[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-123658-en":3,"doc-seo-123658-105":30,"detail-sidebar-cat-0-en-105":91},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":4,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},123658,962075006959,"Anda","https://ap-avatar.wpscdn.com/avatar/e0002397efbe92a78e?_k=1776741047341049297",8,"Research & Report","Drought tolerance classification of grapevine rootstock by machine learning for the São Francisco Valley","Machine Learning (ML) supports pattern recognition and prediction in agricultural plant breeding, enabling faster experimentation and clearer interpretation of results. Identifying drought-tolerant grapevine rootstock cultivars is increasingly important under climate change, yet remains difficult due to perennial growth, polygenic traits, and complex inheritance. This study compares six ML models using a cultivar dataset and selects the best performer to predict drought tolerance classes for three rootstock cultivars with previously unknown tolerance levels, yielding high predictive performance.","Journal Pre-proof  \nDrought tolerance classification of grapevine rootstock by machine learning for the So Francisco Valley  \nNina Iris Verslype , Andr Cmara Alves do Nascimento , Rosimar dos Santos Musser , Raphael Miller de Souza Caldas , Luiza Suely Semen Martins , Patr ´ıcia Coelho de Souza Leo  \nPII: S2772-3755(23)00022-9  \nDOI: [https://doi.org/10.1016/j.atech.2023.100192](https://doi.org/10.1016/j.atech.2023.100192)  \nReference: ATECH 100192  \nTo appear in: Smart Agricultural Technology  \nReceived date: 10 October 2022  \nRevised date: 23 January 2023  \nAccepted date: 29 January 2023  \nPlease cite this article as: Nina Iris Verslype , Andr Cmara Alves do Nascimento , Rosimar dos Santos Musser , Raphael Miller de Souza Caldas , Luiza Suely Semen Martins , Patr ´ıcia Coelho de Souza Leo˜ , Drought tolerance classification of grapevine rootstock by  \nmachine learning for the Sao Francisco Valley, Smart Agricultural Technology (2023), doi:  \n[https://doi.org/10.1016/j.atech.2023.100192](https://doi.org/10.1016/j.atech.2023.100192)  \nThis is a PDF file of an article that has undergone enhancements after acceptance, such as the addition of a cover page and metadata, and formatting for readability, but it is not yet the definitive version of record. This version will undergo additional copyediting, typesetting and review before it is published in its final form, but we are providing this version to give early visibility of the article. Please note that, during the production process, errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain.  \n© 2023 Published by Elsevier B.V.  \nThis is an open access article under the CC BY-NC-ND license ([http://creativecommons.org/l](http://creativecommons.org/l)icenses/by-nc-nd/4 .0/)  \nHighlights  \n􀁸 Comparison of six different ML algorithms to predict drought tolerance classes.  \n􀁸 A dataset with 42 cultivars and 30 variables was used for training and testing.  \n􀁸 The best model predicted drought tolerance classes of three rootstocks cultivars.  \n􀁸 The RF achieved 98.57% accuracy to predict rootstock drought tolerance classes.  \na  \nb  \nc  \nd  \nDrought tolerance classification of grapevine rootstock by machine learning for the  \nSão Francisco Valley  \nNina Iris Verslypea,􀀍 , André Câmara Alves do Nascimentob, Rosimar dos Santos Mussera, Raphael Miller de Souza Caldasa, Luiza Suely Semen Martinsc , Patrícia  \nCoelho de Souza Leãod  \nDepartament of Agronomy, Federal Rural University of Pernambuco, Recife, Brazil. Department of Computing, Federal Rural University of Pernambuco, Recife, Brazil. Department of Biology, Federal Rural University of Pernambuco, Recife, Brazil. Agricultural Research Centre for Semi-arid Tropics , Petrolina, Brazil.  \n􀀍 Corresponding author  \nE-mail adresses:  [nina.verslype@ufrpe.br](nina.verslype@ufrpe.br) (N. I. Verslype),  [andre.camara@ufrpe.br](andre.camara@ufrpe.br) ([A.C.A. do](A.C.A. do)[ ](A.C.A. do)Nascimento), [rosimar.musser@ufrpe.br](rosimar.musser@ufrpe.br) (R. dos S. Musser), [raphaelmillers@gmail.com](raphaelmillers@gmail.com) ( [R. M. de](R. M. de) S. Caldas), [luiza.martins@ufrpe.br](luiza.martins@ufrpe.br) (L. S. S. Martins), patricia. leao@embrapa. br (P. C. de S. Leão) .  \nABSTRACT  \nMachine Learning (ML) algorithms are increasingly being used in several areas of agricultural studies , such as plant breeding. ML can assist in the recognition of relevant patterns or groups, or even in the prediction of the outcome under new settings , thus accelerating experiments and interpretating their results. The identification and selection of drought-tolerant grapevine rootstock (Vitis spp. ) have become more relevant in late years, motivated mostly by global climate change scenarios. However, the grapevine is a perennial species , with polygenic characteristics and a complex traits inheritance by offspring , thus making it very challenging to discover new, drought tolerant cultivars. For this reason, ","cbCaikgPTIV0hhp2","https://ap.wps.com/l/cbCaikgPTIV0hhp2","pdf",1376218,1,28,"English","en",105,"# Highlights\n## Model comparison and dataset overview\n# Abstract\n# Introduction","[{\"question\":\"What is the main objective of this study?\",\"answer\":\"To compare six machine learning models and assess their performance in predicting drought tolerance levels of grapevine rootstock cultivars.\"},{\"question\":\"How was the dataset used for training and testing?\",\"answer\":\"A dataset containing multiple grapevine cultivars and several variables was used to evaluate the models during training and testing.\"},{\"question\":\"Which model performed best and what accuracy was achieved?\",\"answer\":\"The Random Forest (RF) model achieved 98.57% accuracy for predicting grapevine rootstock drought tolerance classes.\"}]","Drought tolerance classification of grapevine rootstock by machine learning for the São Francisco Valley | PDF",1785817885,71,{"code":4,"msg":31,"data":32},"ok",{"site_id":24,"language":23,"slug":33,"title":13,"keywords":34,"description":14,"schema_data":35,"social_meta":86,"head_meta":88,"extra_data":90,"updated_unix":28},"drought-tolerance-classification-of-grapevine-rootstock-by-machine-learning-for-the-sao-francisco-valley","",{"@graph":36,"@context":85},[37,54,68],{"@type":38,"itemListElement":39},"BreadcrumbList",[40,44,48,51],{"item":41,"name":42,"@type":43,"position":20},"https://docshare.wps.com","Home","ListItem",{"item":45,"name":46,"@type":43,"position":47},"https://docshare.wps.com/document/","Document",2,{"item":49,"name":12,"@type":43,"position":50},"https://docshare.wps.com/document/research-report/",3,{"item":52,"name":13,"@type":43,"position":53},"https://docshare.wps.com/document/drought-tolerance-classification-of-grapevine-rootstock-by-machine-learning-for-the-sao-francisco-valley/123658/",4,{"url":52,"name":13,"@type":55,"author":56,"headline":13,"publisher":58,"fileFormat":61,"inLanguage":23,"description":14,"dateModified":62,"datePublished":62,"encodingFormat":61,"isAccessibleForFree":63,"interactionStatistic":64},"DigitalDocument",{"name":9,"@type":57},"Person",{"url":41,"name":59,"@type":60},"DocShare","Organization","application/pdf","2026-08-04",true,{"@type":65,"interactionType":66,"userInteractionCount":4},"InteractionCounter",{"@type":67},"ViewAction",{"@type":69,"mainEntity":70},"FAQPage",[71,77,81],{"name":72,"@type":73,"acceptedAnswer":74},"What is the main objective of this study?","Question",{"text":75,"@type":76},"To compare six machine learning models and assess their performance in predicting drought tolerance levels of grapevine rootstock cultivars.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"How was the dataset used for training and testing?",{"text":80,"@type":76},"A dataset containing multiple grapevine cultivars and several variables was used to evaluate the models during training and testing.",{"name":82,"@type":73,"acceptedAnswer":83},"Which model performed best and what accuracy was achieved?",{"text":84,"@type":76},"The Random Forest (RF) model achieved 98.57% accuracy for predicting grapevine rootstock drought tolerance classes.","https://schema.org",{"og:url":52,"og:type":87,"og:title":13,"og:site_name":59,"og:description":14},"article",{"robots":89,"canonical":52},"index,follow",{"doc_id":7,"site_id":24},{"code":4,"msg":5,"data":92},[93,97,101,105,110,115,120,123,128,131,135],{"id":20,"doc_module":4,"doc_module_name":46,"category_name":94,"show_sort_weight":95,"slug":96},"Story & Novel",90,"story-novel",{"id":47,"doc_module":4,"doc_module_name":46,"category_name":98,"show_sort_weight":99,"slug":100},"Literature",80,"literature",{"id":53,"doc_module":4,"doc_module_name":46,"category_name":102,"show_sort_weight":103,"slug":104},"Exam",70,"exam",{"id":106,"doc_module":4,"doc_module_name":46,"category_name":107,"show_sort_weight":108,"slug":109},5,"Comic",60,"comic",{"id":111,"doc_module":4,"doc_module_name":46,"category_name":112,"show_sort_weight":113,"slug":114},6,"Technology",50,"technology",{"id":116,"doc_module":4,"doc_module_name":46,"category_name":117,"show_sort_weight":118,"slug":119},7,"Healthcare",40,"healthcare",{"id":11,"doc_module":4,"doc_module_name":46,"category_name":12,"show_sort_weight":121,"slug":122},30,"research-report",{"id":124,"doc_module":4,"doc_module_name":46,"category_name":125,"show_sort_weight":126,"slug":127},9,"Religion & Spirituality",20,"religion-spirituality",{"id":126,"doc_module":4,"doc_module_name":46,"category_name":129,"show_sort_weight":126,"slug":130},"World Cup","world-cup",{"id":132,"doc_module":4,"doc_module_name":46,"category_name":133,"show_sort_weight":132,"slug":134},10,"Lifestyle","lifestyle",{"id":136,"doc_module":4,"doc_module_name":46,"category_name":137,"show_sort_weight":106,"slug":138},19,"General","general"]