{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,21]],"date-time":"2026-07-21T05:03:12Z","timestamp":1784610192407,"version":"3.55.0"},"reference-count":26,"publisher":"MDPI AG","issue":"14","license":[{"start":{"date-parts":[[2026,7,19]],"date-time":"2026-07-19T00:00:00Z","timestamp":1784419200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100018542","name":"Natural Science Foundation of Sichuan Province","doi-asserted-by":"crossref","award":["2025ZNSFSC0162"],"award-info":[{"award-number":["2025ZNSFSC0162"]}],"id":[{"id":"10.13039\/501100018542","id-type":"DOI","asserted-by":"crossref"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>To address the challenges of small pest target sizes, dense distributions, strong background interference, and missed detection of small objects in insect pest monitoring lamp images, this study proposes YOLO11-MSC, a lightweight agricultural pest detection model based on an improved YOLO11. First, an Object-Centric Adaptive Slicing (OCAS) strategy is designed to construct the Insect35_OCAS dataset, which increases the relative proportion of small pest targets in the input images while reducing redundant background interference. Second, the Multi-path Nonlinear Star Aggregation (MNS) module and the C2-based Improved Bi-Level Routing Attention (C2iBRA) module are introduced to enhance fine-grained feature extraction for multi-scale pest targets and improve background suppression in complex scenes. Finally, a Semantic-Guided Adaptive Slicing (SGAS) inference strategy is developed to transfer the sliced-image training model to full-image inference on original high-resolution insect pest monitoring lamp images. Experimental results show that YOLO11-MSC achieves an mAP@0.5, Precision, and Recall of 95.2%, 91.8%, and 93.0% on the Insect35_OCAS dataset, respectively, improving the baseline YOLO11n by 1.3, 1.0, and 1.2 percentage points. The model contains only 3.4 M parameters and requires 7.8 Giga Floating-Point Operations (GFLOPs). When combined with SGAS, YOLO11-MSC achieves an mAP@0.5, Precision, and Recall of 86.4%, 81.2%, and 90.1% on the original full-image dataset, respectively. These results demonstrate that the proposed method effectively improves the detection accuracy of small pests and the full-image inference capability in complex insect pest monitoring lamp scenarios while maintaining low model complexity, providing technical support for intelligent agricultural pest monitoring and precision control.<\/jats:p>","DOI":"10.3390\/s26144576","type":"journal-article","created":{"date-parts":[[2026,7,20]],"date-time":"2026-07-20T07:54:13Z","timestamp":1784534053000},"page":"4576","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["A YOLO11-MSC-Based Method for Small Pest Detection in Insect Pest Monitoring Lamp Images"],"prefix":"10.3390","volume":"26","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-2402-7657","authenticated-orcid":false,"given":"Zhiyong","family":"Li","sequence":"first","affiliation":[{"name":"College of Information Engineering, Sichuan Agricultural University, Yaan 625015, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Zuxiang","family":"Lin","sequence":"additional","affiliation":[{"name":"College of Mechanical and Electrical Engineering, Sichuan Agricultural University, Yaan 625015, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xulin","family":"Liu","sequence":"additional","affiliation":[{"name":"College of Information Engineering, Sichuan Agricultural University, Yaan 625015, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yuzhu","family":"Li","sequence":"additional","affiliation":[{"name":"College of Information Engineering, Sichuan Agricultural University, Yaan 625015, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2026,7,19]]},"reference":[{"key":"ref_1","first-page":"45","article-title":"Occurrence trend and cause analysis of major crop diseases and pests nationwide in 2026","volume":"46","author":"Zhang","year":"2026","journal-title":"China Plant Prot."},{"key":"ref_2","doi-asserted-by":"crossref","unstructured":"Zheng, Y.-Y., Kong, J.-L., Jin, X.-B., Wang, X.-Y., Su, T.-L., and Zuo, M. 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