{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,9]],"date-time":"2026-07-09T21:27:21Z","timestamp":1783632441377,"version":"3.55.0"},"reference-count":33,"publisher":"Frontiers Media SA","license":[{"start":{"date-parts":[[2026,6,10]],"date-time":"2026-06-10T00:00:00Z","timestamp":1781049600000},"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 detection of tomato ripeness in complex greenhouse environments presents a significant dual challenge: the interference caused by foliage occlusion and fruit overlapping demands high detection accuracy, while the limited computational resources of harvesting robots necessitate model lightweighting. To address this, we propose YOLO-FLBM, a lightweight, high-performance model based on the enhanced YOLOv8s architecture. First, the backbone network was reconstructed using FasterNet to minimize redundancy, establishing a streamlined foundation for edge deployment. Second, an innovative neck architecture, designated as the LB Neck, was constructed by integrating the C2f-LS module with the BiFPN structure. Crucially, a novel Multi-scale Coordinate Dynamic Attention (MCDA) mechanism was developed. By integrating hybrid perception pooling with full-rank kernel generation, MCDA dynamically captures spatial dependencies to resolve occlusion issues. Experimental results on a custom tomato dataset demonstrated that YOLO-FLBM achieved comprehensive performance enhancements: precision, recall, mAP@50, and mAP@50\u201395 reached 95.2%, 91.9%, 97.4%, and 78.9%, respectively, representing improvements of 3.7%, 2.5%, 1.9%, and 1.7% over the baseline model. Meanwhile, the model\u2019s parameter count was reduced to 3.743 M, a substantial 61.9% reduction compared to the original model. These results confirm the model\u2019s efficiency and accuracy, offering a valuable reference for automated tomato harvesting robots.<\/jats:p>","DOI":"10.3389\/fpls.2026.1824558","type":"journal-article","created":{"date-parts":[[2026,6,10]],"date-time":"2026-06-10T05:39:52Z","timestamp":1781069992000},"update-policy":"https:\/\/doi.org\/10.3389\/crossmark-policy","source":"Crossref","is-referenced-by-count":1,"title":["YOLO-FLBM: a lightweight and high-performance model for tomato ripeness detection in complex greenhouse environments"],"prefix":"10.3389","volume":"17","author":[{"given":"Yuting","family":"Su","sequence":"first","affiliation":[{"name":"College of Computer and Information Sciences, Fujian Agriculture and Forestry University","place":["Fuzhou, China"]},{"name":"Key Laboratory of Smart Agriculture and Forestry, Fujian Province University","place":["Fuzhou, 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