{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,23]],"date-time":"2026-03-23T07:37:15Z","timestamp":1774251435383,"version":"3.50.1"},"reference-count":50,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2026,1,26]],"date-time":"2026-01-26T00:00:00Z","timestamp":1769385600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2026,1,26]],"date-time":"2026-01-26T00:00:00Z","timestamp":1769385600000},"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":["J Classif"],"published-print":{"date-parts":[[2026,4]]},"DOI":"10.1007\/s00357-025-09531-4","type":"journal-article","created":{"date-parts":[[2026,1,26]],"date-time":"2026-01-26T06:10:14Z","timestamp":1769407814000},"page":"215-235","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["A Gaussian Noise Bayesian Deep Learning Approach for Enhancing Uncertainty Quantifications in Classifier Decisions"],"prefix":"10.1007","volume":"43","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-6792-5513","authenticated-orcid":false,"given":"Dalia","family":"Ezzat","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Eman","family":"Ahmed","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Mona","family":"Soliman","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Reda","family":"Alkhoribi","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2026,1,26]]},"reference":[{"key":"9531_CR1","doi-asserted-by":"publisher","DOI":"10.1016\/j.eswa.2023.122983","volume":"244","author":"S Abut","year":"2024","unstructured":"Abut, S., Okut, H., & Kallail, K. J. (2024). Paradigm shift from Artificial Neural Networks (ANNs) to deep Convolutional Neural Networks (DCNNs) in the field of medical image processing. Expert Systems with Applications, 244, Article 122983. https:\/\/doi.org\/10.1016\/j.eswa.2023.122983","journal-title":"Expert Systems with Applications"},{"key":"9531_CR2","doi-asserted-by":"publisher","DOI":"10.1016\/j.autcon.2021.103989","volume":"133","author":"R Ali","year":"2022","unstructured":"Ali, R., Chuah, J. H., Talip, M. S. A., Mokhtar, N., & Shoaib, M. A. (2022). Structural crack detection using deep convolutional neural networks. Automation in Construction, 133, Article 103989. https:\/\/doi.org\/10.1016\/j.autcon.2021.103989","journal-title":"Automation in Construction"},{"key":"9531_CR3","doi-asserted-by":"publisher","first-page":"757","DOI":"10.1016\/j.neunet.2023.02.022","volume":"161","author":"D Bala","year":"2023","unstructured":"Bala, D., Hossain, M. S., Hossain, M. A., Abdullah, M. I., Rahman, M. M., Manavalan, B., Gu, N., Islam, M. S., & Huang, Z. (2023). MonkeyNet: A robust deep convolutional neural network for monkeypox disease detection and classification. Neural Networks, 161, 757\u2013775. https:\/\/doi.org\/10.1016\/j.neunet.2023.02.022","journal-title":"Neural Networks"},{"key":"9531_CR4","doi-asserted-by":"publisher","DOI":"10.1016\/j.compag.2021.106367","volume":"189","author":"W Bao","year":"2021","unstructured":"Bao, W., Yang, X., Liang, D., Hu, G., & Yang, X. (2021). Lightweight convolutional neural network model for field wheat ear disease identification. Computers and Electronics in Agriculture, 189, Article 106367. https:\/\/doi.org\/10.1016\/j.compag.2021.106367","journal-title":"Computers and Electronics in Agriculture"},{"key":"9531_CR5","doi-asserted-by":"publisher","unstructured":"Bobrowska, R., Moskalik, J., Noweiska, A., Spycha\u0142a, J., Tomkowiak, A., & Kwiatek, M. T. (2025). Development and application of duplex and triplex assays for simultaneous detection of resistance genes to leaf rust, Fusarium head blight, powdery mildew, Septoria tritici blotch, eyspot, stem rust and yellow rust in wheat. Journal of Applied Genetics, 1\u201316.\u00a0https:\/\/doi.org\/10.1007\/s13353-025-01004-z","DOI":"10.1007\/s13353-025-01004-z"},{"issue":"1","key":"9531_CR6","doi-asserted-by":"publisher","first-page":"11554","DOI":"10.1038\/s41598-022-15163-0","volume":"12","author":"Y Borhani","year":"2022","unstructured":"Borhani, Y., Khoramdel, J., & Najafi, E. (2022). A deep learning based approach for automated plant disease classification using vision transformer. Scientific Reports, 12(1), 11554. https:\/\/doi.org\/10.1038\/s41598-022-15163-0","journal-title":"Scientific Reports"},{"key":"9531_CR7","doi-asserted-by":"publisher","DOI":"10.1016\/j.jhydrol.2024.130627","volume":"630","author":"I Busari","year":"2024","unstructured":"Busari, I., Sahoo, D., & Jana, R. B. (2024). Prediction of chlorophyll-a as an indicator of harmful algal blooms using deep learning with Bayesian approximation for uncertainty assessment. Journal of Hydrology, 630, Article 130627. https:\/\/doi.org\/10.1016\/j.jhydrol.2024.130627","journal-title":"Journal of Hydrology"},{"key":"9531_CR8","doi-asserted-by":"publisher","first-page":"21","DOI":"10.1016\/j.neucom.2020.07.140","volume":"437","author":"P Chen","year":"2021","unstructured":"Chen, P., Li, W. L., Yao, S. J., Ma, C., Zhang, J., Wang, B., Zheng, C. H., Xie, C. J., & Liang, D. (2021). Recognition and counting of wheat mites in wheat fields by a three-step deep learning method. Neurocomputing, 437, 21\u201330. https:\/\/doi.org\/10.1016\/j.neucom.2020.07.140","journal-title":"Neurocomputing"},{"key":"9531_CR9","volume-title":"Deep learning with Python","author":"F Chollet","year":"2021","unstructured":"Chollet, F. (2021). Deep learning with Python. Simon and Schuster."},{"key":"9531_CR10","doi-asserted-by":"publisher","DOI":"10.1016\/j.mex.2024.102659","volume":"12","author":"RM Devadas","year":"2024","unstructured":"Devadas, R. M., & Hiremani, V. (2024). Integrating dropout and Kullback-Leibler regularization in Bayesian Neural Networks for improved uncertainty estimation in Regression. MethodsX, 12, Article 102659. https:\/\/doi.org\/10.1016\/j.mex.2024.102659","journal-title":"MethodsX"},{"issue":"1","key":"9531_CR11","doi-asserted-by":"publisher","first-page":"5","DOI":"10.1002\/jpln.202400213","volume":"188","author":"D Ejigu","year":"2025","unstructured":"Ejigu, D., Pushpalatha, R., Jayaprakash, S. K., Gangadharan, B., Himanshu, S. K., & Gopakumar, S. (2025). Integrated fertilizers for sustainable wheat production to improve food security\u2014A comprehensive review. Journal of Plant Nutrition and Soil Science, 188(1), 5\u201316.","journal-title":"Journal of Plant Nutrition and Soil Science"},{"key":"9531_CR12","doi-asserted-by":"publisher","DOI":"10.1007\/s00521-024-10008-5","author":"D Ezzat","year":"2024","unstructured":"Ezzat, D., Ahmed, E., Soliman, M., & Hassanien, A. E. (2024). An interpretable Bayesian deep learning-based approach for sustainable clean energy. Neural Computing and Applications. https:\/\/doi.org\/10.1007\/s00521-024-10008-5","journal-title":"Neural Computing and Applications"},{"key":"9531_CR13","doi-asserted-by":"publisher","first-page":"110810","DOI":"10.1016\/j.asoc.2023.110810","volume":"147","author":"D Ezzat","year":"2023","unstructured":"Ezzat, D., & Hassanien, A. E. (2023). Optimized Bayesian convolutional neural networks for invasive breast cancer diagnosis system[Formula presented]. Applied Soft Computing, 147, 110810. https:\/\/doi.org\/10.1016\/j.asoc.2023.110810","journal-title":"Applied Soft Computing"},{"issue":"9","key":"9531_CR14","doi-asserted-by":"publisher","DOI":"10.3390\/app13095801","volume":"13","author":"X Fang","year":"2023","unstructured":"Fang, X., Zhen, T., & Li, Z. (2023). Lightweight multiscale CNN model for wheat disease detection. Applied Sciences, 13(9), Article 5801. https:\/\/doi.org\/10.3390\/app13095801","journal-title":"Applied Sciences"},{"key":"9531_CR15","unstructured":"Gal, Y., & Ghahramani, Z. (2016). Dropout as a Bayesian approximation: Representing model uncertainty in deep learning. 33rd International Conference on Machine Learning, ICML 2016, 3, 1651\u20131660."},{"issue":"Suppl 1","key":"9531_CR16","doi-asserted-by":"publisher","first-page":"1513","DOI":"10.1007\/s10462-023-10562-9","volume":"56","author":"J Gawlikowski","year":"2023","unstructured":"Gawlikowski, J., Tassi, C. R. N., Ali, M., Lee, J., Humt, M., Feng, J., Kruspe, A., Triebel, R., Jung, P., Roscher, R., Shahzad, M., Yang, W., Bamler, R., & Zhu, X. X. (2023). A survey of uncertainty in deep neural networks. Artificial Intelligence Review, 56(Suppl 1), 1513\u20131589. https:\/\/doi.org\/10.1007\/s10462-023-10562-9","journal-title":"Artificial Intelligence Review"},{"issue":"1","key":"9531_CR17","doi-asserted-by":"publisher","first-page":"613","DOI":"10.1007\/s10115-023-01955-x","volume":"66","author":"F Gerges","year":"2024","unstructured":"Gerges, F., Boufadel, M. C., Bou-Zeid, E., Nassif, H., & Wang, J. T. L. (2024). Long-term prediction of daily solar irradiance using Bayesian deep learning and climate simulation data. Knowledge and Information Systems, 66(1), 613\u2013633. https:\/\/doi.org\/10.1007\/s10115-023-01955-x","journal-title":"Knowledge and Information Systems"},{"key":"9531_CR18","unstructured":"Getachew, H. (2021). Wheat Leaf Dataset. Mendeley Data, V1. https:\/\/www.kaggle.com\/datasets\/olyadgetch\/wheat-leaf-dataset"},{"key":"9531_CR19","doi-asserted-by":"publisher","first-page":"64","DOI":"10.1016\/j.neucom.2020.02.067","volume":"398","author":"S Hahn","year":"2020","unstructured":"Hahn, S., & Choi, H. (2020). Understanding dropout as an optimization trick. Neurocomputing, 398, 64\u201370. https:\/\/doi.org\/10.1016\/j.neucom.2020.02.067","journal-title":"Neurocomputing"},{"issue":"3","key":"9531_CR20","doi-asserted-by":"publisher","first-page":"923","DOI":"10.1007\/s42161-021-00886-2","volume":"103","author":"T Hayit","year":"2021","unstructured":"Hayit, T., Erbay, H., Var\u00e7\u0131n, F., Hayit, F., & Akci, N. (2021). Determination of the severity level of yellow rust disease in wheat by using convolutional neural networks. Journal of Plant Pathology, 103(3), 923\u2013934. https:\/\/doi.org\/10.1007\/s42161-021-00886-2","journal-title":"Journal of Plant Pathology"},{"key":"9531_CR21","doi-asserted-by":"publisher","first-page":"630","DOI":"10.1007\/978-3-319-46493-0_38","volume":"9908","author":"K He","year":"2016","unstructured":"He, K., Zhang, X., Ren, S., & Sun, J. (2016). Identity mappings in deep residual networks. Lecture Notes in Computer Science (Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 9908, 630\u2013645. https:\/\/doi.org\/10.1007\/978-3-319-46493-0_38","journal-title":"Lecture Notes in Computer Science (Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)"},{"key":"9531_CR22","doi-asserted-by":"publisher","DOI":"10.1016\/j.rse.2022.113388","volume":"285","author":"V Hertel","year":"2023","unstructured":"Hertel, V., Chow, C., Wani, O., Wieland, M., & Martinis, S. (2023). Probabilistic SAR-based water segmentation with adapted Bayesian convolutional neural network. Remote Sensing of Environment, 285, Article 113388. https:\/\/doi.org\/10.1016\/j.rse.2022.113388","journal-title":"Remote Sensing of Environment"},{"key":"9531_CR23","doi-asserted-by":"publisher","unstructured":"Huang, G., Liu, Z., Van Der Maaten, L., & Weinberger, K. Q. (2017). Densely connected convolutional networks. Proceedings - 30th IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2017, 2017-Janua, 2261\u20132269. https:\/\/doi.org\/10.1109\/CVPR.2017.243","DOI":"10.1109\/CVPR.2017.243"},{"key":"9531_CR24","doi-asserted-by":"publisher","unstructured":"Igrejas, G., Ikeda, T. M., & Guzm\u00e1n, C. (2020). Wheat quality for improving processing and human health. Springer. https:\/\/doi.org\/10.1007\/978-3-030-34163-3","DOI":"10.1007\/978-3-030-34163-3"},{"key":"9531_CR25","doi-asserted-by":"publisher","DOI":"10.1016\/j.jafr.2023.100764","volume":"14","author":"MM Islam","year":"2023","unstructured":"Islam, M. M., Adil, M. A. A., Talukder, M. A., Ahamed, M. K. U., Uddin, M. A., Hasan, M. K., Sharmin, S., Rahman, M. M., & Debnath, S. K. (2023). DeepCrop: Deep learning-based crop disease prediction with web application. Journal of Agriculture and Food Research, 14, Article 100764. https:\/\/doi.org\/10.1016\/j.jafr.2023.100764","journal-title":"Journal of Agriculture and Food Research"},{"key":"9531_CR26","doi-asserted-by":"publisher","DOI":"10.1016\/j.compag.2021.106184","volume":"186","author":"Z Jiang","year":"2021","unstructured":"Jiang, Z., Dong, Z., Jiang, W., & Yang, Y. (2021). Recognition of rice leaf diseases and wheat leaf diseases based on multi-task deep transfer learning. Computers and Electronics in Agriculture, 186, Article 106184. https:\/\/doi.org\/10.1016\/j.compag.2021.106184","journal-title":"Computers and Electronics in Agriculture"},{"key":"9531_CR27","doi-asserted-by":"publisher","DOI":"10.1016\/j.artmed.2024.102798","volume":"149","author":"S Khawaled","year":"2024","unstructured":"Khawaled, S., & Freiman, M. (2024). NPB-REC: A non-parametric Bayesian deep-learning approach for undersampled MRI reconstruction with uncertainty estimation. Artificial Intelligence in Medicine, 149, Article 102798. https:\/\/doi.org\/10.1016\/j.artmed.2024.102798","journal-title":"Artificial Intelligence in Medicine"},{"key":"9531_CR28","doi-asserted-by":"publisher","unstructured":"Kumari, N., & Saini, B. S. (2023). Fully automatic wheat disease detection system by using different CNN models. In Sentiment Analysis and Deep Learning: Proceedings of ICSADL 2022 (pp. 351\u2013365). Springer. https:\/\/doi.org\/10.1007\/978-981-19-5443-6_26","DOI":"10.1007\/978-981-19-5443-6_26"},{"key":"9531_CR29","doi-asserted-by":"publisher","DOI":"10.1016\/j.imavis.2024.105016","volume":"146","author":"V Latke","year":"2024","unstructured":"Latke, V., & Narawade, V. (2024). Detection of dental periapical lesions using retinex based image enhancement and lightweight deep learning model. Image and Vision Computing, 146, Article 105016. https:\/\/doi.org\/10.1016\/j.imavis.2024.105016","journal-title":"Image and Vision Computing"},{"key":"9531_CR30","doi-asserted-by":"publisher","first-page":"180","DOI":"10.1109\/TNSRE.2022.3217929","volume":"31","author":"C Li","year":"2023","unstructured":"Li, C., Deng, Z., Song, R., Liu, X., Qian, R., & Chen, X. (2023). EEG-based seizure prediction via model uncertainty learning. IEEE Transactions on Neural Systems and Rehabilitation Engineering, 31, 180\u2013191. https:\/\/doi.org\/10.1109\/TNSRE.2022.3217929","journal-title":"IEEE Transactions on Neural Systems and Rehabilitation Engineering"},{"issue":"1","key":"9531_CR31","doi-asserted-by":"publisher","first-page":"7256","DOI":"10.1038\/s41598-022-11049-3","volume":"12","author":"RJ Licata","year":"2022","unstructured":"Licata, R. J., & Mehta, P. M. (2022). Uncertainty quantification techniques for data-driven space weather modeling: Thermospheric density application. Scientific Reports, 12(1), 7256. https:\/\/doi.org\/10.1038\/s41598-022-11049-3","journal-title":"Scientific Reports"},{"issue":"7","key":"9531_CR32","doi-asserted-by":"publisher","DOI":"10.3390\/agriculture12071031","volume":"12","author":"A Mohammadi","year":"2022","unstructured":"Mohammadi, A., Venkatesh, G., Eskandari, S., & Rafiee, S. (2022). Eco-efficiency analysis to improve environmental performance of wheat production. Agriculture, 12(7), Article 1031. https:\/\/doi.org\/10.3390\/agriculture12071031","journal-title":"Agriculture"},{"issue":"6","key":"9531_CR33","doi-asserted-by":"publisher","first-page":"3947","DOI":"10.1007\/s10462-019-09784-7","volume":"53","author":"R Moradi","year":"2020","unstructured":"Moradi, R., Berangi, R., & Minaei, B. (2020). A survey of regularization strategies for deep models. Artificial Intelligence Review, 53(6), 3947\u20133986. https:\/\/doi.org\/10.1007\/s10462-019-09784-7","journal-title":"Artificial Intelligence Review"},{"issue":"1","key":"9531_CR34","doi-asserted-by":"publisher","first-page":"29955","DOI":"10.1038\/s41598-025-14847-7","volume":"15","author":"S Murugesan","year":"2025","unstructured":"Murugesan, S., Chinnadurai, J., Srinivasan, S., Mathivanan, S. K., Chandan, R. R., & Moorthy, U. (2025). Robust multiclass classification of crop leaf diseases using hybrid deep learning and Grad-CAM interpretability. Scientific Reports, 15(1), 29955.","journal-title":"Scientific Reports"},{"key":"9531_CR35","doi-asserted-by":"publisher","DOI":"10.1016\/j.ecoinf.2023.102068","volume":"75","author":"S Nigam","year":"2023","unstructured":"Nigam, S., Jain, R., Marwaha, S., Arora, A., Haque, M. A., Dheeraj, A., & Singh, V. K. (2023). Deep transfer learning model for disease identification in wheat crop. Ecological Informatics, 75, Article 102068. https:\/\/doi.org\/10.1016\/j.ecoinf.2023.102068","journal-title":"Ecological Informatics"},{"key":"9531_CR36","doi-asserted-by":"publisher","unstructured":"Prechelt, L. (2002). Early stopping-but when? In neural networks: Tricks of the trade (pp. 55-69). Berlin, Heidelberg: Springer Berlin Heidelberg. https:\/\/doi.org\/10.1007\/978-3-642-35289-8_5","DOI":"10.1007\/978-3-642-35289-8_5"},{"key":"9531_CR37","unstructured":"Simonyan, K., & Zisserman, A. (2015). Very deep convolutional networks for large-scale image recognition. 3rd International Conference on Learning Representations, ICLR 2015 - Conference Track Proceedings."},{"issue":"1","key":"9531_CR38","doi-asserted-by":"publisher","first-page":"221","DOI":"10.1186\/s40537-025-01278-4","volume":"12","author":"MSH Talukder","year":"2025","unstructured":"Talukder, M. S. H., Akter, S., Nur, A. H., Aljaidi, M., Sulaiman, R. B., & Alkoradees, A. F. (2025). SugarcaneNet: An optimized ensemble of LASSO-regularized pre-trained models for accurate sugarcane disease classification. Journal of Big Data, 12(1), 221.","journal-title":"Journal of Big Data"},{"key":"9531_CR39","doi-asserted-by":"publisher","DOI":"10.1016\/j.compag.2023.107709","volume":"207","author":"Z Tang","year":"2023","unstructured":"Tang, Z., Wang, M., Schirrmann, M., Dammer, K. H., Li, X., Brueggeman, R., Sankaran, S., Carter, A. H., Pumphrey, M. O., Hu, Y., Chen, X., & Zhang, Z. (2023). Affordable high throughput field detection of wheat stripe rust using deep learning with semi-automated image labeling. Computers and Electronics in Agriculture, 207, Article 107709. https:\/\/doi.org\/10.1016\/j.compag.2023.107709","journal-title":"Computers and Electronics in Agriculture"},{"issue":"8","key":"9531_CR40","doi-asserted-by":"publisher","first-page":"146","DOI":"10.1145\/3551636","volume":"55","author":"Z Tian","year":"2022","unstructured":"Tian, Z., Cui, L., Liang, J., & Yu, S. (2022). A comprehensive survey on poisoning attacks and countermeasures in machine learning. ACM Computing Surveys, 55(8), 146\u2013166. https:\/\/doi.org\/10.1145\/3551636","journal-title":"ACM Computing Surveys"},{"issue":"1","key":"9531_CR41","doi-asserted-by":"publisher","first-page":"26","DOI":"10.1038\/s41746-024-01007-w","volume":"7","author":"AT Tran","year":"2024","unstructured":"Tran, A. T., Zeevi, T., Haider, S. P., Abou Karam, G., Berson, E. R., Tharmaseelan, H., Qureshi, A. I., Sanelli, P. C., Werring, D. J., Malhotra, A., Petersen, N. H., de Havenon, A., Falcone, G. J., Sheth, K. N., & Payabvash, S. (2024). Uncertainty-aware deep-learning model for prediction of supratentorial hematoma expansion from admission non-contrast head computed tomography scan. Npj Digital Medicine, 7(1), 26. https:\/\/doi.org\/10.1038\/s41746-024-01007-w","journal-title":"Npj Digital Medicine"},{"key":"9531_CR42","doi-asserted-by":"publisher","DOI":"10.1080\/03772063.2023.2181229","author":"S Verma","year":"2023","unstructured":"Verma, S., Kumar, P., & Singh, J. P. (2023). A unified lightweight CNN-based model for disease detection and identification in corn, rice, and wheat. IETE Journal of Research. https:\/\/doi.org\/10.1080\/03772063.2023.2181229","journal-title":"IETE Journal of Research"},{"issue":"3","key":"9531_CR43","doi-asserted-by":"publisher","first-page":"815","DOI":"10.1080\/10095020.2024.2341748","volume":"28","author":"M Vizzari","year":"2025","unstructured":"Vizzari, M., Lesti, G., & Acharki, S. (2025). Crop classification in Google Earth Engine: Leveraging Sentinel-1, Sentinel-2, European CAP data, and object-based machine-learning approaches. Geo-Spatial Information Science, 28(3), 815\u2013830.","journal-title":"Geo-Spatial Information Science"},{"key":"9531_CR44","doi-asserted-by":"publisher","DOI":"10.1016\/j.xplc.2024.101223","author":"H Wang","year":"2025","unstructured":"Wang, H., Yan, S., Wang, W., Chen, Y., Hong, J., He, Q., Diao, X., Lin, Y., Chen, Y., & Cao, Y. (2025). Cropformer: An interpretable deep learning framework for crop genomic prediction. Plant Communications. https:\/\/doi.org\/10.1016\/j.xplc.2024.101223","journal-title":"Plant Communications"},{"key":"9531_CR45","doi-asserted-by":"publisher","first-page":"101940","DOI":"10.1016\/j.pmpp.2022.101940","volume":"123","author":"L Xu","year":"2023","unstructured":"Xu, L., Cao, B., Zhao, F., Ning, S., Xu, P., Zhang, W., & Hou, X. (2023). Wheat leaf disease identification based on deep learning algorithms. Physiological and Molecular Plant Pathology, 123, 101940.","journal-title":"Physiological and Molecular Plant Pathology"},{"key":"9531_CR46","doi-asserted-by":"publisher","DOI":"10.1016\/j.atech.2025.101132","author":"Y Xu","year":"2025","unstructured":"Xu, Y., Mao, Y., Li, H., Li, X., Sun, L., Fan, K., Li, Z., Gong, S., Ding, Z., & Wang, Y. (2025). Construction and application of a drought classification model for tea plantations based on multi-source remote sensing. Smart Agricultural Technology. https:\/\/doi.org\/10.1016\/j.atech.2025.101132","journal-title":"Smart Agricultural Technology"},{"key":"9531_CR47","doi-asserted-by":"publisher","first-page":"110003","DOI":"10.1016\/j.patcog.2023.110003","volume":"146","author":"SH Yelleni","year":"2024","unstructured":"Yelleni, S. H., Kumari, D., Srijith, P. K., & Krishna Mohan, C. (2024). Monte Carlo DropBlock for modeling uncertainty in object detection. Pattern Recognition, 146, 110003. https:\/\/doi.org\/10.1016\/j.patcog.2023.110003","journal-title":"Pattern Recognition"},{"key":"9531_CR48","doi-asserted-by":"publisher","DOI":"10.1016\/j.foodcont.2022.108819","volume":"135","author":"L Yipeng","year":"2022","unstructured":"Yipeng, L., Wenbing, L., Kaixuan, H., Wentao, T., Ling, Z., Shizhuang, W., & Linsheng, H. (2022). Determination of wheat kernels damaged by Fusarium head blight using monochromatic images of effective wavelengths from hyperspectral imaging coupled with an architecture self-search deep network. Food Control, 135, Article 108819. https:\/\/doi.org\/10.1016\/j.foodcont.2022.108819","journal-title":"Food Control"},{"key":"9531_CR49","doi-asserted-by":"publisher","DOI":"10.1016\/j.compbiomed.2024.108324","volume":"172","author":"R Zahari","year":"2024","unstructured":"Zahari, R., Cox, J., & Obara, B. (2024). Uncertainty-aware image classification on 3D CT lung. Computers in Biology and Medicine, 172, Article 108324. https:\/\/doi.org\/10.1016\/j.compbiomed.2024.108324","journal-title":"Computers in Biology and Medicine"},{"key":"9531_CR50","doi-asserted-by":"publisher","DOI":"10.1016\/j.ress.2022.108525","volume":"224","author":"T Zhou","year":"2022","unstructured":"Zhou, T., Han, T., & Droguett, E. L. (2022). Towards trustworthy machine fault diagnosis: A probabilistic Bayesian deep learning framework. Reliability Engineering & System Safety, 224, Article 108525. https:\/\/doi.org\/10.1016\/j.ress.2022.108525","journal-title":"Reliability Engineering & System Safety"}],"container-title":["Journal of Classification"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00357-025-09531-4.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s00357-025-09531-4","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00357-025-09531-4.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,3,23]],"date-time":"2026-03-23T06:43:19Z","timestamp":1774248199000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s00357-025-09531-4"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,1,26]]},"references-count":50,"journal-issue":{"issue":"1","published-print":{"date-parts":[[2026,4]]}},"alternative-id":["9531"],"URL":"https:\/\/doi.org\/10.1007\/s00357-025-09531-4","relation":{},"ISSN":["0176-4268","1432-1343"],"issn-type":[{"value":"0176-4268","type":"print"},{"value":"1432-1343","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,1,26]]},"assertion":[{"value":"8 August 2024","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"16 December 2025","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"26 January 2026","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"Not applicable.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Ethical Approval"}},{"value":"There is no Conflict of Interest.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of Interest"}}]}}