{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,7]],"date-time":"2026-02-07T11:07:19Z","timestamp":1770462439272,"version":"3.49.0"},"reference-count":40,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2025,12,8]],"date-time":"2025-12-08T00:00:00Z","timestamp":1765152000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2025,12,8]],"date-time":"2025-12-08T00:00:00Z","timestamp":1765152000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"funder":[{"name":"Central Government Guided Local Science and Technology Development S&T Program of Hebei","award":["236Z1601G"],"award-info":[{"award-number":["236Z1601G"]}]},{"name":"Central Government Guided Local Science and Technology Development S&T Program of Hebei","award":["236Z1601G"],"award-info":[{"award-number":["236Z1601G"]}]},{"DOI":"10.13039\/501100003787","name":"Natural Science Foundation of Hebei Province","doi-asserted-by":"crossref","award":["F2022203085"],"award-info":[{"award-number":["F2022203085"]}],"id":[{"id":"10.13039\/501100003787","id-type":"DOI","asserted-by":"crossref"}]},{"DOI":"10.13039\/501100003787","name":"Natural Science Foundation of Hebei Province","doi-asserted-by":"crossref","award":["F2022203085"],"award-info":[{"award-number":["F2022203085"]}],"id":[{"id":"10.13039\/501100003787","id-type":"DOI","asserted-by":"crossref"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"crossref","award":["62073234"],"award-info":[{"award-number":["62073234"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"crossref"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"crossref","award":["62073234"],"award-info":[{"award-number":["62073234"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"crossref"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["J Real-Time Image Proc"],"published-print":{"date-parts":[[2026,2]]},"DOI":"10.1007\/s11554-025-01821-9","type":"journal-article","created":{"date-parts":[[2025,12,8]],"date-time":"2025-12-08T06:06:59Z","timestamp":1765174019000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["MSTA-YOLOv11: an improved detection model for maize pests and diseases under complex scenes"],"prefix":"10.1007","volume":"23","author":[{"given":"Lijun","family":"Yuan","sequence":"first","affiliation":[]},{"given":"Fang","family":"Wang","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2025,12,8]]},"reference":[{"issue":"S2","key":"1821_CR1","first-page":"210","volume":"55","author":"H Zhu","year":"2024","unstructured":"Zhu, H., Wang, M., Bai, L., Zhang, Y., Liu, Q., Li, R.: Maize pest and disease detection and precise variable spraying system based on visual recognition. Trans. Chine. Soc. Agric. Mach. 55(S2), 210\u2013221 (2024). (in Chinese)","journal-title":"Trans. Chine. Soc. Agric. Mach."},{"key":"1821_CR2","doi-asserted-by":"publisher","DOI":"10.1016\/j.compag.2025.110128","volume":"234","author":"R George","year":"2025","unstructured":"George, R., Thuseethan, S., Ragel, R.G., Fernando, A.: Past, present and future of deep plant leaf disease recognition: a survey. Comput. Electron. Agric. 234, 110128 (2025)","journal-title":"Comput. Electron. Agric."},{"key":"1821_CR3","first-page":"1","volume":"61","author":"J Deng","year":"2023","unstructured":"Deng, J., Wang, R., Yang, L., Lv, X., Yang, Z., Ma, Z.: Quantitative estimation of wheat stripe rust disease index using unmanned aerial vehicle hyperspectral imagery and innovative vegetation indices. IEEE Trans. Geosci. Remote Sens. 61, 1\u201311 (2023)","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"1821_CR4","doi-asserted-by":"publisher","DOI":"10.1016\/j.cropro.2024.107093","volume":"190","author":"SR Goyal","year":"2025","unstructured":"Goyal, S.R., Kulkarni, V.S., Choudhary, R., Jain, R.: A comparative analysis of efficacy of machine learning techniques for disease detection in some economically important crops. Crop Prot. 190, 107093 (2025)","journal-title":"Crop Prot."},{"issue":"17","key":"1821_CR5","first-page":"1","volume":"40","author":"X Jiang","year":"2024","unstructured":"Jiang, X., Ji, K., Jiang, H., Zhou, H.: Research progress of non-destructive detection of forest fruit quality using deep learning. Trans. Chin. Soc. Agric. Eng. (Trans. CSAE) 40(17), 1\u201316 (2024). (in Chinese)","journal-title":"Trans. Chin. Soc. Agric. Eng. (Trans. CSAE)"},{"key":"1821_CR6","doi-asserted-by":"publisher","DOI":"10.1016\/j.knosys.2025.113539","volume":"318","author":"P Shi","year":"2025","unstructured":"Shi, P., Dong, X., Ge, R., Liu, Z., Yang, A.: Dp-m3d: Monocular 3d object detection algorithm with depth perception capability. Knowl.-Based Syst. 318, 113539 (2025)","journal-title":"Knowl.-Based Syst."},{"key":"1821_CR7","doi-asserted-by":"publisher","DOI":"10.1016\/j.cropro.2023.106488","volume":"176","author":"X Wenqing","year":"2024","unstructured":"Wenqing, X., Li, W.: Deep residual neural networks with feature recalibration for crop image disease recognition. Crop Prot. 176, 106488 (2024)","journal-title":"Crop Prot."},{"key":"1821_CR8","doi-asserted-by":"publisher","first-page":"12935","DOI":"10.1007\/s13369-024-08892-z","volume":"49","author":"C Jin","year":"2024","unstructured":"Jin, C., Zheng, A., Zhaoying, W., Tong, C., Li, H.: Transformer-based multi-layer feature aggregation and rotated anchor matching for oriented object detection in remote sensing images. Arab. J. Sci. Eng. 49, 12935\u201312951 (2024)","journal-title":"Arab. J. Sci. Eng."},{"key":"1821_CR9","doi-asserted-by":"crossref","unstructured":"Girshick, R.: Fast R-CNN. In Proceedings of the IEEE International Conference on Computer Vision, pages 1440\u20131448 (2015)","DOI":"10.1109\/ICCV.2015.169"},{"key":"1821_CR10","doi-asserted-by":"crossref","unstructured":"Liu, W., Anguelov, D., Erhan, D., Szegedy, C., Reed, S., Fu, C-Y., Berg, A.C.: SSD: single shot multibox detector. In Proceedings of the European Conference on Computer Vision (ECCV), pages 21\u201337 (2016)","DOI":"10.1007\/978-3-319-46448-0_2"},{"key":"1821_CR11","doi-asserted-by":"crossref","unstructured":"Redmon, J., Divvala, S., Girshick, R., Farhadi, A.: You Only Look Once: unified, real-time object detection. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pages 779\u2013788, (2016)","DOI":"10.1109\/CVPR.2016.91"},{"issue":"1","key":"1821_CR12","first-page":"62","volume":"44","author":"J Xie","year":"2025","unstructured":"Xie, J., Liao, F., Wang, W., Liu, Z., Zhang, X., Li, J.: Litchi pest and disease detection based on improved faster r-cnn. Journal of Huazhong Agricultural University 44(1), 62\u201373 (2025). (in Chinese)","journal-title":"Journal of Huazhong Agricultural University"},{"key":"1821_CR13","doi-asserted-by":"crossref","unstructured":"Li, Z., Tang, J., Kuang, Y.: Lightweight small-target detection model for litchi pests based on improved yolov10n. Smart Agriculture, pages 1\u201314, (2025a)","DOI":"10.3390\/agriculture14112066"},{"key":"1821_CR14","doi-asserted-by":"publisher","DOI":"10.1016\/j.ecoinf.2024.102543","volume":"80","author":"BP de Melo Lima","year":"2024","unstructured":"de Melo Lima, B.P., Borges, L.D.A.B., Hirose, E., de Almeida, L.F.: A lightweight and enhanced model for detecting the neotropical brown stink bug, euschistus heros (hemiptera: Pentatomidae) based on yolov8 for soybean fields. Eco. Inform. 80, 102543 (2024)","journal-title":"Eco. Inform."},{"key":"1821_CR15","doi-asserted-by":"crossref","unstructured":"Yang, H., Sheng, S., Jiang, F., Zhang, T., Wang, S., Xiao, J.: YOLO-SDW: a method for detecting infection in corn leaves. Energy Reports, 12, (2024)","DOI":"10.1016\/j.egyr.2024.11.072"},{"key":"1821_CR16","doi-asserted-by":"publisher","first-page":"2925","DOI":"10.1007\/s13369-025-09997-9","volume":"50","author":"H Liu","year":"2025","unstructured":"Liu, H., Ma, Y., Jiang, H., Hong, T.: Spr-yolo: A traffic flow detection algorithm for fuzzy scenarios. Arab. J. Sci. Eng. 50, 2925\u20132943 (2025)","journal-title":"Arab. J. Sci. Eng."},{"issue":"5","key":"1821_CR17","doi-asserted-by":"publisher","first-page":"167","DOI":"10.1007\/s11554-025-01742-7","volume":"22","author":"L Li","year":"2025","unstructured":"Li, L., Sun, R., Xu, Y.: Design and optimization of a new corn-weed detection model with YOLOv8-GAS based on artificial intelligence. J. Real-Time Image Proc. 22(5), 167 (2025b)","journal-title":"J. Real-Time Image Proc."},{"issue":"5","key":"1821_CR18","doi-asserted-by":"publisher","first-page":"172","DOI":"10.1007\/s11554-025-01748-1","volume":"22","author":"Z Li","year":"2025","unstructured":"Li, Z., Xiaonan, H., Zhao, X., Ye, H., Chen, F., Chen, X., Li, X.: Beyond obstacles: feather-light YOLO11-LES for real-time ripeness detection of occluded strawberries in greenhouses. J. Real-Time Image Proc. 22(5), 172 (2025c)","journal-title":"J. Real-Time Image Proc."},{"issue":"1","key":"1821_CR19","doi-asserted-by":"publisher","DOI":"10.1117\/1.JEI.33.1.013023","volume":"33","author":"P Yan","year":"2024","unstructured":"Yan, P., Liu, Y., Lyu, L., Xu, X., Song, B., et al.: IOD-YOLO: an algorithm for object detection in low-altitude aerial images. J. Electron. Imaging 33(1), 013023 (2024)","journal-title":"J. Electron. Imaging"},{"key":"1821_CR20","doi-asserted-by":"crossref","unstructured":"Gao, S., Zhang, P., Yan, T., Lu, H.: Multi-scale and detail-enhanced segment anything model for salient object detection. In Proceedings of the 32nd ACM International Conference on Multimedia (MM \u201924), pages 9894\u20139903, (2024)","DOI":"10.1145\/3664647.3680650"},{"key":"1821_CR21","unstructured":"Rafael, C.: Gonzalez and Richard E, 4th edn. Woods. Digital Image Processing. Pearson, Harlow, UK (2018)"},{"key":"1821_CR22","doi-asserted-by":"publisher","DOI":"10.1016\/j.eswa.2023.121638","volume":"237","author":"B Chang","year":"2024","unstructured":"Chang, B., Wang, Y., Zhao, X., Li, G., Yuan, P.: A general-purpose edge-feature guidance module to enhance vision transformers for plant disease identification. Expert Syst. Appl. 237, 121638 (2024)","journal-title":"Expert Syst. Appl."},{"key":"1821_CR23","doi-asserted-by":"crossref","unstructured":"Chen, X., Li, H., Li, M., Pan, J.: Learning a sparse transformer network for effective image deraining. In Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pages 5896\u20135905, (2023)","DOI":"10.1109\/CVPR52729.2023.00571"},{"key":"1821_CR24","doi-asserted-by":"publisher","DOI":"10.1016\/j.dsp.2024.104611","volume":"153","author":"W Lai","year":"2024","unstructured":"Lai, W., Tong, Y.: Enhanced-YOLOv8: a new small target detection model. Digital Signal Processing 153, 104611 (2024)","journal-title":"Digital Signal Processing"},{"key":"1821_CR25","doi-asserted-by":"publisher","DOI":"10.1016\/j.ins.2024.121366","volume":"686","author":"Q Fan","year":"2025","unstructured":"Fan, Q., Li, Y., Deveci, M., Zhong, K., Kadry, S.: LUD-YOLO: a novel lightweight object detection network for unmanned aerial vehicle. Inf. Sci. 686, 121336 (2025)","journal-title":"Inf. Sci."},{"issue":"1","key":"1821_CR26","doi-asserted-by":"publisher","first-page":"1947","DOI":"10.1177\/03611981241258753","volume":"2679","author":"X Dong","year":"2025","unstructured":"Dong, X., Shi, P., Liang, T., Yang, A.: CTAFFNet: Cnn-transformer adaptive feature fusion object detection algorithm for complex traffic scenarios. Transp. Res. Rec. 2679(1), 1947\u20131965 (2025)","journal-title":"Transp. Res. Rec."},{"key":"1821_CR27","doi-asserted-by":"publisher","unstructured":"Chollet, F.: Xception: deep learning with depthwise separable convolutions. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pages 1251\u20131258, (2017). https:\/\/doi.org\/10.1109\/CVPR.2017.195","DOI":"10.1109\/CVPR.2017.195"},{"key":"1821_CR28","unstructured":"Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, L., Polosukhin, I.: Attention is all you need. In Advances in Neural Information Processing Systems 30 (NeurIPS 2017), pages 5998\u20136008, Long Beach, CA, USA, (2017). Curran Associates, Inc"},{"key":"1821_CR29","doi-asserted-by":"publisher","first-page":"1373104","DOI":"10.3389\/fpls.2024.1373104","volume":"15","author":"R Ye","year":"2024","unstructured":"Ye, R., Gao, Q., Li, T.: BRA-YOLOv7: improvements on large leaf disease object detection using fasternet and dual-level routing attention in yolov7. Front. Plant Sci. 15, 1373104 (2024). https:\/\/doi.org\/10.3389\/fpls.2024.1373104","journal-title":"Front. Plant Sci."},{"issue":"2","key":"1821_CR30","doi-asserted-by":"publisher","DOI":"10.1117\/1.JEI.34.2.023049","volume":"34","author":"Y Wang","year":"2025","unstructured":"Wang, Y., Ke, H., Cai, H.: PC-YOLO: enhancing object detection in adverse weather through physics-aware and dynamic network structures. J. Electron. Imaging 34(2), 023049 (2025). https:\/\/doi.org\/10.1117\/1.JEI.34.2.023049","journal-title":"J. Electron. Imaging"},{"key":"1821_CR31","doi-asserted-by":"publisher","DOI":"10.1016\/j.displa.2024.102814","volume":"84","author":"X Dong","year":"2024","unstructured":"Dong, X., Shi, P., Qi, H., Yang, A., Liang, T.: Ts-bev: Bev object detection algorithm based on temporal-spatial feature fusion. Displays 84, 102814 (2024). https:\/\/doi.org\/10.1016\/j.displa.2024.102814","journal-title":"Displays"},{"key":"1821_CR32","doi-asserted-by":"publisher","unstructured":"Ding, X., Zhang, X., Han, J., Ding, G.: Diverse Branch Block: building a convolution as an inception-like unit. In Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pages 10881\u201310890, (2021). https:\/\/doi.org\/10.1109\/CVPR46437.2021.01074","DOI":"10.1109\/CVPR46437.2021.01074"},{"key":"1821_CR33","doi-asserted-by":"publisher","unstructured":"Yu, W., Zhang, P., Yan, S., Wang, X.: InceptionNeXt: when inception meets convnext. In Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pages 5672\u20135683, (2024). https:\/\/doi.org\/10.1109\/CVPR60749.2024.00547","DOI":"10.1109\/CVPR60749.2024.00547"},{"key":"1821_CR34","doi-asserted-by":"publisher","first-page":"1495222","DOI":"10.3389\/fpls.2024.1495222","volume":"15","author":"Z Qiu","year":"2024","unstructured":"Qiu, Z., Wang, F., Wang, W., Li, T., Jin, X., Qing, S., Shi, Y.: YOLO-SDL: a lightweight wheat grain detection technology based on an improved yolov8n model. Front. Plant Sci. 15, 1495222 (2024). https:\/\/doi.org\/10.3389\/fpls.2024.1495222","journal-title":"Front. Plant Sci."},{"key":"1821_CR35","doi-asserted-by":"publisher","first-page":"1419","DOI":"10.3389\/fpls.2016.01419","volume":"7","author":"SP Mohanty","year":"2016","unstructured":"Mohanty, S.P., Hughes, D.P., Salath, M.: Using deep learning for image-based plant disease detection. Front. Plant Sci. 7, 1419 (2016). https:\/\/doi.org\/10.3389\/fpls.2016.01419","journal-title":"Front. Plant Sci."},{"key":"1821_CR36","doi-asserted-by":"publisher","unstructured":"Singh, D., Jain, N., Jain, P., Kayal,P., Kumawat, S., Batra, N.: PlantDoc: a dataset for visual plant disease detection. In Proceedings of the 7th ACM IKDD CoDS and 25th COMAD, pages 249\u2013253, (2020). https:\/\/doi.org\/10.1145\/3371158.3371196","DOI":"10.1145\/3371158.3371196"},{"key":"1821_CR37","doi-asserted-by":"publisher","unstructured":"Wu, X., Zhan, C., Lai, Y.K., Cheng, M-M., Yang, J.: IP102: a large-scale benchmark dataset for insect pest recognition. In Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pages 8787\u20138796, (2019). https:\/\/doi.org\/10.1109\/CVPR.2019.00899","DOI":"10.1109\/CVPR.2019.00899"},{"key":"1821_CR38","doi-asserted-by":"publisher","unstructured":"Wang, C.-Y., Yeh, I.-H., Liao, H.-Y.M.: YOLOv9: learning what you want to learn using programmable gradient information. arXiv preprint, arXiv:2402.13616, (2024a). https:\/\/doi.org\/10.48550\/arXiv.2402.13616","DOI":"10.48550\/arXiv.2402.13616"},{"key":"1821_CR39","doi-asserted-by":"publisher","unstructured":"Wang, A., Zhang, Y., Li, X., Wang, Q., Sun, J.: YOLOv10: real-time end-to-end object detection. arXiv preprint, arXiv:2405.14458, (2024b). https:\/\/doi.org\/10.48550\/arXiv.2405.14458","DOI":"10.48550\/arXiv.2405.14458"},{"key":"1821_CR40","doi-asserted-by":"publisher","DOI":"10.1016\/j.compag.2020.105585","volume":"175","author":"Q-J Wang","year":"2020","unstructured":"Wang, Q.-J., Zhang, S.-Y., Dong, S.-F., Zhang, G.-C., Yang, J., Li, R., Wang, H.-Q.: Pest24: a large-scale very small object data set of agricultural pests for multi-target detection. Comput. Electron. Agric. 175, 105585 (2020). https:\/\/doi.org\/10.1016\/j.compag.2020.105585","journal-title":"Comput. Electron. Agric."}],"container-title":["Journal of Real-Time Image Processing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11554-025-01821-9.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s11554-025-01821-9","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11554-025-01821-9.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,2,6]],"date-time":"2026-02-06T16:48:33Z","timestamp":1770396513000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s11554-025-01821-9"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,12,8]]},"references-count":40,"journal-issue":{"issue":"1","published-print":{"date-parts":[[2026,2]]}},"alternative-id":["1821"],"URL":"https:\/\/doi.org\/10.1007\/s11554-025-01821-9","relation":{},"ISSN":["1861-8200","1861-8219"],"issn-type":[{"value":"1861-8200","type":"print"},{"value":"1861-8219","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,12,8]]},"assertion":[{"value":"13 August 2025","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"1 December 2025","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"8 December 2025","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"The authors declare there are no financial interests, commercial affiliations, or other potential conflict of interest that have influenced the objectivity of this research or the writing of this paper. The authors declare no conflict of interest.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}}],"article-number":"22"}}