{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,23]],"date-time":"2026-10-23T22:04:47Z","timestamp":1792793087046,"version":"4.5.0"},"reference-count":30,"publisher":"Wiley","issue":"6","license":[{"start":{"date-parts":[[2024,1,31]],"date-time":"2024-01-31T00:00:00Z","timestamp":1706659200000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/onlinelibrary.wiley.com\/termsAndConditions#vor"}],"funder":[{"DOI":"10.13039\/501100012166","name":"National Key Research and Development Program of China","doi-asserted-by":"publisher","id":[{"id":"10.13039\/501100012166","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["scijournals.onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["Pest Management Science"],"published-print":{"date-parts":[[2024,6]]},"abstract":"<jats:title>Abstract<\/jats:title><jats:sec><jats:title>BACKGROUND<\/jats:title><jats:p>Pest infestation is one of the primary causes of decreased cotton yield and quality. Rapid and accurate identification of cotton pest categories is essential for producers to implement effective and expeditious control measures. Existing multi\u2010scale cotton pest detection technology still suffers from poor accuracy and rapidity of detection. This study proposed the pruned GBW\u2010YOLOv5 (Ghost\u2010BiFPN\u2010WIoU You Only Look Once version 5), a novel model for the rapid detection of cotton pests.<\/jats:p><\/jats:sec><jats:sec><jats:title>RESULTS<\/jats:title><jats:p>The detection performance of the pruned GBW\u2010YOLOv5 model for cotton pests was evaluated based on the self\u2010built cotton pest dataset. In comparison with the original YOLOv5 model, the pruned GBW\u2010YOLOv5 model demonstrated significant reductions in complexity, size, and parameters by 68.4%, 66.7%, and 68.2%, respectively. Remarkably, the mean average precision (mAP) decreased by a mere 3.8%. The pruned GBW\u2010YOLOv5 model outperformed other classic object detection models, achieving an outstanding detection speed of 114.9 FPS.<\/jats:p><\/jats:sec><jats:sec><jats:title>CONCLUSION<\/jats:title><jats:p>The methodology proposed by our research enabled rapid and accurate identification of cotton pests, laying a solid foundation for the implementation of precise pest control measures. The pruned GBW\u2010YOLOv5 model provided theoretical research and technical support for detecting cotton pests under field conditions. \u00a9 2024 Society of Chemical Industry.<\/jats:p><\/jats:sec>","DOI":"10.1002\/ps.7978","type":"journal-article","created":{"date-parts":[[2024,1,31]],"date-time":"2024-01-31T06:11:30Z","timestamp":1706681490000},"page":"2738-2750","update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":11,"title":["Rapid detection of multi\u2010scale cotton pests based on lightweight <scp>GBW\u2010YOLOv5<\/scp> model"],"prefix":"10.1002","volume":"80","author":[{"ORCID":"https:\/\/orcid.org\/0009-0006-0694-7483","authenticated-orcid":false,"given":"Jinyan","family":"Liang","sequence":"first","affiliation":[{"name":"College of Mechanical and Electrical Engineering, Shihezi University  Shihezi China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Min","family":"Tian","sequence":"additional","affiliation":[{"name":"College of Mechanical and Electrical Engineering, Shihezi University  Shihezi China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xiang","family":"Liu","sequence":"additional","affiliation":[{"name":"College of Mechanical and Electrical Engineering, Shihezi University  Shihezi China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"311","published-online":{"date-parts":[[2024,1,31]]},"reference":[{"key":"e_1_2_8_2_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.scitotenv.2022.153407"},{"key":"e_1_2_8_3_1","doi-asserted-by":"publisher","DOI":"10.1016\/S2095-3119(20)63318-4"},{"key":"e_1_2_8_4_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.biosystemseng.2015.11.005"},{"key":"e_1_2_8_5_1","doi-asserted-by":"crossref","first-page":"85","DOI":"10.1016\/j.ifacol.2018.08.066","article-title":"A cognitive vision method for insect pest image segmentation","volume":"51","author":"Wang Z","year":"2018","journal-title":"IFAC\u2010PapersOnLine"},{"key":"e_1_2_8_6_1","doi-asserted-by":"publisher","DOI":"10.1017\/S002185962200017X"},{"key":"e_1_2_8_7_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.compag.2022.107534"},{"key":"e_1_2_8_8_1","doi-asserted-by":"publisher","DOI":"10.3389\/fpls.2022.814681"},{"key":"e_1_2_8_9_1","unstructured":"LeeN AjanthanTandTorrPH Snip: Single\u2010shot network pruning based on connection sensitivity.arXiv preprint arXiv:1810.02340(2018)."},{"key":"e_1_2_8_10_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2017.106"},{"key":"e_1_2_8_11_1","first-page":"6054","volume-title":"Proceedings of the IEEE\/CVF international conference on computer vision, 27 October\u20102 November 2019","author":"Li Y","year":"2019"},{"key":"e_1_2_8_12_1","unstructured":"BochkovskiyA WangC\u2010YandLiao H\u2010YM Yolov4: Optimal speed and accuracy of object detection.arXiv preprint arXiv:2004.10934(2020)."},{"key":"e_1_2_8_13_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2019.00075"},{"key":"e_1_2_8_14_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2017.211"},{"key":"e_1_2_8_15_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.compag.2020.105222"},{"key":"e_1_2_8_16_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.compag.2022.106827"},{"key":"e_1_2_8_17_1","doi-asserted-by":"publisher","DOI":"10.3390\/insects13060554"},{"key":"e_1_2_8_18_1","unstructured":"HowardAG ZhuM ChenB KalenichenkoD WangW WeyandTet al. 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