{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,24]],"date-time":"2026-02-24T17:25:01Z","timestamp":1771953901511,"version":"3.50.1"},"publisher-location":"Cham","reference-count":30,"publisher":"Springer Nature Switzerland","isbn-type":[{"value":"9783031918346","type":"print"},{"value":"9783031918353","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\/978-3-031-91835-3_21","type":"book-chapter","created":{"date-parts":[[2025,5,26]],"date-time":"2025-05-26T06:50:17Z","timestamp":1748242217000},"page":"319-334","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["Beyond Annotations: Efficient Wheat Head Segmentation Using L-Systems, Game Engines, and\u00a0Student-Teacher Models"],"prefix":"10.1007","author":[{"ORCID":"https:\/\/orcid.org\/0009-0003-5143-2751","authenticated-orcid":false,"given":"Hosein","family":"Beheshtifard","sequence":"first","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0009-0005-1451-2685","authenticated-orcid":false,"given":"Elijah","family":"Mickelson","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3050-8107","authenticated-orcid":false,"given":"Keyhan","family":"Najafian","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5673-8210","authenticated-orcid":false,"given":"Farhad","family":"Maleki","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2025,5,12]]},"reference":[{"key":"21_CR1","doi-asserted-by":"crossref","unstructured":"Arazo, E., Ortego, D., Albert, P., O\u2019Connor, N.E., McGuinness, K.: Pseudo-labeling and confirmation bias in deep semi-supervised learning. 2020 International Joint Conference on Neural Networks (IJCNN), pp.\u00a01\u20138 (2019)","DOI":"10.1109\/IJCNN48605.2020.9207304"},{"key":"21_CR2","doi-asserted-by":"crossref","unstructured":"Basu, S.: Semi-supervised learning. In: Encyclopedia of Database Systems (2019)","DOI":"10.1007\/978-1-4614-8265-9_609"},{"key":"21_CR3","unstructured":"Blender Online\u00a0Community D: Blender-A 3D modelling and rendering package. Blender Foundation (2018)"},{"key":"21_CR4","doi-asserted-by":"crossref","unstructured":"Bolya, D., Zhou, C., Xiao, F., Lee, Y.J.: YOLACT: real-time instance segmentation. 2019 IEEE\/CVF International Conference on Computer Vision (ICCV), pp. 9156\u20139165 (2019)","DOI":"10.1109\/ICCV.2019.00925"},{"key":"21_CR5","doi-asserted-by":"crossref","unstructured":"David, E., et al.: Global wheat head detection 2021: An improved dataset for benchmarking wheat head detection methods. Plant Phenomics 2021 (2021)","DOI":"10.34133\/2021\/9846158"},{"key":"21_CR6","doi-asserted-by":"crossref","unstructured":"Deng, J., Dong, W., Socher, R., Li, L.J., Li, K., Fei-Fei, L.: Imagenet: a large-scale hierarchical image database. In: 2009 IEEE Conference on Computer Vision and Pattern Recognition, pp. 248\u2013255 (2009)","DOI":"10.1109\/CVPR.2009.5206848"},{"key":"21_CR7","doi-asserted-by":"crossref","unstructured":"Ennadifi, E., Dandrifosse, S., Mokhtari, M.E.A., Carlier, A., Laraba, S., Mercatoris, B., Gosselin, B.: Local unsupervised wheat head segmentation. In: 2022 IEEE 18th International Conference on Intelligent Computer Communication and Processing (ICCP), pp. 55\u201362 (2022)","DOI":"10.1109\/ICCP56966.2022.10053964"},{"issue":"4","key":"21_CR8","doi-asserted-by":"publisher","first-page":"3557","DOI":"10.3390\/su15043557","volume":"15","author":"S Gokool","year":"2023","unstructured":"Gokool, S., et al.: Crop monitoring in smallholder farms using unmanned aerial vehicles to facilitate precision agriculture practices: a scoping review and bibliometric analysis. Sustainability 15(4), 3557 (2023)","journal-title":"Sustainability"},{"key":"21_CR9","doi-asserted-by":"crossref","unstructured":"He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 770\u2013778 (2016)","DOI":"10.1109\/CVPR.2016.90"},{"key":"21_CR10","unstructured":"Iakubovskii, P.: Segmentation models pytorch (2019). https:\/\/github.com\/qubvel\/segmentation_models.pytorch"},{"key":"21_CR11","unstructured":"Laine, S., Aila, T.: Temporal ensembling for semi-supervised learning (2017)"},{"key":"21_CR12","unstructured":"Lee, D.H., et\u00a0al.: Pseudo-label: the simple and efficient semi-supervised learning method for deep neural networks. In: Workshop on Challenges in Representation Learning, ICML, vol.\u00a03, p.\u00a0896. Atlanta (2013)"},{"key":"21_CR13","doi-asserted-by":"crossref","unstructured":"Li, Y., Wang, D., Yuan, C., Li, H., Hu, J.: Enhancing agricultural image segmentation with an agricultural segment anything model adapter. Sensors (Basel, Switzerland) 23 (2023)","DOI":"10.3390\/s23187884"},{"key":"21_CR14","doi-asserted-by":"crossref","unstructured":"Myers, J., Najafian, K., Maleki, F., Ovens, K.: Efficient wheat head segmentation with minimal annotation: a generative approach. J. Imaging 10(7) (2024)","DOI":"10.3390\/jimaging10070152"},{"key":"21_CR15","doi-asserted-by":"publisher","first-page":"0025","DOI":"10.34133\/plantphenomics.0025","volume":"5","author":"K Najafian","year":"2023","unstructured":"Najafian, K., et al.: Semi-self-supervised learning for semantic segmentation in images with dense patterns. Plant Phenomics 5, 0025 (2023)","journal-title":"Plant Phenomics"},{"key":"21_CR16","doi-asserted-by":"crossref","unstructured":"Napier, C.C., Cook, D.M., Armstrong, L., Diepeveen, D.: A synthetic wheat l-system to accurately detect and visualise wheat head anomalies (2023)","DOI":"10.2991\/978-94-6463-122-7_36"},{"key":"21_CR17","doi-asserted-by":"crossref","unstructured":"Nayak, N., Kumar, D., Chattopadhay, S., Kukreja, V., Verma, A.: Improved detection of fusarium head blight in wheat ears through YOLACT instance segmentation. 2024 11th International Conference on Reliability, Infocom Technologies and Optimization (Trends and Future Directions) (ICRITO), pp.\u00a01\u20134 (2024)","DOI":"10.1109\/ICRITO61523.2024.10522220"},{"key":"21_CR18","unstructured":"Oliver, A., Odena, A., Raffel, C.A., Cubuk, E.D., Goodfellow, I.: Realistic evaluation of deep semi-supervised learning algorithms. Adv. Neural Inform. Process. Syst. 31 (2018)"},{"key":"21_CR19","unstructured":"Prusinkiewicz, P., Lindenmayer, A.: The algorithmic beauty of plants. Springer Science & Business Media (2012)"},{"key":"21_CR20","unstructured":"Reis, D., Kupec, J., Hong, J., Daoudi, A.: Real-time flying object detection with yolov8 (2023). arXiv preprint arXiv:2305.09972"},{"key":"21_CR21","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"234","DOI":"10.1007\/978-3-319-24574-4_28","volume-title":"Medical Image Computing and Computer-Assisted Intervention \u2013 MICCAI 2015","author":"O Ronneberger","year":"2015","unstructured":"Ronneberger, O., Fischer, P., Brox, T.: U-Net: convolutional networks for biomedical image segmentation. In: Navab, N., Hornegger, J., Wells, W.M., Frangi, A.F. (eds.) MICCAI 2015. LNCS, vol. 9351, pp. 234\u2013241. Springer, Cham (2015). https:\/\/doi.org\/10.1007\/978-3-319-24574-4_28"},{"key":"21_CR22","unstructured":"Ruder, S.: An overview of gradient descent optimization algorithms. ArXiv abs\/ arXiv: 1609.04747 (2016)"},{"key":"21_CR23","doi-asserted-by":"crossref","unstructured":"Shiferaw, B.A., Smale, M., Braun, H.J., Duveiller, E., Reynolds, M., Muricho, G.: Crops that feed the world 10. Past successes and future challenges to the role played by wheat in global food security. Food Sec. 5, 291\u2013317 (2013)","DOI":"10.1007\/s12571-013-0263-y"},{"key":"21_CR24","first-page":"596","volume":"33","author":"K Sohn","year":"2020","unstructured":"Sohn, K., et al.: Fixmatch: simplifying semi-supervised learning with consistency and confidence. Adv. Neural. Inf. Process. Syst. 33, 596\u2013608 (2020)","journal-title":"Adv. Neural. Inf. Process. Syst."},{"key":"21_CR25","unstructured":"Sun, L., Zhao, C., Stolkin, R.: Weakly-supervised DCNN for RGB-D object recognition in real-world applications which lack large-scale annotated training data. arXiv preprint arXiv:1703.06370 (2017)"},{"key":"21_CR26","unstructured":"Tan, M., Le, Q.V.: Efficientnet: Rethinking model scaling for convolutional neural networks. ArXiv abs\/ arXiv: 1905.11946 (2019)"},{"key":"21_CR27","unstructured":"Tarvainen, A., Valpola, H.: Mean teachers are better role models: weight-averaged consistency targets improve semi-supervised deep learning results. Adv. Neural Inform. Process. Syst. 30 (2017)"},{"key":"21_CR28","doi-asserted-by":"publisher","first-page":"791","DOI":"10.1007\/s00778-022-00775-9","volume":"32","author":"SE Whang","year":"2021","unstructured":"Whang, S.E., Roh, Y., Song, H., Lee, J.G.: Data collection and quality challenges in deep learning: a data-centric AI perspective. VLDB J. 32, 791\u2013813 (2021)","journal-title":"VLDB J."},{"issue":"2","key":"21_CR29","doi-asserted-by":"publisher","first-page":"327","DOI":"10.3390\/agriculture14020327","volume":"14","author":"R Zhang","year":"2024","unstructured":"Zhang, R., Yao, M., Qiu, Z., Zhang, L., Li, W., Shen, Y.: Wheat teacher: a one-stage anchor-based semi-supervised wheat head detector utilizing pseudo-labeling and consistency regularization methods. Agriculture 14(2), 327 (2024)","journal-title":"Agriculture"},{"key":"21_CR30","doi-asserted-by":"crossref","unstructured":"Zhu, J.Y., Park, T., Isola, P., Efros, A.A.: Unpaired image-to-image translation using cycle-consistent adversarial networks. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2223\u20132232 (2017)","DOI":"10.1109\/ICCV.2017.244"}],"container-title":["Lecture Notes in Computer Science","Computer Vision \u2013 ECCV 2024 Workshops"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-031-91835-3_21","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,5,26]],"date-time":"2025-05-26T06:50:35Z","timestamp":1748242235000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-91835-3_21"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025]]},"ISBN":["9783031918346","9783031918353"],"references-count":30,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-91835-3_21","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"value":"0302-9743","type":"print"},{"value":"1611-3349","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025]]},"assertion":[{"value":"12 May 2025","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"ECCV","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"European Conference on Computer Vision","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Milan","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Italy","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":"29 September 2024","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"4 October 2024","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"18","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"eccv2024","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/eccv2024.ecva.net\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}}]}}