{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,17]],"date-time":"2026-04-17T22:00:01Z","timestamp":1776463201402,"version":"3.51.2"},"reference-count":46,"publisher":"MDPI AG","issue":"12","license":[{"start":{"date-parts":[[2021,11,27]],"date-time":"2021-11-27T00:00:00Z","timestamp":1637971200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100012166","name":"National Key Research and Development Program of China","doi-asserted-by":"publisher","award":["No. 2020YFD1100605"],"award-info":[{"award-number":["No. 2020YFD1100605"]}],"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","award":["No.61966016 and No.61861021"],"award-info":[{"award-number":["No.61966016 and No.61861021"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"name":"National-level Student Innovation and Entrepreneurship Training Program","award":["No.202110410026"],"award-info":[{"award-number":["No.202110410026"]}]},{"name":"Science and Technology Project of Jiangxi Provincial Education Department","award":["No.190194"],"award-info":[{"award-number":["No.190194"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Entropy"],"abstract":"<jats:p>Traditional pest detection methods are challenging to use in complex forestry environments due to their low accuracy and speed. To address this issue, this paper proposes the YOLOv4_MF model. The YOLOv4_MF model utilizes MobileNetv2 as the feature extraction block and replaces the traditional convolution with depth-wise separated convolution to reduce the model parameters. In addition, the coordinate attention mechanism was embedded in MobileNetv2 to enhance feature information. A symmetric structure consisting of a three-layer spatial pyramid pool is presented, and an improved feature fusion structure was designed to fuse the target information. For the loss function, focal loss was used instead of cross-entropy loss to enhance the network\u2019s learning of small targets. The experimental results showed that the YOLOv4_MF model has 4.24% higher mAP, 4.37% higher precision, and 6.68% higher recall than the YOLOv4 model. The size of the proposed model was reduced to 1\/6 of that of YOLOv4. Moreover, the proposed algorithm achieved 38.62% mAP with respect to some state-of-the-art algorithms on the COCO dataset.<\/jats:p>","DOI":"10.3390\/e23121587","type":"journal-article","created":{"date-parts":[[2021,11,29]],"date-time":"2021-11-29T05:23:02Z","timestamp":1638163382000},"page":"1587","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":61,"title":["A Lightweight YOLOv4-Based Forestry Pest Detection Method Using Coordinate Attention and Feature Fusion"],"prefix":"10.3390","volume":"23","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-0186-2940","authenticated-orcid":false,"given":"Mingfeng","family":"Zha","sequence":"first","affiliation":[{"name":"School of Software, Jiangxi Agricultural University, Nanchang 330045, China"}]},{"given":"Wenbin","family":"Qian","sequence":"additional","affiliation":[{"name":"School of Software, Jiangxi Agricultural University, Nanchang 330045, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9266-3498","authenticated-orcid":false,"given":"Wenlong","family":"Yi","sequence":"additional","affiliation":[{"name":"School of Software, Jiangxi Agricultural University, Nanchang 330045, China"}]},{"given":"Jing","family":"Hua","sequence":"additional","affiliation":[{"name":"School of Software, Jiangxi Agricultural University, Nanchang 330045, China"}]}],"member":"1968","published-online":{"date-parts":[[2021,11,27]]},"reference":[{"key":"ref_1","first-page":"114","article-title":"Analysis of identification and classification methods of forestry pests","volume":"8","author":"Yi","year":"2017","journal-title":"Flora"},{"key":"ref_2","doi-asserted-by":"crossref","unstructured":"Qi, S.F., and Li, Y.H. 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