[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-120222-en":3,"doc-seo-120222-105":30,"detail-sidebar-cat-0-en-105":95},{"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":20,"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},120222,7971461740886,"Theodore","https://ap-avatar.wpscdn.com/davatar_3d24733baf745e90a7e4bdd5f77d97b2",8,"Research & Report","Machine Learning for Tomato Late Blight Outbreak and Progress Forecast in the Espírito Santo Region, Brazil","Tomato late blight, caused by Phytophthora infestans, is a highly destructive disease for Solanum lycopersicum, making improved forecasting essential for integrated management. The study evaluates machine learning approaches to overcome limitations of current weather-based alert and empirical models. Six field trials across two years collected disease severity time series together with hyperlocal meteorological data. A Support Vector Machine (SVM) predicted disease onset with 95% accuracy. For disease progress, Random Forest Regressor (RF) and Extreme Gradient Boosting Regressor (XGBR) were tested; XGBR performed better for the exponential stage and similarly for the asymptote stage. Water availability emerged as a key driver of progress. Despite a limited dataset, ML models support onset and progress forecasting for decision-support systems aimed at better disease control.","FEDERICO JOSÉ GONZÁLEZ VILLASANTI  \nMACHINE LEARNING FOR TOMATO LATE BLIGHT OUTBREAK AND PROGRESS FORECAST IN THE ESPÍRITO SANTO REGION, BRAZIL  \nDissertation submitted to the Plant Pathology Graduate Program of the Universidade Federal de Viçosa in partial fulfillment of the requirements for the degree of Magister Scientiae.  \nAdviser: Eduardo Seiti Gomide Mizubuti  \nFicha catalográfica elaborada pela Biblioteca Central da Universidade Federal de Viçosa-Campus Viçosa  \n\n| T\u003Cbr>G643m 2023 | González Villasanti, Federico José, 1991-\u003Cbr>Machine learning for tomato tate blight outbreak and progress forecast in the Espírito Santo region, Brazil / Federico José González Villasanti.– Viçosa, MG, 2023.\u003Cbr>1 dissertação digital (38 f.): il. (algumas color.) .\u003Cbr>Orientador: Eduardo Seiti Gomide Mizubuti.\u003Cbr>Dissertação (mestrado) -Universidade Federal de Viçosa, Departamento de Fitopatologia, 2023.\u003Cbr>Referências bibliográficas: f.33-38 .\u003Cbr>DOI: [https://doi.org/10.47328/ufvbbt.2024.157](https://doi.org/10.47328/ufvbbt.2024.157)\u003Cbr>[Modo de acesso: World Wide Web.](Modo de acesso: World Wide Web.)\u003Cbr>1. Tomate-Resistência a doenças e pragas-Controle. 2. Phytopththora infestans. 3. Solanum lycopersicum: Inteligência artificial. 4. Predição. I. Mizubuti, Eduardo Seiti Gomide, 1966- .\u003Cbr>II. Universidade Federal de Viçosa. Departamento de Fitopatologia. Programa de Pós-graduação em Fitopatologia.\u003Cbr>III. Título.\u003Cbr>CDD 22. ed. 635.642 |\n| --- | --- |\n\nBibliotecário(a) responsável: Advania Elza da Silva CRB-6/3263  \nVIÇOSA-MINAS GERAIS  \n2023  \nFEDERICO JOSÉ GONZÁLEZ VILLASANTI  \nMACHINE LEARNING FOR TOMATO LATE BLIGHT OUTBREAK AND PROGRESS FORECAST IN THE ESPÍRITO SANTO REGION, BRAZIL  \nDissertation submitted to the Plant Pathology Graduate Program of the Universidade Federal de Viçosa in partial fulfillment of the requirements for the degree of Magister Scientiae.  \nFederico José González Villsanti  \nAuthor  \nEduardo Seiti Gomide Mizubuti  \nAdviser  \nAGRADECIMENTOS  \nAo meu orientador, Professor Eduardo Mizubuti, pela oportunidade, suporte epaciência.  \nAos membros do laboratório de Biologia de Populações, pelo suporte e ajuda.  \nÀ Universidade Federal de Viçosa, pela oportunidade de realizar a pósgraduação.  \nO presente trabalho foi realizado com apoio da Coordenação de Aperfeiçoamento de Pessoal de Nível Superior – Brasil (CAPES) – Código de Financiamento 001.  \nABSTRACT  \nVILLASANTI, Federico José González, M.Sc. candidate, Universidade Federal de Viçosa, August, 2023. Machine learning for tomato late blight outbreak and progress forecast in the Espírito Santo region, Brazil. Adviser: Eduardo Seiti Gomide Mizubuti.  \nTomato late blight (TLB) caused by Phytophthora infestans (Mont.) de Bary is one of the most destructive diseases of tomato crops (Solanum lycopersicum) . Due to its economic importance, several integrated management tools were developed to improve its control, including disease forecast models. Available models in the market rely mostly on weather-based risk alerts and empirical approaches, while recent technologies such as machine learning provide new capabilities for modeling and forecasting. Six field trials in two years were conducted to gather disease measurements. Each trial had a hyperlocal weather station installed to record meteorological data. A Support Vector Machine (SVM) model was used to forecast disease onset with an accuracy of 95% . Two machine learning models constructed to forecast TLB progress were tested and compared: Random Forest Regressor (RF) and an Extreme Gradient Boosting Regressor (XGBR) . The XGBR returned a lower symmetric mean absolute percentage error when compared to the RF for the exponential stage of the epidemics and a similar error for the asymptote stage. The weather variables that affected TLB progress were related to water availability. ML models can predict the onset and development of TLB, despite clear limitations regarding a small disease dataset. Machine learning models can","cbCaivxVRXYT4sjP","https://ap.wps.com/l/cbCaivxVRXYT4sjP","pdf",714167,1,40,"English","en",105,"# Abstract / Resumo\n## Disease background and rationale\n## Field trials and data collection\n## Models for onset prediction and progress forecasting\n## Key drivers and performance comparison","[{\"question\":\"What disease and pathogen does the study focus on?\",\"answer\":\"The study focuses on tomato late blight caused by Phytophthora infestans, which severely affects Solanum lycopersicum.\"},{\"question\":\"How was the dataset collected for modeling?\",\"answer\":\"Six field trials were conducted over two years, collecting disease severity measurements over time and hyperlocal meteorological data from station records.\"},{\"question\":\"Which machine learning models were used and how did they perform?\",\"answer\":\"An SVM predicted disease onset with 95% accuracy. For progress, Random Forest Regressor and Extreme Gradient Boosting Regressor were compared; XGBR showed lower error in the exponential stage and similar error in the asymptote stage.\"},{\"question\":\"What meteorological factor was most associated with disease progress?\",\"answer\":\"Water availability was identified as the meteorological variable most correlated with disease development.\"}]","Machine Learning for Tomato Late Blight Outbreak and Progress Forecast in the Espírito Santo Region, Brazil | PDF",1785728791,101,{"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":90,"head_meta":92,"extra_data":94,"updated_unix":28},"machine-learning-for-tomato-late-blight-outbreak-and-progress-forecast-in-the-espirito-santo-region-brazil","",{"@graph":36,"@context":89},[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/machine-learning-for-tomato-late-blight-outbreak-and-progress-forecast-in-the-espirito-santo-region-brazil/120222/",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","2026-08-03",true,{"@type":65,"interactionType":66,"userInteractionCount":20},"InteractionCounter",{"@type":67},"ViewAction",{"@type":69,"mainEntity":70},"FAQPage",[71,77,81,85],{"name":72,"@type":73,"acceptedAnswer":74},"What disease and pathogen does the study focus on?","Question",{"text":75,"@type":76},"The study focuses on tomato late blight caused by Phytophthora infestans, which severely affects Solanum lycopersicum.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"How was the dataset collected for modeling?",{"text":80,"@type":76},"Six field trials were conducted over two years, collecting disease severity measurements over time and hyperlocal meteorological data from station records.",{"name":82,"@type":73,"acceptedAnswer":83},"Which machine learning models were used and how did they perform?",{"text":84,"@type":76},"An SVM predicted disease onset with 95% accuracy. 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