{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,25]],"date-time":"2026-06-25T11:50:33Z","timestamp":1782388233255,"version":"3.54.5"},"reference-count":168,"publisher":"Wiley","issue":"2","license":[{"start":{"date-parts":[[2026,6,22]],"date-time":"2026-06-22T00:00:00Z","timestamp":1782086400000},"content-version":"vor","delay-in-days":21,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"},{"start":{"date-parts":[[2026,6,1]],"date-time":"2026-06-01T00:00:00Z","timestamp":1780272000000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/doi.wiley.com\/10.1002\/tdm_license_1.1"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["32560154"],"award-info":[{"award-number":["32560154"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["acsess.onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["The Plant Genome"],"published-print":{"date-parts":[[2026,6]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:p>\n                    Deep learning, as a pivotal branch of machine learning, has demonstrated remarkable potential in advancing crop science by effectively integrating genomics and phenomics. This review systematically outlines the application of diverse deep learning architectures\u2014such as convolutional neural networks, recurrent neural networks, and transformers\u2014across key crop genomic tasks, including gene expression prediction, alternative splicing analysis,\n                    <jats:italic>cis<\/jats:italic>\n                    \u2010regulatory element identification, epigenomic profiling, and genome\u2010based trait prediction. In phenomics, these models facilitate high\u2010throughput extraction of crop phenotypic traits from multispectral, unmanned aerial vehicle, and ground\u2010based imagery, supporting yield forecasting, disease diagnosis, and stress response monitoring. We critically evaluate the performance and limitations of each model type across tasks, considering trade\u2010offs between complexity, accuracy, and interpretability, to offer practical guidance for crop researchers. Additionally, the review addresses major challenges in deploying deep learning\u2014such as data scarcity, model transparency, and computational demands\u2014and proposes future pathways to enhance model generalizability, multimodal data integration, and applications in intelligent breeding and sustainable agriculture.\n                  <\/jats:p>","DOI":"10.1002\/tpg2.70268","type":"journal-article","created":{"date-parts":[[2026,6,23]],"date-time":"2026-06-23T05:37:43Z","timestamp":1782193063000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Application of deep learning in crop research: From genomics to phenomics"],"prefix":"10.1002","volume":"19","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-4404-238X","authenticated-orcid":false,"given":"Zefeng","family":"Wu","sequence":"first","affiliation":[{"name":"State Key Laboratory of Aridland Crop Science Gansu Agricultural University  Lanzhou China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yali","family":"Sun","sequence":"additional","affiliation":[{"name":"State Key Laboratory of Aridland Crop Science Gansu Agricultural University  Lanzhou China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Qian","family":"Luo","sequence":"additional","affiliation":[{"name":"State Key Laboratory of Aridland Crop Science Gansu Agricultural University  Lanzhou China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jiaping","family":"Wei","sequence":"additional","affiliation":[{"name":"State Key Laboratory of Aridland Crop Science Gansu Agricultural University  Lanzhou China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Junmei","family":"Cui","sequence":"additional","affiliation":[{"name":"State Key Laboratory of Aridland Crop Science Gansu Agricultural University  Lanzhou China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yan","family":"Fang","sequence":"additional","affiliation":[{"name":"State Key Laboratory of Aridland Crop Science Gansu Agricultural University  Lanzhou China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yining","family":"Niu","sequence":"additional","affiliation":[{"name":"State Key Laboratory of Aridland Crop Science Gansu Agricultural University  Lanzhou China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Zhaohong","family":"Li","sequence":"additional","affiliation":[{"name":"Key Laboratory of Animal Genetics, Breeding and Reproduction of Shaanxi Province, College of Animal Science and Technology Northwest A&amp;F University  Yangling China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xiaolin","family":"Wang","sequence":"additional","affiliation":[{"name":"College of Agriculture South China Agricultural University  Guangzhou China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Zigang","family":"Liu","sequence":"additional","affiliation":[{"name":"State Key Laboratory of Aridland Crop Science Gansu Agricultural University  Lanzhou China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"311","published-online":{"date-parts":[[2026,6,22]]},"reference":[{"key":"e_1_2_14_2_1","doi-asserted-by":"publisher","DOI":"10.1038\/s41598\u2010026\u201039182\u20103"},{"key":"e_1_2_14_3_1","doi-asserted-by":"publisher","DOI":"10.1093\/plcell\/koac079"},{"key":"e_1_2_14_4_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.gene.2020.100035"},{"issue":"93","key":"e_1_2_14_5_1","first-page":"91","article-title":"iNNvestigate neural networks!","volume":"20","author":"Alber M.","year":"2018","journal-title":"Journal of Machine Learning Research"},{"key":"e_1_2_14_6_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.molp.2024.03.007"},{"key":"e_1_2_14_7_1","doi-asserted-by":"publisher","DOI":"10.1146\/annurev\u2010arplant\u2010083123\u2010090055"},{"key":"e_1_2_14_8_1","doi-asserted-by":"publisher","DOI":"10.3389\/fpls.2020.01086"},{"key":"e_1_2_14_9_1","doi-asserted-by":"publisher","DOI":"10.1038\/s41592\u2010021\u201001252\u2010x"},{"key":"e_1_2_14_10_1","doi-asserted-by":"publisher","DOI":"10.1186\/s13040\u2010024\u201000391\u2010z"},{"key":"e_1_2_14_11_1","doi-asserted-by":"publisher","DOI":"10.1038\/nrm.2017.27"},{"key":"e_1_2_14_12_1","doi-asserted-by":"publisher","DOI":"10.1038\/s41576\u2010025\u201000841\u20102"},{"key":"e_1_2_14_13_1","doi-asserted-by":"publisher","DOI":"10.1186\/s13007\u2010025\u201001325\u20104"},{"key":"e_1_2_14_14_1","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-319-44781-0_8"},{"key":"e_1_2_14_15_1","doi-asserted-by":"publisher","DOI":"10.1146\/annurev.arplant.49.1.77"},{"key":"e_1_2_14_16_1","doi-asserted-by":"publisher","DOI":"10.1261\/rna.048769.114"},{"key":"e_1_2_14_17_1","doi-asserted-by":"publisher","DOI":"10.1007\/s11119\u2010022\u201009940\u20100"},{"key":"e_1_2_14_18_1","doi-asserted-by":"publisher","DOI":"10.1186\/s13059\u2010024\u201003379\u20104"},{"key":"e_1_2_14_19_1","doi-asserted-by":"publisher","DOI":"10.34133\/plantphenomics.0270"},{"key":"e_1_2_14_20_1","doi-asserted-by":"publisher","DOI":"10.1093\/bioinformatics\/btw074"},{"key":"e_1_2_14_21_1","doi-asserted-by":"publisher","DOI":"10.3389\/fpls.2022.1090970"},{"key":"e_1_2_14_22_1","doi-asserted-by":"publisher","DOI":"10.3389\/fpls.2023.1175837"},{"key":"e_1_2_14_23_1","doi-asserted-by":"publisher","DOI":"10.1093\/plcell\/koaf223"},{"key":"e_1_2_14_24_1","doi-asserted-by":"publisher","DOI":"10.1038\/s42256\u2010025\u201001007\u20109"},{"key":"e_1_2_14_25_1","doi-asserted-by":"publisher","DOI":"10.1038\/s41586\u2010019\u20101517\u20104"},{"key":"e_1_2_14_26_1","doi-asserted-by":"publisher","DOI":"10.1038\/s42256\u2010024\u201000839\u20101"},{"key":"e_1_2_14_27_1","doi-asserted-by":"publisher","DOI":"10.34133\/plantphenomics.0054"},{"key":"e_1_2_14_28_1","doi-asserted-by":"publisher","DOI":"10.1093\/nar\/gkad917"},{"key":"e_1_2_14_29_1","doi-asserted-by":"publisher","DOI":"10.1109\/TGRS.2025.3544434"},{"key":"e_1_2_14_30_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.jare.2023.11.017"},{"key":"e_1_2_14_31_1","doi-asserted-by":"crossref","unstructured":"Ennadifi E. Laraba S. Vincke D. Mercatoris B. &Gosselin B.(2020).Wheat diseases classification and localization using convolutional neural networks and GradCAM visualization. In2020 international conference on intelligent systems and computer vision (ISCV)(pp.1\u20135).IEEE.","DOI":"10.1109\/ISCV49265.2020.9204258"},{"key":"e_1_2_14_32_1","doi-asserted-by":"publisher","DOI":"10.3390\/genes13050907"},{"key":"e_1_2_14_33_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.tplants.2011.09.005"},{"key":"e_1_2_14_34_1","doi-asserted-by":"publisher","DOI":"10.1093\/hr\/uhaf128"},{"key":"e_1_2_14_35_1","doi-asserted-by":"publisher","DOI":"10.1109\/ACCESS.2018.2825996"},{"key":"e_1_2_14_36_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.plaphe.2025.100039"},{"key":"e_1_2_14_37_1","unstructured":"Goodfellow I. J. Pouget\u2010Abadie J. Mirza M. Xu B. Warde\u2010Farley D. Ozair S. Courville A. &Bengio Y.(2014).Generative adversarial nets. InNIPS'14: Proceedings of the 28th international conference on neural information processing systems(pp.2672\u20132680).ACM Digital Library."},{"key":"e_1_2_14_38_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.compag.2024.109111"},{"key":"e_1_2_14_39_1","doi-asserted-by":"publisher","DOI":"10.3390\/plants14111724"},{"key":"e_1_2_14_40_1","unstructured":"Gupta A. K. &Singh A. K. J. A.(2023).Agri\u2010GNN: A novel genotypic\u2010topological graph neural network framework built on GraphSAGE for optimized yield prediction. ArXiv.https:\/\/doi.org\/10.48550\/arXiv.2310.13037"},{"key":"e_1_2_14_41_1","doi-asserted-by":"publisher","DOI":"10.3390\/drones8110680"},{"key":"e_1_2_14_42_1","doi-asserted-by":"publisher","DOI":"10.3390\/su17177602"},{"key":"e_1_2_14_43_1","doi-asserted-by":"publisher","DOI":"10.1002\/advs.202412423"},{"key":"e_1_2_14_44_1","doi-asserted-by":"crossref","unstructured":"He K. Zhang X. Ren S. &Sun J.(2016).Deep residual learning for image recognition. In2016 IEEE conference on computer vision and pattern recognition (CVPR)(pp.770\u2013778).IEEE.","DOI":"10.1109\/CVPR.2016.90"},{"key":"e_1_2_14_45_1","unstructured":"Howard A. G. Zhu M. Chen B. Kalenichenko D. Wang W. Weyand T. Andreetto M. &Adam H.(2017).MobileNets: Efficient convolutional neural networks for mobile vision applications.arXiv.https:\/\/doi.org\/10.48550\/arXiv.1704.04861"},{"key":"e_1_2_14_46_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.copbio.2022.102887"},{"key":"e_1_2_14_47_1","doi-asserted-by":"publisher","DOI":"10.1111\/nph.16221"},{"key":"e_1_2_14_48_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.biochi.2006.03.006"},{"key":"e_1_2_14_49_1","doi-asserted-by":"publisher","DOI":"10.3389\/fpls.2024.1421503"},{"key":"e_1_2_14_50_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.cell.2018.12.015"},{"key":"e_1_2_14_51_1","doi-asserted-by":"publisher","DOI":"10.34133\/plantphenomics.0017"},{"key":"e_1_2_14_52_1","doi-asserted-by":"publisher","DOI":"10.1093\/bioinformatics\/btab083"},{"key":"e_1_2_14_53_1","doi-asserted-by":"publisher","DOI":"10.34133\/2020\/4152816"},{"key":"e_1_2_14_54_1","doi-asserted-by":"publisher","DOI":"10.1111\/nph.17580"},{"key":"e_1_2_14_55_1","doi-asserted-by":"publisher","DOI":"10.1038\/s42003\u2010024\u201007298\u20109"},{"key":"e_1_2_14_56_1","doi-asserted-by":"publisher","DOI":"10.3389\/fpls.2019.00621"},{"key":"e_1_2_14_57_1","unstructured":"Kokhlikyan N. Miglani V. Martin M. Wang E. Alsallakh B. Reynolds J. Melnikov A. Kliushkina N. Araya C. Yan S. &Reblitz\u2010Richardson O.(2020).Captum: A unified and generic model interpretability library for PyTorch. ArXiv.https:\/\/doi.org\/10.48550\/arXiv.2009.07896"},{"key":"e_1_2_14_58_1","doi-asserted-by":"publisher","DOI":"10.1145\/3065386"},{"key":"e_1_2_14_59_1","doi-asserted-by":"publisher","DOI":"10.1007\/s12892\u2010025\u201000334\u2010y"},{"issue":"114","key":"e_1_2_14_60_1","first-page":"3938","article-title":"The LRP toolbox for artificial neural networks","volume":"17","author":"Lapuschkin S.","year":"2016","journal-title":"The Journal of Machine Learning Research"},{"key":"e_1_2_14_61_1","doi-asserted-by":"publisher","DOI":"10.3390\/plants13060758"},{"key":"e_1_2_14_62_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.molp.2024.03.002"},{"key":"e_1_2_14_63_1","doi-asserted-by":"publisher","DOI":"10.1038\/s41467\u2010026\u201071035\u20105"},{"key":"e_1_2_14_64_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.xplc.2024.100975"},{"key":"e_1_2_14_65_1","doi-asserted-by":"publisher","DOI":"10.3390\/agriculture15111171"},{"key":"e_1_2_14_66_1","doi-asserted-by":"publisher","DOI":"10.1073\/pnas.2319811121"},{"key":"e_1_2_14_67_1","doi-asserted-by":"publisher","DOI":"10.1093\/bib\/bbaf493"},{"key":"e_1_2_14_68_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.compag.2022.106702"},{"key":"e_1_2_14_69_1","doi-asserted-by":"publisher","DOI":"10.1186\/s12859\u2010021\u201004437\u20105"},{"key":"e_1_2_14_70_1","unstructured":"Lipton Z. C. Berkowitz J. &Elkan C. J. C. S.(2015).A critical review of recurrent neural networks for sequence learning. ArXiv.https:\/\/doi.org\/10.48550\/arXiv.1506.00019"},{"issue":"3","key":"e_1_2_14_71_1","first-page":"31","article-title":"The mythos of model interpretability: In machine learning, the concept of interpretability is both important and slippery","volume":"16","author":"Lipton Z. C.","year":"2018","journal-title":"Communications of the ACM"},{"key":"e_1_2_14_72_1","doi-asserted-by":"publisher","DOI":"10.34133\/plantphenomics.0199"},{"key":"e_1_2_14_73_1","doi-asserted-by":"publisher","DOI":"10.1093\/bioinformatics\/btaa1100"},{"key":"e_1_2_14_74_1","doi-asserted-by":"publisher","DOI":"10.1093\/plphys\/kiab322"},{"key":"e_1_2_14_75_1","first-page":"4766","article-title":"A unified approach to interpreting model predictions","volume":"30","author":"Lundberg S. M.","year":"2017","journal-title":"Neural Information Processing Systems"},{"key":"e_1_2_14_76_1","doi-asserted-by":"publisher","DOI":"10.34133\/plantphenomics.0080"},{"key":"e_1_2_14_77_1","doi-asserted-by":"publisher","DOI":"10.3390\/agriengineering7100353"},{"key":"e_1_2_14_78_1","doi-asserted-by":"publisher","DOI":"10.3390\/rs17030443"},{"key":"e_1_2_14_79_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.gde.2011.04.001"},{"key":"e_1_2_14_80_1","doi-asserted-by":"publisher","DOI":"10.1038\/s42003\u2010024\u201006465\u20102"},{"key":"e_1_2_14_81_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.pbi.2015.02.002"},{"key":"e_1_2_14_82_1","doi-asserted-by":"publisher","DOI":"10.1146\/annurev\u2010arplant\u2010070523\u2010042828"},{"key":"e_1_2_14_83_1","doi-asserted-by":"publisher","DOI":"10.1038\/s41598\u2010025\u201014847\u20107"},{"key":"e_1_2_14_84_1","unstructured":"Nagasubramanian K. Singh A. K. Singh A. Sarkar S. &Ganapathysubramanian B. J. A.(2020).Usefulness of interpretability methods to explain deep learning based plant stress phenotyping.arXiv.https:\/\/doi.org\/10.48550\/arXiv.2007.05729arXiv"},{"key":"e_1_2_14_85_1","unstructured":"Nahian M. J. A. Ghosh T. Sheikhi F. &Maleki F.(2025).Agri\u2010FM+: A self\u2010supervised foundation model for agricultural vision. In2025 IEEE\/CVF conference on computer vision and pattern recognition workshops (CVPRW)(pp.5520\u20135532).CVF Open Access."},{"key":"e_1_2_14_86_1","doi-asserted-by":"publisher","DOI":"10.3389\/fpls.2025.1718810"},{"key":"e_1_2_14_87_1","doi-asserted-by":"publisher","DOI":"10.34133\/plantphenomics.0244"},{"key":"e_1_2_14_88_1","doi-asserted-by":"publisher","DOI":"10.1111\/nph.16544"},{"key":"e_1_2_14_89_1","doi-asserted-by":"publisher","DOI":"10.1038\/s41467\u2010024\u201047744\u20100"},{"key":"e_1_2_14_90_1","doi-asserted-by":"publisher","DOI":"10.1186\/s12864\u2010018\u20105360\u2010z"},{"key":"e_1_2_14_91_1","doi-asserted-by":"crossref","unstructured":"Ribeiro M. T. Singh S. &Guestrin C.(2016).\u201cWhy should I trust you?\u201d: Explaining the predictions of any classifier. InProceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining(pp.1135\u20131144).ACM Digital Library.","DOI":"10.1145\/2939672.2939778"},{"key":"e_1_2_14_92_1","doi-asserted-by":"publisher","DOI":"10.1080\/08839514.2025.2565169"},{"key":"e_1_2_14_93_1","doi-asserted-by":"publisher","DOI":"10.3389\/fgene.2021.829131"},{"key":"e_1_2_14_94_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.aiia.2024.07.001"},{"key":"e_1_2_14_95_1","doi-asserted-by":"crossref","unstructured":"Schlegel L. Cano F. G. Marand A. P. &Johannes F.(2025).Polymer\u2010derived distance penalties improve chromatin interaction predictions from single\u2010cell data across crop genomes. bioRxiv.https:\/\/doi.org\/10.1101\/2025.08.20.671329","DOI":"10.1101\/2025.08.20.671329"},{"key":"e_1_2_14_96_1","doi-asserted-by":"publisher","DOI":"10.1007\/978\u20103\u2010540\u201076776\u20103_3"},{"key":"e_1_2_14_97_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.jplph.2020.153352"},{"key":"e_1_2_14_98_1","doi-asserted-by":"publisher","DOI":"10.1038\/s42256\u2010024\u201000851\u20105"},{"key":"e_1_2_14_99_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.tplants.2021.06.016"},{"key":"e_1_2_14_100_1","doi-asserted-by":"publisher","DOI":"10.1270\/jsbbs.20146"},{"key":"e_1_2_14_101_1","doi-asserted-by":"publisher","DOI":"10.3389\/fpls.2023.1283235"},{"key":"e_1_2_14_102_1","doi-asserted-by":"publisher","DOI":"10.1093\/aobpla\/plq008"},{"key":"e_1_2_14_103_1","unstructured":"Shrikumar A. Greenside P. &Kundaje A.(2017).Learning important features through propagating activation differences. InICML'17: Proceedings of the 34th international conference on machine learning(pp.3145\u20133153).ACM Digital Library."},{"key":"e_1_2_14_104_1","unstructured":"Simonyan K. Vedaldi A. &Zisserman A. J. C.(2013).Deep inside convolutional networks: Visualising image classification models and saliency maps. ArXiv.https:\/\/doi.org\/10.48550\/arXiv.1312.6034"},{"key":"e_1_2_14_105_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.tplants.2018.07.004"},{"key":"e_1_2_14_106_1","doi-asserted-by":"publisher","DOI":"10.1134\/S0361768824700051"},{"key":"e_1_2_14_107_1","doi-asserted-by":"publisher","DOI":"10.1038\/s41467\u2010026\u201071877\u2010z"},{"key":"e_1_2_14_108_1","unstructured":"Sundararajan M. Taly A. &Yan Q.(2017).Axiomatic attribution for deep networks. InICML'17: Proceedings of the 34th international conference on machine learning(pp.3319\u20133328).ACM Digital Library."},{"key":"e_1_2_14_109_1","doi-asserted-by":"publisher","DOI":"10.34133\/plantphenomics.0073"},{"key":"e_1_2_14_110_1","doi-asserted-by":"publisher","DOI":"10.34133\/2020\/3194308"},{"key":"e_1_2_14_111_1","doi-asserted-by":"publisher","DOI":"10.34133\/2019\/9237136"},{"key":"e_1_2_14_112_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.jplph.2020.153354"},{"key":"e_1_2_14_113_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.isprsjprs.2024.02.021"},{"key":"e_1_2_14_114_1","doi-asserted-by":"publisher","DOI":"10.34133\/plantphenomics.0155"},{"key":"e_1_2_14_115_1","doi-asserted-by":"publisher","DOI":"10.34133\/research.0491"},{"key":"e_1_2_14_116_1","doi-asserted-by":"publisher","DOI":"10.3168\/jds.2007\u20100980"},{"key":"e_1_2_14_117_1","unstructured":"Vaswani A. Shazeer N. Parmar N. Uszkoreit J. Jones L. Gomez A. N. Kaiser L. &Polosukhin I.(2017).Attention is all you need. InProceedings of the 31st international conference on neural information processing systems(pp.6000\u20136010).Curran Associates Inc."},{"key":"e_1_2_14_118_1","doi-asserted-by":"publisher","DOI":"10.34133\/plantphenomics.0153"},{"key":"e_1_2_14_119_1","doi-asserted-by":"publisher","DOI":"10.1007\/s11265-022-01808-w"},{"key":"e_1_2_14_120_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.pbi.2019.12.010"},{"key":"e_1_2_14_121_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.xplc.2024.101223"},{"key":"e_1_2_14_122_1","doi-asserted-by":"publisher","DOI":"10.34133\/plantphenomics.0197"},{"key":"e_1_2_14_123_1","doi-asserted-by":"publisher","DOI":"10.1002\/tpg2.20155"},{"key":"e_1_2_14_124_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.molp.2022.11.004"},{"key":"e_1_2_14_125_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.indcrop.2024.118358"},{"key":"e_1_2_14_126_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.cj.2018.03.001"},{"key":"e_1_2_14_127_1","doi-asserted-by":"publisher","DOI":"10.1007\/s11434\u2010015\u20100791\u20102"},{"key":"e_1_2_14_128_1","doi-asserted-by":"publisher","DOI":"10.1111\/nph.17630"},{"key":"e_1_2_14_129_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.xplc.2024.100985"},{"key":"e_1_2_14_130_1","doi-asserted-by":"publisher","DOI":"10.1111\/tpj.15166"},{"key":"e_1_2_14_131_1","doi-asserted-by":"publisher","DOI":"10.1007\/s00122\u2010021\u201003943\u20107"},{"key":"e_1_2_14_132_1","doi-asserted-by":"publisher","DOI":"10.1073\/pnas.1814551116"},{"key":"e_1_2_14_133_1","doi-asserted-by":"publisher","DOI":"10.1093\/bib\/bbae309"},{"key":"e_1_2_14_134_1","doi-asserted-by":"publisher","DOI":"10.34133\/plantphenomics.0178"},{"key":"e_1_2_14_135_1","doi-asserted-by":"publisher","DOI":"10.1038\/sdata.2016.18"},{"key":"e_1_2_14_136_1","doi-asserted-by":"publisher","DOI":"10.1038\/s41576\u2010022\u201000514\u20104"},{"key":"e_1_2_14_137_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.xplc.2025.101240"},{"key":"e_1_2_14_138_1","doi-asserted-by":"publisher","DOI":"10.1093\/pcp\/pcz051"},{"key":"e_1_2_14_139_1","doi-asserted-by":"publisher","DOI":"10.1093\/bioinformatics\/btae716"},{"key":"e_1_2_14_140_1","doi-asserted-by":"publisher","DOI":"10.1111\/nph.19554"},{"key":"e_1_2_14_141_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.engappai.2024.109890"},{"key":"e_1_2_14_142_1","doi-asserted-by":"publisher","DOI":"10.1038\/s41477\u2010026\u201002289\u20106"},{"key":"e_1_2_14_143_1","doi-asserted-by":"publisher","DOI":"10.1093\/bib\/bbac425"},{"key":"e_1_2_14_144_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.molp.2025.01.020"},{"key":"e_1_2_14_145_1","doi-asserted-by":"publisher","DOI":"10.1093\/bib\/bbad095"},{"key":"e_1_2_14_146_1","doi-asserted-by":"publisher","DOI":"10.1038\/s41467\u2010024\u201053088\u20106"},{"key":"e_1_2_14_147_1","doi-asserted-by":"publisher","DOI":"10.1093\/plphys\/kiaf126"},{"key":"e_1_2_14_148_1","doi-asserted-by":"publisher","DOI":"10.1073\/pnas.2421738122"},{"key":"e_1_2_14_149_1","doi-asserted-by":"publisher","DOI":"10.1002\/advs.202503135"},{"key":"e_1_2_14_150_1","doi-asserted-by":"publisher","DOI":"10.1038\/s41580\u2010024\u201000769\u20101"},{"key":"e_1_2_14_151_1","doi-asserted-by":"publisher","DOI":"10.34133\/plantphenomics.0123"},{"key":"e_1_2_14_152_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.eswa.2025.129827"},{"key":"e_1_2_14_153_1","doi-asserted-by":"crossref","unstructured":"Zhang X. Zhou X. Lin M. &Sun J.(2017).ShuffleNet: An extremely efficient convolutional neural network for mobile devices. In2018 IEEE\/CVF conference on computer vision and pattern recognition(pp.6848\u20136856).CVF Open Access.","DOI":"10.1109\/CVPR.2018.00716"},{"key":"e_1_2_14_154_1","doi-asserted-by":"publisher","DOI":"10.1038\/s41467\u2010024\u201047678\u20107"},{"key":"e_1_2_14_155_1","doi-asserted-by":"publisher","DOI":"10.1038\/s42256\u2010021\u201000316\u2010z"},{"key":"e_1_2_14_156_1","doi-asserted-by":"publisher","DOI":"10.3389\/fpls.2019.00714"},{"key":"e_1_2_14_157_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.plaphe.2025.100004"},{"key":"e_1_2_14_158_1","doi-asserted-by":"publisher","DOI":"10.1038\/s41422\u2010024\u201001027\u2010x"},{"key":"e_1_2_14_159_1","doi-asserted-by":"publisher","DOI":"10.34133\/2022\/9850486"},{"key":"e_1_2_14_160_1","doi-asserted-by":"publisher","DOI":"10.34133\/plantphenomics.0200"},{"key":"e_1_2_14_161_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.agsy.2024.104214"},{"key":"e_1_2_14_162_1","doi-asserted-by":"publisher","DOI":"10.34133\/2021\/9892570"},{"key":"e_1_2_14_163_1","doi-asserted-by":"publisher","DOI":"10.1038\/s41588\u2010018\u20100160\u20106"},{"key":"e_1_2_14_164_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.plaphe.2025.100028"},{"key":"e_1_2_14_165_1","doi-asserted-by":"publisher","DOI":"10.34133\/plantphenomics.0022"},{"key":"e_1_2_14_166_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.jgg.2024.12.007"},{"key":"e_1_2_14_167_1","doi-asserted-by":"publisher","DOI":"10.3389\/fpls.2025.1579355"},{"key":"e_1_2_14_168_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.compag.2024.109833"},{"key":"e_1_2_14_169_1","doi-asserted-by":"publisher","DOI":"10.1093\/bioinformatics\/bty497"}],"container-title":["The Plant Genome"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/acsess.onlinelibrary.wiley.com\/doi\/pdf\/10.1002\/tpg2.70268","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/acsess.onlinelibrary.wiley.com\/doi\/full-xml\/10.1002\/tpg2.70268","content-type":"application\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/acsess.onlinelibrary.wiley.com\/doi\/pdf\/10.1002\/tpg2.70268","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,6,25]],"date-time":"2026-06-25T11:31:39Z","timestamp":1782387099000},"score":1,"resource":{"primary":{"URL":"https:\/\/acsess.onlinelibrary.wiley.com\/doi\/10.1002\/tpg2.70268"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,6]]},"references-count":168,"journal-issue":{"issue":"2","published-print":{"date-parts":[[2026,6]]}},"alternative-id":["10.1002\/tpg2.70268"],"URL":"https:\/\/doi.org\/10.1002\/tpg2.70268","archive":["Portico"],"relation":{},"ISSN":["1940-3372","1940-3372"],"issn-type":[{"value":"1940-3372","type":"print"},{"value":"1940-3372","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,6]]},"assertion":[{"value":"2026-03-02","order":0,"name":"received","label":"Received","group":{"name":"publication_history","label":"Publication History"}},{"value":"2026-05-28","order":2,"name":"accepted","label":"Accepted","group":{"name":"publication_history","label":"Publication History"}},{"value":"2026-06-22","order":3,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}],"article-number":"e70268"}}