{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,29]],"date-time":"2026-07-29T06:05:09Z","timestamp":1785305109792,"version":"3.55.0"},"reference-count":58,"publisher":"Frontiers Media SA","license":[{"start":{"date-parts":[[2026,7,29]],"date-time":"2026-07-29T00:00:00Z","timestamp":1785283200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100005230","name":"Natural Science Foundation of Chongqing Municipality","doi-asserted-by":"publisher","award":["CSTB2025NSCQ WZQLHJJMSX0016, CSTB2025TIAD-LYKJXZLHGGX0003"],"award-info":[{"award-number":["CSTB2025NSCQ WZQLHJJMSX0016, CSTB2025TIAD-LYKJXZLHGGX0003"]}],"id":[{"id":"10.13039\/501100005230","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100012477","name":"Fundamental Research Funds for the Key Research Program of Chongqing Science and Technology Commission","doi-asserted-by":"publisher","award":["KJZD-K202501201"],"award-info":[{"award-number":["KJZD-K202501201"]}],"id":[{"id":"10.13039\/501100012477","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["frontiersin.org"],"crossmark-restriction":true},"short-container-title":["Front. Plant Sci."],"abstract":"<jats:sec>\n                    <jats:title>Introduction<\/jats:title>\n                    <jats:p>To achieve high-precision and high-efficiency recognition of maize seedlings and weeds in complex field environments while meeting the deployment requirements of resource-constrained edge devices, this study constructed a lightweight object detection model, DCC-YOLOv8n.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>Based on YOLOv8n, the model incorporates three key improvements: a dynamic convolution module to enhance feature diversity, a context-guided module to improve target identification in complex backgrounds, and a content-aware reassembly of features (CARAFE) module to improve small-object detection. A self-built dataset of maize seedlings and weeds containing 1,200 images was utilized, incorporating multidimensional data augmentation.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>Experimental results demonstrated that DCC-YOLOv8n achieved a precision of 90.1% and a recall of 95.5%, with mAP@0.5 and mAP@0.5:0.95 reaching 93.7% and 73.9%, respectively, outperforming YOLOv8n and mainstream comparison models, including Faster R-CNN and YOLOv5n. Deployment on the NVIDIA Jetson Nano edge-computing platform achieved a real-time inference speed of 18.6 FPS with mAP@0.5 of 90.8%, verifying the feasibility of its application and deployment on actual farmland edge devices.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Discussion<\/jats:title>\n                    <jats:p>The proposed DCC-YOLOv8n model achieved an optimal balance between accuracy, lightweight architecture, and real-time performance. The constructed field recognition system effectively addresses the challenges of complex farmland scenarios and provides a viable technical solution for intelligent operations in precision agriculture.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.3389\/fpls.2026.1901787","type":"journal-article","created":{"date-parts":[[2026,7,29]],"date-time":"2026-07-29T05:57:56Z","timestamp":1785304676000},"update-policy":"https:\/\/doi.org\/10.3389\/crossmark-policy","source":"Crossref","is-referenced-by-count":0,"title":["DCC-YOLOv8n: a lightweight model for maize seedling and weed recognition in complex farmland environments"],"prefix":"10.3389","volume":"17","author":[{"given":"Jiapeng","family":"Cui","sequence":"first","affiliation":[{"name":"College of Mechanical Engineering, Chongqing Sanxia University of Science and Technology","place":["Chongqing, China"]},{"name":"Chongqing Key Laboratory of Light Mountain Intelligent Agricultural Machinery, Chongqing Sanxia University of Science and Technology","place":["Chongqing, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Shengqiang","family":"Hao","sequence":"additional","affiliation":[{"name":"College of Mechanical Engineering, Chongqing Sanxia University of Science and Technology","place":["Chongqing, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yinyin","family":"Yang","sequence":"additional","affiliation":[{"name":"College of Mechanical Engineering, Chongqing Sanxia University of Science and Technology","place":["Chongqing, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Zhuo","family":"Chen","sequence":"additional","affiliation":[{"name":"College of Mechanical Engineering, Chongqing Sanxia University of Science and Technology","place":["Chongqing, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Nan","family":"Bai","sequence":"additional","affiliation":[{"name":"College of Information Engineering, Liaoning Vocational University of Technology","place":["Jinzhou, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1965","published-online":{"date-parts":[[2026,7,29]]},"reference":[{"key":"B1","doi-asserted-by":"publisher","DOI":"10.1016\/j.compag.2021.106081","article-title":"Performance of deep learning models for classifying and detecting common weeds in corn and soybean production systems","volume":"184","author":"Ahmad","year":"2021","journal-title":"Comput. 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