{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,12,10]],"date-time":"2025-12-10T12:49:13Z","timestamp":1765370953032,"version":"3.46.0"},"reference-count":37,"publisher":"Springer Science and Business Media LLC","issue":"4","license":[{"start":{"date-parts":[[2025,5,26]],"date-time":"2025-05-26T00:00:00Z","timestamp":1748217600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2025,5,26]],"date-time":"2025-05-26T00:00:00Z","timestamp":1748217600000},"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":["Iran J Comput Sci"],"published-print":{"date-parts":[[2025,12]]},"DOI":"10.1007\/s42044-025-00274-4","type":"journal-article","created":{"date-parts":[[2025,5,26]],"date-time":"2025-05-26T07:03:43Z","timestamp":1748243023000},"page":"1503-1514","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Plant leaf disease recognition using deep dynamic joint adaptation networks based on partially labeled data"],"prefix":"10.1007","volume":"8","author":[{"given":"Isack","family":"Bulugu","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2025,5,26]]},"reference":[{"issue":"2","key":"274_CR1","doi-asserted-by":"publisher","first-page":"249","DOI":"10.56093\/ijas.v90i2.98996","volume":"90","author":"S Nigam","year":"2020","unstructured":"Nigam, S., Jain, R.: Plant disease identification using Deep Learning: A review. Indian J. Agric. Sci. 90(2), 249\u2013257 (2020)","journal-title":"Indian J. Agric. Sci."},{"key":"274_CR2","doi-asserted-by":"publisher","DOI":"10.1016\/j.engappai.2023.106458","author":"H Jin","year":"2023","unstructured":"Jin, H., Chu, X., Qi, J., Zhang, X., Mu, W.: CWAN: Self-supervised learning for deep grape disease image composition. Eng. Appl. Artif. Intell. (2023). https:\/\/doi.org\/10.1016\/j.engappai.2023.106458","journal-title":"Eng. Appl. Artif. Intell."},{"unstructured":"J. Donahue, Y. Jia, O. Vinyals, J. Hoffman, N. Zhang, E. Tzeng and T. Darrell, \"A deep Convolutional Activation Feature for Generic Visual Recognition,\" In Proceedings of the 31st International Conference on Machine Learning, 2014.","key":"274_CR3"},{"unstructured":"X. Glorot, A. Bordes and Y. Bengio, \"Domain Adaptation for Large-Scale Sentiment Classification: A Deep Learning Approach,\" In Proceedings of the 28 th International Conference on Machine Learning, 2011.","key":"274_CR4"},{"unstructured":"E. Tzeng, J. Hoffman, K. Saenko, N. Zhang and T. Darrell, \"Deep Domain Confusion:Maximizing for Domain Invariance,\" Cornell University, 2014.","key":"274_CR5"},{"key":"274_CR6","first-page":"3320","volume":"27","author":"J Yosinski","year":"2014","unstructured":"Yosinski, J., Clune, J., Bengio, Y., Lipson, H.: How transferable are features in deep neural networks? Adv. Neural Inf. Process. Syst. 27, 3320\u20133328 (2014)","journal-title":"Adv. Neural Inf. Process. Syst."},{"unstructured":"M. Long, Y. Cao, J. Wang and M. I. Jordan, \"Learning Transferable Fetures with Deep Adaptation Networks,\" In Proceedings of the 32nd International Conference on Machine, 2015.","key":"274_CR7"},{"key":"274_CR8","doi-asserted-by":"publisher","DOI":"10.1016\/j.neucom.2024.128608","author":"AYH Chai","year":"2024","unstructured":"Chai, A.Y.H., Lee, S.H., Tay, F.S., Bonnet, P., Joly, A.: Beyond supervision: Harnessing self-supervised learning in unseen plant disease recognition. Neurocomputing (2024). https:\/\/doi.org\/10.1016\/j.neucom.2024.128608","journal-title":"Neurocomputing"},{"key":"274_CR9","doi-asserted-by":"publisher","DOI":"10.1016\/j.eswa.2025.127101","author":"G Guo","year":"2025","unstructured":"Guo, G., Lai, S., Wu, Q., Shou, Y., Shi, W.: Enhancing domain adaptation for plant diseases detection through Masked Image Consistency in Multi-Granularity Alignment. Expert Syst. Appl. (2025). https:\/\/doi.org\/10.1016\/j.eswa.2025.127101","journal-title":"Expert Syst. Appl."},{"key":"274_CR10","doi-asserted-by":"publisher","DOI":"10.1016\/j.compag.2024.109574","author":"MH Tunio","year":"2024","unstructured":"Tunio, M.H., Li, J., Zeng, X., Ahmed, A., Attique, S.S., Shaikh, H.-U., Yahya, I.A.: Advancing plant disease classification: A robust and generalized approach with transformer-fused convolution and Wasserstein domain adaptation. Comput. Electron. Agric. (2024). https:\/\/doi.org\/10.1016\/j.compag.2024.109574","journal-title":"Comput. Electron. Agric."},{"doi-asserted-by":"crossref","unstructured":"Ouamane, A., Chouchane, A., Himeur, Y., Miniaoui, S., Atalla, S. Mansoor, W.: Optimized vision transformers for superior plant disease detection. IEEE Access. 13, 48552\u201348570 (2025)","key":"274_CR11","DOI":"10.1109\/ACCESS.2025.3547416"},{"unstructured":"M. Long, H. Zhu,. J. Wang and M. I. Jordan, \"Deep Transfer Learning with Joint Adaptation Networks,\" In Proceedings of the 34th International Conference on Machine Learning, 2017.","key":"274_CR12"},{"doi-asserted-by":"crossref","unstructured":"K. He, X. Zhang, S. Ren and J. Sun, \"Deep Residual Learning for Image Recognition,\" In 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2016.","key":"274_CR13","DOI":"10.1109\/CVPR.2016.90"},{"unstructured":"Y. Grandvalet and Y. Bengio, \"Semi-supervised Learning by Entropy Minimization,\" In Proceedings of the 17th International Conference on Neural Information Processing Systems, 2004.","key":"274_CR14"},{"key":"274_CR15","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-01424-7_27","volume-title":"A Survey on Deep Transfer Learning","author":"C Tan","year":"2018","unstructured":"Tan, C., Sun, F., Kong, T., Zhang, W., Yang, C., Liu, C.: A Survey on Deep Transfer Learning. Springer, Cham (2018)"},{"key":"274_CR16","doi-asserted-by":"publisher","first-page":"213","DOI":"10.1007\/978-3-642-15561-1_16","volume-title":"Computer Vision \u2013 ECCV 2010","author":"K Saenko","year":"2010","unstructured":"Saenko, K., Kulis, B., Fritz, M., Darrell, T.: Adapting Visual Category Models to New Domains. In: Computer Vision \u2013 ECCV 2010, pp. 213\u2013226. Springer, Berlin, Heidelberg (2010)"},{"unstructured":"Collobert, R., Weston, J., Bottou, L., Karlen, M., Kavukcuoglu, K., Kuksa, P.: Natural language processing(almost) from scratch. J. Mach. Learn. Res. 12, 2493\u20132537 (2011)","key":"274_CR17"},{"key":"274_CR18","first-page":"898","volume-title":"PRICAI 2014: Trends in Artificial Intelligenc","author":"M Ghifary","year":"2014","unstructured":"Ghifary, M., Kleijn, B., Zhang, M.: Domain Adaptive Neural Networks for Object Recognition. In: PRICAI 2014: Trends in Artificial Intelligenc, pp. 898\u2013904. Springer, Switzerland (2014)"},{"key":"274_CR19","doi-asserted-by":"publisher","DOI":"10.1093\/bioinformatics\/btl242","author":"KM Borgwardt","year":"2006","unstructured":"Borgwardt, K.M., Gretton, A., Rasch, M.J., Kriegel, H.-P., Scholkopf, B., Smola, A.J.: Integrating structured biological data by Kernel Maximum Mean Discrepancy. Bioinformatics (2006). https:\/\/doi.org\/10.1093\/bioinformatics\/btl242","journal-title":"Bioinformatics"},{"unstructured":"M. Long, H. Zhu, J. Wang and M. I. Jordan, \"Unsupervised Domain Adaptation with Residual Transfer Networks,\" In 30th Conference on Neural Information Processing Systems, Barcelona, Spain, 2016.","key":"274_CR20"},{"key":"274_CR21","doi-asserted-by":"publisher","DOI":"10.1016\/j.rineng.2025.103922","author":"G Yilma","year":"2025","unstructured":"Yilma, G., Dagne, M., Ahmed, M.K., Bellam, R.B.: Attentive Self-supervised Contrastive Learning (ASCL) for plant disease classification. Results Eng. (2025). https:\/\/doi.org\/10.1016\/j.rineng.2025.103922","journal-title":"Results Eng."},{"key":"274_CR22","doi-asserted-by":"publisher","first-page":"179912","DOI":"10.1109\/ACCESS.2024.3505989","volume":"12","author":"ZU Rahman","year":"2024","unstructured":"Rahman, Z.U., Asaari, M.S.M., Ibrahim, H., Abidin, I.S.Z., Ishak, M.K.: Generative adversarial networks (GANs) for image augmentation in farming: A Review. IEEE Access 12, 179912\u2013179943 (2024)","journal-title":"IEEE Access"},{"key":"274_CR23","doi-asserted-by":"publisher","first-page":"171926","DOI":"10.1109\/ACCESS.2024.3475819","volume":"12","author":"A Al Mamun","year":"2024","unstructured":"Al Mamun, A., Ahmedt-Aristizabal, D., Zhang, M., Hossen, M.I., Hayder, Z., Awrangjeb, M.: Plant disease detection using self-supervised learning: A systematic review. IEEE Access 12, 171926\u2013171943 (2024)","journal-title":"IEEE Access"},{"key":"274_CR24","doi-asserted-by":"publisher","first-page":"443","DOI":"10.1007\/978-3-319-49409-8_35","volume-title":"Computer Vision \u2013 ECCV 2016 Workshops","author":"B Sun","year":"2016","unstructured":"Sun, B., Saenko, K.: Deep CORAL: Correlation Alignment for Deep Domain Adaptation. In: Computer Vision \u2013 ECCV 2016 Workshops, pp. 443\u2013450. Springer, Cham (2016)"},{"unstructured":"W. Zellinger, T. Grubinger, E. Lughofer, T. Natschlager and S. Saminger-Platz, \"Central Moment Discrepancy (CMD) for Domain-Invariant Representation,\" in International Conference on Learning Representations (ICLR), 2017.","key":"274_CR25"},{"unstructured":"I. J. Goodfellow, J. Pouget-Abadie, M. Mirza, B. Xu, D. Warde-Farley, S. Ozair, A. Courville and Y. Bengio, \"Generative Adversarial Nets,\" in Proceedings of the 27th International Conference on Neural Information Processing Systems , 2014.","key":"274_CR26"},{"unstructured":"Ganin, Y., Ustinova, E., Ajakan, H., Germain, P., Larochelle, H., Laviolette, F., Marchand, M., Lempitsky, V.: Domain advesarial training of neural networks. J. Mach. Learn. Res. 17, 1\u201335 (2016)","key":"274_CR27"},{"doi-asserted-by":"crossref","unstructured":"Cao, Z., Long, M., Wang, J., Jordan, M.I.: Partial transfer learning with selective adversarial network. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 2724\u20132732 (2018)","key":"274_CR28","DOI":"10.1109\/CVPR.2018.00288"},{"key":"274_CR29","doi-asserted-by":"publisher","DOI":"10.1007\/s10994-009-5152-4","author":"S Ben-David","year":"2010","unstructured":"Ben-David, S., Blitzer, J., Crammer, K., Kulesza, A., Pereira, F., Wortman, J.: A theory of learning from different domains. Mach. Learn. (2010). https:\/\/doi.org\/10.1007\/s10994-009-5152-4","journal-title":"Mach. Learn."},{"key":"274_CR30","doi-asserted-by":"publisher","DOI":"10.1109\/TNN.2010.2091281","author":"SJ Pan","year":"2011","unstructured":"Pan, S.J., Tsang, I.W., Kwok, J.T., Yang, Q.: Domain adaptation via transfer component analysis. IEEE Trans. Neural Netw. (2011). https:\/\/doi.org\/10.1109\/TNN.2010.2091281","journal-title":"IEEE Trans. Neural Netw."},{"doi-asserted-by":"crossref","unstructured":"M. Long, J. Wang, G. Ding, J. Sun and P. S. Yu, \"Transfer Joint Matching for Unsupervised Domain Adaptation,\" In IEEE Conference on Computer Vision and Pattern Recognition, 2014.","key":"274_CR31","DOI":"10.1109\/CVPR.2014.183"},{"doi-asserted-by":"crossref","unstructured":"M. Long, J. Wang, G. Ding, J. Sun and P. S. Yu, \"Transfer Feature Learning with Joint Distribution Adaptation,\" In IEEE International Conference on Computer Vision, Sydney, NSW, Australia, 2013.","key":"274_CR32","DOI":"10.1109\/ICCV.2013.274"},{"doi-asserted-by":"crossref","unstructured":"J. Wang , Y. Chen , S. Hao , W. Feng and Z. Shen, \"Balanced Distribution Adaptation for Transfer Learning,\" In IEEE International Conference on Data Mining (ICDM), 2017.","key":"274_CR33","DOI":"10.1109\/ICDM.2017.150"},{"doi-asserted-by":"crossref","unstructured":"J. Wang, W. Feng, Y. Chen, H. Yu, M. Huang and P. . S. Yu, \"Visual Domain Adaptation with Manifold Embedded Distribution Alignment,\" in Proceedings of the 26th ACM international conference on Multimedia, New York, NY, United States, 2018.","key":"274_CR34","DOI":"10.1145\/3240508.3240512"},{"doi-asserted-by":"crossref","unstructured":"Ben-David, S., Blitzer, J., Crammer, K., Pereira, F.: Analysis of representations for domain adaptation. In: Proceedings of the Conference: Advances in Neural Information Processing Systems, pp. 137\u2013144 (2019)","key":"274_CR35","DOI":"10.7551\/mitpress\/7503.003.0022"},{"key":"274_CR36","first-page":"2579","volume":"9","author":"L van der Maaten","year":"2008","unstructured":"van der Maaten, L., Hinton, G.: Visualizing Data using t-SNE. J. Mach. Learn. Res. 9, 2579\u20132605 (2008)","journal-title":"J. Mach. Learn. Res."},{"unstructured":"A. Gretton, B. Sriperumbudur, D. Sejdinovic, H. Strahmann, S. Balakrishnan, M. Pontil and K. Fukumizu, \"Optimal kernel choice for large-scale two-sample tests,\" In Proceedings of the 25th International Conference on Neural Information Processing Systems, 2012.","key":"274_CR37"}],"container-title":["Iran Journal of Computer Science"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s42044-025-00274-4.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s42044-025-00274-4\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s42044-025-00274-4.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,12,10]],"date-time":"2025-12-10T09:09:57Z","timestamp":1765357797000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s42044-025-00274-4"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,5,26]]},"references-count":37,"journal-issue":{"issue":"4","published-print":{"date-parts":[[2025,12]]}},"alternative-id":["274"],"URL":"https:\/\/doi.org\/10.1007\/s42044-025-00274-4","relation":{},"ISSN":["2520-8438","2520-8446"],"issn-type":[{"type":"print","value":"2520-8438"},{"type":"electronic","value":"2520-8446"}],"subject":[],"published":{"date-parts":[[2025,5,26]]},"assertion":[{"value":"5 January 2025","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"6 May 2025","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"26 May 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 no competing interests.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflicts of interest"}}]}}