{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,4]],"date-time":"2026-02-04T18:20:31Z","timestamp":1770229231796,"version":"3.49.0"},"publisher-location":"Singapore","reference-count":42,"publisher":"Springer Nature Singapore","isbn-type":[{"value":"9789819561957","type":"print"},{"value":"9789819561964","type":"electronic"}],"license":[{"start":{"date-parts":[[2026,1,1]],"date-time":"2026-01-01T00:00:00Z","timestamp":1767225600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2026,1,1]],"date-time":"2026-01-01T00:00:00Z","timestamp":1767225600000},"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":[[2026]]},"DOI":"10.1007\/978-981-95-6196-4_1","type":"book-chapter","created":{"date-parts":[[2026,2,4]],"date-time":"2026-02-04T05:59:02Z","timestamp":1770184742000},"page":"3-17","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Parameter-Efficient Wheat Disease Segmentation"],"prefix":"10.1007","author":[{"given":"Shijie","family":"Wang","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Zijian","family":"Wang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yadan","family":"Luo","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Pengfei","family":"Zhang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xin","family":"Yu","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2026,2,5]]},"reference":[{"key":"1_CR1","doi-asserted-by":"crossref","unstructured":"Aich, S., Stavness, I.: Leaf counting with deep convolutional and deconvolutional networks. In: 2017 IEEE International Conference on Computer Vision Workshops, ICCV Workshops 2017, Venice, Italy, 22\u201329 October 2017, pp. 2080\u20132089. IEEE Computer Society (2017)","DOI":"10.1109\/ICCVW.2017.244"},{"key":"1_CR2","first-page":"2020","volume":"1","author":"S Arya","year":"2020","unstructured":"Arya, S., Singh, B.: Wheat nitrogen deficiency and leaf rust image dataset. Mendeley Data 1, 2020 (2020)","journal-title":"Mendeley Data"},{"key":"1_CR3","doi-asserted-by":"publisher","unstructured":"Chen, L.-C., Zhu, Y., Papandreou, G., Schroff, F., Adam, H.: Encoder-decoder with Atrous separable convolution for semantic image segmentation. In: Ferrari, V., Hebert, M., Sminchisescu, C., Weiss, Y. (eds.) ECCV 2018. LNCS, vol. 11211, pp. 833\u2013851. Springer, Cham (2018). https:\/\/doi.org\/10.1007\/978-3-030-01234-2_49","DOI":"10.1007\/978-3-030-01234-2_49"},{"key":"1_CR4","unstructured":"Chen, S., et al.: AdaptFormer: adapting vision transformers for scalable visual recognition. In: Koyejo, S., Mohamed, S., Agarwal, A., Belgrave, D., Cho, K., Oh, A. (eds.) Advances in Neural Information Processing Systems 35: Annual Conference on Neural Information Processing Systems 2022, NeurIPS 2022, New Orleans, LA, USA, November 28 - December 9, 2022 (2022)"},{"key":"1_CR5","doi-asserted-by":"crossref","unstructured":"David, E., et\u00a0al.: Global wheat head detection (GWHD) dataset: a large and diverse dataset of high-resolution RGB-labelled images to develop and benchmark wheat head detection methods. Plant Phenomics (2020)","DOI":"10.34133\/2020\/3521852"},{"issue":"4","key":"1_CR6","doi-asserted-by":"publisher","first-page":"681","DOI":"10.1007\/s11023-020-09548-1","volume":"30","author":"L Floridi","year":"2020","unstructured":"Floridi, L., Chiriatti, M.: GPT-3: its nature, scope, limits, and consequences. Minds Mach. 30(4), 681\u2013694 (2020)","journal-title":"Minds Mach."},{"issue":"9","key":"1_CR7","doi-asserted-by":"publisher","first-page":"2045","DOI":"10.1109\/TMM.2017.2729019","volume":"19","author":"L Gao","year":"2017","unstructured":"Gao, L., Guo, Z., Zhang, H., Xu, X., Shen, H.T.: Video captioning with attention-based LSTM and semantic consistency. IEEE Trans. Multimedia 19(9), 2045\u20132055 (2017)","journal-title":"IEEE Trans. Multimedia"},{"key":"1_CR8","unstructured":"Han, B., et\u00a0al.: FoMo4Wheat: toward reliable crop vision foundation models with globally curated data. arXiv preprint: arXiv:2509.06907 (2025)"},{"key":"1_CR9","doi-asserted-by":"crossref","unstructured":"He, H., Cai, J., Zhang, J., Tao, D., Zhuang, B.: Sensitivity-aware visual parameter-efficient fine-tuning. In: IEEE\/CVF International Conference on Computer Vision, ICCV 2023, Paris, France, 1\u20136 October 2023, pp. 11791\u201311801. IEEE (2023)","DOI":"10.1109\/ICCV51070.2023.01086"},{"key":"1_CR10","doi-asserted-by":"crossref","unstructured":"He, X., Li, C., Zhang, P., Yang, J., Wang, X.E.: Parameter-efficient model adaptation for vision transformers. In: Williams, B., Chen, Y., Neville, J. (eds.) Thirty-Seventh AAAI Conference on Artificial Intelligence, AAAI 2023, Thirty-Fifth Conference on Innovative Applications of Artificial Intelligence, IAAI 2023, Thirteenth Symposium on Educational Advances in Artificial Intelligence, EAAI 2023, Washington, DC, USA, 7\u201314 February 2023, pp. 817\u2013825. AAAI Press (2023)","DOI":"10.1609\/aaai.v37i1.25160"},{"key":"1_CR11","unstructured":"Hu, E.J., et al.: LoRA: low-rank adaptation of large language models. In: The Tenth International Conference on Learning Representations, ICLR 2022, Virtual Event, 25\u201329 April 2022. OpenReview.net (2022)"},{"key":"1_CR12","unstructured":"Hussain, S., Moreno, A., Chauda, S.: CGIAR computer vision for crop disease. Kaggle (2021)"},{"key":"1_CR13","unstructured":"Jia, M., et al.: Visual prompt tuning. In: Avidan, S., Brostow, G.J., Ciss\u00e9, M., Farinella, G.M., Hassner, T. (eds.) Computer Vision - ECCV 2022 - 17th European Conference, Tel Aviv, Israel, 23\u201327 October 2022, Proceedings, Part XXXIII. Lecture Notes in Computer Science, vol. 13693, pp. 709\u2013727. Springer (2022)"},{"key":"1_CR14","doi-asserted-by":"crossref","unstructured":"Jiang, F., Wang, S., Gong, X.: Task-conditional adapter for multi-task dense prediction. In: Cai, J., et al. (eds.) Proceedings of the 32nd ACM International Conference on Multimedia, MM 2024, Melbourne, VIC, Australia, 28 October 2024 - 1 November 2024, pp. 2059\u20132068. ACM (2024)","DOI":"10.1145\/3664647.3681581"},{"issue":"6","key":"1_CR15","doi-asserted-by":"publisher","first-page":"4503","DOI":"10.1109\/TCSVT.2023.3340225","volume":"34","author":"H Li","year":"2024","unstructured":"Li, H., Li, M., Peng, Q., Wang, S., Yu, H., Wang, Z.: Correlation-guided semantic consistency network for visible-infrared person re-identification. IEEE Trans. Circuits Syst. Video Technol. 34(6), 4503\u20134515 (2024)","journal-title":"IEEE Trans. Circuits Syst. Video Technol."},{"issue":"12","key":"1_CR16","doi-asserted-by":"publisher","first-page":"2933","DOI":"10.3390\/agronomy12122933","volume":"12","author":"Y Li","year":"2022","unstructured":"Li, Y., et al.: Semantic segmentation of wheat stripe rust images using deep learning. Agronomy 12(12), 2933 (2022)","journal-title":"Agronomy"},{"key":"1_CR17","unstructured":"Lian, D., Zhou, D., Feng, J., Wang, X.: Scaling & shifting your features: a new baseline for efficient model tuning. In: Koyejo, S., Mohamed, S., Agarwal, A., Belgrave, D., Cho, K., Oh, A. (eds.) Advances in Neural Information Processing Systems 35: Annual Conference on Neural Information Processing Systems 2022, NeurIPS 2022, New Orleans, LA, USA, November 28 - December 9, 2022 (2022)"},{"key":"1_CR18","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":"1_CR19","unstructured":"Pan, T., et al.: Structure-aware semantic discrepancy and consistency for 3d medical image self-supervised learning. arXiv preprint: arXiv:2507.02581 (2025)"},{"issue":"6","key":"1_CR20","doi-asserted-by":"publisher","first-page":"e66428","DOI":"10.1371\/journal.pone.0066428","volume":"8","author":"DK Ray","year":"2013","unstructured":"Ray, D.K., Mueller, N.D., West, P.C., Foley, J.A.: Yield trends are insufficient to double global crop production by 2050. PLoS ONE 8(6), e66428 (2013)","journal-title":"PLoS ONE"},{"key":"1_CR21","doi-asserted-by":"crossref","unstructured":"Reynolds, M.P., Braun, H.J.: Wheat Improvement: Food Security in a Changing Climate. Springer Nature (2022)","DOI":"10.1007\/978-3-030-90673-3"},{"issue":"12","key":"1_CR22","doi-asserted-by":"publisher","first-page":"3034","DOI":"10.1109\/TPAMI.2018.2789887","volume":"40","author":"F Shen","year":"2018","unstructured":"Shen, F., Xu, Y., Liu, L., Yang, Y., Huang, Z., Shen, H.T.: Unsupervised deep hashing with similarity-adaptive and discrete optimization. IEEE Trans. Pattern Anal. Mach. Intell. 40(12), 3034\u20133044 (2018)","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"1_CR23","unstructured":"Si, C., et al.: FloRA: low-rank core space for N-dimension. CoRR abs\/2405.14739 (2024)"},{"issue":"1","key":"1_CR24","first-page":"41","volume":"4","author":"V Singh","year":"2017","unstructured":"Singh, V., Misra, A.K.: Detection of plant leaf diseases using image segmentation and soft computing techniques. Inf. Process. Agric. 4(1), 41\u201349 (2017)","journal-title":"Inf. Process. Agric."},{"key":"1_CR25","unstructured":"Touvron, H., et al.: Llama 2: open foundation and fine-tuned chat models. CoRR abs\/2307.09288 (2023)"},{"key":"1_CR26","doi-asserted-by":"crossref","unstructured":"Wang, S., Chang, J., Li, H., Wang, Z., Ouyang, W., Tian, Q.: Open-set fine-grained retrieval via prompting vision-language evaluator. In: IEEE\/CVF Conference on Computer Vision and Pattern Recognition, CVPR 2023, Vancouver, BC, Canada, 17\u201324 June 2023, pp. 19381\u201319391. IEEE (2023)","DOI":"10.1109\/CVPR52729.2023.01857"},{"key":"1_CR27","doi-asserted-by":"crossref","unstructured":"Wang, S., Chang, J., Wang, Z., Li, H., Ouyang, W., Tian, Q.: Fine-grained retrieval prompt tuning. In: Williams, B., Chen, Y., Neville, J. (eds.) Thirty-Seventh AAAI Conference on Artificial Intelligence, AAAI 2023, Thirty-Fifth Conference on Innovative Applications of Artificial Intelligence, IAAI 2023, Thirteenth Symposium on Educational Advances in Artificial Intelligence, EAAI 2023, Washington, DC, USA, 7\u201314 February 2023, pp. 2644\u20132652. AAAI Press (2023)","DOI":"10.1609\/aaai.v37i2.25363"},{"key":"1_CR28","doi-asserted-by":"crossref","unstructured":"Wang, Z., Wang, S., Yang, S., Li, H., Li, J., Li, Z.: Weakly supervised fine-grained image classification via Guassian mixture model oriented discriminative learning. In: 2020 IEEE\/CVF Conference on Computer Vision and Pattern Recognition, CVPR 2020, Seattle, WA, USA, 13\u201319 June 2020, pp. 9746\u20139755. Computer Vision Foundation\/IEEE (2020)","DOI":"10.1109\/CVPR42600.2020.00977"},{"key":"1_CR29","doi-asserted-by":"crossref","unstructured":"Wang, Z., et\u00a0al.: The global wheat full semantic organ segmentation (GWFSS) dataset. Plant Phenomics, 100084 (2025)","DOI":"10.1016\/j.plaphe.2025.100084"},{"key":"1_CR30","unstructured":"Xin, Y., et al.: Parameter-efficient fine-tuning for pre-trained vision models: a survey. CoRR abs\/2402.02242 (2024)"},{"key":"1_CR31","unstructured":"Xu, Y., et al.: QA-LoRA: auantization-aware low-rank adaptation of large language models. In: The Twelfth International Conference on Learning Representations, ICLR 2024, Vienna, Austria, 7\u201311 May 2024. OpenReview.net (2024)"},{"key":"1_CR32","doi-asserted-by":"crossref","unstructured":"Yin, D., Yang, Y., Wang, Z., Yu, H., Wei, K., Sun, X.: 1% VS 100%: parameter-efficient low rank adapter for dense predictions. In: IEEE\/CVF Conference on Computer Vision and Pattern Recognition, CVPR 2023, Vancouver, BC, Canada, 17\u201324 June 2023, pp. 20116\u201320126. IEEE (2023)","DOI":"10.1109\/CVPR52729.2023.01926"},{"key":"1_CR33","unstructured":"Yosinski, J., Clune, J., Bengio, Y., Lipson, H.: How transferable are features in deep neural networks? In: Ghahramani, Z., Welling, M., Cortes, C., Lawrence, N.D., Weinberger, K.Q. (eds.) Advances in Neural Information Processing Systems 27: Annual Conference on Neural Information Processing Systems 2014, 8\u201313 December 2014, Montreal, Quebec, Canada, pp. 3320\u20133328 (2014)"},{"issue":"12","key":"1_CR34","first-page":"1","volume":"56","author":"BXB Yu","year":"2024","unstructured":"Yu, B.X.B., et al.: Visual tuning. ACM Comput. Surv. 56(12), 1\u201338 (2024)","journal-title":"ACM Comput. Surv."},{"key":"1_CR35","doi-asserted-by":"crossref","unstructured":"Yuan, B., Song, S., Fernandez, J., Luo, Y., Baktashmotlagh, M., Wang, Z.: WisWheat: a three-tiered vision-language dataset for wheat management. arXiv preprint: arXiv:2506.06084 (2025)","DOI":"10.1145\/3746027.3758256"},{"key":"1_CR36","doi-asserted-by":"crossref","unstructured":"Yuan, B., Wang, Z., Yu, X.: Towards reliable and efficient vegetation segmentation for Australian wheat data analysis. In: Australasian Database Conference, pp. 119\u2013135. Springer (2023)","DOI":"10.1007\/978-3-031-47843-7_9"},{"key":"1_CR37","doi-asserted-by":"crossref","unstructured":"Zaken, E.B., Goldberg, Y., Ravfogel, S.: BitFit: simple parameter-efficient fine-tuning for transformer-based masked language-models. In: Muresan, S., Nakov, P., Villavicencio, A. (eds.) Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers), ACL 2022, Dublin, Ireland, 22\u201327 May 2022, pp.\u00a01\u20139. Association for Computational Linguistics (2022)","DOI":"10.18653\/v1\/2022.acl-short.1"},{"key":"1_CR38","doi-asserted-by":"crossref","unstructured":"Zenkl, R., McDonald, B.A., Walter, A., Unvericht, M., Saintenac, C., Anderegg, J.: From canopy images to organ-level disease assessments: a scalable approach to measure quantitative resistance in the field. bioRxiv, pp. 2025\u201304 (2025)","DOI":"10.1101\/2025.04.30.651476"},{"key":"1_CR39","doi-asserted-by":"publisher","first-page":"774068","DOI":"10.3389\/fpls.2021.774068","volume":"12","author":"R Zenkl","year":"2022","unstructured":"Zenkl, R., et al.: Outdoor plant segmentation with deep learning for high-throughput field phenotyping on a diverse wheat dataset. Front. Plant Sci. 12, 774068 (2022)","journal-title":"Front. Plant Sci."},{"key":"1_CR40","doi-asserted-by":"crossref","unstructured":"Zhang, P.F., Huang, Z., Bai, G.: Universal adversarial perturbations for vision-language pre-trained models. In: Proceedings of the 47th International ACM SIGIR Conference on Research and Development in Information Retrieval, pp. 862\u2013871 (2024)","DOI":"10.1145\/3626772.3657781"},{"key":"1_CR41","doi-asserted-by":"publisher","first-page":"9477","DOI":"10.1109\/TMM.2024.3394677","volume":"26","author":"PF Zhang","year":"2024","unstructured":"Zhang, P.F., Huang, Z., Xu, X.S., Bai, G.: Effective and robust adversarial training against data and label corruptions. IEEE Trans. Multimedia 26, 9477\u20139488 (2024)","journal-title":"IEEE Trans. Multimedia"},{"key":"1_CR42","doi-asserted-by":"crossref","unstructured":"Zhang, Z., et al.: Gradient-based parameter selection for efficient fine-tuning. In: IEEE\/CVF Conference on Computer Vision and Pattern Recognition, CVPR 2024, Seattle, WA, USA, 16\u201322 June 2024, pp. 28566\u201328577. IEEE (2024)","DOI":"10.1109\/CVPR52733.2024.02699"}],"container-title":["Lecture Notes in Computer Science","Databases Theory and Applications"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-981-95-6196-4_1","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,2,4]],"date-time":"2026-02-04T05:59:17Z","timestamp":1770184757000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-981-95-6196-4_1"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026]]},"ISBN":["9789819561957","9789819561964"],"references-count":42,"URL":"https:\/\/doi.org\/10.1007\/978-981-95-6196-4_1","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"value":"0302-9743","type":"print"},{"value":"1611-3349","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026]]},"assertion":[{"value":"5 February 2026","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"ADC","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Australasian Database Conference","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Sydney","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Australia","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2025","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"4 December 2025","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"7 December 2025","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"36","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"adc2025","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/adc-conference.github.io\/2025\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}}]}}