{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,23]],"date-time":"2026-10-23T21:03:29Z","timestamp":1792789409596,"version":"4.5.0"},"reference-count":36,"publisher":"MDPI AG","issue":"6","license":[{"start":{"date-parts":[[2026,3,20]],"date-time":"2026-03-20T00:00:00Z","timestamp":1773964800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Central Government\u2019s Guidance Fund for Local Science and Technology Development","award":["246Z0309G"],"award-info":[{"award-number":["246Z0309G"]}]},{"name":"Higher Education Research Project of Hebei Province","award":["BJ2025097"],"award-info":[{"award-number":["BJ2025097"]}]},{"name":"Higher Education Research Project of Hebei Province","award":["BJ2026072"],"award-info":[{"award-number":["BJ2026072"]}]},{"name":"Hebei Province Doctoral Student Innovation Capability Training Fund","award":["CXZZBS2024073"],"award-info":[{"award-number":["CXZZBS2024073"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Foods"],"abstract":"<jats:p>Accurate and non-destructive evaluation of grape quality is crucial for intelligent viticulture, yet most existing approaches address cultivar classification and soluble solid content (SSC) prediction as independent tasks based on single-modality data, limiting robustness and practical applicability. This study proposes DualStream-RTNet, a unified multimodal deep learning framework that simultaneously performs grape cultivar classification and SSC prediction by integrating RGB-HSV fused images and PCA-compressed hyperspectral spectra. The dual-stream architecture enables the complementary learning of external chromatic\u2013textural cues and internal physicochemical information, while a Transformer-enhanced fusion module strengthens global representation and cross-modal correlation. A dataset of 864 berries from five grape cultivars was used to validate the model. DualStream-RTNet achieved 93.64% classification accuracy, outperforming ResNet18 and other CNN baselines, and produced more compact and consistent confusion-matrix patterns. For SSC prediction, it consistently yielded the highest performance across cultivars, with R2p values up to 0.9693 and RMSE as low as 0.2567, surpassing the PLSR, SVR, LSTM, and Transformer regression models. These results demonstrate the superiority of the proposed framework in capturing both visual and spectral characteristics. DualStream-RTNet provides an efficient and scalable solution for comprehensive grape quality assessment, offering strong potential for real-time sorting, precision grading, and smart agricultural applications.<\/jats:p>","DOI":"10.3390\/foods15061095","type":"journal-article","created":{"date-parts":[[2026,3,20]],"date-time":"2026-03-20T10:49:58Z","timestamp":1774003798000},"page":"1095","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["DualStream-RTNet: A Multimodal Deep Learning Framework for Grape Cultivar Classification and Soluble Solid Content Prediction"],"prefix":"10.3390","volume":"15","author":[{"given":"Zhiguo","family":"Liu","sequence":"first","affiliation":[{"name":"College of Information Science and Technology, Hebei Agricultural University, Baoding 071001, China"},{"name":"Hebei Key Laboratory of IoT Blockchain Integration, School of Future Information Technology, Shijiazhuang University, Shijiazhuang 050037, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yufei","family":"Song","sequence":"additional","affiliation":[{"name":"Hebei Key Laboratory of IoT Blockchain Integration, School of Future Information Technology, Shijiazhuang University, Shijiazhuang 050037, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Aoran","family":"Liu","sequence":"additional","affiliation":[{"name":"Hebei Key Laboratory of IoT Blockchain Integration, School of Future Information Technology, Shijiazhuang University, Shijiazhuang 050037, China"},{"name":"School of College of Computer and Cyber Security, Hebei Normal University, Hebei Provincial Key Laboratory of Network & Information Security, Hebei Provincial Engineering Research Center for Supply Chain Big Data Analytics & Data Security, Shijiazhuang 050025, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xi","family":"Meng","sequence":"additional","affiliation":[{"name":"Hebei Key Laboratory of IoT Blockchain Integration, School of Future Information Technology, Shijiazhuang University, Shijiazhuang 050037, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Chang","family":"Liu","sequence":"additional","affiliation":[{"name":"Hebei Key Laboratory of IoT Blockchain Integration, School of Future Information Technology, Shijiazhuang University, Shijiazhuang 050037, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Shanshan","family":"Li","sequence":"additional","affiliation":[{"name":"Hebei Key Laboratory of IoT Blockchain Integration, School of Future Information Technology, Shijiazhuang University, Shijiazhuang 050037, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xiangqing","family":"Wang","sequence":"additional","affiliation":[{"name":"Hebei Key Laboratory of IoT Blockchain Integration, School of Future Information Technology, Shijiazhuang University, Shijiazhuang 050037, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Guifa","family":"Teng","sequence":"additional","affiliation":[{"name":"College of Information Science and Technology, Hebei Agricultural University, Baoding 071001, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2026,3,20]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"100457","DOI":"10.1016\/j.atech.2024.100457","article-title":"SSC and pH prediction and maturity classification of grapes based on hyperspectral imaging","volume":"8","author":"Gao","year":"2024","journal-title":"Smart Agric. Technol."},{"key":"ref_2","doi-asserted-by":"crossref","unstructured":"Zhao, J., Hu, Q., Li, B., Xie, Y., Lu, H., and Xu, S. (2023). Research on an improved non-destructive detection method for the soluble solids content in bunch-harvested grapes based on deep learning and hyperspectral imaging. Appl. Sci., 13.","DOI":"10.3390\/app13116776"},{"key":"ref_3","first-page":"161","article-title":"Current status and prospects of the development of grape industry in China","volume":"50","author":"Li","year":"2021","journal-title":"S. China Fruits"},{"key":"ref_4","first-page":"168","article-title":"Fusion deep learning with pre-post harvest quality management of grapes within the realm of supply chain management","volume":"15","author":"Patil","year":"2024","journal-title":"Sci. Temper"},{"key":"ref_5","doi-asserted-by":"crossref","unstructured":"Gong, Z., Zhi, Z., Zhang, C., and Cao, D. (2025). Non-Destructive Detection of Soluble Solids Content in Fruits: A Review. Chemistry, 7.","DOI":"10.3390\/chemistry7040115"},{"key":"ref_6","first-page":"101734","article-title":"Enhancing optical non-destructive methods for food quality and safety assessments with machine learning techniques: A survey","volume":"19","author":"Wang","year":"2025","journal-title":"J. Agric. Food Res."},{"key":"ref_7","doi-asserted-by":"crossref","unstructured":"Ye, W., Xu, W., Yan, T., Yan, J., Gao, P., and Zhang, C. (2022). Application of near-infrared spectroscopy and hyperspectral imaging combined with machine learning algorithms for quality inspection of grape: A review. Foods, 12.","DOI":"10.3390\/foods12010132"},{"key":"ref_8","doi-asserted-by":"crossref","unstructured":"Yu, K., Zhong, M., Zhu, W., Rashid, A., Han, R., Virk, M.S., Duan, K., Zhao, Y., and Ren, X. (2025). Advances in computer vision and spectroscopy techniques for non-destructive quality assessment of citrus fruits: A comprehensive review. Foods, 14.","DOI":"10.3390\/foods14030386"},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"618","DOI":"10.1093\/jigpal\/jzac029","article-title":"RGB images-driven recognition of grapevine varieties using a densely connected convolutional network","volume":"31","year":"2023","journal-title":"Log. J. IGPL"},{"key":"ref_10","first-page":"555","article-title":"Non-destructive Determination of the Soluble Solid Content in Winter Jujubes Using Hyperspectral Technology and the SCARS-PLSR Prediction Model","volume":"52","author":"Liu","year":"2025","journal-title":"IAENG Int. J. Comput. Sci."},{"key":"ref_11","doi-asserted-by":"crossref","unstructured":"Zhai, H., Xie, P., Xie, X., and Sha, S.S. (2025). Deep learning-enabled hyperspectral imaging for high-accuracy non-destructive quantification of nutritional components in multi-variety apples. Front. Plant Sci., 16.","DOI":"10.3389\/fpls.2025.1634785"},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"10019","DOI":"10.1021\/acs.jafc.4c11492","article-title":"Hyperspectral imaging and deep learning for quality and safety inspection of fruits and vegetables: A review","volume":"73","author":"Yang","year":"2025","journal-title":"J. Agric. Food Chem."},{"key":"ref_13","first-page":"226","article-title":"Multi-features Identification of Grape Cultivars Based on Attention Mechanism","volume":"52","author":"Su","year":"2021","journal-title":"Trans. Chin. Soc. Agric. Mach."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"470","DOI":"10.1007\/s12393-024-09374-6","article-title":"Visible\/near-infrared spectroscopy and hyperspectral imaging facilitate the rapid determination of soluble solids content in fruits","volume":"16","author":"Zhao","year":"2024","journal-title":"Food Eng. Rev."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"104576","DOI":"10.1016\/j.infrared.2023.104576","article-title":"Detection of mango soluble solid content using hyperspectral imaging technology","volume":"129","author":"Tian","year":"2023","journal-title":"Infrared Phys. Technol."},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Qiu, G., Chen, B., Lu, H., Yue, X., Deng, X., Ouyang, H., Li, B., and Wei, X. (2024). Nondestructively determining soluble solids content of blueberries using reflection hyperspectral imaging technique. Agronomy, 14.","DOI":"10.3390\/agronomy14102296"},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"6124","DOI":"10.1039\/D5AY00403A","article-title":"NIRS-based fresh grape ripeness prediction with SPA-LASSO spectral feature selection","volume":"17","author":"Hu","year":"2025","journal-title":"Anal. Methods"},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"106822","DOI":"10.1016\/j.compag.2022.106822","article-title":"Hyperspectral image information fusion-based detection of soluble solids content in red globe grapes","volume":"196","author":"Gao","year":"2022","journal-title":"Comput. Electron. Agric."},{"key":"ref_19","first-page":"229","article-title":"Prediction of soluble solids content for wine grapes during maturing based on visible and near-infrared spectroscopy","volume":"41","author":"Zhang","year":"2021","journal-title":"Spectrosc. Spectr. Anal."},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"Bi, X., Xie, H., Song, Z., Li, J., Liu, C., Zhou, X., Yu, H., Bi, C., and Zhao, M. (2025). DualCMNet: A lightweight dual-branch network for maize variety identification based on multi-modal feature fusion. Front. Plant Sci., 16.","DOI":"10.3389\/fpls.2025.1588901"},{"key":"ref_21","doi-asserted-by":"crossref","unstructured":"Long, Z., Wang, T., Zhang, Z., and Liu, Y. (2025). Quality and Maturity Detection of Korla Fragrant Pears via Integrating Hyperspectral Imaging with Multiscale CNN\u2013LSTM. Foods, 14.","DOI":"10.3390\/foods14203561"},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"187","DOI":"10.1038\/s41538-025-00551-3","article-title":"A maturity classification model for winter jujubes based on DSAF-ResNet","volume":"9","author":"Song","year":"2025","journal-title":"npj Sci. Food"},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"8088","DOI":"10.1007\/s11694-024-02789-z","article-title":"Sensor fusion techniques in deep learning for multimodal fruit and vegetable quality assessment: A comprehensive review","volume":"18","author":"Singh","year":"2024","journal-title":"J. Food Meas. Charact."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"829","DOI":"10.1162\/neco_a_01273","article-title":"A survey on deep learning for multimodal data fusion","volume":"32","author":"Gao","year":"2020","journal-title":"Neural Comput."},{"key":"ref_25","doi-asserted-by":"crossref","unstructured":"Sindagi, V.A., Zhou, Y., and Tuzel, O. (2019, January 20\u201324). Mvx-net: Multimodal voxelnet for 3d object detection. Proceedings of the 2019 International Conference on Robotics and Automation (ICRA), Montreal, QC, Canada.","DOI":"10.1109\/ICRA.2019.8794195"},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"Pang, S., Morris, D., and Radha, H. (2020\u201324, January 24). CLOCs: Camera-LiDAR object candidates fusion for 3D object detection. Proceedings of the 2020 IEEE\/RSJ International Conference on Intelligent Robots and Systems (IROS), Las Vegas, NV, USA.","DOI":"10.1109\/IROS45743.2020.9341791"},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"12113","DOI":"10.1109\/TPAMI.2023.3275156","article-title":"Multimodal learning with transformers: A survey","volume":"45","author":"Peng","year":"2023","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_28","doi-asserted-by":"crossref","unstructured":"Guo, H., Tian, B., and Liu, W. (2025). CCFormer: Cross-Modal Cross-Attention Transformer for Classification of Hyperspectral and LiDAR Data. Sensors, 25.","DOI":"10.3390\/s25185698"},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"109583","DOI":"10.1016\/j.engappai.2024.109583","article-title":"Cross-attention interaction learning network for multi-model image fusion via transformer","volume":"139","author":"Wang","year":"2025","journal-title":"Eng. Appl. Artif. Intell."},{"key":"ref_30","first-page":"2326","article-title":"Hyperspectral Estimation of Soluble Solids Contentin Winter Jujube BasedonLSTM-TE Model","volume":"45","author":"Liu","year":"2025","journal-title":"Spectrosc. Spectr. Anal."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"108533","DOI":"10.1016\/j.compag.2023.108533","article-title":"MTD-YOLO: Multi-task deep convolutional neural network for cherry tomato fruit bunch maturity detection","volume":"216","author":"Chen","year":"2024","journal-title":"Comput. Electron. Agric."},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"100733","DOI":"10.1016\/j.crfs.2024.100733","article-title":"Fruit freshness detection based on multi-task convolutional neural network","volume":"8","author":"Zhang","year":"2024","journal-title":"Curr. Res. Food Sci."},{"key":"ref_33","unstructured":"(2014). China National Institute of Standardization. Guidelines for Quality Grading of Agricultural Products (Standard No. GB\/T 30763-2014)."},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"137","DOI":"10.5344\/ajev.2020.20050","article-title":"Soft, sweet, and colorful: Stratified sampling reveals sequence of events at the onset of grape ripening","volume":"72","author":"Zhang","year":"2021","journal-title":"Am. J. Enol. Vitic."},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"10","DOI":"10.48130\/fia-0025-0005","article-title":"Detection of soluble solid content in table grapes during storage based on visible-near-infrared spectroscopy","volume":"4","author":"Su","year":"2024","journal-title":"Food Innov. Adv."},{"key":"ref_36","doi-asserted-by":"crossref","unstructured":"Song, D.-J., Chun, S.-W., Kim, M.-J., Park, S.-H., Ahn, C.-K., and Mo, C. (2024). Performance improvement of partial least squares regression soluble solid content prediction model based on adjusting distance between light source and spectral sensor according to apple size. Sensors, 24.","DOI":"10.3390\/s24020316"}],"container-title":["Foods"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2304-8158\/15\/6\/1095\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,3,20]],"date-time":"2026-03-20T11:14:03Z","timestamp":1774005243000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2304-8158\/15\/6\/1095"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,3,20]]},"references-count":36,"journal-issue":{"issue":"6","published-online":{"date-parts":[[2026,3]]}},"alternative-id":["foods15061095"],"URL":"https:\/\/doi.org\/10.3390\/foods15061095","relation":{},"ISSN":["2304-8158"],"issn-type":[{"value":"2304-8158","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,3,20]]}}}