{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,2]],"date-time":"2026-10-02T16:56:19Z","timestamp":1790960179833,"version":"4.1.0"},"reference-count":43,"publisher":"MDPI AG","issue":"19","license":[{"start":{"date-parts":[[2026,10,2]],"date-time":"2026-10-02T00:00:00Z","timestamp":1790899200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100003624","name":"Ministry of Agriculture, Food and Rural Affairs","doi-asserted-by":"publisher","award":["Project No. RS-2023-00231026"],"award-info":[{"award-number":["Project No. RS-2023-00231026"]}],"id":[{"id":"10.13039\/501100003624","id-type":"DOI","asserted-by":"publisher"}]},{"award":["Project No. RS-2023-00231026"],"award-info":[{"award-number":["Project No. RS-2023-00231026"]}],"id":[{"id":"https:\/\/ror.org\/009g8rq41","id-type":"ROR","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Accurate plant counting supports stand assessment and crop management, but canopy overlap, camera motion, and temporary occlusion complicate video-based monitoring. This study developed an RGB image- and video-based framework for detecting, tracking, and counting cabbage seedlings and grown plants under greenhouse and open-field conditions. The framework integrated YOLOv8n with convolutional block attention modules (CBAM) and an improved BoT-SORT procedure incorporating trajectory persistence, short-gap relinking, class-consistent identity management, and counting-event filtering. Detector performance was evaluated across five independent training runs, counting performance across nine held-out videos, and tracking performance on four manually annotated videos. YOLOv8n-CBAM achieved mean precision, recall, mAP@0.50, and mAP@0.50:0.95 values of 0.880 \u00b1 0.007, 0.920 \u00b1 0.007, 0.960 \u00b1 0.006, and 0.600 \u00b1 0.008, respectively (mean \u00b1 sample standard deviation). The complete framework achieved 99.27% mean counting accuracy, a mean absolute error of 0.56 plants, a root mean square error of 0.88 plants, and an R2 of 0.997. On the annotated subset, Improved BoT-SORT increased mean IDF1 from 79.6% to 82.3% and mean HOTA from 74.3% to 83.2%, while reducing total identity switches from 23 to 12. Complete-pipeline throughput was 23.38 frames per second on an RTX 3090 workstation. These findings support cabbage population monitoring under the evaluated conditions; independent-site validation, tracking-component ablation, and deployment-device testing remain necessary to establish broader applicability.<\/jats:p>","DOI":"10.3390\/s26196268","type":"journal-article","created":{"date-parts":[[2026,10,2]],"date-time":"2026-10-02T16:50:38Z","timestamp":1790959838000},"page":"6268","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Cabbage Detection and Counting Using YOLOv8-CBAM and Improved BoT-SORT for Greenhouse and Open Field Cultivation in Korea"],"prefix":"10.3390","volume":"26","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-7793-400X","authenticated-orcid":false,"given":"Md Nasim","family":"Reza","sequence":"first","affiliation":[{"name":"Department of Agricultural Machinery Engineering, Graduate School, Chungnam National University, Daejeon 34134, Republic of 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