SMC-YOLO: A High-Precision Maize Insect Pest-Detection Method
Qinghao Wang, Yongkang Liu, Yongkang Liu, Qi Zheng, Rui Tao, Yong Liu, Yong Liu
Abstract
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, [email protected]:0.95 and [email protected] 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.