{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,30]],"date-time":"2026-07-30T14:02:46Z","timestamp":1785420166212,"version":"3.56.0"},"reference-count":41,"publisher":"MDPI AG","issue":"5","license":[{"start":{"date-parts":[[2026,5,12]],"date-time":"2026-05-12T00:00:00Z","timestamp":1778544000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100012166","name":"National Key Research and Development Program of China","doi-asserted-by":"publisher","award":["2022YFD2101204"],"award-info":[{"award-number":["2022YFD2101204"]}],"id":[{"id":"10.13039\/501100012166","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"crossref","award":["22138004"],"award-info":[{"award-number":["22138004"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"crossref"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"crossref","award":["22422807"],"award-info":[{"award-number":["22422807"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"crossref"}]},{"award":["22138004"],"award-info":[{"award-number":["22138004"]}],"id":[{"id":"https:\/\/ror.org\/01h0zpd94","id-type":"ROR","asserted-by":"publisher"}]},{"award":["22422807"],"award-info":[{"award-number":["22422807"]}],"id":[{"id":"https:\/\/ror.org\/01h0zpd94","id-type":"ROR","asserted-by":"publisher"}]},{"name":"Zhejiang Province and Local Collaborative Project","award":["2024SDXT001-4"],"award-info":[{"award-number":["2024SDXT001-4"]}]},{"DOI":"10.13039\/100017888","name":"National Engineering Research Center of Solid-state Brewing","doi-asserted-by":"crossref","id":[{"id":"10.13039\/100017888","id-type":"DOI","asserted-by":"crossref"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Computers"],"abstract":"<jats:p>The objective evaluation of Daqu cross-sectional quality is challenging due to its heterogeneous structure, small features, and low contrast. This study proposes a semantic-segmentation-based framework for the automated identification and quantitative analysis of Luzhou-flavor Daqu cross-sections. Four representative architectures\u2014including three convolutional neural network (CNN)-based models (U-Net, U-Net++, and U2-Net) and one Transformer-based model (SegFormer)\u2014were systematically benchmarked. To address severe class imbalance and enhance model robustness, a task-specific data augmentation pipeline was implemented. With these optimized augmentation strategies, the U2-Net model demonstrated the best performance, with a peak mean Intersection over Union (mIoU) of 87.54% and a Dice score of 98.30%. Based on the predicted masks, quantitative indicators such as plaque area ratio, pizhang thickness, and fissure length were precisely extracted. The proposed framework provides an objective and scalable solution for Daqu quality inspection, offering significant practical value for industrial scenarios involving complex materials and fine-grained defect patterns.<\/jats:p>","DOI":"10.3390\/computers15050307","type":"journal-article","created":{"date-parts":[[2026,5,12]],"date-time":"2026-05-12T16:04:36Z","timestamp":1778601876000},"page":"307","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Semantic Segmentation-Based Identification and Quantitative Analysis of Cross-Sectional Quality Features in Luzhou-Flavor Liquor Daqu"],"prefix":"10.3390","volume":"15","author":[{"given":"Zheli","family":"Song","sequence":"first","affiliation":[{"name":"School of Artificial Intelligence and Computer Science, Jiangnan University, Wuxi 214122, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yi","family":"Dong","sequence":"additional","affiliation":[{"name":"Luzhou Laojiao Group Co., Ltd., Luzhou 646000, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Chao","family":"Wang","sequence":"additional","affiliation":[{"name":"Luzhou Laojiao Group Co., Ltd., Luzhou 646000, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xiu","family":"Zhang","sequence":"additional","affiliation":[{"name":"Luzhou Laojiao Group Co., Ltd., Luzhou 646000, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Aibao","family":"Sun","sequence":"additional","affiliation":[{"name":"National Engineering Research Center of Huangjiu, Zhejiang Guyuelongshan Shaoxing Wine Co., Ltd., Shaoxing 312000, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Cuiping","family":"You","sequence":"additional","affiliation":[{"name":"School of Biotechnology, Jiangnan University, Wuxi 214122, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3221-2492","authenticated-orcid":false,"given":"Jian","family":"Mao","sequence":"additional","affiliation":[{"name":"State Key Laboratory of Food Science and Technology, National Engineering Research Center of Cereal Fermentation and Food Biomanufacturing, School of Food Science and Technology, Jiangnan University, Wuxi 214122, China"},{"name":"Shaoxing Key Laboratory of Traditional Fermentation Food and Human Health, Jiangnan University (Shaoxing) Industrial Technology Research Institute, Shaoxing 312000, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Shuangping","family":"Liu","sequence":"additional","affiliation":[{"name":"School of Artificial Intelligence and Computer Science, Jiangnan University, Wuxi 214122, China"},{"name":"State Key Laboratory of Food Science and Technology, National Engineering Research Center of Cereal Fermentation and Food Biomanufacturing, School of Food Science and Technology, Jiangnan University, Wuxi 214122, China"},{"name":"Shaoxing Key Laboratory of Traditional Fermentation Food and Human Health, Jiangnan University (Shaoxing) Industrial Technology Research Institute, Shaoxing 312000, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2026,5,12]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"18","DOI":"10.1016\/j.tifs.2017.02.016","article-title":"Mystery behind Chinese liquor fermentation","volume":"63","author":"Jin","year":"2017","journal-title":"Trends Food Sci. Technol."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"108851","DOI":"10.1016\/j.foodres.2019.108851","article-title":"Bioturbation effect of fortified Daqu on microbial community and flavor metabolite in Chinese strong-flavor liquor brewing microecosystem","volume":"129","author":"He","year":"2020","journal-title":"Food Res. Int."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"114257","DOI":"10.1016\/j.foodres.2024.114257","article-title":"Microbial communities, functional, and flavor differences among three different-colored high-temperature Daqu: A comprehensive metagenomic, physicochemical, and electronic sensory analysis","volume":"184","author":"Hou","year":"2024","journal-title":"Food Res. Int."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"243","DOI":"10.1002\/jsfa.12136","article-title":"Bacteria-induced amino acid metabolism involved in appearance characteristics of high-temperature Daqu","volume":"103","author":"Zhang","year":"2023","journal-title":"J. Sci. Food Agric."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"104982","DOI":"10.1016\/j.tifs.2025.104982","article-title":"Application of computer vision techniques to fermented foods: An overview","volume":"160","author":"Song","year":"2025","journal-title":"Trends Food Sci. Technol."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"100822","DOI":"10.1016\/j.fufo.2025.100822","article-title":"Image-based discrimination of ultrasound-assisted frozen meat using deep learning","volume":"12","author":"Sardari","year":"2025","journal-title":"Future Foods"},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"110102","DOI":"10.1016\/j.jfoodeng.2020.110102","article-title":"On line detection of defective apples using computer vision system combined with deep learning methods","volume":"286","author":"Fan","year":"2020","journal-title":"J. Food Eng."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"510","DOI":"10.1093\/jas\/skaf300.580","article-title":"Microbial evaluation of inoculated fresh meat utilizing imaging and computer vision","volume":"103","author":"Davis","year":"2025","journal-title":"J. Anim. Sci."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"e70172","DOI":"10.1111\/jfpe.70172","article-title":"Hyperspectral Reconstruction in Combination With a Crown Porcupine Optimization-Optimized Support Vector Regression (CPO-SVR) Machine Learning Model for Predicting the Total Acid Content of Daqu","volume":"48","author":"Xia","year":"2025","journal-title":"J. Food Process Eng."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"e13633","DOI":"10.1111\/jfpe.13633","article-title":"Multigranularity cascade forest algorithm based on hyperspectral imaging to detect moisture content in Daqu","volume":"44","author":"Huang","year":"2021","journal-title":"J. Food Process Eng."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"e13844","DOI":"10.1111\/jfpe.13844","article-title":"Detecting total acid content quickly and accurately by combining hyperspectral imaging and an optimized algorithm method","volume":"44","author":"Jiang","year":"2021","journal-title":"J. Food Process Eng."},{"key":"ref_12","doi-asserted-by":"crossref","unstructured":"Zhao, M.K., Han, C.Y., Xue, T.H., Ren, C., Nie, X., Jing, X., Hao, H.Y., Liu, Q.F., and Jia, L.Y. (2025). Establishment of a Daqu Grade Classification Model Based on Computer Vision and Machine Learning. Foods, 14.","DOI":"10.3390\/foods14040668"},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"111660","DOI":"10.1016\/j.foodcont.2025.111660","article-title":"Multimodal nondestructive testing of tender coconut water quality using spectroscopy, computer vision, and deep learning","volume":"180","author":"Wang","year":"2026","journal-title":"Food Control"},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"140911","DOI":"10.1016\/j.foodchem.2024.140911","article-title":"Smartphone video imaging: A versatile, low-cost technology for food authentication","volume":"462","author":"Song","year":"2025","journal-title":"Food Chem."},{"key":"ref_15","first-page":"81","article-title":"Understanding Deep Learning Techniques for Image Segmentation","volume":"52","author":"Ghosh","year":"2019","journal-title":"ACM Comput. Surv."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"203","DOI":"10.1038\/s41592-020-01008-z","article-title":"nnU-Net: A self-configuring method for deep learning-based biomedical image segmentation","volume":"18","author":"Isensee","year":"2021","journal-title":"Nat. Methods"},{"key":"ref_17","first-page":"9491376","article-title":"Semantic Segmentation of Remote Sensing Image Based on GAN and FCN Network Model","volume":"2021","author":"Tian","year":"2021","journal-title":"Sci. Program."},{"key":"ref_18","first-page":"1314","article-title":"Attention U-Net-based semantic segmentation for welding line detection","volume":"15","author":"Beregi","year":"2025","journal-title":"Sci. Rep."},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Bastian, M., Angela, M.-A., Constanza, A., and Eduardo, A. (2025). Lightweight DeepLabv3+ for Semantic Food Segmentation. Foods, 14.","DOI":"10.3390\/foods14081306"},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"Cao, M.Y., Tang, F.F., Ji, P., and Ma, F.Y. (2023). Improved real-time semantic segmentation network model for crop vision navigation line detection. Front. Plant Sci., 14.","DOI":"10.3389\/fpls.2023.1140560"},{"key":"ref_21","first-page":"2013","article-title":"Food Calorie Estimation System Based on Semantic Segmentation Network","volume":"35","author":"Kong","year":"2023","journal-title":"Sens. Mater."},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Gonzalez, B., Garcia, G., Velastin, S.A., Gholamhosseini, H., Tejeda, L., and Farias, G. (2024). Automated Food Weight and Content Estimation Using Computer Vision and AI Algorithms. Sensors, 24.","DOI":"10.3390\/s24237660"},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"108440","DOI":"10.1016\/j.compag.2023.108440","article-title":"High-precision target ranging in complex orchard scenes by utilizing semantic segmentation results and binocular vision","volume":"215","author":"Wen","year":"2023","journal-title":"Comput. Electron. Agric."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"100611","DOI":"10.1016\/j.fufo.2025.100611","article-title":"Detection of external defects of tomato crop using appearance parameters by convolutional neural networks","volume":"11","author":"Nima","year":"2025","journal-title":"Future Foods"},{"key":"ref_25","doi-asserted-by":"crossref","unstructured":"Casado-Garc\u00eda, A., Dom\u00ednguez, C., Garc\u00eda-Dom\u00ednguez, M., Heras, J., In\u00e9s, A., Mata, E., and Pascual, V. (2019). CLoDSA: A tool for augmentation in classification, localization, detection, semantic segmentation and instance segmentation tasks. BMC Bioinform., 20.","DOI":"10.1186\/s12859-019-2931-1"},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"961","DOI":"10.1007\/s11263-018-1070-x","article-title":"Augmented Reality Meets Computer Vision: Efficient Data Generation for Urban Driving Scenes","volume":"126","author":"Mustikovela","year":"2018","journal-title":"Int. J. Comput. Vis."},{"key":"ref_27","doi-asserted-by":"crossref","unstructured":"Navab, N., Hornegger, J., Wells, W.M., and Frangi, A.F. (2015). U-Net: Convolutional Networks for Biomedical Image Segmentation. Medical Image Computing and Computer-Assisted Intervention\u2014MICCAI 2015, Springer International Publishing.","DOI":"10.1007\/978-3-319-24553-9"},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"3365","DOI":"10.1007\/s00542-020-05123-x","article-title":"Deep learning based real-time Industrial framework for rotten and fresh fruit detection using semantic segmentation","volume":"27","author":"Roy","year":"2021","journal-title":"Microsyst. Technol."},{"key":"ref_29","doi-asserted-by":"crossref","unstructured":"Zang, H.C., Wang, C.S., Zhao, Q., Zhang, J., Wang, J.M., Zheng, G.Q., and Li, G.Q. (2025). Constructing segmentation method for wheat powdery mildew using deep learning. Front. Plant Sci., 16.","DOI":"10.3389\/fpls.2025.1524283"},{"key":"ref_30","first-page":"3","article-title":"UNet++: A Nested U-Net Architecture for Medical Image Segmentation","volume":"Volume 11045","author":"Zhou","year":"2018","journal-title":"Deep Learning in Medical Image Analysis and Multimodal Learning for Clinical Decision Support"},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"1856","DOI":"10.1109\/TMI.2019.2959609","article-title":"UNet++: Redesigning Skip Connections to Exploit Multiscale Features in Image Segmentation","volume":"39","author":"Zhou","year":"2020","journal-title":"IEEE Trans. Med. Imaging"},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"107404","DOI":"10.1016\/j.patcog.2020.107404","article-title":"U2-Net: Going deeper with nested U-structure for salient object detection","volume":"106","author":"Qin","year":"2020","journal-title":"Pattern Recognit."},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"139","DOI":"10.1016\/j.neucom.2019.01.036","article-title":"DeepCrack: A deep hierarchical feature learning architecture for crack segmentation","volume":"338","author":"Liu","year":"2019","journal-title":"Neurocomputing"},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"640","DOI":"10.1109\/TPAMI.2016.2572683","article-title":"Fully Convolutional Networks for Semantic Segmentation","volume":"39","author":"Shelhamer","year":"2017","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_35","first-page":"78","article-title":"Relationship between sensory indexes, physicochemical indexes, microbial community and volatile compounds in high-temperature Daqu","volume":"23","author":"Can","year":"2022","journal-title":"Food Ferment. Ind."},{"key":"ref_36","first-page":"90","article-title":"Isolation, identification and control of molds on the surface of medium-high-temperature Daqu","volume":"43","author":"Wang","year":"2024","journal-title":"China Brew."},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"113214","DOI":"10.1016\/j.lwt.2022.113214","article-title":"Analysis of the microbial community and the metabolic profile in medium-temperature Daqu after inoculation with Bacillus licheniformis and Bacillus velezensis","volume":"160","author":"Xu","year":"2022","journal-title":"LWT"},{"key":"ref_38","first-page":"80","article-title":"Research on the Daqu quality detection system based on machine vision","volume":"4","author":"Dan","year":"2018","journal-title":"Food Mach."},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"107222","DOI":"10.1016\/j.compag.2022.107222","article-title":"Semantic segmentation and path planning for orchards based on UAV images","volume":"200","author":"Qilin","year":"2022","journal-title":"Comput. Electron. Agric."},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"309","DOI":"10.1016\/j.isprsjprs.2020.01.023","article-title":"Aerial image semantic segmentation using DCNN predicted distance maps","volume":"161","author":"Chai","year":"2020","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_41","first-page":"11844","article-title":"Research on underwater robot ranging technology based on semantic segmentation and binocular vision","volume":"14","author":"Hu","year":"2024","journal-title":"Sci. Rep."}],"container-title":["Computers"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2073-431X\/15\/5\/307\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,5,14]],"date-time":"2026-05-14T04:17:01Z","timestamp":1778732221000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2073-431X\/15\/5\/307"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,5,12]]},"references-count":41,"journal-issue":{"issue":"5","published-online":{"date-parts":[[2026,5]]}},"alternative-id":["computers15050307"],"URL":"https:\/\/doi.org\/10.3390\/computers15050307","relation":{},"ISSN":["2073-431X"],"issn-type":[{"value":"2073-431X","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,5,12]]}}}