 model. And the model was small in size, and had been deployed in mobile devices.

Conclusion The results demonstrate that the improved network performs better than the original model in detecting maize leaf diseases under imbalanced sample conditions. This approach can be applied to intelligent diagnosis of maize diseases in farmland scenarios with imbalanced data, providing a theoretical foundation for real-time maize disease detection in the agricultural sector.