{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,24]],"date-time":"2026-08-24T12:26:30Z","timestamp":1787574390499,"version":"build-2736575974"},"reference-count":33,"publisher":"MDPI AG","issue":"17","license":[{"start":{"date-parts":[[2026,8,24]],"date-time":"2026-08-24T00:00:00Z","timestamp":1787529600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Humanities and Social Sciences Foundation of Ministry of Education of China","award":["25XJA860001"],"award-info":[{"award-number":["25XJA860001"]}]},{"DOI":"10.13039\/501100012456","name":"Key Program of National Social Science Foundation of China","doi-asserted-by":"publisher","award":["23AGL039"],"award-info":[{"award-number":["23AGL039"]}],"id":[{"id":"10.13039\/501100012456","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["22578366"],"award-info":[{"award-number":["22578366"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"award":["22578366"],"award-info":[{"award-number":["22578366"]}],"id":[{"id":"https:\/\/ror.org\/01h0zpd94","id-type":"ROR","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Accurate and rapid detection of citrus leaf diseases is important for early diagnosis, precision orchard management, and the reduction of economic losses in citrus production. Automatic detection remains difficult because early lesions are often small and irregular. Several disease categories also share similar visual appearances, and localization is easily affected by veins, shadows, and cluttered backgrounds. To address these task-specific challenges, we propose HGSM-YOLO, where HGSM denotes the coordinated use of heterogeneous convolution, a GSConv-based slim neck, and multi-scale dilated local attention. The framework is built on YOLO11n because its 2.59 M-parameter and 6.4 GFLOP design provides a stringent compact baseline for edge-oriented improvement. The method follows a hierarchical design: C3k2-HetConv preserves lesion edges and local morphology in the backbone; the GSConv-based slim neck reduces part of the feature fusion cost; and an MSDA module in the high-resolution P3 branch enhances the context of small lesions. Following model selection on the validation split, the final locked models were evaluated once on the held-out test split, with HGSM-YOLO reaching 77.5% precision, 66.8% recall, 71.7% F1-score, 70.8% mAP@0.5, and 44.2% mAP@0.5:0.95, compared with 68.5%, 61.5%, 64.8%, 66.0%, and 40.2% for YOLO11n. A stratified outer five-fold cross-validation further yields 71.0% \u00b1 1.4% mAP@0.5 and 44.4% \u00b1 1.1% mAP@0.5:0.95 for HGSM-YOLO, versus 65.9% \u00b1 1.1% and 40.2% \u00b1 0.9% for YOLO11n. On the independent 1871-image citrus-leaf-disease-2 dataset, retraining under the same protocol gives 94.4% mAP@0.5 for HGSM-YOLO versus 92.2% for YOLO11n and 93.1% for the public Roboflow YOLOv11 reference model. The complete HGSM-YOLO architecture uses 7.2 GFLOPs, 2.82 M parameters, and runs at 90.9 FPS on the RTX 4090, compared with 6.4 GFLOPs, 2.59 M parameters, and 110.1 FPS for the baseline. Thus, the contribution provides a recall- and localization-oriented accuracy\u2013efficiency trade-off rather than universal superiority in every individual metric.<\/jats:p>","DOI":"10.3390\/s26175345","type":"journal-article","created":{"date-parts":[[2026,8,24]],"date-time":"2026-08-24T11:47:19Z","timestamp":1787572039000},"page":"5345","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["HGSM-YOLO: A Small-Lesion-Oriented Lightweight YOLO11n Framework for Citrus Leaf Disease Detection"],"prefix":"10.3390","volume":"26","author":[{"given":"Rui","family":"Zheng","sequence":"first","affiliation":[{"name":"School of Electronic Information, Xijing University, Xi\u2019an 710123, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jing","family":"Zhao","sequence":"additional","affiliation":[{"name":"School of Electronic Information, Xijing University, Xi\u2019an 710123, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xinwei","family":"Wang","sequence":"additional","affiliation":[{"name":"School of Electronic Information, Xijing University, Xi\u2019an 710123, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Feng","family":"Wang","sequence":"additional","affiliation":[{"name":"School of Electronic Information, Xijing University, Xi\u2019an 710123, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2026,8,24]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Saleem, M.H., Potgieter, J., and Arif, K.M. (2019). Plant Disease Detection and Classification by Deep Learning. Plants, 8.","DOI":"10.3390\/plants8110468"},{"key":"ref_2","unstructured":"Ren, S., He, K., Girshick, R., and Sun, J. (2015, January 7\u201312). Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks. Proceedings of the Advances in Neural Information Processing Systems, Montreal, QC, Canada."},{"key":"ref_3","doi-asserted-by":"crossref","unstructured":"Redmon, J., Divvala, S., Girshick, R., and Farhadi, A. (2016, January 27\u201330). You Only Look Once: Unified, Real-Time Object Detection. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Las Vegas, NV, USA.","DOI":"10.1109\/CVPR.2016.91"},{"key":"ref_4","doi-asserted-by":"crossref","unstructured":"Wang, C.-Y., Bochkovskiy, A., and Liao, H.-Y.M. (2023, January 18\u201319). YOLOv7: Trainable Bag-of-Freebies Sets New State-of-the-Art for Real-Time Object Detectors. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), Vancouver, BC, Canada.","DOI":"10.1109\/CVPR52729.2023.00721"},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"102133","DOI":"10.1016\/j.jksuci.2024.102133","article-title":"Lightweight citrus leaf disease detection model based on ARMS and cross-domain dynamic attention","volume":"36","author":"Mo","year":"2024","journal-title":"J. King Saud-Univ. Comput. Inf. Sci."},{"key":"ref_6","doi-asserted-by":"crossref","unstructured":"Xie, W., Feng, F., and Zhang, H. (2024). A Detection Algorithm for Citrus Huanglongbing Disease Based on RT-DETR and Early Attention Fusion. Sensors, 24.","DOI":"10.3390\/s24144448"},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"61","DOI":"10.1186\/s13007-024-01188-1","article-title":"An efficient deep learning model for tomato disease detection","volume":"20","author":"Wang","year":"2024","journal-title":"Plant Methods"},{"key":"ref_8","doi-asserted-by":"crossref","unstructured":"Lv, Q., Sun, F., Bian, Y., Wu, H., Li, X., Li, X., and Zhou, J. (2025). A Lightweight Citrus Object Detection Method in Complex Environments. Agriculture, 15.","DOI":"10.3390\/agriculture15101046"},{"key":"ref_9","doi-asserted-by":"crossref","unstructured":"Feng, W., Liu, J., Li, Z., and Lyu, S. (2025). YOLO-Citrus: A lightweight and efficient model for citrus leaf disease detection in complex agricultural environments. Front. Plant Sci., 16.","DOI":"10.3389\/fpls.2025.1668036"},{"key":"ref_10","doi-asserted-by":"crossref","unstructured":"Xue, R., and Wang, L. (2025). Research on Lightweight Citrus Leaf Pest and Disease Detection Based on PEW-YOLO. Processes, 13.","DOI":"10.3390\/pr13051365"},{"key":"ref_11","doi-asserted-by":"crossref","unstructured":"Dai, Q., Liang, S., Li, Z., Lyu, S., Xue, X., Song, S., Huang, Y., Zhang, S., and Fu, J. (2025). YOLOv11-RDTNet: A Lightweight Model for Citrus Pest and Disease Detection Based on an Improved YOLO11n. Agronomy, 15.","DOI":"10.3390\/agronomy15051252"},{"key":"ref_12","doi-asserted-by":"crossref","unstructured":"Zhao, J., Xu, L., Ma, Z., Li, J., Wang, X., Liu, Y., and Du, X. (2025). A review of plant leaf disease identification by deep learning algorithms. Front. Plant Sci., 16.","DOI":"10.3389\/fpls.2025.1637241"},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"7969","DOI":"10.1038\/s41598-025-92143-0","article-title":"Leveraging YOLO deep learning models to enhance plant disease identification","volume":"15","author":"Alhwaiti","year":"2025","journal-title":"Sci. Rep."},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"Fang, K., Zhou, R., Deng, N., Li, C., and Zhu, X. (2025). RLDD-YOLOv11n: Research on Rice Leaf Disease Detection Based on YOLOv11. Agronomy, 15.","DOI":"10.3390\/agronomy15061266"},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"Tao, J., Li, X., He, Y., and Islam, M.A. (2025). CEFW-YOLO: A High-Precision Model for Plant Leaf Disease Detection in Natural Environments. Agriculture, 15.","DOI":"10.3390\/agriculture15080833"},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Wang, X., Tang, S.H., Mohd Ariffin, M.K.A.B., Ismail, M.I.S.B., and Shen, J. (2025). YOLO-LF: Application of multi-scale information fusion and small target detection in agricultural disease detection. Front. Plant Sci., 16.","DOI":"10.3389\/fpls.2025.1609284"},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Yang, C., Feng, Q., Mai, J., Zhang, R., Chong, N., Ru, Z., and Liang, T. (2026). LSL-YOLO11n: A YOLO11n-based model for maize leaf disease detection in complex field environments. Front. Plant Sci., 17.","DOI":"10.3389\/fpls.2026.1840425"},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"Wang, Q., and Wang, Z. (2026). TGL-YOLO: A Multi-Scale Feature Enhancement Method for Plant Disease Detection Based on Improved YOLO11. Agriculture, 16.","DOI":"10.3390\/agriculture16090947"},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"28513","DOI":"10.1038\/s41598-025-14021-z","article-title":"YOLO-LeafNet: A robust deep learning framework for multispecies plant disease detection with data augmentation","volume":"15","author":"Kaur","year":"2025","journal-title":"Sci. Rep."},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"Nguyen, D.T., Bui, T.D., Ngo, T.M., and Ngo, U.Q. (2025). Improving YOLO-Based Plant Disease Detection Using alphaSILU: A Novel Activation Function for Smart Agriculture. AgriEngineering, 7.","DOI":"10.3390\/agriengineering7090271"},{"key":"ref_21","doi-asserted-by":"crossref","unstructured":"Yang, X., Wang, H., Zhou, Q., Lu, L., Zhang, L., Sun, C., and Wu, G. (2025). A Lightweight and Efficient Plant Disease Detection Method Integrating Knowledge Distillation and Dual-Scale Weighted Convolutions. Algorithms, 18.","DOI":"10.3390\/a18070433"},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"62","DOI":"10.1007\/s11554-024-01436-6","article-title":"Slim-neck by GSConv: A lightweight-design for real-time detector architectures","volume":"21","author":"Li","year":"2024","journal-title":"J. Real Time Image Process."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"8906","DOI":"10.1109\/TMM.2023.3243616","article-title":"DilateFormer: Multi-Scale Dilated Transformer for Visual Recognition","volume":"25","author":"Jiao","year":"2023","journal-title":"IEEE Trans. Multimed."},{"key":"ref_24","unstructured":"Jocher, G., and Qiu, J. (2026, June 10). Ultralytics YOLO11. Ultralytics, 2024. Software Documentation. Available online: https:\/\/docs.ultralytics.com\/models\/yolo11."},{"key":"ref_25","doi-asserted-by":"crossref","unstructured":"Lin, T.-Y., Doll\u00e1r, P., Girshick, R., He, K., Hariharan, B., and Belongie, S. (2017, January 21\u201326). Feature Pyramid Networks for Object Detection. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Honolulu, HI, USA.","DOI":"10.1109\/CVPR.2017.106"},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"Liu, S., Qi, L., Qin, H., Shi, J., and Jia, J. (2018, January 18\u201323). Path Aggregation Network for Instance Segmentation. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), Salt Lake City, UT, USA.","DOI":"10.1109\/CVPR.2018.00913"},{"key":"ref_27","doi-asserted-by":"crossref","unstructured":"Singh, P., Verma, V.K., Rai, P., and Namboodiri, V.P. (2019, January 15\u201320). HetConv: Heterogeneous Kernel-Based Convolutions for Deep CNNs. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), Long Beach, CA, USA.","DOI":"10.1109\/CVPR.2019.00497"},{"key":"ref_28","doi-asserted-by":"crossref","unstructured":"Sandler, M., Howard, A., Zhu, M., Zhmoginov, A., and Chen, L.-C. (2018, January 18\u201323). MobileNetV2: Inverted Residuals and Linear Bottlenecks. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Salt Lake City, UT, USA.","DOI":"10.1109\/CVPR.2018.00474"},{"key":"ref_29","doi-asserted-by":"crossref","unstructured":"Zhang, X., Zhou, X., Lin, M., and Sun, J. (2018, January 18\u201323). ShuffleNet: An Extremely Efficient Convolutional Neural Network for Mobile Devices. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), Salt Lake City, UT, USA.","DOI":"10.1109\/CVPR.2018.00716"},{"key":"ref_30","unstructured":"(2026, August 08). Citrus Leaf Disease. Citrus Leaves Diseases Dataset, Version 1. Roboflow Universe, 2023. 1250 Images; CC BY 4.0. Available online: https:\/\/universe.roboflow.com\/citrus-leaf-disease\/citrus-leaves-diseases-ykhpu\/dataset\/1."},{"key":"ref_31","unstructured":"Ultralytics (2026, August 08). Ultralytics YOLO26 Documentation. Official YOLO26 Implementation and Model Family, Released 25 September 2025. Available online: https:\/\/docs.ultralytics.com\/models\/yolo26\/."},{"key":"ref_32","doi-asserted-by":"crossref","unstructured":"Selvaraju, R.R., Cogswell, M., Das, A., Vedantam, R., Parikh, D., and Batra, D. (2017, January 21\u201326). Grad-CAM: Visual Explanations from Deep Networks via Gradient-Based Localization. Proceedings of the IEEE International Conference on Computer Vision (ICCV), Honolulu, HI, USA.","DOI":"10.1109\/ICCV.2017.74"},{"key":"ref_33","unstructured":"(2026, August 09). Roboflow Universe User \u201c1\u201d. citrus-leaf-disease-2 Dataset. Roboflow Universe, 2026. Public Object-Detection Project with 1871 Original Images, Six Classes, and CC BY 4.0 License. The Public Project Page Reports Precision 91.3%, Recall 92.1%, and mAP@0.5 93.1% for its Hosted YOLOv11 Reference Model. Available online: https:\/\/universe.roboflow.com\/1-eytaw\/citrus-leaf-disease-2."}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/26\/17\/5345\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,8,24]],"date-time":"2026-08-24T11:57:23Z","timestamp":1787572643000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/26\/17\/5345"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,8,24]]},"references-count":33,"journal-issue":{"issue":"17","published-online":{"date-parts":[[2026,9]]}},"alternative-id":["s26175345"],"URL":"https:\/\/doi.org\/10.3390\/s26175345","relation":{},"ISSN":["1424-8220"],"issn-type":[{"value":"1424-8220","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,8,24]]}}}