{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,26]],"date-time":"2026-09-26T00:41:02Z","timestamp":1790383262200,"version":"4.1.0"},"reference-count":84,"publisher":"MDPI AG","issue":"8","license":[{"start":{"date-parts":[[2026,7,30]],"date-time":"2026-07-30T00:00:00Z","timestamp":1785369600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/100015548","name":"Vietnam National University, Hanoi","doi-asserted-by":"crossref","award":["QG.25.61"],"award-info":[{"award-number":["QG.25.61"]}],"id":[{"id":"10.13039\/100015548","id-type":"DOI","asserted-by":"crossref"}]},{"award":["QG.25.61"],"award-info":[{"award-number":["QG.25.61"]}],"id":[{"id":"https:\/\/ror.org\/02jmfj006","id-type":"ROR","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Genes"],"abstract":"<jats:p>Background: Rice breeding requires faster development of high-yielding, climate-resilient, resource-efficient, and high-quality cultivars for production systems exposed to environmental variability and increasing input constraints. Genomic selection offers an opportunity to predict breeding value before extensive field evaluation, although its effectiveness depends on the integration of genomic, phenotypic, and environmental information. Methods: This narrative review critically examines recent advances in genomic selection for rice and its integration with high-throughput genotyping, high-throughput phenotyping, machine learning, multi-environment prediction, and speed breeding. Results: Genome-wide marker data can support early ranking of breeding materials for grain yield, grain quality, disease resistance, drought tolerance, salinity tolerance, and nutrient-use efficiency. Prediction performance is influenced by trait architecture, marker density, training-population size, genetic relatedness between training and candidate populations, phenotypic data quality, and genotype-by-environment interaction. Red-green-blue, multispectral, hyperspectral, thermal, and light detection and ranging platforms can generate temporal traits associated with plant architecture, biomass, water status, nutrient status, and stress responses, which may improve prediction under suitable population and validation designs. Speed-breeding systems shorten generation intervals and facilitate rapid advancement, recurrent selection, and recycling of superior parental lines. Conclusions: Integrated breeding pipelines that combine genomic prediction, high-throughput phenotyping, environmental data, and speed breeding can improve selection efficiency and shorten rice improvement cycles. Wider adoption will require affordable technology platforms, standardized data systems, multi-environment validation, breeder capacity development, and collaborative data-sharing frameworks for smart and greener agriculture.<\/jats:p>","DOI":"10.3390\/genes17080900","type":"journal-article","created":{"date-parts":[[2026,7,30]],"date-time":"2026-07-30T11:38:54Z","timestamp":1785411534000},"page":"900","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["Genomic Selection Integrated with High-Throughput Phenotyping and Speed Breeding for Smart and Greener Rice (Oryza sativa) Improvement"],"prefix":"10.3390","volume":"17","author":[{"given":"Ha Duc","family":"Chu","sequence":"first","affiliation":[{"name":"Faculty of Agricultural Technology, University of Engineering and Technology, Vietnam National University Hanoi, Hanoi City 100000, Vietnam"},{"name":"VinUni Bigdata Research Institute, VinUniversity, Hanoi City 100000, Vietnam"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Trung Quoc","family":"Nguyen","sequence":"additional","affiliation":[{"name":"Faculty of Biotechnology, Vietnam National University of Agriculture, Hanoi City 100000, Vietnam"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Loc Van","family":"Nguyen","sequence":"additional","affiliation":[{"name":"Faculty of Agronomy, Vietnam National University of Agriculture, Hanoi City 100000, Vietnam"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Nguyen Nguyen","family":"Chuong","sequence":"additional","affiliation":[{"name":"Institute of Genomics for Crop Abiotic Stress Tolerance, Department of Plant and Soil Science, Texas Tech University, Lubbock, TX 79409, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Quyen Thi","family":"Ha","sequence":"additional","affiliation":[{"name":"Faculty of Agricultural Technology, University of Engineering and Technology, Vietnam National University Hanoi, Hanoi City 100000, Vietnam"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0006-9480-7535","authenticated-orcid":false,"given":"Nguyen Thi Phuong","family":"Thao","sequence":"additional","affiliation":[{"name":"VinUni Bigdata Research Institute, VinUniversity, Hanoi City 100000, Vietnam"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1995-494X","authenticated-orcid":false,"given":"Touhidur Rahman","family":"Anik","sequence":"additional","affiliation":[{"name":"Institute of Genomics for Crop Abiotic Stress Tolerance, Department of Plant and Soil Science, Texas Tech University, Lubbock, TX 79409, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2070-6660","authenticated-orcid":false,"given":"Saad","family":"Sulieman","sequence":"additional","affiliation":[{"name":"Department of Integrative Agriculture, College of Agriculture and Veterinary Medicine, United Arab Emirates University, Al Ain P.O. Box 15551, Abu Dhabi, United Arab Emirates"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2324-0387","authenticated-orcid":false,"given":"Weiqiang","family":"Li","sequence":"additional","affiliation":[{"name":"State Key Laboratory of Black Soils Conservation and Utilization, Northeast Institute of Geography and Agroecology, Chinese Academy of Sciences, Changchun 130102, China"},{"name":"Jilin Da\u2019an Agro-Ecosystem National Observation and Research Station, Changchun Jingyuetan Remote Sensing Experiment Station, Northeast Institute of Geography and Agroecology, Chinese Academy of Sciences, Changchun 130102, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9883-9768","authenticated-orcid":false,"given":"Lam-Son Phan","family":"Tran","sequence":"additional","affiliation":[{"name":"Institute of Genomics for Crop Abiotic Stress Tolerance, Department of Plant and Soil Science, Texas Tech University, Lubbock, TX 79409, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2026,7,30]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"S2","DOI":"10.3177\/jnsv.65.S2","article-title":"Rice: Importance for global nutrition","volume":"65","author":"Fukagawa","year":"2019","journal-title":"J. Nutr. Sci. Vitaminol."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"7","DOI":"10.1111\/nyas.12540","article-title":"An overview of global rice production, supply, trade, and consumption","volume":"1324","author":"Muthayya","year":"2014","journal-title":"Ann. N. Y. Acad. Sci."},{"key":"ref_3","doi-asserted-by":"crossref","unstructured":"Geng, Y., Raza, Q.U., Bashir, M.A., Xie, S., Song, X., Yan, M., Jia, G., Liu, R., Ran, W., and Long, Y. (2025). Spatio-temporal analysis of rice production and trade between Southwest China and major rice producers in Southeast Asia. Front. Plant Sci., 16.","DOI":"10.3389\/fpls.2025.1543314"},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"456","DOI":"10.1016\/j.molp.2026.02.009","article-title":"Rice2035: A decadal vision for rice research and breeding","volume":"19","author":"Yu","year":"2026","journal-title":"Mol. Plant"},{"key":"ref_5","doi-asserted-by":"crossref","unstructured":"Lin, Y., Mao, F., Huang, Y., Huang, D., Wang, K., Yan, S., and Liu, C. (2025). Advances in doubled haploid technology for rice breeding: Mechanisms, applications, and future perspectives. Front. Plant Sci., 16.","DOI":"10.3389\/fpls.2025.1709033"},{"key":"ref_6","doi-asserted-by":"crossref","unstructured":"Sandhu, N., Yadav, S., Catolos, M., Cruz, M.T.S., and Kumar, A. (2021). Developing climate-resilient, direct-seeded, adapted multiple-stress-tolerant rice applying genomics-assisted breeding. Front. Plant Sci., 12.","DOI":"10.3389\/fpls.2021.637488"},{"key":"ref_7","first-page":"524847","article-title":"Rice molecular breeding laboratories in the genomics era: Current status and future considerations","volume":"2008","author":"Collard","year":"2008","journal-title":"Int. J. Plant Genom."},{"key":"ref_8","doi-asserted-by":"crossref","unstructured":"Kou, S., Ci, Z., Liu, W., Wu, Z., Peng, H., Yuan, P., Jiang, C., Li, H., Mansour, E., and Huang, P. (2026). Conservation and sustainable development of rice landraces for enhancing resilience to climate change, with a case study of \u2018Pantiange Heigu\u2019 in China. Life, 16.","DOI":"10.3390\/life16010143"},{"key":"ref_9","doi-asserted-by":"crossref","unstructured":"Bhat, J.A., Ali, S., Salgotra, R.K., Mir, Z.A., Dutta, S., Jadon, V., Tyagi, A., Mushtaq, M., Jain, N., and Singh, P.K. (2016). Genomic selection in the era of Next generation sequencing for complex traits in plant breeding. Front. Genet., 7.","DOI":"10.3389\/fgene.2016.00221"},{"key":"ref_10","doi-asserted-by":"crossref","unstructured":"Budhlakoti, N., Kushwaha, A.K., Rai, A., Chaturvedi, K.K., Kumar, A., Pradhan, A.K., Kumar, U., Kumar, R.R., Juliana, P., and Mishra, D.C. (2022). Genomic selection: A tool for accelerating the efficiency of molecular breeding for development of climate-resilient crops. Front. Genet., 13.","DOI":"10.3389\/fgene.2022.832153"},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"669","DOI":"10.1016\/j.cj.2021.03.008","article-title":"Genomic selection: A breakthrough technology in rice breeding","volume":"9","author":"Xu","year":"2021","journal-title":"Crop J."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"55","DOI":"10.1186\/s12711-021-00647-w","article-title":"Predicting the accuracy of genomic predictions","volume":"53","author":"Dekkers","year":"2021","journal-title":"Genet. Sel. Evol."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"1446","DOI":"10.1038\/s41598-018-38081-6","article-title":"Design of training populations for selective phenotyping in genomic prediction","volume":"9","author":"Akdemir","year":"2019","journal-title":"Sci. Rep."},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"Zhang, Z., Liu, J., Ding, X., Bijma, P., de Koning, D.J., and Zhang, Q. (2010). Best linear unbiased prediction of genomic breeding values using a trait-specific marker-derived relationship matrix. PLoS ONE, 5.","DOI":"10.1371\/journal.pone.0012648"},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"1093","DOI":"10.2135\/cropsci2011.11.0592","article-title":"Efficient computation of ridge-regression best linear unbiased prediction in genomic selection in plant breeding","volume":"52","author":"Piepho","year":"2012","journal-title":"Crop Sci."},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Lu, Y., Xia, C., Wang, Z., Liu, Q., Zhu, M., Xie, X., Zhao, Y., Jiang, Y., Luo, X., and Zou, M. (2025). Assessment of the prediction accuracy of genomic selection for rice amylose content and gel consistency. Agronomy, 15.","DOI":"10.3390\/agronomy15020336"},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"81","DOI":"10.1007\/s11032-023-01423-y","article-title":"Integrating genome-wide association study into genomic selection for the prediction of agronomic traits in rice (Oryza sativa L.)","volume":"43","author":"Zhang","year":"2023","journal-title":"Mol. Breed."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"347","DOI":"10.1534\/genetics.112.147983","article-title":"Genomic prediction in animals and plants: Simulation of data, validation, reporting, and benchmarking","volume":"193","author":"Daetwyler","year":"2013","journal-title":"Genetics"},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"7","DOI":"10.1186\/s12284-023-00623-6","article-title":"Multi-environment genomic selection in rice elite breeding lines","volume":"16","author":"Nguyen","year":"2023","journal-title":"Rice"},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"100","DOI":"10.1038\/s41437-017-0010-9","article-title":"Factors affecting GEBV accuracy with single-step Bayesian models","volume":"120","author":"Zhou","year":"2018","journal-title":"Heredity"},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"40","DOI":"10.1186\/s12284-017-0181-2","article-title":"Large-scale deployment of a rice 6 K SNP array for genetics and breeding applications","volume":"10","author":"Thomson","year":"2017","journal-title":"Rice"},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"e20219","DOI":"10.1002\/tpg2.20219","article-title":"Development and validation of an optimized marker set for genomic selection in southern U.S. rice breeding programs","volume":"15","author":"Cerioli","year":"2022","journal-title":"Plant Genome"},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"21","DOI":"10.1186\/s12284-019-0272-3","article-title":"Development of a core SNP arrays based on the KASP method for molecular breeding of rice","volume":"12","author":"Yang","year":"2019","journal-title":"Rice"},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"55","DOI":"10.1186\/s12284-019-0311-0","article-title":"1k-RiCA (1K-Rice Custom Amplicon) a novel genotyping amplicon-based SNP assay for genetics and breeding applications in rice","volume":"12","author":"Arbelaez","year":"2019","journal-title":"Rice"},{"key":"ref_25","doi-asserted-by":"crossref","unstructured":"Reyes, V.P., Kitony, J.K., Nishiuchi, S., Makihara, D., and Doi, K. (2022). Utilization of genotyping-by-sequencing (GBS) for rice pre-breeding and improvement: A review. Life, 12.","DOI":"10.3390\/life12111752"},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"2056","DOI":"10.1016\/j.molp.2021.08.007","article-title":"Cytoplasmic and nuclear genome variations of rice hybrids and their parents inform the trajectory and strategy of hybrid rice breeding","volume":"14","author":"Gu","year":"2021","journal-title":"Mol. Plant"},{"key":"ref_27","first-page":"9272363","article-title":"Whole-genome characteristics and polymorphic analysis of Vietnamese rice landraces as a comprehensive information resource for marker-assisted selection","volume":"2017","author":"Trinh","year":"2017","journal-title":"Int. J. Genom."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"38","DOI":"10.1007\/s11032-018-0777-2","article-title":"Accelerating public sector rice breeding with high-density KASP markers derived from whole genome sequencing of indica rice","volume":"38","author":"Steele","year":"2018","journal-title":"Mol. Breed."},{"key":"ref_29","doi-asserted-by":"crossref","unstructured":"Spindel, J., Begum, H., Akdemir, D., Virk, P., Collard, B., Redo\u00f1a, E., Atlin, G., Jannink, J.L., and McCouch, S.R. (2015). Genomic selection and association mapping in rice (Oryza sativa): Effect of trait genetic architecture, training population composition, marker number and statistical model on accuracy of rice genomic selection in elite, tropical rice breeding lines. PLoS Genet., 11.","DOI":"10.1371\/journal.pgen.1005350"},{"key":"ref_30","doi-asserted-by":"crossref","unstructured":"Bartholom\u00e9, J., Frouin, J., Brottier, L., Cao, T.-V., Boisnard, A., Ahmadi, N., and Courtois, B. (2023). Genomic selection for salinity tolerance in japonica rice. PLoS ONE, 18.","DOI":"10.1371\/journal.pone.0291833"},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"330","DOI":"10.1016\/j.cj.2018.03.001","article-title":"Genomic selection methods for crop improvement: Current status and prospects","volume":"6","author":"Wang","year":"2018","journal-title":"Crop J."},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"759","DOI":"10.1534\/genetics.115.177907","article-title":"Modeling epistasis in genomic selection","volume":"201","author":"Jiang","year":"2015","journal-title":"Genetics"},{"key":"ref_33","doi-asserted-by":"crossref","unstructured":"Bhandari, A., Bartholom\u00e9, J., Cao-Hamadoun, T.-V., Kumari, N., Frouin, J., Kumar, A., and Ahmadi, N. (2019). Selection of trait-specific markers and multi-environment models improve genomic predictive ability in rice. PLoS ONE, 14.","DOI":"10.1371\/journal.pone.0208871"},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"2319","DOI":"10.1534\/g3.118.200098","article-title":"Genomic prediction accounting for genotype by environment interaction offers an effective framework for breeding simultaneously for adaptation to an abiotic stress and performance under normal cropping conditions in rice","volume":"8","author":"Cao","year":"2018","journal-title":"G3"},{"key":"ref_35","doi-asserted-by":"crossref","unstructured":"Muvunyi, B.P., Zou, W., Zhan, J., He, S., and Ye, G. (2022). Multi-trait genomic prediction models enhance the predictive ability of grain trace elements in rice. Front. Genet., 13.","DOI":"10.3389\/fgene.2022.883853"},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"268","DOI":"10.1016\/j.rsci.2021.04.006","article-title":"Genomic prediction of arsenic tolerance and grain yield in rice: Contribution of trait-specific markers and multi-environment models","volume":"28","author":"Ahmadi","year":"2021","journal-title":"Rice Sci."},{"key":"ref_37","first-page":"16","article-title":"Genome-wide association study and genomic prediction for yield and grain quality traits of hybrid rice","volume":"42","author":"Yu","year":"2022","journal-title":"Mol. Breed. New Strateg. Plant Improv."},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"52","DOI":"10.1007\/s00122-026-05159-z","article-title":"Enhancing genomic prediction ability of blast resistance using genome-wide association study-derived marker weights in two rice (Oryza sativa L.) populations","volume":"139","author":"Akohoue","year":"2026","journal-title":"Theor. Appl. Genet."},{"key":"ref_39","doi-asserted-by":"crossref","unstructured":"Labroo, M.R., Ali, J., Aslam, M.U., de Asis, E.J., dela Paz, M.A., Sevilla, M.A., Lipka, A.E., Studer, A.J., and Rutkoski, J.E. (2021). Genomic prediction of yield traits in single-cross hybrid rice (Oryza sativa L.). Front. Genet., 12.","DOI":"10.3389\/fgene.2021.692870"},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"e70068","DOI":"10.1002\/tpg2.70068","article-title":"Implementing marker covariates and multi-trait genomic selection models to improve grain milling, appearance, cooking, and edible quality in rice (Oryza sativa L.)","volume":"18","author":"Dhakal","year":"2025","journal-title":"Plant Genome"},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"114","DOI":"10.1007\/s11032-019-1023-2","article-title":"Use of genomic selection in breeding rice (Oryza sativa L.) for resistance to rice blast (Magnaporthe oryzae)","volume":"39","author":"Huang","year":"2019","journal-title":"Mol. Breed."},{"key":"ref_42","first-page":"158","article-title":"Validation of genomic selection approach for predicting sheath blight resistance in rice (Oryza sativa L.)","volume":"43","author":"Mahantesh","year":"2021","journal-title":"J. Exp. Agric. Int."},{"key":"ref_43","first-page":"1000264","article-title":"Breeding for nitrogen use efficiency: Lessons from genomic prediction experiments based on a broad-based population of upland rice","volume":"9","author":"Rakotomalala","year":"2021","journal-title":"Rice Res."},{"key":"ref_44","doi-asserted-by":"crossref","unstructured":"Khanna, A., Anumalla, M., Catolos, M., Bhosale, S., Jarquin, D., and Hussain, W. (2022). Optimizing predictions in IRRI\u2019s rice drought breeding program by leveraging 17 years of historical data and pedigree information. Front. Plant Sci., 13.","DOI":"10.3389\/fpls.2022.983818"},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"43","DOI":"10.1186\/s12284-023-00661-0","article-title":"Optimization of multi-generation multi-location genomic prediction models for recurrent genomic selection in an upland rice population","volume":"16","author":"Baertschi","year":"2023","journal-title":"Rice"},{"key":"ref_46","doi-asserted-by":"crossref","unstructured":"Werner, C.R., Gaynor, R.C., Gorjanc, G., Hickey, J.M., Kox, T., Abbadi, A., Leckband, G., Snowdon, R.J., and Stahl, A. (2020). How population structure impacts genomic selection accuracy in cross-validation: Implications for practical breeding. Front. Plant Sci., 11.","DOI":"10.3389\/fpls.2020.592977"},{"key":"ref_47","doi-asserted-by":"crossref","first-page":"3","DOI":"10.1270\/jsbbs.21069","article-title":"High-throughput field crop phenotyping: Current status and challenges","volume":"72","author":"Ninomiya","year":"2022","journal-title":"Breed. Sci."},{"key":"ref_48","doi-asserted-by":"crossref","unstructured":"Garcia-Oliveira, A.L., Dwivedi, S.L., Chander, S., Nelimor, C., Abd El Moneim, D., and Ortiz, R.O. (2026). Breeding smarter: Artificial intelligence and machine learning tools in modern breeding\u2014A review. Agronomy, 16.","DOI":"10.3390\/agronomy16010137"},{"key":"ref_49","doi-asserted-by":"crossref","unstructured":"Pokharel, R., Gentimis, T., Kongchum, M., Tubana, B., Adhikari, R., and Setiyono, T. (2026). Rice yield estimation based on machine learning applied to UAV remote sensing data. Remote Sens., 18.","DOI":"10.3390\/rs18101575"},{"key":"ref_50","doi-asserted-by":"crossref","first-page":"293","DOI":"10.1007\/s00122-025-05060-1","article-title":"Accelerated breeding modernization: A global blueprint for driving genetic gains, climate resilience, and food security in rice","volume":"138","author":"Katiyar","year":"2025","journal-title":"Theor. Appl. Genet."},{"key":"ref_51","doi-asserted-by":"crossref","first-page":"6","DOI":"10.1186\/s40529-025-00452-4","article-title":"Assessing water status in rice plants in water-deficient environments using thermal imaging","volume":"66","author":"Yang","year":"2025","journal-title":"Bot. Stud."},{"key":"ref_52","doi-asserted-by":"crossref","first-page":"38","DOI":"10.1007\/s00425-020-03436-9","article-title":"High-throughput phenotyping platform for analyzing drought tolerance in rice","volume":"252","author":"Kim","year":"2020","journal-title":"Planta"},{"key":"ref_53","doi-asserted-by":"crossref","first-page":"9851096","DOI":"10.34133\/2022\/9851096","article-title":"End-to-end fusion of hyperspectral and chlorophyll fluorescence imaging to identify rice stresses","volume":"2022","author":"Zhang","year":"2022","journal-title":"Plant Phenomics"},{"key":"ref_54","doi-asserted-by":"crossref","unstructured":"Herzig, P., Borrmann, P., Knauer, U., Kl\u00fcck, H.-C., Kilias, D., Seiffert, U., Pillen, K., and Maurer, A. (2021). Evaluation of RGB and multispectral unmanned aerial vehicle (UAV) imagery for high-throughput phenotyping and yield prediction in barley breeding. Remote Sens., 13.","DOI":"10.3390\/rs13142670"},{"key":"ref_55","doi-asserted-by":"crossref","first-page":"617","DOI":"10.1016\/j.rsci.2024.04.007","article-title":"Estimating key phenological dates of multiple rice accessions using unmanned aerial vehicle-based plant height dynamics for breeding","volume":"31","author":"Weiyuan","year":"2024","journal-title":"Rice Sci."},{"key":"ref_56","doi-asserted-by":"crossref","first-page":"440","DOI":"10.1111\/nph.17580","article-title":"Combining UAV-RGB high-throughput field phenotyping and genome-wide association study to reveal genetic variation of rice germplasms in dynamic response to drought stress","volume":"232","author":"Jiang","year":"2021","journal-title":"New Phytol."},{"key":"ref_57","doi-asserted-by":"crossref","first-page":"9765952","DOI":"10.34133\/2021\/9765952","article-title":"Classification of rice yield using UAV-based hyperspectral imagery and lodging feature","volume":"2021","author":"Wang","year":"2021","journal-title":"Plant Phenomics"},{"key":"ref_58","doi-asserted-by":"crossref","first-page":"0019","DOI":"10.34133\/plantphenomics.0019","article-title":"Dynamic UAV phenotyping for rice disease resistance analysis based on multisource data","volume":"5","author":"Bai","year":"2023","journal-title":"Plant Phenomics"},{"key":"ref_59","doi-asserted-by":"crossref","first-page":"198","DOI":"10.1016\/j.crope.2022.09.001","article-title":"Role of canopy temperature depression in rice","volume":"1","author":"Fukai","year":"2022","journal-title":"Crop Environ."},{"key":"ref_60","doi-asserted-by":"crossref","unstructured":"Cipra-Rodriguez, J.A., Fern\u00e1ndez-Guisuraga, J.M., and Quintano, C. (2026). Comparative assessment of hyperspectral and multispectral vegetation indices for estimating fire severity in Mediterranean ecosystems. Remote Sens., 18.","DOI":"10.3390\/rs18020244"},{"key":"ref_61","doi-asserted-by":"crossref","first-page":"545","DOI":"10.1093\/jxb\/ery373","article-title":"Combining high-throughput micro-CT-RGB phenotyping and genome-wide association study to dissect the genetic architecture of tiller growth in rice","volume":"70","author":"Wu","year":"2019","journal-title":"J. Exp. Bot."},{"key":"ref_62","doi-asserted-by":"crossref","unstructured":"Taniguchi, S., Sakamoto, T., Imase, R., Nonoue, Y., Tsunematsu, H., Goto, A., Matsushita, K., Ohmori, S., Maeda, H., and Takeuchi, Y. (2022). Prediction of heading date, culm length, and biomass from canopy-height-related parameters derived from time-series UAV observations of rice. Front. Plant Sci., 13.","DOI":"10.3389\/fpls.2022.998803"},{"key":"ref_63","doi-asserted-by":"crossref","first-page":"73","DOI":"10.1186\/s13007-025-01398-1","article-title":"Unified estimation of rice canopy leaf area index over multiple periods based on UAV multispectral imagery and deep learning","volume":"21","author":"Li","year":"2025","journal-title":"Plant Methods"},{"key":"ref_64","doi-asserted-by":"crossref","first-page":"3139","DOI":"10.1094\/PDIS-10-22-2294-RE","article-title":"Characterization of rice\u2014Magnaporthe oryzae interactions by hyperspectral imaging","volume":"107","author":"Maina","year":"2023","journal-title":"Plant Dis."},{"key":"ref_65","doi-asserted-by":"crossref","first-page":"e00080","DOI":"10.1002\/pld3.80","article-title":"Utilizing random regression models for genomic prediction of a longitudinal trait derived from high-throughput phenotyping","volume":"2","author":"Campbell","year":"2018","journal-title":"Plant Direct"},{"key":"ref_66","doi-asserted-by":"crossref","unstructured":"Toda, Y., Wakatsuki, H., Aoike, T., Kajiya-Kanegae, H., Yamasaki, M., Yoshioka, T., Ebana, K., Hayashi, T., Nakagawa, H., and Hasegawa, T. (2020). Predicting biomass of rice with intermediate traits: Modeling method combining crop growth models and genomic prediction models. PLoS ONE, 15.","DOI":"10.1371\/journal.pone.0233951"},{"key":"ref_67","doi-asserted-by":"crossref","unstructured":"Baba, T., Momen, M., Campbell, M.T., Walia, H., and Morota, G. (2020). Multi-trait random regression models increase genomic prediction accuracy for a temporal physiological trait derived from high-throughput phenotyping. PLoS ONE, 15.","DOI":"10.1371\/journal.pone.0228118"},{"key":"ref_68","doi-asserted-by":"crossref","unstructured":"Montesinos-L\u00f3pez, A., Montesinos-L\u00f3pez, O.A., Ramos-Pulido, S., Mosqueda-Gonz\u00e1lez, B.A., Guerrero-Arroyo, E.A., Crossa, J., and Ortiz, R. (2025). Artificial intelligence meets genomic selection: Comparing deep learning and GBLUP across diverse plant datasets. Front. Genet., 16.","DOI":"10.3389\/fgene.2025.1568705"},{"key":"ref_69","doi-asserted-by":"crossref","unstructured":"Maldonado, C., Mora-Poblete, F., Contreras-Soto, R.I., Ahmar, S., Chen, J.-T., do Amaral J\u00fanior, A.T., and Scapim, C.A. (2020). Genome-wide prediction of complex traits in two outcrossing plant species through deep learning and Bayesian regularized neural network. Front. Plant Sci., 11.","DOI":"10.3389\/fpls.2020.593897"},{"key":"ref_70","doi-asserted-by":"crossref","first-page":"1829","DOI":"10.1038\/s41467-024-46150-w","article-title":"Data leakage inflates prediction performance in connectome-based machine learning models","volume":"15","author":"Rosenblatt","year":"2024","journal-title":"Nat. Commun."},{"key":"ref_71","doi-asserted-by":"crossref","unstructured":"Novielli, P., Romano, D., Pavan, S., Losciale, P., Stellacci, A.M., Diacono, D., Bellotti, R., and Tangaro, S. (2024). Explainable artificial intelligence for genotype-to-phenotype prediction in plant breeding: A case study with a dataset from an almond germplasm collection. Front. Plant Sci., 15.","DOI":"10.3389\/fpls.2024.1434229"},{"key":"ref_72","doi-asserted-by":"crossref","unstructured":"Westhues, C.C., Mahone, G.S., da Silva, S., Thorwarth, P., Schmidt, M., Richter, J.-C., Simianer, H., and Beissinger, T.M. (2021). Prediction of maize phenotypic traits with genomic and environmental predictors using gradient boosting frameworks. Front. Plant Sci., 12.","DOI":"10.3389\/fpls.2021.699589"},{"key":"ref_73","doi-asserted-by":"crossref","first-page":"259","DOI":"10.1016\/j.eng.2024.09.019","article-title":"Plant factory speed breeding significantly shortens rice generation time and enhances metabolic diversity","volume":"50","author":"Liu","year":"2025","journal-title":"Engineering"},{"key":"ref_74","doi-asserted-by":"crossref","first-page":"1921","DOI":"10.1007\/s12298-022-01247-8","article-title":"Strategies for accelerating genetic gains in crop plants: Special focus on speed breeding","volume":"28","author":"Gudi","year":"2022","journal-title":"Physiol. Mol. Biol. Plants"},{"key":"ref_75","doi-asserted-by":"crossref","first-page":"1051","DOI":"10.1111\/pbi.14245","article-title":"SpeedFlower: A comprehensive speed breeding protocol for indica and japonica rice","volume":"22","author":"Kabade","year":"2024","journal-title":"Plant Biotechnol. J."},{"key":"ref_76","doi-asserted-by":"crossref","first-page":"109","DOI":"10.1186\/s13007-024-01235-x","article-title":"SpeedyPaddy: A revolutionized cost-effective protocol for large scale offseason advancement of rice germplasm","volume":"20","author":"Sandhu","year":"2024","journal-title":"Plant Methods"},{"key":"ref_77","doi-asserted-by":"crossref","first-page":"e70225","DOI":"10.1002\/csc2.70225","article-title":"Toward an art of genomic selection in vegetable breeding","volume":"66","author":"Hernandez","year":"2026","journal-title":"Crop Sci."},{"key":"ref_78","doi-asserted-by":"crossref","unstructured":"Silva, \u00c9.D.B., Xavier, A., and Faria, M.V. (2021). Impact of genomic prediction model, selection intensity, and breeding strategy on the long-term genetic gain and genetic erosion in soybean breeding. Front. Genet., 12.","DOI":"10.3389\/fgene.2021.637133"},{"key":"ref_79","doi-asserted-by":"crossref","first-page":"jkad195","DOI":"10.1093\/g3journal\/jkad195","article-title":"Comparison of genomic-enabled cross selection criteria for the improvement of inbred line breeding populations","volume":"13","author":"Durand","year":"2023","journal-title":"G3"},{"key":"ref_80","doi-asserted-by":"crossref","unstructured":"Zhou, D., Zhou, X., Sun, C., Tang, G., Liu, L., Chen, L., He, H., and Xiong, Q. (2022). Transcriptome and metabolome analysis provides insights into the heterosis of yield and quality traits in two hybrid rice varieties (Oryza sativa L.). Int. J. Mol. Sci., 23.","DOI":"10.3390\/ijms232112934"},{"key":"ref_81","doi-asserted-by":"crossref","first-page":"6856","DOI":"10.1038\/s41467-024-50701-6","article-title":"Prediction of plant complex traits via integration of multi-omics data","volume":"15","author":"Wang","year":"2024","journal-title":"Nat. Commun."},{"key":"ref_82","doi-asserted-by":"crossref","first-page":"102283","DOI":"10.1016\/j.atech.2026.102283","article-title":"Digital twins in agriculture: A systematic literature review on modeling, semantics, and interoperability","volume":"14","author":"Usman","year":"2026","journal-title":"Smart Agric. Technol."},{"key":"ref_83","doi-asserted-by":"crossref","unstructured":"Chen, B., Su, Q., Li, Y., Chen, R., Yang, W., and Huang, C. (2025). Field rice growth monitoring and fertilization management based on UAV spectral and deep image feature fusion. Agronomy, 15.","DOI":"10.3390\/agronomy15040886"},{"key":"ref_84","doi-asserted-by":"crossref","first-page":"e70087","DOI":"10.1002\/csc2.70087","article-title":"Onerice breeding framework: An end-to-end system to develop better varieties faster","volume":"65","author":"Bhosale","year":"2025","journal-title":"Crop Sci."}],"container-title":["Genes"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2073-4425\/17\/8\/900\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,8,1]],"date-time":"2026-08-01T04:33:34Z","timestamp":1785558814000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2073-4425\/17\/8\/900"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,7,30]]},"references-count":84,"journal-issue":{"issue":"8","published-online":{"date-parts":[[2026,8]]}},"alternative-id":["genes17080900"],"URL":"https:\/\/doi.org\/10.3390\/genes17080900","relation":{},"ISSN":["2073-4425"],"issn-type":[{"value":"2073-4425","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,7,30]]}}}