{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,23]],"date-time":"2026-04-23T05:40:34Z","timestamp":1776922834388,"version":"3.51.2"},"reference-count":36,"publisher":"Elsevier BV","license":[{"start":{"date-parts":[[2026,6,1]],"date-time":"2026-06-01T00:00:00Z","timestamp":1780272000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/tdm\/userlicense\/1.0\/"},{"start":{"date-parts":[[2026,6,1]],"date-time":"2026-06-01T00:00:00Z","timestamp":1780272000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/legal\/tdmrep-license"},{"start":{"date-parts":[[2026,6,1]],"date-time":"2026-06-01T00:00:00Z","timestamp":1780272000000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-017"},{"start":{"date-parts":[[2026,6,1]],"date-time":"2026-06-01T00:00:00Z","timestamp":1780272000000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-037"},{"start":{"date-parts":[[2026,6,1]],"date-time":"2026-06-01T00:00:00Z","timestamp":1780272000000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-012"},{"start":{"date-parts":[[2026,6,1]],"date-time":"2026-06-01T00:00:00Z","timestamp":1780272000000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2026,6,1]],"date-time":"2026-06-01T00:00:00Z","timestamp":1780272000000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-004"}],"funder":[{"DOI":"10.13039\/100022963","name":"Key Research and Development Program of Zhejiang Province","doi-asserted-by":"publisher","id":[{"id":"10.13039\/100022963","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100012844","name":"Huzhou University","doi-asserted-by":"publisher","award":["2024ZD2045"],"award-info":[{"award-number":["2024ZD2045"]}],"id":[{"id":"10.13039\/100012844","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["elsevier.com","sciencedirect.com"],"crossmark-restriction":true},"short-container-title":["Computers and Electronics in Agriculture"],"published-print":{"date-parts":[[2026,6]]},"DOI":"10.1016\/j.compag.2026.111709","type":"journal-article","created":{"date-parts":[[2026,4,3]],"date-time":"2026-04-03T03:34:20Z","timestamp":1775187260000},"page":"111709","update-policy":"https:\/\/doi.org\/10.1016\/elsevier_cm_policy","source":"Crossref","is-referenced-by-count":0,"special_numbering":"C","title":["DC-MMoE: A deep Cross multi-gate Mixture-of-Experts model for simultaneous rice seed vigor assessment and variety classification using near-infrared hyperspectral imaging"],"prefix":"10.1016","volume":"247","author":[{"given":"Zihong","family":"Huang","sequence":"first","affiliation":[]},{"given":"Xiaoping","family":"Wu","sequence":"additional","affiliation":[]},{"given":"Hengnian","family":"Qi","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6760-3154","authenticated-orcid":false,"given":"Chu","family":"Zhang","sequence":"additional","affiliation":[]}],"member":"78","reference":[{"issue":"10","key":"10.1016\/j.compag.2026.111709_b0005","doi-asserted-by":"crossref","first-page":"773","DOI":"10.1071\/CP20373","article-title":"Accelerated aging test of seed vigour for predicting field emergence of wet direct-seeded rice","volume":"72","author":"Alahakoon","year":"2021","journal-title":"Crop Pasture Sci."},{"issue":"11","key":"10.1016\/j.compag.2026.111709_b0010","first-page":"1037","article-title":"Change in storage enzymes activities in natural and accelerated aged seed of wheat (Triticum aestivum)","volume":"81","author":"Chauhan","year":"2011","journal-title":"Indian J. Agric. Sci."},{"issue":"3","key":"10.1016\/j.compag.2026.111709_b0015","doi-asserted-by":"crossref","first-page":"673","DOI":"10.1007\/s10681-014-1304-0","article-title":"Mapping QTLs related to rice seed storability under natural and artificial aging storage conditions","volume":"203","author":"Hang","year":"2015","journal-title":"Euphytica"},{"key":"10.1016\/j.compag.2026.111709_b0020","doi-asserted-by":"crossref","DOI":"10.1016\/j.fochx.2024.101481","article-title":"Simultaneous determination of pigments of spinach (Spinacia oleracea L.) leaf for quality inspection using hyperspectral imaging and multi-task deep learning regression approaches","volume":"22","author":"He","year":"2024","journal-title":"Food Chemistry-X"},{"key":"10.1016\/j.compag.2026.111709_b0025","doi-asserted-by":"crossref","DOI":"10.1016\/j.infrared.2022.104097","article-title":"Determination of viability and vigor of naturally-aged rice seeds using hyperspectral imaging with machine learning","volume":"122","author":"Jin","year":"2022","journal-title":"Infrared Phys. Technol."},{"key":"10.1016\/j.compag.2026.111709_b0030","article-title":"Identification of Rice seed Varieties based on Near-Infrared Hyperspectral Imaging Technology combined with Deep Learning","author":"Jin","year":"2022","journal-title":"ACS Omega"},{"issue":"16","key":"10.1016\/j.compag.2026.111709_b0035","doi-asserted-by":"crossref","first-page":"22355","DOI":"10.1007\/s11042-021-11282-4","article-title":"Ensemble of multi-task deep convolutional neural networks using transfer learning for fruit freshness classification","volume":"81","author":"Kang","year":"2022","journal-title":"Multimed. Tools Appl."},{"issue":"1","key":"10.1016\/j.compag.2026.111709_b0040","doi-asserted-by":"crossref","first-page":"118","DOI":"10.56093\/ijas.v85i1.46063","article-title":"Molecular characterization of farmers' varieties of rice (Oryza sativa)","volume":"85","author":"Kumar","year":"2015","journal-title":"Indian J. Agric. Sci."},{"issue":"6","key":"10.1016\/j.compag.2026.111709_b0045","doi-asserted-by":"crossref","DOI":"10.3390\/axioms12060569","article-title":"Multi-Task Deep Learning Games: investigating Nash Equilibria and Convergence Properties","volume":"12","author":"Lee","year":"2023","journal-title":"Axioms"},{"issue":"5","key":"10.1016\/j.compag.2026.111709_b0050","doi-asserted-by":"crossref","first-page":"623","DOI":"10.1109\/TCSVT.2011.2129430","article-title":"Multi-Task Rank Learning for Visual Saliency Estimation","volume":"21","author":"Li","year":"2011","journal-title":"IEEE Trans. Circuits Syst. Video Technol."},{"key":"10.1016\/j.compag.2026.111709_b0055","doi-asserted-by":"crossref","DOI":"10.1016\/j.microc.2025.113133","article-title":"Research on identification of common bean seed vigor based on hyperspectral and deep learning","volume":"211","author":"Li","year":"2025","journal-title":"Microchem. J."},{"key":"10.1016\/j.compag.2026.111709_b0060","article-title":"A Hybrid Multitask Learning Network for Hyperspectral image Classification with few Labels","volume":"62","author":"Liu","year":"2024","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"issue":"6","key":"10.1016\/j.compag.2026.111709_b0065","doi-asserted-by":"crossref","first-page":"5085","DOI":"10.1109\/TGRS.2020.3018879","article-title":"Few-Shot Hyperspectral image Classification with unknown classes using Multitask Deep Learning","volume":"59","author":"Liu","year":"2021","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"10.1016\/j.compag.2026.111709_b0070","series-title":"Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining","article-title":"Modeling task relationships in multi-task learning with multi-gate mixture-of-experts","author":"Ma","year":"2018"},{"key":"10.1016\/j.compag.2026.111709_b0075","series-title":"Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition","article-title":"Cross-stitch networks for multi-task learning","author":"Misra","year":"2016"},{"key":"10.1016\/j.compag.2026.111709_b0080","doi-asserted-by":"crossref","first-page":"123026","DOI":"10.1109\/ACCESS.2020.3006495","article-title":"Rapid Vitality Estimation and Prediction of Corn Seeds based on Spectra and Images using Deep Learning and Hyperspectral Imaging Techniques","volume":"8","author":"Pang","year":"2020","journal-title":"IEEE Access"},{"key":"10.1016\/j.compag.2026.111709_b0085","article-title":"Enhancing rice seed purity recognition accuracy based on optimal feature selection","volume":"86","author":"Phan","year":"2025","journal-title":"Eco. Inform."},{"key":"10.1016\/j.compag.2026.111709_b0090","doi-asserted-by":"crossref","DOI":"10.1016\/j.compag.2023.108473","article-title":"SAM-GAN: an improved DCGAN for rice seed viability determination using near-infrared hyperspectral imaging","volume":"216","author":"Qi","year":"2024","journal-title":"Comput. Electron. Agric."},{"key":"10.1016\/j.compag.2026.111709_b0095","doi-asserted-by":"crossref","DOI":"10.3389\/fpls.2023.1283921","article-title":"Rice seed vigor detection based on near-infrared hyperspectral imaging and deep transfer learning","volume":"14","author":"Qi","year":"2023","journal-title":"Front. Plant Sci."},{"key":"10.1016\/j.compag.2026.111709_b0100","doi-asserted-by":"crossref","DOI":"10.3389\/fpls.2023.1194701","article-title":"Vigour testing for the rice seed with computer vision-based techniques","volume":"14","author":"Qiao","year":"2023","journal-title":"Front. Plant Sci."},{"key":"10.1016\/j.compag.2026.111709_b0105","doi-asserted-by":"crossref","unstructured":"Ramcharan, A., Baranowski, K., McCloskey, P., Ahmed, B., Legg, J., & Hughes, D. P. (2017). Deep Learning for Image-Based Cassava Disease Detection. Frontiers in Plant Science, 8, Article 1852. Doi: 10.3389\/fpls.2017.01852.","DOI":"10.3389\/fpls.2017.01852"},{"issue":"10","key":"10.1016\/j.compag.2026.111709_b0110","doi-asserted-by":"crossref","first-page":"e3585","DOI":"10.1002\/cem.3585","article-title":"Nondestructive Identification of Wheat seed Variety and Geographical Origin using Near\u2010Infrared Hyperspectral Imagery and Deep Learning","volume":"38","author":"Sharma","year":"2024","journal-title":"J. Chemom."},{"issue":"2","key":"10.1016\/j.compag.2026.111709_b0115","first-page":"231","article-title":"Rapid and non-destructive detection method for water status and water distribution of rice seeds with different vigor","volume":"14","author":"Song","year":"2021","journal-title":"Int. J. Agric. Biol. Eng."},{"issue":"1","key":"10.1016\/j.compag.2026.111709_b0120","doi-asserted-by":"crossref","first-page":"97","DOI":"10.15258\/sst.2023.51.1.08","article-title":"Tetrazolium test for evaluating viability of stored rice (Oryza sativa) seeds","volume":"51","author":"Sukkaew","year":"2023","journal-title":"Seed Sci. Technol."},{"key":"10.1016\/j.compag.2026.111709_b0125","series-title":"Proceedings of the 14th ACM Conference on Recommender Systems","article-title":"Progressive layered extraction (ple): a novel multi-task learning (mtl) model for personalized recommendations","author":"Tang","year":"2020"},{"issue":"7","key":"10.1016\/j.compag.2026.111709_b0130","doi-asserted-by":"crossref","first-page":"5205","DOI":"10.1007\/s10462-021-10018-y","article-title":"A review of deep learning used in the hyperspectral image analysis for agriculture","volume":"54","author":"Wang","year":"2021","journal-title":"Artif. Intell. Rev."},{"key":"10.1016\/j.compag.2026.111709_b0135","doi-asserted-by":"crossref","DOI":"10.1016\/j.infrared.2023.104611","article-title":"Variety identification of sweet maize seeds based on hyperspectral imaging combined with deep learning","volume":"130","author":"Wang","year":"2023","journal-title":"Infrared Phys. Technol."},{"key":"10.1016\/j.compag.2026.111709_b0140","doi-asserted-by":"crossref","DOI":"10.1016\/j.compag.2022.106850","article-title":"Deep convolution neural network with weighted loss to detect rice seeds vigor based on hyperspectral imaging under the sample-imbalanced condition","volume":"196","author":"Wu","year":"2022","journal-title":"Comput. Electron. Agric."},{"issue":"3","key":"10.1016\/j.compag.2026.111709_b0145","article-title":"Physiological Alterations and Nondestructive Test Methods of Crop seed Vigor","volume":"13","author":"Xing","year":"2023","journal-title":"A Comprehensive Review. Agriculture-Basel"},{"key":"10.1016\/j.compag.2026.111709_b0150","doi-asserted-by":"crossref","DOI":"10.1016\/j.compag.2024.108790","article-title":"A novel cascaded multi-task method for crop prescription recommendation based on electronic medical record","volume":"219","author":"Xu","year":"2024","journal-title":"Comput. Electron. Agric."},{"key":"10.1016\/j.compag.2026.111709_b0155","doi-asserted-by":"crossref","unstructured":"Yang, L. X., Zhang, H., Xu, J. W., Lyu, J., Zhou, X., Shao, D., Gao, S., & Bacchelli, A. (2025). A preliminary investigation on using multi-task learning to predict change performance in code reviews (vol 129, pg 157, 2024). Empirical Software Engineering, 30(3), Article 92. Doi: 10.1007\/s10664-025-10628-y.","DOI":"10.1007\/s10664-025-10628-y"},{"issue":"6","key":"10.1016\/j.compag.2026.111709_b0160","article-title":"Cross-Stitch Networks for Joint State of Charge and State of Health Online Estimation of Lithium-Ion Batteries","volume":"10","author":"Yao","year":"2024","journal-title":"Batteries-Basel"},{"issue":"3","key":"10.1016\/j.compag.2026.111709_b0165","article-title":"Detection of Aging Maize seed Vigor and Calculation of Germ Growth speed using an improved YOLOv8-Seg Network","volume":"15","author":"Yu","year":"2025","journal-title":"Agriculture-Basel"},{"key":"10.1016\/j.compag.2026.111709_b0170","doi-asserted-by":"crossref","DOI":"10.1016\/j.patcog.2021.108401","article-title":"Co-attentive multi-task convolutional neural network for facial expression recognition","volume":"123","author":"Yu","year":"2022","journal-title":"Pattern Recogn."},{"issue":"2","key":"10.1016\/j.compag.2026.111709_b0175","doi-asserted-by":"crossref","first-page":"397","DOI":"10.47836\/ifrj.29.2.17","article-title":"Application of hyperspectral imaging to discriminate waxy corn seed vigour after aging","volume":"29","author":"Yuan","year":"2022","journal-title":"Int. Food Res. J."},{"key":"10.1016\/j.compag.2026.111709_b0180","doi-asserted-by":"crossref","first-page":"10440","DOI":"10.1109\/ACCESS.2023.3240410","article-title":"An Alternative Hard-Parameter Sharing Paradigm for Multi-Domain Learning","volume":"11","author":"Zhang","year":"2023","journal-title":"IEEE Access"}],"container-title":["Computers and Electronics in Agriculture"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S0168169926003042?httpAccept=text\/xml","content-type":"text\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S0168169926003042?httpAccept=text\/plain","content-type":"text\/plain","content-version":"vor","intended-application":"text-mining"}],"deposited":{"date-parts":[[2026,4,23]],"date-time":"2026-04-23T04:48:59Z","timestamp":1776919739000},"score":1,"resource":{"primary":{"URL":"https:\/\/linkinghub.elsevier.com\/retrieve\/pii\/S0168169926003042"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,6]]},"references-count":36,"alternative-id":["S0168169926003042"],"URL":"https:\/\/doi.org\/10.1016\/j.compag.2026.111709","relation":{},"ISSN":["0168-1699"],"issn-type":[{"value":"0168-1699","type":"print"}],"subject":[],"published":{"date-parts":[[2026,6]]},"assertion":[{"value":"Elsevier","name":"publisher","label":"This article is maintained by"},{"value":"DC-MMoE: A deep Cross multi-gate Mixture-of-Experts model for simultaneous rice seed vigor assessment and variety classification using near-infrared hyperspectral imaging","name":"articletitle","label":"Article Title"},{"value":"Computers and Electronics in Agriculture","name":"journaltitle","label":"Journal Title"},{"value":"https:\/\/doi.org\/10.1016\/j.compag.2026.111709","name":"articlelink","label":"CrossRef DOI link to publisher maintained version"},{"value":"article","name":"content_type","label":"Content Type"},{"value":"\u00a9 2026 Elsevier B.V. All rights are reserved, including those for text and data mining, AI training, and similar technologies.","name":"copyright","label":"Copyright"}],"article-number":"111709"}}