{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,15]],"date-time":"2026-03-15T06:31:58Z","timestamp":1773556318058,"version":"3.50.1"},"publisher-location":"Cham","reference-count":39,"publisher":"Springer Nature Switzerland","isbn-type":[{"value":"9783031882258","type":"print"},{"value":"9783031882265","type":"electronic"}],"license":[{"start":{"date-parts":[[2025,1,1]],"date-time":"2025-01-01T00:00:00Z","timestamp":1735689600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2025,1,1]],"date-time":"2025-01-01T00:00:00Z","timestamp":1735689600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2025]]},"DOI":"10.1007\/978-3-031-88226-5_10","type":"book-chapter","created":{"date-parts":[[2025,4,20]],"date-time":"2025-04-20T17:43:12Z","timestamp":1745170992000},"page":"141-155","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["Identification of Non-Plant Elements in\u00a0Herbarium Images Using YOLO"],"prefix":"10.1007","author":[{"given":"Youcef","family":"Sklab","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Hanane","family":"Ariouat","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Edi","family":"Prifti","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jean-Daniel","family":"Zucker","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Eric","family":"Chenin","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2025,4,21]]},"reference":[{"key":"10_CR1","doi-asserted-by":"publisher","unstructured":"Sklab Y, et al.: Towards a deep learning-powered herbarium image analysis platform. Biodivers. Inf. Sci. Stan. 8 e135629 (2024). https:\/\/doi.org\/10.3897\/biss.8.135629","DOI":"10.3897\/biss.8.135629"},{"key":"10_CR2","doi-asserted-by":"crossref","unstructured":"Ariouat, H., et al.: Extracting masks from herbarium specimen images based on object detection and image segmentation techniques. Biodivers. Inf. Sci. Stan. 7 (2023)","DOI":"10.3897\/biss.7.112161"},{"issue":"2023","key":"10_CR3","volume":"7","author":"M Sahraoui","year":"2023","unstructured":"Sahraoui, M., Sklab, Y., Pignal, M., Lebbe, R.V., Guigue, V.: Leveraging multimodality for biodiversity data: exploring joint representations of species descriptions and specimen images using CLIP. Biodivers. Inf. Sci. Stan. 7(2023), e112666 (2023)","journal-title":"Biodivers. Inf. Sci. Stan."},{"key":"10_CR4","doi-asserted-by":"crossref","unstructured":"Meredith, L.: Roles of natural history collections. Ann. Mo. Bot. Gard. 4(83) (1996)","DOI":"10.2307\/2399994"},{"key":"10_CR5","doi-asserted-by":"crossref","unstructured":"Raven, P.H.: Saving plants, saving ourselves. Plants, People, Planet 1 (2019)","DOI":"10.1002\/ppp3.3"},{"key":"10_CR6","doi-asserted-by":"crossref","unstructured":"Younis, S., Schmidt, M., Weiland, C., Dressler,S., Seeger, B., Hickler, T.: Detection and annotation of plant organs from digitised herbarium scans using deep learning. Biodivers. Data J. 8 (2020)","DOI":"10.3897\/BDJ.8.e57090"},{"key":"10_CR7","doi-asserted-by":"crossref","unstructured":"Besnard, G., et al.: Herbarium-based science in the twenty-first century. Bot. Lett. 165 (2018)","DOI":"10.1080\/23818107.2018.1482783"},{"key":"10_CR8","doi-asserted-by":"crossref","unstructured":"Soltis, P.: Digitization of herbaria enables novel research. Am. J. Bot. 104 (2017)","DOI":"10.3732\/ajb.1700281"},{"key":"10_CR9","doi-asserted-by":"crossref","unstructured":"Abdelaziz, T., Bassem,B., Walid, M.: A deep learning-based approach for detecting plant organs from digitized herbarium specimen images. Ecol. Inf. 69 (2022)","DOI":"10.1016\/j.ecoinf.2022.101590"},{"key":"10_CR10","unstructured":"Patrick W.S., et al: Large-scale digitization of herbarium specimens: development and usage of an automated, high-throughput conveyor system. Taxon (2018)"},{"key":"10_CR11","doi-asserted-by":"crossref","unstructured":"Wenli, Z.,et al.: Deep-learning-based in-field citrus fruit detection and tracking. Hortic. Res. 9 (2022)","DOI":"10.1093\/hr\/uhac003"},{"key":"10_CR12","doi-asserted-by":"crossref","unstructured":"Jiang, Y., Li, C.: Convolutional neural networks for image-based high-throughput plant phenotyping: a review. Plant Phenomics 9 (2020)","DOI":"10.34133\/2020\/4152816"},{"key":"10_CR13","doi-asserted-by":"crossref","unstructured":"Borhani, Y., Khoramdel, J., Najafi, E.: A deep learning based approach for automated plant disease classification using vision transformer. Sci. Rep. 12 (2022)","DOI":"10.1038\/s41598-022-15163-0"},{"key":"10_CR14","unstructured":"Ashish, V., et al.: Attention is all you need. CoRR (2017). vol. abs\/1706.03762"},{"key":"10_CR15","doi-asserted-by":"crossref","unstructured":"Sue Han, L., Chee Seng, C., Simon, J., Paolo, R.: How deep learning extracts and learns leaf features for plant classification. Pattern Recognit. 71 (2017)","DOI":"10.1016\/j.patcog.2017.05.015"},{"key":"10_CR16","doi-asserted-by":"crossref","unstructured":"Mochida, K., et al.: Computer vision-based phenotyping for improvement of plant productivity: a machine learning perspective. Gigascience 8 (2018)","DOI":"10.1093\/gigascience\/giy153"},{"key":"10_CR17","unstructured":"Shaoqing, R., Kaiming, H., Ross B.G., Jian, S.: Faster R-CNN: towards real-time object detection with region proposal networks. Comput. Vis. Pattern Recognit. (2015). vol. abs\/1506.01497"},{"key":"10_CR18","unstructured":"Kaiming, H., Georgia, G., Piotr, D., Ross B.G.: Mask R-CNN. Comput. Vis. Pattern Recognit. (2017). vol. abs\/1703.06870"},{"key":"10_CR19","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 (2016)","DOI":"10.1109\/CVPR.2016.91"},{"key":"10_CR20","unstructured":"Xiao, Z., Lang, J., Shuai, L., Tingting, Z., Xingang, M.: YOLO-SASE: an improved YOLO algorithm for the small targets detection in complex backgrounds. Comput. Vis. Pattern Recognit. (2022). vol. abs\/2207.02696"},{"key":"10_CR21","unstructured":"Chien-Yao, W., Alexey, B., Hong-Yuan Mark, L.: YOLOv7: trainable bag-of-freebies sets new state-of-the-art for real-time object detectors. Comput. Vis. Pattern Recognit. (2022). vol. abs\/2207.02696"},{"key":"10_CR22","unstructured":"Ozan,O., et al.: Attention U-net: learning where to look for the pancreas. Comput. Vis. Pattern Recognit. (2018). vol. abs\/1804.03999"},{"key":"10_CR23","doi-asserted-by":"crossref","unstructured":"Lang, P.M., Willems, F., Scheepens, J.F., Burbano, H., Bossdorf, O.: Using herbaria to study global environmental change. New Phytol. 2021 (2019)","DOI":"10.7287\/peerj.preprints.26886v1"},{"key":"10_CR24","doi-asserted-by":"crossref","unstructured":"Zhao, H., Zhang, H., Zhao, Y.: YOLOv7-sea: object detection of maritime UAV images based on improved YOLOv7. In: Proceedings of the IEEE\/CVF Winter Conference on Applications of Computer Vision (WACV) Workshops (2023)","DOI":"10.1109\/WACVW58289.2023.00029"},{"key":"10_CR25","unstructured":"Zixiao, Z., et al.: ViT-YOLO: transformer-based YOLO for object detection. In: IEEE\/CVF International Conference on Computer Vision Workshops, ICCVW 2021, Montreal, BC, Canada, 11\u201317 Oct 2021"},{"key":"10_CR26","doi-asserted-by":"crossref","unstructured":"James, C., et al.: From prototype to inference: a pipeline to apply deep learning in sorghum panicle detection. Plant Phenomics 5 (2023)","DOI":"10.34133\/plantphenomics.0017"},{"key":"10_CR27","doi-asserted-by":"crossref","unstructured":"Jie, X., et al.: TrichomeYOLO: a neural network for automatic maize trichome counting. Plant Phenomics 5 (2023)","DOI":"10.34133\/plantphenomics.0024"},{"key":"10_CR28","doi-asserted-by":"crossref","unstructured":"Zhaoyang, N., Guoqiang, Z., Hui, Y.: A review on the attention mechanism of deep learning. Plant Neurocomput. 452 (2021)","DOI":"10.1016\/j.neucom.2021.03.091"},{"key":"10_CR29","unstructured":"Dillon R., Jordan K., Jacqueline H., Ahmad D.: Real-time flying object detection with YOLOv8. Comput. Vis. Pattern Recognit. (2023)"},{"key":"10_CR30","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"223","DOI":"10.1007\/978-981-97-2253-2_18","volume-title":"Advances in Knowledge Discovery and Data Mining","author":"H Ariouat","year":"2024","unstructured":"Ariouat, H., et al.: Enhancing YOLOv7 for plant organs detection using attention-gate mechanism. In: Yang, D.N., Xie, X., Tseng, V.S., Pei, J., Huang, J.W., Lin, J. (eds.) PAKDD 2024. LNCS, vol. 14646, pp. 223\u2013234. Springer, Singapore (2024). https:\/\/doi.org\/10.1007\/978-981-97-2253-2_18"},{"key":"10_CR31","doi-asserted-by":"crossref","unstructured":"Wang, C.Y., Yeh, I.H., Mark Liao, H.Y.: YOLOv9: learning what you want to learn using programmable gradient information. Comput. Vis. Pattern Recognit. (2024)","DOI":"10.1007\/978-3-031-72751-1_1"},{"key":"10_CR32","doi-asserted-by":"crossref","unstructured":"Vondrick, C., Khosla, A., Pirsiavash, H., Malisiewicz, T., Torralba, A.: Visualizing object detection features. Comput. Vis. Pattern Recognit. (2015)","DOI":"10.1007\/s11263-016-0884-7"},{"key":"10_CR33","doi-asserted-by":"publisher","unstructured":"Felzenszwalb, P., McAllester, D., Ramanan, D.: A discriminatively trained, multiscale, deformable part model. In: 2008 IEEE Conference on Computer Vision and Pattern Recognition, Anchorage, AK, USA, pp. 1\u20138 (2008). https:\/\/doi.org\/10.1109\/CVPR.2008.4587597","DOI":"10.1109\/CVPR.2008.4587597"},{"key":"10_CR34","doi-asserted-by":"publisher","first-page":"340","DOI":"10.1016\/j.proeng.2012.07.182","volume":"41","author":"R Hussin","year":"2012","unstructured":"Hussin, R., Juhari, M.R., Kang, N.W., Ismail, R.C., Kamarudin, A.: Digital image processing techniques for object detection from complex background image. Procedia Eng. 41, 340\u2013344 (2012)","journal-title":"Procedia Eng."},{"key":"10_CR35","unstructured":"Cox, L.: Heavy lifting at Sydney\u2019s herbarium: the quest to move and catalogue more than 1m plant specimens (2022). https:\/\/www.theguardian.com\/ australia-news\/2022\/jan\/12\/heavy-lifting-at-sydneys-herbariumthe-quest-to-move-and-catalogue-more-than-1m-plant-specimens"},{"key":"10_CR36","doi-asserted-by":"publisher","unstructured":"Thompson, K.M., Turnbull, R., Fitzgerald, E., Birch, J.L.: Identification of herbarium specimen sheet components from high-resolution images using deep learning. Ecol. Evol. 13, e10395 (2023). https:\/\/doi.org\/10.1002\/ece3.10395","DOI":"10.1002\/ece3.10395"},{"key":"10_CR37","doi-asserted-by":"publisher","unstructured":"Triki, A., Bouaziz, B., Mahdi, W., Gaikwad, J.: Objects detection from digitized herbarium specimen based on improved YOLO V3. 523\u2013529 (2020). https:\/\/doi.org\/10.5220\/0009170005230529","DOI":"10.5220\/0009170005230529"},{"key":"10_CR38","doi-asserted-by":"crossref","unstructured":"Redmon, J., Divvala, S., Girshick, R., Farhadi, A.: You only look once: unified, real-time object detection. In: 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) (2016)","DOI":"10.1109\/CVPR.2016.91"},{"key":"10_CR39","unstructured":"Jocher, G.: YOLOv5 by ultralytics. Github (2020). https:\/\/github.com\/ ultralytics\/yolov5. https:\/\/doi.org\/10.5281\/zenodo.3908559"}],"container-title":["Communications in Computer and Information Science","Research in Computer Science"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-031-88226-5_10","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,4,20]],"date-time":"2025-04-20T17:43:26Z","timestamp":1745171006000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-88226-5_10"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025]]},"ISBN":["9783031882258","9783031882265"],"references-count":39,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-88226-5_10","relation":{},"ISSN":["1865-0929","1865-0937"],"issn-type":[{"value":"1865-0929","type":"print"},{"value":"1865-0937","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025]]},"assertion":[{"value":"21 April 2025","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"CARI","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"African Conference on Research in Computer Science and Applied Mathematics","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Beja\u00efa","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Algeria","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2024","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"23 November 2024","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"26 November 2024","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"17","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"cari2024","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/www.cari-info.org\/call-cari2024-2\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}}]}}