{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,14]],"date-time":"2026-09-14T06:04:16Z","timestamp":1789365856651,"version":"build-2803163510"},"reference-count":35,"publisher":"Frontiers Media SA","license":[{"start":{"date-parts":[[2026,9,14]],"date-time":"2026-09-14T00:00:00Z","timestamp":1789344000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":["frontiersin.org"],"crossmark-restriction":true},"short-container-title":["Front. Plant Sci."],"abstract":"<jats:p>Real-time and accurate detection of crop pests in field environments is critical for intelligent plant protection and ensuring food security. However, practical farmland settings introduce substantial challenges, including small and densely distributed targets, high inter-class morphological similarity, complex backgrounds, and a long-tailed class distribution. To address these issues, an improved lightweight detection model named YOLOv8s-BD is proposed based on the YOLOv8s architecture. The Pest24 dataset is first augmented using a Generative Adversarial Network (FastGAN) to mitigate data scarcity for minority categories and enrich intra-class feature diversity. Subsequently, a dynamic upsampling module (DySample) is incorporated to enhance high-resolution feature representation for small-scale pests, and the original feature pyramid is replaced with a weighted bidirectional feature pyramid (BiFPN) to optimize multi-scale feature fusion. Experimental results demonstrate that YOLOv8s-BD achieves 72.3% mAP@0.5 and 46.9% mAP@0.5:0.95 on the Pest24 dataset, with only 7.4 million parameters and an inference speed of 1.9 ms per image, demonstrating superior accuracy and efficiency over mainstream YOLO series models. Both visualization results and error analysis confirm that the proposed model can accurately localize pest distribution regions and effectively reduce misclassification rate among similar species. The model thus offers a practical and efficient solution for multi-class pest detection in real-world agricultural scenarios.<\/jats:p>","DOI":"10.3389\/fpls.2026.1913220","type":"journal-article","created":{"date-parts":[[2026,9,14]],"date-time":"2026-09-14T05:38:39Z","timestamp":1789364319000},"update-policy":"https:\/\/doi.org\/10.3389\/crossmark-policy","source":"Crossref","is-referenced-by-count":0,"title":["Lightweight YOLOv8s-BD for small and dense agricultural pest detection using dynamic upsampling and bidirectional feature fusion"],"prefix":"10.3389","volume":"17","author":[{"given":"Qiang","family":"Gao","sequence":"first","affiliation":[{"name":"Xi\u2019an University School of Artificial Intelligence and Big Data","place":["Xi\u2019an, Shaanxi, China"]},{"name":"Shaanxi Joint Laboratory of Artificial Intelligence, Xi\u2019an University","place":["Xi\u2019an, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Chongchong","family":"Shi","sequence":"additional","affiliation":[{"name":"Xi\u2019an University School of Artificial Intelligence and Big Data","place":["Xi\u2019an, Shaanxi, China"]},{"name":"Shaanxi Joint Laboratory of Artificial Intelligence, Xi\u2019an University","place":["Xi\u2019an, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yu","family":"Ji","sequence":"additional","affiliation":[{"name":"School of Computer Science, Xijing University","place":["Xi\u2019an, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xiaoning","family":"Chen","sequence":"additional","affiliation":[{"name":"Xi\u2019an University School of Artificial Intelligence and Big Data","place":["Xi\u2019an, Shaanxi, China"]},{"name":"Shaanxi Joint Laboratory of Artificial Intelligence, Xi\u2019an University","place":["Xi\u2019an, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Huan","family":"Wang","sequence":"additional","affiliation":[{"name":"Xi\u2019an University School of Artificial Intelligence and Big Data","place":["Xi\u2019an, Shaanxi, China"]},{"name":"Shaanxi Joint Laboratory of Artificial Intelligence, Xi\u2019an University","place":["Xi\u2019an, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Mengke","family":"Cao","sequence":"additional","affiliation":[{"name":"Xi\u2019an University School of Artificial Intelligence and Big Data","place":["Xi\u2019an, Shaanxi, China"]},{"name":"Shaanxi Joint Laboratory of Artificial Intelligence, Xi\u2019an University","place":["Xi\u2019an, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Meili","family":"Wang","sequence":"additional","affiliation":[{"name":"Shaanxi Joint Laboratory of Artificial Intelligence, Xi\u2019an University","place":["Xi\u2019an, China"]},{"name":"School of Information Engineering, Northwest A&F University","place":["Yangling, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1965","published-online":{"date-parts":[[2026,9,14]]},"reference":[{"key":"B1","doi-asserted-by":"publisher","DOI":"10.3390\/agronomy15092022","article-title":"YOLO-LCE: A lightweight YOLOv8 model for agricultural pest detection","volume":"15","author":"Cen","year":"2025","journal-title":"Agronomy"},{"key":"B2","doi-asserted-by":"publisher","DOI":"10.1007\/s11042-023-15221-3","article-title":"Survey on crop pest detection using deep learning and machine learning approaches","volume":"1500","author":"Chithambarathanu","year":"2023","journal-title":"Multimedia Tools Appl"},{"key":"B3","first-page":"1160","article-title":"Effective fusion factor in FPN for tiny object detection","author":"Gong","year":"2021"},{"key":"B4","doi-asserted-by":"publisher","first-page":"156","DOI":"10.11975\/j.issn.1002-6819.202409183","article-title":"Detecting rice diseases using improved lightweight YOLOv8n","volume":"41","author":"Guo","year":"2025","journal-title":"Trans. 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