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 de...
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MDPI AG
2025-01-01
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| Series: | Agronomy |
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| Online Access: | https://www.mdpi.com/2073-4395/15/1/195 |
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| author | Qinghao Wang Yongkang Liu Qi Zheng Rui Tao Yong Liu |
| author_facet | Qinghao Wang Yongkang Liu Qi Zheng Rui Tao Yong Liu |
| author_sort | Qinghao Wang |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-ffb1f94a08d24bfa8cb29ec17d736121 |
| institution | Kabale University |
| issn | 2073-4395 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Agronomy |
| spelling | doaj-art-ffb1f94a08d24bfa8cb29ec17d7361212025-01-24T13:17:08ZengMDPI AGAgronomy2073-43952025-01-0115119510.3390/agronomy15010195SMC-YOLO: A High-Precision Maize Insect Pest-Detection MethodQinghao Wang0Yongkang Liu1Qi Zheng2Rui Tao3Yong Liu4College of Electronic Engineering, Heilongjiang University, Harbin 150000, ChinaCollege of Electronic Engineering, Heilongjiang University, Harbin 150000, ChinaCollege of Electronic Engineering, Heilongjiang University, Harbin 150000, ChinaHarbin Dongshui Intelligent Agriculture Technology Co., Harbin 150000, ChinaCollege of Electronic Engineering, Heilongjiang University, Harbin 150000, ChinaMaize 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.https://www.mdpi.com/2073-4395/15/1/195YOLOobject detectionattentionmaize pestartificial intelligence |
| spellingShingle | Qinghao Wang Yongkang Liu Qi Zheng Rui Tao Yong Liu SMC-YOLO: A High-Precision Maize Insect Pest-Detection Method Agronomy YOLO object detection attention maize pest artificial intelligence |
| title | SMC-YOLO: A High-Precision Maize Insect Pest-Detection Method |
| title_full | SMC-YOLO: A High-Precision Maize Insect Pest-Detection Method |
| title_fullStr | SMC-YOLO: A High-Precision Maize Insect Pest-Detection Method |
| title_full_unstemmed | SMC-YOLO: A High-Precision Maize Insect Pest-Detection Method |
| title_short | SMC-YOLO: A High-Precision Maize Insect Pest-Detection Method |
| title_sort | smc yolo a high precision maize insect pest detection method |
| topic | YOLO object detection attention maize pest artificial intelligence |
| url | https://www.mdpi.com/2073-4395/15/1/195 |
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