{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,2]],"date-time":"2026-08-02T18:29:45Z","timestamp":1785695385799,"version":"3.56.0"},"reference-count":36,"publisher":"MDPI AG","issue":"8","license":[{"start":{"date-parts":[[2026,4,15]],"date-time":"2026-04-15T00:00:00Z","timestamp":1776211200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Plants"],"abstract":"<jats:p>In natural agricultural environments, plant disease monitoring faces significant challenges, including a highly uneven (long-tail) distribution of disease species, tiny scales of early-stage lesions, and complex, variable backgrounds. These factors hinder the ability of existing lightweight models to balance detection accuracy and computational efficiency. To address these issues, this paper proposes a detection scheme driven by the synergy of data distribution reshaping and model architecture optimization. At the data level, we propose the CALM-Aug augmentation strategy. Based on the statistical distribution characteristics of disease categories, this strategy utilizes object-level copy-paste logic to specifically compensate for the feature shortcomings of rare disease samples. It introduces a teacher-guided screening mechanism and employs accept\u2013reject sampling to ensure the pathological consistency of the augmented samples, thereby alleviating the model\u2019s inductive bias toward head categories. At the model architecture level, using YOLOv11 as the baseline, the YOLO11-ARL model adapted to agricultural scenarios is constructed. It enhances sensitivity to early point-like disease spots through Efficient Multi-Scale Convolutional Pyramids and lightweight decoupled detection heads. Furthermore, a Layer-wise Adaptive Feature-guided Distillation Pruning (LAFDP) algorithm is utilized to extract a lightweight version, YOLO11-ARL-PD, achieving a significant reduction in parameters and computational cost. Experimental results on the PlantDoc dataset show that the final model achieves a precision of 89.0% and an mAP@0.5 of 85.3%. Compared to the baseline model YOLOv11n, YOLO11-ARL-PD improves precision and average precision by 7.7 and 2.6 percentage points, respectively, while reducing parameters by 51.93% and weights by 46.15%. Cross-dataset tests prove the good generalization performance of the proposed method. This study indicates that, under lightweight constraints, jointly optimizing the training distribution and model architecture is an effective way to improve plant disease monitoring and to support the edge deployment of smart crop-protection systems. All resources for CALM-Aug are available at wyz-2004\/CALM-Aug on GitHub.<\/jats:p>","DOI":"10.3390\/plants15081206","type":"journal-article","created":{"date-parts":[[2026,4,15]],"date-time":"2026-04-15T10:30:23Z","timestamp":1776249023000},"page":"1206","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["A Lightweight Plant Disease Detection Model for Long-Tailed Agricultural Scenarios"],"prefix":"10.3390","volume":"15","author":[{"given":"Luyun","family":"Chen","sequence":"first","affiliation":[{"name":"School of Information Science and Engineering, Northeastern University, Shenyang 110819, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0004-8539-8173","authenticated-orcid":false,"given":"Yuzhu","family":"Wu","sequence":"additional","affiliation":[{"name":"College of Engineering, Northeast Agricultural University, Harbin 150030, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yangyuzhi","family":"Meng","sequence":"additional","affiliation":[{"name":"College of Electrical and Information Engineering, Northeast Agricultural University, Harbin 150030, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0360-5526","authenticated-orcid":false,"given":"Qiang","family":"Tang","sequence":"additional","affiliation":[{"name":"School of Artificial Intelligence, Anhui University of Science and Technology, Hefei 231131, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0009-2962-6334","authenticated-orcid":false,"given":"Zhen","family":"Tian","sequence":"additional","affiliation":[{"name":"James Watt School of Engineering, University of Glasgow, Glasgow G12 8QQ, UK"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8940-8058","authenticated-orcid":false,"given":"Shengyu","family":"Li","sequence":"additional","affiliation":[{"name":"School of Civil and Hydraulic Engineering, Chongqing University of Science and Technology, Chongqing 401331, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Siyuan","family":"Liu","sequence":"additional","affiliation":[{"name":"School of Information Science and Engineering, Northeastern University, Shenyang 110819, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2026,4,15]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Muhammad, A., Salman, Z., Lee, K., and Han, D. 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