Machine learning analysis of in vitro tuber formation of potato cultivars (A) tuberization, (B) tubers per plant, (C) tuber size, and (D) tuber weight.
<p>Machine learning analysis of in vitro tuber formation of potato cultivars (A) tuberization, (B) tubers per plant, (C) tuber size, and (D) tuber weight.</p>
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2025
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| _version_ | 1852019086750384128 |
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| author | Rajermani Thinakaran (19644730) |
| author2 | Ecenur Korkmaz (21594438) Başak Ünver (21594441) Seyid Amjad Ali (13301451) Zeshan Iqbal (2836625) Muhammad Aasim (3666946) |
| author2_role | author author author author author |
| author_facet | Rajermani Thinakaran (19644730) Ecenur Korkmaz (21594438) Başak Ünver (21594441) Seyid Amjad Ali (13301451) Zeshan Iqbal (2836625) Muhammad Aasim (3666946) |
| author_role | author |
| dc.creator.none.fl_str_mv | Rajermani Thinakaran (19644730) Ecenur Korkmaz (21594438) Başak Ünver (21594441) Seyid Amjad Ali (13301451) Zeshan Iqbal (2836625) Muhammad Aasim (3666946) |
| dc.date.none.fl_str_mv | 2025-06-24T17:42:13Z |
| dc.identifier.none.fl_str_mv | 10.1371/journal.pone.0325754.g006 |
| dc.relation.none.fl_str_mv | https://figshare.com/articles/figure/Machine_learning_analysis_of_in_vitro_tuber_formation_of_potato_cultivars_A_tuberization_B_tubers_per_plant_C_tuber_size_and_D_tuber_weight_/29394120 |
| dc.rights.none.fl_str_mv | CC BY 4.0 info:eu-repo/semantics/openAccess |
| dc.subject.none.fl_str_mv | Microbiology Cell Biology Pharmacology Biotechnology Plant Biology Biological Sciences not elsewhere classified solanum tuberosum </ scale commercial production response surface methodology potentially improving large offers efficient propagation network plot analyses manipulating key variables improved feature engineering high error rates free seed production div >< p particularly tuber size vitro tuber production selective auxin application rsra analysis confirmed rsm ), followed maximum tuberization rate optimizing sucrose concentration 2 </ sup tuber formation synergistic application sucrose concentration auxin interactions 2 mg vitro tuberization vitro regeneration study concludes study aimed significant factor random forest promising strategy potato tubers potato (< modern agriculture medium supplemented machine learning linear effects l sucrose l indole influential variable including cultivar iba ). highly significant growth parameters genetic enhancement data validation butyric acid artificial intelligence ai presents 90 g |
| dc.title.none.fl_str_mv | Machine learning analysis of in vitro tuber formation of potato cultivars (A) tuberization, (B) tubers per plant, (C) tuber size, and (D) tuber weight. |
| dc.type.none.fl_str_mv | Image Figure info:eu-repo/semantics/publishedVersion image |
| description | <p>Machine learning analysis of in vitro tuber formation of potato cultivars (A) tuberization, (B) tubers per plant, (C) tuber size, and (D) tuber weight.</p> |
| eu_rights_str_mv | openAccess |
| id | Manara_10a78aad2a6e69ac0bbcfdcdb6b35430 |
| identifier_str_mv | 10.1371/journal.pone.0325754.g006 |
| network_acronym_str | Manara |
| network_name_str | ManaraRepo |
| oai_identifier_str | oai:figshare.com:article/29394120 |
| publishDate | 2025 |
| repository.mail.fl_str_mv | |
| repository.name.fl_str_mv | |
| repository_id_str | |
| rights_invalid_str_mv | CC BY 4.0 |
| spelling | Machine learning analysis of in vitro tuber formation of potato cultivars (A) tuberization, (B) tubers per plant, (C) tuber size, and (D) tuber weight.Rajermani Thinakaran (19644730)Ecenur Korkmaz (21594438)Başak Ünver (21594441)Seyid Amjad Ali (13301451)Zeshan Iqbal (2836625)Muhammad Aasim (3666946)MicrobiologyCell BiologyPharmacologyBiotechnologyPlant BiologyBiological Sciences not elsewhere classifiedsolanum tuberosum </scale commercial productionresponse surface methodologypotentially improving largeoffers efficient propagationnetwork plot analysesmanipulating key variablesimproved feature engineeringhigh error ratesfree seed productiondiv >< pparticularly tuber sizevitro tuber productionselective auxin applicationrsra analysis confirmedrsm ), followedmaximum tuberization rateoptimizing sucrose concentration2 </ suptuber formationsynergistic applicationsucrose concentrationauxin interactions2 mgvitro tuberizationvitro regenerationstudy concludesstudy aimedsignificant factorrandom forestpromising strategypotato tuberspotato (<modern agriculturemedium supplementedmachine learninglinear effectsl sucrosel indoleinfluential variableincluding cultivariba ).highly significantgrowth parametersgenetic enhancementdata validationbutyric acidartificial intelligenceai presents90 g<p>Machine learning analysis of in vitro tuber formation of potato cultivars (A) tuberization, (B) tubers per plant, (C) tuber size, and (D) tuber weight.</p>2025-06-24T17:42:13ZImageFigureinfo:eu-repo/semantics/publishedVersionimage10.1371/journal.pone.0325754.g006https://figshare.com/articles/figure/Machine_learning_analysis_of_in_vitro_tuber_formation_of_potato_cultivars_A_tuberization_B_tubers_per_plant_C_tuber_size_and_D_tuber_weight_/29394120CC BY 4.0info:eu-repo/semantics/openAccessoai:figshare.com:article/293941202025-06-24T17:42:13Z |
| spellingShingle | Machine learning analysis of in vitro tuber formation of potato cultivars (A) tuberization, (B) tubers per plant, (C) tuber size, and (D) tuber weight. Rajermani Thinakaran (19644730) Microbiology Cell Biology Pharmacology Biotechnology Plant Biology Biological Sciences not elsewhere classified solanum tuberosum </ scale commercial production response surface methodology potentially improving large offers efficient propagation network plot analyses manipulating key variables improved feature engineering high error rates free seed production div >< p particularly tuber size vitro tuber production selective auxin application rsra analysis confirmed rsm ), followed maximum tuberization rate optimizing sucrose concentration 2 </ sup tuber formation synergistic application sucrose concentration auxin interactions 2 mg vitro tuberization vitro regeneration study concludes study aimed significant factor random forest promising strategy potato tubers potato (< modern agriculture medium supplemented machine learning linear effects l sucrose l indole influential variable including cultivar iba ). highly significant growth parameters genetic enhancement data validation butyric acid artificial intelligence ai presents 90 g |
| status_str | publishedVersion |
| title | Machine learning analysis of in vitro tuber formation of potato cultivars (A) tuberization, (B) tubers per plant, (C) tuber size, and (D) tuber weight. |
| title_full | Machine learning analysis of in vitro tuber formation of potato cultivars (A) tuberization, (B) tubers per plant, (C) tuber size, and (D) tuber weight. |
| title_fullStr | Machine learning analysis of in vitro tuber formation of potato cultivars (A) tuberization, (B) tubers per plant, (C) tuber size, and (D) tuber weight. |
| title_full_unstemmed | Machine learning analysis of in vitro tuber formation of potato cultivars (A) tuberization, (B) tubers per plant, (C) tuber size, and (D) tuber weight. |
| title_short | Machine learning analysis of in vitro tuber formation of potato cultivars (A) tuberization, (B) tubers per plant, (C) tuber size, and (D) tuber weight. |
| title_sort | Machine learning analysis of in vitro tuber formation of potato cultivars (A) tuberization, (B) tubers per plant, (C) tuber size, and (D) tuber weight. |
| topic | Microbiology Cell Biology Pharmacology Biotechnology Plant Biology Biological Sciences not elsewhere classified solanum tuberosum </ scale commercial production response surface methodology potentially improving large offers efficient propagation network plot analyses manipulating key variables improved feature engineering high error rates free seed production div >< p particularly tuber size vitro tuber production selective auxin application rsra analysis confirmed rsm ), followed maximum tuberization rate optimizing sucrose concentration 2 </ sup tuber formation synergistic application sucrose concentration auxin interactions 2 mg vitro tuberization vitro regeneration study concludes study aimed significant factor random forest promising strategy potato tubers potato (< modern agriculture medium supplemented machine learning linear effects l sucrose l indole influential variable including cultivar iba ). highly significant growth parameters genetic enhancement data validation butyric acid artificial intelligence ai presents 90 g |