{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,2]],"date-time":"2026-06-02T18:27:14Z","timestamp":1780424834777,"version":"3.54.1"},"reference-count":56,"publisher":"Springer Science and Business Media LLC","issue":"2","license":[{"start":{"date-parts":[[2026,2,6]],"date-time":"2026-02-06T00:00:00Z","timestamp":1770336000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"},{"start":{"date-parts":[[2026,2,6]],"date-time":"2026-02-06T00:00:00Z","timestamp":1770336000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"}],"funder":[{"DOI":"10.13039\/501100020957","name":"Universidade Tecnol\u00f3gica Federal Do Paran\u00e1","doi-asserted-by":"crossref","id":[{"id":"10.13039\/501100020957","id-type":"DOI","asserted-by":"crossref"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Multimed Tools Appl"],"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:p>The demand for soybean tends to increase worldwide with population growth. Despite farmers adopting strategies such as crop rotation, soil preparation, and the application of chemical products, the most critical factor to increase productivity is choosing cultivars adapted to the region so that they are resistant to deceases. In addition, seed quality also directly influences crop productivity. To help a farmer select suitable cultivars and analyze the quality of soybean seeds, we present an application that uses the user\u2019s location to identify suitable cultivars based on the edaphoclimatic characteristics of a region. The details of these cultivars are shown in text, images, and video, making it possible to compare the selected varieties. Our app also contains a trained convolution neural network capable of classifying the quality of soybean seeds based on an image captured by the user or stored on a smartphone. The convolutional neural network architecture allowed an excellent performance, with an accuracy of 94.06% in the classification of soybean seeds. All app functionality runs comfortably on mobile devices. Compared to others that have the same purpose, our application has a more significant number of features.<\/jats:p>","DOI":"10.1007\/s11042-026-21239-0","type":"journal-article","created":{"date-parts":[[2026,2,6]],"date-time":"2026-02-06T08:52:45Z","timestamp":1770367965000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["Soybean farmer mobile application"],"prefix":"10.1007","volume":"85","author":[{"given":"Matheus Amaral","family":"Silva","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Leandro Alfredo","family":"Carlos","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Hugo Soares","family":"Kern","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3635-7477","authenticated-orcid":false,"given":"Silvio Ricardo Rodrigues","family":"Sanches","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Cl\u00e9ber Gimenez","family":"Corr\u00eaa","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Claiton","family":"Oliveira","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Reginaldo","family":"R\u00e9","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2026,2,6]]},"reference":[{"key":"21239_CR1","unstructured":"0nTimeTech (2023) Soybean price. https:\/\/play.google.com\/store\/apps\/details?id=de.ontimetech.android.Soybeans, Accessed 5 June 2023"},{"key":"21239_CR2","unstructured":"Abadi M, Agarwal A, Barham P, Brevdo E, Chen Z, Citro C, Corrado GS, Davis A, Dean J, Devin M, Ghemawat S, Goodfellow I, Harp A, Irving G, Isard M, Jia Y, Jozefowicz R, Kaiser L, Kudlur M, Levenberg J, Man\u00e9 D, Monga R, Moore S, Murray D, Olah C, Schuster M, Shlens J, Steiner B, Sutskever I, Talwar K, Tucker P, Vanhoucke V, Vasudevan V, Vi\u00e9gas F, Vinyals O, Warden P, Wattenberg M, Wicke M, Yu Y, Zheng X (2015) TensorFlow: Large-scale machine learning on heterogeneous systems. https:\/\/www.tensorflow.org\/, software available from tensorflow.org"},{"key":"21239_CR3","unstructured":"ADAMA Brasil (2023) ADAMA alvo. https:\/\/www.adama.com\/brasil\/pt\/adama-alvo, Accessed 22 June 2023"},{"issue":"8","key":"21239_CR4","doi-asserted-by":"publisher","first-page":"265","DOI":"10.3316\/informit.732079049502769","volume":"6","author":"M Arshad","year":"2022","unstructured":"Arshad M, Ranamukhaarachchi S (2022) Effects of legume type, planting pattern and time of establishment on growth and yield of sweet sorghum-legume intercropping. Australian J Crop Sci 6(8):265\u20131274. https:\/\/doi.org\/10.3316\/informit.732079049502769","journal-title":"Australian J Crop Sci"},{"key":"21239_CR5","unstructured":"de Beer A, Cochrane N (2023) Soybean cultivar recommendations 2022-23. https:\/\/www.arc.agric.za\/arc-gci\/Documents\/Soybeans\/Soybean%20Cultivar%20Recommendations%202022-2023.pdf, Accessed 23 June 2023"},{"key":"21239_CR6","unstructured":"Associa\u00e7\u00e3o Brasileira de Marketing Rural e Agroneg\u00f3cio (2017) 7$$^a$$ pesquisa de h\u00e1bitos do produtor rural ABMRA. http:\/\/www.webrural.com.br\/wp-content\/uploads\/2018\/11\/7_PESQUISA_HABITOS_DO_PR_RELATORIOFINAL.pdf, Accessed 20 June 2023"},{"key":"21239_CR7","doi-asserted-by":"publisher","DOI":"10.1016\/j.jfca.2021.103803","volume":"98","author":"M Azam","year":"2021","unstructured":"Azam M, Zhang S, Qi J, Abdelghany AM, Shaibu AS, Ghosh S, Feng Y, Huai Y, Gebregziabher BS, Li J, Li B, Sun J (2021) Profiling and associations of seed nutritional characteristics in chinese and usa soybean cultivars. J Food Composition Anal 98:103803. https:\/\/doi.org\/10.1016\/j.jfca.2021.103803","journal-title":"J Food Composition Anal"},{"key":"21239_CR8","doi-asserted-by":"publisher","unstructured":"Barbosa JZ, Prior SA, Pedreira GQ, Motta ACV, Poggere GC, Goularte GD (2020) Global trends in apps for agriculture. Multi-Sci J 3(1):16\u201320. https:\/\/doi.org\/10.33837\/msj.v3i1.1095","DOI":"10.33837\/msj.v3i1.1095"},{"key":"21239_CR9","doi-asserted-by":"publisher","first-page":"83","DOI":"10.1016\/j.cropro.2015.01.019","volume":"70","author":"M Carmona","year":"2015","unstructured":"Carmona M, Sautua F, Perelman S, Gally M, Reis EM (2015) Development and validation of a fungicide scoring system for management of late season soybean diseases in argentina. Crop Prot 70:83\u201391. https:\/\/doi.org\/10.1016\/j.cropro.2015.01.019","journal-title":"Crop Prot"},{"key":"21239_CR10","doi-asserted-by":"publisher","first-page":"310","DOI":"10.1016\/j.compag.2017.11.028","volume":"144","author":"MA Carmona","year":"2018","unstructured":"Carmona MA, Sautua FJ, P\u00e9rez-Hern\u00e1ndez O, Mandolesi JI (2018) Agrodecisor efc: First android\u2122\u00a0app decision support tool for timing fungicide applications for management of late-season soybean diseases. Comput Electron Agric 144:310\u2013313. https:\/\/doi.org\/10.1016\/j.compag.2017.11.028","journal-title":"Comput Electron Agric"},{"key":"21239_CR11","unstructured":"Chollet F et\u00a0al (2015) Keras. https:\/\/keras.io"},{"key":"21239_CR12","unstructured":"Crops Team (2023) Best cultivar. https:\/\/www.cropsteam.com\/, Accessed 27 June 2023"},{"key":"21239_CR13","doi-asserted-by":"crossref","unstructured":"Deng J, Dong W, Socher R, Li LJ, Li K, Fei-Fei L (2009) Imagenet: A large-scale hierarchical image database. In: 2009 IEEE conference on computer vision and pattern recognition, Ieee, pp 248\u2013255","DOI":"10.1109\/CVPR.2009.5206848"},{"key":"21239_CR14","unstructured":"Dreamco Design (2023) Smart soy. https:\/\/play.google.com\/store\/apps\/details?id=edu.illinois.smartsoyapp, Accessed 20 MayJune 2023"},{"key":"21239_CR15","unstructured":"Embrapa Soja (2019) Cultivares de soja - centro-sul do brasil | macrorregi\u00f5es 1, 2, 3 e rec 401. https:\/\/ainfo.cnptia.embrapa.br\/digital\/bitstream\/item\/206337\/1\/Catalogo-4-Soja-2019-OL.pdf, Accessed 14 June 2023"},{"key":"21239_CR16","unstructured":"Farias JRB, Nepomuceno AL, Neumaier N (2021) Soja. https:\/\/www.embrapa.br\/en\/agencia-de-informacao-tecnologica\/cultivos\/soja, Accessed 18 June 2023"},{"key":"21239_CR17","doi-asserted-by":"publisher","unstructured":"Gledhill D (2008) The International Code of Nomenclature for Cultivated Plants, 4th edn., Cambridge University Press, pp 26\u201329. https:\/\/doi.org\/10.1017\/CBO9780511550898.007","DOI":"10.1017\/CBO9780511550898.007"},{"key":"21239_CR18","doi-asserted-by":"publisher","unstructured":"Grau CR, Dorrance AE, Bond J, Russin JS (2004) Fungal Diseases, American Society of Agronomy, Inc., Wisconsin, pp 679\u2013763. https:\/\/doi.org\/10.2134\/agronmonogr16.3ed.c14","DOI":"10.2134\/agronmonogr16.3ed.c14"},{"issue":"3","key":"21239_CR19","doi-asserted-by":"publisher","first-page":"303","DOI":"10.2134\/jpa1992.0303","volume":"5","author":"DR Hicks","year":"1992","unstructured":"Hicks DR, Stucker RE, Orf J (1992) Choosing soybean varieties from yield trials. J Production Agric 5(3):303\u2013307. https:\/\/doi.org\/10.2134\/jpa1992.0303","journal-title":"J Production Agric"},{"key":"21239_CR20","doi-asserted-by":"publisher","first-page":"62199","DOI":"10.1109\/ACCESS.2024.3395532","volume":"12","author":"A Holzinger","year":"2024","unstructured":"Holzinger A, Fister I, Fister I, Kaul HP, Asseng S (2024) Human-centered ai in smart farming: Toward agriculture 5.0. IEEE Access 12:62199\u201362214. https:\/\/doi.org\/10.1109\/ACCESS.2024.3395532","journal-title":"IEEE Access"},{"key":"21239_CR21","unstructured":"Howard AG, 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:1704.04861"},{"key":"21239_CR22","doi-asserted-by":"publisher","DOI":"10.1016\/j.compag.2022.107393","volume":"202","author":"Z Huang","year":"2022","unstructured":"Huang Z, Wang R, Cao Y, Zheng S, Teng Y, Wang F, Wang L, Du J (2022) Deep learning based soybean seed classification. Comput Electron Agric 202:107393. https:\/\/doi.org\/10.1016\/j.compag.2022.107393","journal-title":"Comput Electron Agric"},{"key":"21239_CR23","unstructured":"Instituto Brasileiro de Geografia e Estat\u00edstica - IBGE (2017) Censo abropecu\u00e1rio 2017. https:\/\/censoagro2017.ibge.gov.br\/, Accessed 6 June 2023"},{"key":"21239_CR24","unstructured":"Kingma DP, Ba J (2017) Adam: A method for stochastic optimization. arXiv:1412.6980"},{"key":"21239_CR25","doi-asserted-by":"publisher","DOI":"10.1016\/j.fcr.2022.108539","volume":"283","author":"E Kumagai","year":"2022","unstructured":"Kumagai E, Yabiku T, Hasegawa T (2022) A strong negative trade-off between seed number and 100-seed weight stalls genetic yield gains in northern japanese soybean cultivars in comparison with midwestern us cultivars. Field Crops Res 283:108539. https:\/\/doi.org\/10.1016\/j.fcr.2022.108539","journal-title":"Field Crops Res"},{"issue":"4","key":"21239_CR26","doi-asserted-by":"publisher","first-page":"140","DOI":"10.3390\/fermentation8040140","volume":"8","author":"Q Li","year":"2022","unstructured":"Li Q, Zeng T, Hu Y, Du Z, Liu Y, Jin M, Tahir M, Wang X, Yang W, Yan Y (2022) Effects of soybean density and sowing time on the yield and the quality of mixed silage in corn-soybean strip intercropping system. Fermentation 8(4):140. https:\/\/doi.org\/10.3390\/fermentation8040140","journal-title":"Fermentation"},{"issue":"4","key":"21239_CR27","doi-asserted-by":"publisher","first-page":"401","DOI":"10.1007\/s40271-020-00416-9","volume":"13","author":"SL Lim","year":"2020","unstructured":"Lim SL, Yang JC, Ehrisman J, Havrilesky LJ, Reed SD (2020) Are videos or text better for describing attributes in stated-preference surveys? The Patient - Patient-Centered Outcomes Res 13(4):401\u2013408. https:\/\/doi.org\/10.1007\/s40271-020-00416-9","journal-title":"The Patient - Patient-Centered Outcomes Res"},{"issue":"11","key":"21239_CR28","doi-asserted-by":"publisher","first-page":"3773","DOI":"10.1007\/s00122-022-04101-3","volume":"135","author":"F Lin","year":"2022","unstructured":"Lin F, Chhapekar SS, Vieira CC, Silva MPD, Rojas A, Lee D, Liu N, Pardo EM, Lee YC, Dong Z, Pinheiro JB, Ploper LD, Rupe J, Chen P, Wang D, Nguyen HT (2022) Breeding for disease resistance in soybean: a global perspective. Theoretical Appl Genetics 135(11):3773\u20133872. https:\/\/doi.org\/10.1007\/s00122-022-04101-3","journal-title":"Theoretical Appl Genetics"},{"key":"21239_CR29","doi-asserted-by":"publisher","unstructured":"Lin W, Fu Y, Xu P, Liu S, Ma D, Jiang Z, Zang S, Yao H, Su Q (2023) Soybean seeds. https:\/\/doi.org\/10.17632\/V6VZVFSZJ6.6, https:\/\/data.mendeley.com\/datasets\/v6vzvfszj6\/6","DOI":"10.17632\/V6VZVFSZJ6.6"},{"key":"21239_CR30","doi-asserted-by":"publisher","DOI":"10.1016\/j.engappai.2023.106434","volume":"123","author":"W Lin","year":"2023","unstructured":"Lin W, Shu L, Zhong W, Lu W, Ma D, Meng Y (2023) Online classification of soybean seeds based on deep learning. Eng Appl Artif Intell 123:106434. https:\/\/doi.org\/10.1016\/j.engappai.2023.106434","journal-title":"Eng Appl Artif Intell"},{"key":"21239_CR31","unstructured":"Lorini I (2018) Qualidade de sementes e gr\u00e3os comerciais de soja no brasil - safra 2016\/17. https:\/\/www.embrapa.br\/en\/busca-de-publicacoes\/-\/publicacao\/1097658\/qualidade-de-sementes-e-graos-comerciais-de-soja-no-brasil---safra-201617, Accessed 4 July 2023"},{"issue":"3","key":"21239_CR32","doi-asserted-by":"publisher","first-page":"222","DOI":"10.1590\/1983-40632018v4852340","volume":"48","author":"AD Medeiros","year":"2018","unstructured":"Medeiros AD, Pereira MD (2018) SAPL: a free software for determining the physiological potential in soybean seeds. Pesquisa Agropecu\u00e1ria Tropical 48(3):222\u2013228. https:\/\/doi.org\/10.1590\/1983-40632018v4852340","journal-title":"Pesquisa Agropecu\u00e1ria Tropical"},{"key":"21239_CR33","unstructured":"Mehta S, Rastegari M (2022) Mobilevit: Light-weight, general-purpose, and mobile-friendly vision transformer. arXiv:2110.02178"},{"key":"21239_CR34","unstructured":"Mehta S, Rastegari M (2022) Separable self-attention for mobile vision transformers. arXiv:2206.02680"},{"key":"21239_CR35","doi-asserted-by":"crossref","unstructured":"Mehta S, Abdolhosseini F, Rastegari M (2022) Cvnets: High performance library for computer vision. In: Proceedings of the 30th ACM international conference on multimedia, MM \u201922","DOI":"10.1145\/3503161.3548540"},{"key":"21239_CR36","unstructured":"Melo MLA, Andrade CLT, Rios SA, Favarin AM, Vasconcelos JH (2018) Perfil de usu\u00e1rios de tecnologias para a agricultura irrigada. https:\/\/ainfo.cnptia.embrapa.br\/digital\/bitstream\/item\/180145\/1\/doc-221.pdf"},{"issue":"3","key":"21239_CR37","doi-asserted-by":"publisher","first-page":"387","DOI":"10.1590\/S0103-84781997000300004","volume":"27","author":"NL de Menezes","year":"1997","unstructured":"de Menezes NL, Garcia DC, de Assis Librelotto Rubin S, Bernardi GE (1997) Characterization of soybean legumes and seeds. Ci\u00eancia Rural 27(3):387\u2013391. https:\/\/doi.org\/10.1590\/S0103-84781997000300004","journal-title":"Ci\u00eancia Rural"},{"key":"21239_CR38","unstructured":"Paszke A, Gross S, Massa F, Lerer A, Bradbury J, Chanan G, Killeen T, Lin Z, Gimelshein N, Antiga L, Desmaison A, K\u00f6pf A, Yang E, DeVito Z, Raison M, Tejani A, Chilamkurthy S, Steiner B, Fang L, Bai J, Chintala S (2019) Pytorch: An imperative style, high-performance deep learning library. arXiv:1912.01703"},{"issue":"3","key":"21239_CR39","doi-asserted-by":"publisher","first-page":"509","DOI":"10.1007\/s11219-014-9239-1","volume":"23","author":"B Peischl","year":"2015","unstructured":"Peischl B, Ferk M, Holzinger A (2015) The fine art of user-centered software development. Softw Quality J 23(3):509\u2013536. https:\/\/doi.org\/10.1007\/s11219-014-9239-1","journal-title":"Softw Quality J"},{"issue":"4","key":"21239_CR40","doi-asserted-by":"publisher","first-page":"285","DOI":"10.1017\/S0960258516000234","volume":"26","author":"A Rahman","year":"2016","unstructured":"Rahman A, Cho BK (2016) Assessment of seed quality using non-destructive measurement techniques: a review. Seed Sci Res 26(4):285\u2013305. https:\/\/doi.org\/10.1017\/S0960258516000234","journal-title":"Seed Sci Res"},{"issue":"3","key":"21239_CR41","doi-asserted-by":"publisher","first-page":"430","DOI":"10.1038\/s41559-018-0793-y","volume":"3","author":"S Savary","year":"2019","unstructured":"Savary S, Willocquet L, Pethybridge SJ, Esker P, McRoberts N, Nelson A (2019) The global burden of pathogens and pests on major food crops. Nat Ecol Evolution 3(3):430\u2013439. https:\/\/doi.org\/10.1038\/s41559-018-0793-y","journal-title":"Nat Ecol Evolution"},{"issue":"4","key":"21239_CR42","doi-asserted-by":"publisher","first-page":"380","DOI":"10.1270\/jsbbs.60.380","volume":"60","author":"T Sayama","year":"2010","unstructured":"Sayama T, Hwang TY, Yamazaki H, Yamaguchi N, Komatsu K, Takahashi M, Suzuki C, Miyoshi T, Tanaka Y, Xia Z, Tsubokura Y, Watanabe S, Harada K, Funatsuki H, Ishimoto M (2010) Mapping and comparison of quantitative trait loci for soybean branching phenotype in two locations. Breeding Sci 60(4):380\u2013389. https:\/\/doi.org\/10.1270\/jsbbs.60.380","journal-title":"Breeding Sci"},{"issue":"2","key":"21239_CR43","doi-asserted-by":"publisher","first-page":"492","DOI":"10.3390\/agronomy12020492","volume":"12","author":"F Shan","year":"2022","unstructured":"Shan F, Sun K, Gong S, Wang C, Ma C, Zhang R, Yan C (2022) Effects of shading on the internode critical for soybean (glycine max) lodging. Agronomy 12(2):492. https:\/\/doi.org\/10.3390\/agronomy12020492","journal-title":"Agronomy"},{"key":"21239_CR44","doi-asserted-by":"publisher","first-page":"26647","DOI":"10.1007\/s11042-016-4191-7","volume":"76","author":"S Shrivastava","year":"2017","unstructured":"Shrivastava S, Singh S, Hooda D (2017) Soybean plant foliar disease detection using image retrieval approaches. Multimedia Tools Appl 76:26647\u201326674. https:\/\/doi.org\/10.1007\/s11042-016-4191-7","journal-title":"Multimedia Tools Appl"},{"key":"21239_CR45","unstructured":"Smartirrigationapps (2023) Smartirrigation soybean. https:\/\/smartirrigationapps.org\/soybean-app\/, Accessed 20 June 2023"},{"issue":"3","key":"21239_CR46","doi-asserted-by":"publisher","first-page":"2256","DOI":"10.3390\/ijms24032256","volume":"24","author":"H Song","year":"2023","unstructured":"Song H, Taylor DC, Zhang M (2023) Bioengineering of soybean oil and its impact on agronomic traits. Int J Molecular Sci 24(3):2256. https:\/\/doi.org\/10.3390\/ijms24032256","journal-title":"Int J Molecular Sci"},{"key":"21239_CR47","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1007\/s11042-023-14775-6","volume":"1","author":"A Srilakshmi","year":"2023","unstructured":"Srilakshmi A, Geetha K (2023) A novel framework for soybean leaves disease detection using DIM-U-net and LSTM. Multimed Tools Appl 1:1\u201321. https:\/\/doi.org\/10.1007\/s11042-023-14775-6","journal-title":"Multimed Tools Appl"},{"key":"21239_CR48","unstructured":"Standardization Administration of the People\u2019s Republic of China (2009) General Administration of Quality Supervision, Inspection and Quarantine of the People\u2019s Republic of China - GB 1352-2009 (GB1352-2009). https:\/\/www.chinesestandard.net\/PDF.aspx\/GB1352-2009, Accessed 3 July 2023"},{"key":"21239_CR49","unstructured":"Stoller Brasil (2023) Stoller solu\u00e7\u00f5es essenciais. https:\/\/play.google.com\/store\/apps\/details?id=br.com.stoller.stollerapp, Accessed 5 May 2023"},{"key":"21239_CR50","unstructured":"TMG (2023) Tmg soja. https:\/\/www.tmg.agr.br\/cultivares\/soja\/, Accessed 27 June 2023"},{"key":"21239_CR51","unstructured":"Vissani C, Murgio M, Carrio A (2024) SoyVAR. https:\/\/agroempresario.com\/publicacion\/84957\/soyvar-la-app-del-inta-que-pone-a-un-clic-los-cultivares-de-soja\/, Red Nacional de Ensayos de Cultivares de Soja, Accessed 21 Out 2024"},{"key":"21239_CR52","doi-asserted-by":"publisher","DOI":"10.1016\/j.compag.2022.106914","volume":"197","author":"B Wang","year":"2022","unstructured":"Wang B, Li H, You J, Chen X, Yuan X, Feng X (2022) Fusing deep learning features of triplet leaf image patterns to boost soybean cultivar identification. Comput Electron Agric 197:106914. https:\/\/doi.org\/10.1016\/j.compag.2022.106914","journal-title":"Comput Electron Agric"},{"key":"21239_CR53","doi-asserted-by":"publisher","unstructured":"Yang Y, Zhao T, Wang F, Liu L, Liu B, Zhang K, Qin J, Yang C, Qiao Y (2023) Identification of candidate genes for soybean seed coat-related traits using qtl mapping and gwas. Front Plant Sci 14. https:\/\/doi.org\/10.3389\/fpls.2023.1190503","DOI":"10.3389\/fpls.2023.1190503"},{"key":"21239_CR54","doi-asserted-by":"publisher","DOI":"10.1016\/j.compag.2021.106064","volume":"183","author":"K Zhang","year":"2021","unstructured":"Zhang K, Wu Q, Chen Y (2021) Detecting soybean leaf disease from synthetic image using multi-feature fusion faster R-CNN. Comput Electron Agric 183:106064. https:\/\/doi.org\/10.1016\/j.compag.2021.106064","journal-title":"Comput Electron Agric"},{"key":"21239_CR55","doi-asserted-by":"publisher","DOI":"10.1016\/j.compag.2021.106230","volume":"187","author":"G Zhao","year":"2021","unstructured":"Zhao G, Quan L, Li H, Feng H, Li S, Zhang S, Liu R (2021) Real-time recognition system of soybean seed full-surface defects based on deep learning. Comput Electron Agric 187:106230. https:\/\/doi.org\/10.1016\/j.compag.2021.106230","journal-title":"Comput Electron Agric"},{"issue":"3","key":"21239_CR56","doi-asserted-by":"publisher","first-page":"187","DOI":"10.1002\/aocs.12676","volume":"100","author":"Y Zhao","year":"2023","unstructured":"Zhao Y, Tian R, Xu Z, Jiang L, Sui X (2023) Recent advances in soy protein extraction technology. J Am Oil Chemists\u2019 Soc 100(3):187\u2013195. https:\/\/doi.org\/10.1002\/aocs.12676","journal-title":"J Am Oil Chemists\u2019 Soc"}],"container-title":["Multimedia Tools and Applications"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11042-026-21239-0.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s11042-026-21239-0","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11042-026-21239-0.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,2,6]],"date-time":"2026-02-06T08:52:56Z","timestamp":1770367976000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s11042-026-21239-0"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,2,6]]},"references-count":56,"journal-issue":{"issue":"2","published-online":{"date-parts":[[2026,2]]}},"alternative-id":["21239"],"URL":"https:\/\/doi.org\/10.1007\/s11042-026-21239-0","relation":{},"ISSN":["1573-7721"],"issn-type":[{"value":"1573-7721","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,2,6]]},"assertion":[{"value":"13 July 2023","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"3 November 2024","order":2,"name":"revised","label":"Revised","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"28 October 2025","order":3,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"6 February 2026","order":4,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"The authors have no relevant financial or non-financial interests to disclose.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflicts of Interest"}},{"value":"This article does not contain any studies with human participants or animals performed by any of the authors.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Ethical Approval"}},{"value":"The manuscript is submitted with the consent of all authors.","order":4,"name":"Ethics","group":{"name":"EthicsHeading","label":"Authors Consent"}}],"article-number":"129"}}