{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,14]],"date-time":"2026-08-14T02:06:09Z","timestamp":1786673169050,"version":"build-2736575974"},"reference-count":16,"publisher":"Wiley","issue":"9","license":[{"start":{"date-parts":[[2024,5,27]],"date-time":"2024-05-27T00:00:00Z","timestamp":1716768000000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/onlinelibrary.wiley.com\/termsAndConditions#vor"}],"content-domain":{"domain":["scijournals.onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["Pest Management Science"],"published-print":{"date-parts":[[2024,9]]},"abstract":"<jats:title>Abstract<\/jats:title><jats:sec><jats:title>BACKGROUND<\/jats:title><jats:p><jats:italic>Hylurgus ligniperda<\/jats:italic>, an invasive species originating from Eurasia, is now a major forestry quarantine pest worldwide. In recent years, it has caused significant damage in China. While traps have been effective in monitoring and controlling pests, manual inspections are labor\u2010intensive and require expertise in insect classification. To address this, we applied a two\u2010stage cascade convolutional neural network, YOLOX\u2010MobileNetV2 (YOLOX\u2010Mnet), for identifying <jats:italic>H. ligniperda<\/jats:italic> and other pests captured in traps. This method streamlines target and non\u2010target insect detection from trap images, offering a more efficient alternative to manual inspections.<\/jats:p><\/jats:sec><jats:sec><jats:title>RESULTS<\/jats:title><jats:p>Two cascade convolutional neural network models were employed in two stages to detect both target and non\u2010target insects from images captured in the same forest. Initially, You Only Look Once X (YOLOX) served as the target detection model, identifying insects and non\u2010insects from the collected images, with non\u2010insect targets subsequently filtered out. In the second stage, MobileNetV2, a classification network, classified the captured insects. This approach effectively reduced false positives from non\u2010insect objects, enabled the inclusion of additional classification terms for multi\u2010class insect classification models, and utilized sample control strategies to enhance classification performance.<\/jats:p><\/jats:sec><jats:sec><jats:title>CONCLUSION<\/jats:title><jats:p>Application of the cascade convolutional neural network model accurately identified <jats:italic>H. ligniperda<\/jats:italic>, and Mean F<jats:sub>1<\/jats:sub>\u2010score of all kinds of insects in the trap was 0.98. Compared to traditional insect classification, this method offers great improvement in the identification and early warning of forest pests, as well as provide technical support for the early prevention and control of forest pests. \u00a9 2024 Society of Chemical Industry.<\/jats:p><\/jats:sec>","DOI":"10.1002\/ps.8126","type":"journal-article","created":{"date-parts":[[2024,4,17]],"date-time":"2024-04-17T08:39:30Z","timestamp":1713343170000},"page":"4223-4230","update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":7,"title":["Detection and recognition of the invasive species, <scp><i>Hylurgus ligniperda<\/i><\/scp>, in traps, based on a cascaded convolution neural network"],"prefix":"10.1002","volume":"80","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-2658-5639","authenticated-orcid":false,"given":"Xiahui","family":"Zhang","sequence":"first","affiliation":[{"name":"The Key Laboratory of Forest Pest Control, College of Forestry Beijing Forestry University  Beijing China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Zhengyi","family":"Li","sequence":"additional","affiliation":[{"name":"The Key Laboratory of Forest Pest Control, College of Forestry Beijing Forestry University  Beijing China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Lili","family":"Ren","sequence":"additional","affiliation":[{"name":"The Key Laboratory of Forest Pest Control, College of Forestry Beijing Forestry University  Beijing China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xuanxin","family":"Liu","sequence":"additional","affiliation":[{"name":"College of Information Beijing Forestry University  Beijing China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Tian","family":"Zeng","sequence":"additional","affiliation":[{"name":"Communication Service Center of Transportation Bureau of Chengde City  Chengde China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jing","family":"Tao","sequence":"additional","affiliation":[{"name":"The Key Laboratory of Forest Pest Control, College of Forestry Beijing Forestry University  Beijing China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"311","published-online":{"date-parts":[[2024,5,27]]},"reference":[{"key":"e_1_2_11_2_1","doi-asserted-by":"publisher","DOI":"10.1007\/s42729-020-00327-6"},{"key":"e_1_2_11_3_1","doi-asserted-by":"publisher","DOI":"10.1093\/jee\/tov303"},{"key":"e_1_2_11_4_1","doi-asserted-by":"publisher","DOI":"10.3390\/f12070950"},{"key":"e_1_2_11_5_1","doi-asserted-by":"publisher","DOI":"10.1088\/1742-6596\/1345\/2\/022043"},{"key":"e_1_2_11_6_1","article-title":"Lightweight and computationally faster Hypermetropic Convolutional Neural Network for small size object detection [J]","volume":"119","author":"Amudhan A","year":"2022","journal-title":"Image and Vision Computing."},{"key":"e_1_2_11_7_1","doi-asserted-by":"publisher","DOI":"10.3390\/e23121587"},{"key":"e_1_2_11_8_1","doi-asserted-by":"publisher","DOI":"10.1109\/ACCESS.2020.3021830"},{"key":"e_1_2_11_9_1","article-title":"Applications of deep learning for dense scenes analysis in agriculture: a review[J]","volume":"20","author":"Zhang Q","year":"2020","journal-title":"Sensors"},{"key":"e_1_2_11_10_1","doi-asserted-by":"publisher","DOI":"10.1038\/s41598-016-0015-2"},{"key":"e_1_2_11_11_1","doi-asserted-by":"publisher","DOI":"10.1080\/00305316.2016.1252805"},{"key":"e_1_2_11_12_1","unstructured":"ZhengG SongtaoL FengWet al. YOLOX: Exceeding YOLO Series in 2021[J]. arXiv \u2010 CS \u2010 Computer Vision and Pattern Recognition(2021)."},{"key":"e_1_2_11_13_1","doi-asserted-by":"publisher","DOI":"10.3390\/sym13122260"},{"key":"e_1_2_11_14_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.chaos.2021.110714"},{"key":"e_1_2_11_15_1","doi-asserted-by":"crossref","unstructured":"MarkS AndrewH MenglongZet al. MobileNetV2: Inverted Residuals and Linear Bottlenecks[J]. arXiv \u2010 CS \u2010 Computer Vision and Pattern Recognition(2018).","DOI":"10.1109\/CVPR.2018.00474"},{"key":"e_1_2_11_16_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.aspen.2019.11.006"},{"key":"e_1_2_11_17_1","article-title":"Two\u2010stage detection algorithm for Kiwifruit leaf diseases based on deep learning[J]","volume":"11","author":"Yao J","year":"2022","journal-title":"Plants\u2010Basel"}],"container-title":["Pest Management Science"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/scijournals.onlinelibrary.wiley.com\/doi\/pdf\/10.1002\/ps.8126","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,21]],"date-time":"2025-10-21T12:33:34Z","timestamp":1761050014000},"score":1,"resource":{"primary":{"URL":"https:\/\/scijournals.onlinelibrary.wiley.com\/doi\/10.1002\/ps.8126"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,5,27]]},"references-count":16,"journal-issue":{"issue":"9","published-print":{"date-parts":[[2024,9]]}},"alternative-id":["10.1002\/ps.8126"],"URL":"https:\/\/doi.org\/10.1002\/ps.8126","archive":["Portico"],"relation":{},"ISSN":["1526-498X","1526-4998"],"issn-type":[{"value":"1526-498X","type":"print"},{"value":"1526-4998","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,5,27]]},"assertion":[{"value":"2023-03-14","order":0,"name":"received","label":"Received","group":{"name":"publication_history","label":"Publication History"}},{"value":"2024-04-10","order":2,"name":"accepted","label":"Accepted","group":{"name":"publication_history","label":"Publication History"}},{"value":"2024-05-27","order":3,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}