SP-YOLO: A Real-Time and Efficient Multi-Scale Model for Pest Detection in Sugar Beet Fields
Beet crops are highly vulnerable to pest infestations throughout their growth cycle, which significantly affects crop development and yield. Timely and accurate pest identification is crucial for implementing effective control measures. Current pest detection tasks face two primary challenges: first...
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2025-01-01
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| author | Ke Tang Yurong Qian Hualong Dong Yuning Huang Yi Lu Palidan Tuerxun Qin Li |
| author_facet | Ke Tang Yurong Qian Hualong Dong Yuning Huang Yi Lu Palidan Tuerxun Qin Li |
| author_sort | Ke Tang |
| collection | DOAJ |
| description | Beet crops are highly vulnerable to pest infestations throughout their growth cycle, which significantly affects crop development and yield. Timely and accurate pest identification is crucial for implementing effective control measures. Current pest detection tasks face two primary challenges: first, pests frequently blend into their environment due to similar colors, making it difficult to capture distinguishing features in the field; second, pest images exhibit scale variations under different viewing angles, lighting conditions, and distances, which complicates the detection process. This study constructed the BeetPest dataset, a multi-scale pest dataset for beets in complex backgrounds, and proposed the SP-YOLO model, which is an improved real-time detection model based on YOLO11. The model integrates a CNN and transformer (CAT) into the backbone network to capture global features. The lightweight depthwise separable convolution block (DSCB) module is designed to extract multi-scale features and enlarge the receptive field. The neck utilizes the cross-layer path aggregation network (CLPAN) module, further merging low-level and high-level features. SP-YOLO effectively differentiates between the background and target, excelling in handling scale variations in pest images. In comparison with the original YOLO11 model, SP-YOLO shows a 4.9% improvement in mean average precision (mAP@50), a 9.9% increase in precision, and a 1.3% rise in average recall. Furthermore, SP-YOLO achieves a detection speed of 136 frames per second (FPS), meeting real-time pest detection requirements. The model demonstrates remarkable robustness on other pest datasets while maintaining a manageable parameter size and computational complexity suitable for edge devices. |
| format | Article |
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| institution | Kabale University |
| issn | 2075-4450 |
| language | English |
| publishDate | 2025-01-01 |
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| series | Insects |
| spelling | doaj-art-18413ea060e944f29fc5a04eae1a65202025-01-24T13:35:54ZengMDPI AGInsects2075-44502025-01-0116110210.3390/insects16010102SP-YOLO: A Real-Time and Efficient Multi-Scale Model for Pest Detection in Sugar Beet FieldsKe Tang0Yurong Qian1Hualong Dong2Yuning Huang3Yi Lu4Palidan Tuerxun5Qin Li6School of Software, Xinjiang University, Urumqi 830091, ChinaSchool of Software, Xinjiang University, Urumqi 830091, ChinaSchool of Software, Xinjiang University, Urumqi 830091, ChinaSchool of Software, Xinjiang University, Urumqi 830091, ChinaSchool of Computer Science and Technology, Xinjiang University, Urumqi 830046, ChinaSchool of Computer Science and Technology, Xinjiang Normal University, Urumqi 830054, ChinaCollege of Life Science and Technology, Xinjiang University, Urumqi 830017, ChinaBeet crops are highly vulnerable to pest infestations throughout their growth cycle, which significantly affects crop development and yield. Timely and accurate pest identification is crucial for implementing effective control measures. Current pest detection tasks face two primary challenges: first, pests frequently blend into their environment due to similar colors, making it difficult to capture distinguishing features in the field; second, pest images exhibit scale variations under different viewing angles, lighting conditions, and distances, which complicates the detection process. This study constructed the BeetPest dataset, a multi-scale pest dataset for beets in complex backgrounds, and proposed the SP-YOLO model, which is an improved real-time detection model based on YOLO11. The model integrates a CNN and transformer (CAT) into the backbone network to capture global features. The lightweight depthwise separable convolution block (DSCB) module is designed to extract multi-scale features and enlarge the receptive field. The neck utilizes the cross-layer path aggregation network (CLPAN) module, further merging low-level and high-level features. SP-YOLO effectively differentiates between the background and target, excelling in handling scale variations in pest images. In comparison with the original YOLO11 model, SP-YOLO shows a 4.9% improvement in mean average precision (mAP@50), a 9.9% increase in precision, and a 1.3% rise in average recall. Furthermore, SP-YOLO achieves a detection speed of 136 frames per second (FPS), meeting real-time pest detection requirements. The model demonstrates remarkable robustness on other pest datasets while maintaining a manageable parameter size and computational complexity suitable for edge devices.https://www.mdpi.com/2075-4450/16/1/102sugar beet pestpest detectionmulti-scale feature fusiondeep learningintelligent pest management |
| spellingShingle | Ke Tang Yurong Qian Hualong Dong Yuning Huang Yi Lu Palidan Tuerxun Qin Li SP-YOLO: A Real-Time and Efficient Multi-Scale Model for Pest Detection in Sugar Beet Fields Insects sugar beet pest pest detection multi-scale feature fusion deep learning intelligent pest management |
| title | SP-YOLO: A Real-Time and Efficient Multi-Scale Model for Pest Detection in Sugar Beet Fields |
| title_full | SP-YOLO: A Real-Time and Efficient Multi-Scale Model for Pest Detection in Sugar Beet Fields |
| title_fullStr | SP-YOLO: A Real-Time and Efficient Multi-Scale Model for Pest Detection in Sugar Beet Fields |
| title_full_unstemmed | SP-YOLO: A Real-Time and Efficient Multi-Scale Model for Pest Detection in Sugar Beet Fields |
| title_short | SP-YOLO: A Real-Time and Efficient Multi-Scale Model for Pest Detection in Sugar Beet Fields |
| title_sort | sp yolo a real time and efficient multi scale model for pest detection in sugar beet fields |
| topic | sugar beet pest pest detection multi-scale feature fusion deep learning intelligent pest management |
| url | https://www.mdpi.com/2075-4450/16/1/102 |
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