SMC-YOLO: A High-Precision Maize Insect Pest-Detection Method

Maize is an excellent crop with high yields and versatility, and the extent and frequency of pest outbreaks will have a serious impact on maize yields. Therefore, helping growers accurately identify pest species is important for improving corn yields. Thus, in this study, we propose to use a pest detector called SMC-YOLO, which is proposed using You Only Look Once (YOLO) v8 as a reference model. First, the Spatial Pyramid Convolutional Pooling Module (SPCPM) is utilized in lieu of the Spatial Pyramid Pooling-Fast (SPPF) to enrich the diversity of feature information. Subsequently, a Multi-Dimensional Feature-Enhancement Module (MDFEM) is incorporated into the neck network. This module serves the purpose of augmenting the feature information associated with pests. Finally, a cross-scale feature-level non-local module (CSFLNLM) is incorporated in front of the detector head, which improves the global perception of the detector head. The results showed that SMC-YOLO achieved excellent results in several metrics, with its F1 Score (F1), mean Average Precision (mAP) @0.50, mAP@0.50:0.95 and mAP@0.75 reaching 83.18%, 86.7%, 60.6% and 70%, respectively, outperforming YOLOv11. This study provides a more reliable method of pest identification for the development of smart agriculture.