{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,16]],"date-time":"2026-09-16T06:04:53Z","timestamp":1789538693361,"version":"build-2803163510"},"reference-count":50,"publisher":"Frontiers Media SA","license":[{"start":{"date-parts":[[2026,9,16]],"date-time":"2026-09-16T00:00:00Z","timestamp":1789516800000},"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>As a common greenhouse-grown commercial crop, chili pepper yields have always been a focus of attention. Effective management is essential for both environmental sustainability and high productivity in greenhouses. Whether for growth monitoring, yield estimates, and automated production, precise identification of chili flowers is critical. To this goal, this paper works upon the YOLO11 object detection model, focusing on the precise detection of chili flowers. The diversity of samples across various scenarios is increased by utilizing a self-built dataset of chili flowers in greenhouses and integrating data augmentation. To address YOLO11\u2019s limitations in this task, this paper proposes a unified framework that integrates three modules: (1) full shuffle to enhance information exchange between model channels and optimize weights; (2) Since YOLO11 cannot incorporate external environmental data during training, the environment-aware C3k2 is introduced; and (3) The proposed p-rectified Semi-inner Powerful IoU accelerates model convergence and flexibility. Experiments show that our method obtains 84.8% mAP50, 50.2% mAP50-95, 81.1% Precision, and 75.6% Recall, outperforming the baseline YOLO11\u2019s 77.3% by 7.5 percentage points, as well as YOLOv8, YOLO12, RTDETR, and Faster R-CNN under the same experimental conditions. Therefore, in terms of performance, our method generally surpasses existing algorithms for chili flower detection. Research on detecting chili flowers in greenhouses remains limited. The study offers valuable insights for the advancement of smart agriculture. Future work will further explore the model\u2019s generalization capabilities across multiple varieties and growth stages, as well as deploy it on embedded devices for practical application.<\/jats:p>","DOI":"10.3389\/fpls.2026.1935123","type":"journal-article","created":{"date-parts":[[2026,9,16]],"date-time":"2026-09-16T05:32:21Z","timestamp":1789536741000},"update-policy":"https:\/\/doi.org\/10.3389\/crossmark-policy","source":"Crossref","is-referenced-by-count":0,"title":["Full shuffle and p-rectified semi-inner powerful IoU-based chili pepper flower recognition with environment-aware YOLO11"],"prefix":"10.3389","volume":"17","author":[{"given":"Cui-Ping","family":"Zhang","sequence":"first","affiliation":[{"name":"School of Artificial Intelligence, Weifang University of Science and Technology","place":["Weifang, Shandong, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Zhi-Yong","family":"Wang","sequence":"additional","affiliation":[{"name":"School of Information Science and Engineering, Weifang University of Science and Technology","place":["Weifang, Shandong, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xue-Wei","family":"Wang","sequence":"additional","affiliation":[{"name":"School of Information Science and Engineering, Weifang University of Science and Technology","place":["Weifang, Shandong, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Zhi","family":"Li","sequence":"additional","affiliation":[{"name":"Academic Affairs Department, Sichuan Technology and Business University","place":["Chengdu, Sichuan, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1965","published-online":{"date-parts":[[2026,9,16]]},"reference":[{"key":"B1","doi-asserted-by":"publisher","DOI":"10.3390\/agronomy15040809","article-title":"Bayesian ensemble model with detection of potential misclassification of wax bloom in blueberry images","volume":"15","author":"Arellano","year":"2025","journal-title":"Agronomy"},{"key":"B2","doi-asserted-by":"publisher","DOI":"10.1016\/j.engappai.2025.111775","article-title":"Performance evaluation of low-power and lightweight object detectors for real-time monitoring in resource-constrained drone systems","volume":"159","author":"Bakirci","year":"2025","journal-title":"Eng. 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