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This study introduces an improved YOLOv10n model named WD-YOLO (Weighted and Double-scale YOLO), an advanced architecture for efficient plant disease detection. The PlantDoc dataset was initially enhanced using data augmentation techniques. Subsequently, we developed the DSConv module\u2014a novel convolutional structure employing double-scale weighted convolutions that dynamically adjust to different scale perceptions and optimize attention allocation. This module replaces the conventional Conv module in YOLOv10. Furthermore, the WTConcat module was introduced, dynamically merging weighted concatenation with a channel attention mechanism to replace the Concat module in YOLOv10. The training of WD-YOLO incorporated knowledge distillation techniques using YOLOv10l as a teacher model to refine and compress the architectural learning. Empirical results reveal that WD-YOLO achieved an mAP50 of 65.4%, outperforming YOLOv10n by 9.1% without data augmentation and YOLOv10l by 2.3%, despite having significantly fewer parameters (9.3 times less than YOLOv10l), demonstrating substantial gains in detection efficiency and model compactness.<\/jats:p>","DOI":"10.3390\/a18070433","type":"journal-article","created":{"date-parts":[[2025,7,15]],"date-time":"2025-07-15T09:45:52Z","timestamp":1752572752000},"page":"433","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":5,"title":["A Lightweight and Efficient Plant Disease Detection Method Integrating Knowledge Distillation and Dual-Scale Weighted Convolutions"],"prefix":"10.3390","volume":"18","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-1981-6179","authenticated-orcid":false,"given":"Xiong","family":"Yang","sequence":"first","affiliation":[{"name":"School of Cyberspace Security, Changzhou College of Information Technology, Changzhou 213164, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Hao","family":"Wang","sequence":"additional","affiliation":[{"name":"Faculty of Electronic and Information Engineering, Xi\u2019an Jiaotong University, Xi\u2019an 710049, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Qi","family":"Zhou","sequence":"additional","affiliation":[{"name":"Faculty of Electronic and Information Engineering, Xi\u2019an Jiaotong University, Xi\u2019an 710049, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Lei","family":"Lu","sequence":"additional","affiliation":[{"name":"Faculty of Electronic and Information Engineering, Xi\u2019an Jiaotong University, Xi\u2019an 710049, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7364-6070","authenticated-orcid":false,"given":"Lijuan","family":"Zhang","sequence":"additional","affiliation":[{"name":"College of Internet of Things Engineering, Wuxi University, Wuxi 214105, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5943-1989","authenticated-orcid":false,"given":"Changming","family":"Sun","sequence":"additional","affiliation":[{"name":"CSIRO Data61, P.O. Box 76, Epping, NSW 1710, Australia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8752-9358","authenticated-orcid":false,"given":"Guilu","family":"Wu","sequence":"additional","affiliation":[{"name":"School of Information and Communication Engineering, Hainan University, Haikou 570228, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2025,7,15]]},"reference":[{"key":"ref_1","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, Las Vegas, NV, USA.","DOI":"10.1109\/CVPR.2016.91"},{"key":"ref_2","unstructured":"Hinton, G. (2015). Distilling the Knowledge in a Neural Network. arXiv."},{"key":"ref_3","doi-asserted-by":"crossref","unstructured":"Liu, S., Qiao, Y., Li, J., Zhang, H., Zhang, M., and Wang, M. (2022). An improved lightweight network for real-time detection of apple leaf diseases in natural scenes. Agronomy, 12.","DOI":"10.3390\/agronomy12102363"},{"key":"ref_4","doi-asserted-by":"crossref","unstructured":"Kumar, D., Ishak, M.K., and Maruzuki, M.I.F. (2022, January 24\u201327). EfficientNet based Convolutional Neural Network for Visual Plant Disease Detection. Proceedings of the 2022 19th International Conference on Electrical Engineering\/Electronics, Computer, Telecommunications and Information Technology (ECTI-CON), Prachuap Khiri Khan, Thailand.","DOI":"10.1109\/ECTI-CON54298.2022.9795496"},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"46","DOI":"10.1016\/j.compag.2018.08.013","article-title":"Impact of dataset size and variety on the effectiveness of deep learning and transfer learning for plant disease classification","volume":"153","author":"Barbedo","year":"2018","journal-title":"Comput. Electron. Agric."},{"key":"ref_6","doi-asserted-by":"crossref","unstructured":"Singh, D., Jain, N., Jain, P., Kayal, P., Kumawat, S., and Batra, N. (2020, January 5\u20137). PlantDoc: A dataset for visual plant disease detection. Proceedings of the 7th ACM IKDD CoDS and 25th COMAD, Hyderabad, India.","DOI":"10.1145\/3371158.3371196"},{"key":"ref_7","doi-asserted-by":"crossref","unstructured":"Redmon, J., and Farhadi, A. (2017, January 21\u201326). YOLO9000: Better, faster, stronger. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Honolulu, HI, USA.","DOI":"10.1109\/CVPR.2017.690"},{"key":"ref_8","unstructured":"Redmon, J., and Farhadi, A. (2018). YOLOv3: An incremental improvement. arXiv."},{"key":"ref_9","unstructured":"Bochkovskiy, A., Wang, C.Y., and Liao, H.Y.M. (2020). YOLOv4: Optimal speed and accuracy of object detection. arXiv."},{"key":"ref_10","unstructured":"Li, C., Li, L., Jiang, H., Weng, K., Geng, Y., Li, L., Ke, Z., Li, Q., Cheng, M., and Nie, W. (2022). YOLOv6: A Single-Stage Object Detection Framework for Industrial Applications. arXiv."},{"key":"ref_11","doi-asserted-by":"crossref","unstructured":"Wang, C.Y., and Liao, H.Y.M. (2022). YOLOv7: Trainable bag-of-freebies sets new state-of-the-art for real-time object detectors. arXiv.","DOI":"10.1109\/CVPR52729.2023.00721"},{"key":"ref_12","doi-asserted-by":"crossref","unstructured":"Wang, C.Y., Yeh, I.H., and Liao, H.Y.M. (2024). YOLOv9: Learning what you want to learn using programmable gradient information. arXiv.","DOI":"10.1007\/978-3-031-72751-1_1"},{"key":"ref_13","unstructured":"Wang, A., Chen, H., Liu, L., Chen, K., Lin, Z., Han, J., and Ding, G. (2024). YOLOv10: Real-time end-to-end object detection. arXiv."},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"Shu, C., Liu, Y., Gao, J., Yan, Z., and Shen, C. (2021, January 11\u201317). Channel-wise knowledge distillation for dense prediction. Proceedings of the IEEE\/CVF International Conference on Computer Vision, Virtual.","DOI":"10.1109\/ICCV48922.2021.00526"},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"Zhao, B., Cui, Q., Song, R., Qiu, Y., and Liang, J. (2022, January 18\u201324). Decoupled knowledge distillation. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, New Orleans, LA, USA.","DOI":"10.1109\/CVPR52688.2022.01165"},{"key":"ref_16","unstructured":"Pan, H., Wang, C., Qiu, M., Zhang, Y., Li, Y., and Huang, J. (2020). Meta-KD: A meta knowledge distillation framework for language model compression across domains. arXiv."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"4150","DOI":"10.1109\/TGRS.2020.3014313","article-title":"A lightweight convolutional neural network for hyperspectral image classification","volume":"59","author":"Jia","year":"2020","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_18","unstructured":"Hu, J. (2025). YOLO-FDA: Integrating Hierarchical Attention and Detail Enhancement for Surface Defect Detection. arXiv."},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Hu, J., Shen, L., and Sun, G. (2018, January 18\u201322). Squeeze-and-excitation networks. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Salt Lake City, UT, USA.","DOI":"10.1109\/CVPR.2018.00745"},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"Selvaraju, R.R., Cogswell, M., Das, A., Vedantam, R., Parikh, D., and Batra, D. (2017, January 22\u201329). Grad-CAM: Visual explanations from deep networks via gradient-based localization. Proceedings of the IEEE International Conference on Computer Vision, Venice, Italy.","DOI":"10.1109\/ICCV.2017.74"},{"key":"ref_21","doi-asserted-by":"crossref","unstructured":"Shill, A., and Rahman, M.A. (2021, January 8\u20139). Plant disease detection based on YOLOv3 and YOLOv4. Proceedings of the 2021 International Conference on Automation, Control and Mechatronics for Industry 4.0 (ACMI), Online.","DOI":"10.1109\/ACMI53878.2021.9528179"},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Li, J., Qiao, Y., Liu, S., Zhang, J., Yang, Z., and Wang, M. (2022). An improved YOLOv5-based vegetable disease detection method. Comput. Electron. Agric., 202.","DOI":"10.1016\/j.compag.2022.107345"},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"Li, W., Zhu, L., and Liu, J. (2024). PL-DINO: An Improved Transformer-Based Method for Plant Leaf Disease Detection. Agriculture, 14.","DOI":"10.3390\/agriculture14050691"},{"key":"ref_24","doi-asserted-by":"crossref","unstructured":"Qadri, S.A.A., Huang, N.F., Wani, T.M., and Bhat, S.A. (2023, January 25\u201326). Plant Disease Detection and Segmentation using End-to-End YOLOv8: A Comprehensive Approach. Proceedings of the 2023 IEEE 13th International Conference on Control System, Computing and Engineering (ICCSCE), Penang, Malaysia.","DOI":"10.1109\/ICCSCE58721.2023.10237169"},{"key":"ref_25","doi-asserted-by":"crossref","unstructured":"Yu, S., Xie, L., and Huang, Q. (2023). Inception convolutional vision transformers for plant disease identification. Internet Things, 21.","DOI":"10.1016\/j.iot.2022.100650"},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"Rezaei, M., Diepeveen, D., Laga, H., Jones, M.G., and Sohel, F. (2024). Plant disease recognition in a low data scenario using few-shot learning. Comput. Electron. 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