[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-126203-en":3,"doc-seo-126203-105":31,"detail-sidebar-cat-0-en-105":93},{"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":21,"is_downloadable":21,"audit_status":21,"page_count":22,"language":23,"language_code":24,"site_id":25,"html_lang":24,"table_of_contents":26,"faqs":27,"seo_title":28,"seo_description":14,"update_tm":29,"read_time":30},126203,549768702563,"Sage","https://ap-avatar.wpscdn.com/avatar/8000c4aa63b76e948b?x-image-process=image/resize,m_fixed,w_180,h_180&k=1786536092046926083",8,"Research & Report","Machine Learning Applied to Indoor Hydroponic Lettuce Optimization - Thesis","Optimizing indoor hydroponic lettuce production supports stronger economic viability in controlled-environment agriculture. The study evaluates machine learning techniques and selects an artificial neural network (ANN) combined with a brute-force algorithm. Fresh matter mass gain is defined as the optimization objective, while irrigation time off, electric conductivity, and nutrient solution pH serve as optimization parameters. The workflow includes exploratory plantings, ANN regression model training, brute-force search for the optimal parameter combination, and validation plantings to compare predicted and achieved outputs.","Machine Learning Applied to Indoor Hydroponic Lettuce Optimization  \nMiguel Afonso Beckers-57403  \nThesis presented to the School of Technology and Management in the scope of the  \nMaster in Informatics.  \nSupervisors:  \nProf. Dr. Arnaldo Candido Junior  \nProf. Dr. Pedro João Soares Rodrigues  \nProf. Dr. Pedro Luiz de Paula Filho  \nThis document does not include the suggestions made by the board.  \nBragança  \nii  \nMachine Learning Applied to Indoor Hydroponic Lettuce Optimization  \nMiguel Afonso Beckers  \nDissertation presented to the School of Technology and Management of Bragança to obtain the master’s degree in Informatics within the scope of the double degree program with the Federal University of Technology – Paraná  \nSupervisors:  \nProf. Dr. Arnaldo Candido Junior  \nProf. Dr. Pedro João Soares Rodrigues  \nProf. Dr. Pedro Luiz de Paula Filho  \nBragança  \niv  \nAcknowledgment  \nI want to express my deepest gratitude to my supervisors, Prof. Dr. Arnaldo Candido Junior, Prof. Dr. Pedro João Soares Rodrigues, and Prof. Dr. Pedro Luiz de Paula Filho, for their invaluable guidance, patience, and support. I am also grateful to Prof. Dr. Glauco Vieira Miranda and his team, whose collaboration in carrying out the lettuce cultivation, data collection, and advice were exceptional. My appreciation extends to the Universidade Tecnológica Federal do Paraná (UTFPR) and the Instituto Politécnico de Bragança (IPB) for allowing me to participate in the double degree program in which this work was developed. I must also thank my family, especially my parents, Lidio Beckers and Rosalha Beckers, and my brother Carlos Alberto Beckers, for their support and encouragement during my education. My friends who accompanied me on this journey deserve special mention, in particular my friend Daniel Augusto Rodrigues Farina, whose collaboration was significant for the development of this work. Thank you all.  \nvi  \nAbstract  \nOptimizing indoor hydroponic lettuce production is important for enhancing its economic viability. This study examined machine learning approaches for its optimization. Among the options analyzed, an artificial neural network (ANN) combined with a brute-force algorithm was chosen. This choice proved compatible with the available time and materials. The gain in fresh matter mass was defined as the optimization goal. And, the irrigation time off, the electric conductivity, and the pH of the nutrient solution were defined asthe optimization parameters. The employed method involved four steps: conducting exploratory plantings to generate a database; training an ANN to create a regression model; using the brute-force algorithm to find the optimal combination in the generated model; and conducting validation plantings to verify if the found combination produces the predicted value. The exploratory plantings generated results with variations. The variations were normalized and additional lighting data were collected. The optimization result predicted that the found combinations would generate an average normalized production of 114.22g per individual. Two validation plantings were conducted: the first exceeded the prediction by 34 .43%, while the second was 17 .20% below the expected. The first result surpassed the values of the exploratory plantings, but the second did not. Due to this divergence, further studies will be necessary to reinforce the validation of the results.  \nKeywords: Modified hydroponic shipping container, controlled-environment agriculture, artificial neural networks, brute-force algorithm.  \nResumo  \nA otimização da produção de alface hidropónica em ambiente fechado é importante para ampliar a sua viabilidade económica. Este trabalho analisou as abordagens de aprendizagem automática para a realização da sua otimização. Entre as opções analisadas, optou-sepelo uso de uma rede neuronal artificial (RNA) combinada com um algoritmo de forçabruta. Esta opção revelou-se compatível com a disponibilidade de tempo e de materia","cbCaitw3WxKT6wnn","https://ap.wps.com/l/cbCaitw3WxKT6wnn","pdf",3425099,6,1,96,"English","en",105,"# Introduction\n## Objectives\n## Document Organization\n# Literature Review\n## Controlled-Environment Agriculture\n## Hydroponic Lettuce (Lactuca Sativa L.)","[{\"question\":\"What optimization goal is used for the hydroponic lettuce system?\",\"answer\":\"The gain in fresh matter mass is used as the optimization goal.\"},{\"question\":\"Which machine learning approach is selected in the study?\",\"answer\":\"An artificial neural network (ANN) combined with a brute-force algorithm is selected for the optimization.\"},{\"question\":\"How are the optimal parameters validated?\",\"answer\":\"Validation plantings are conducted to test whether the found irrigation time off, electric conductivity, and pH combination produces the predicted production value.\"}]","Machine Learning Applied to Indoor Hydroponic Lettuce Optimization - Thesis | PDF",1785903775,242,{"code":4,"msg":32,"data":33},"ok",{"site_id":25,"language":24,"slug":34,"title":13,"keywords":35,"description":14,"schema_data":36,"social_meta":88,"head_meta":90,"extra_data":92,"updated_unix":29},"machine-learning-applied-to-indoor-hydroponic-lettuce-optimization-thesis","",{"@graph":37,"@context":87},[38,55,70],{"@type":39,"itemListElement":40},"BreadcrumbList",[41,45,49,52],{"item":42,"name":43,"@type":44,"position":21},"https://docshare.wps.com","Home","ListItem",{"item":46,"name":47,"@type":44,"position":48},"https://docshare.wps.com/document/","Document",2,{"item":50,"name":12,"@type":44,"position":51},"https://docshare.wps.com/document/research-report/",3,{"item":53,"name":13,"@type":44,"position":54},"https://docshare.wps.com/document/machine-learning-applied-to-indoor-hydroponic-lettuce-optimization-thesis/126203/",4,{"url":53,"name":13,"@type":56,"author":57,"headline":13,"publisher":59,"fileFormat":62,"inLanguage":24,"description":14,"dateModified":63,"datePublished":64,"encodingFormat":62,"isAccessibleForFree":65,"interactionStatistic":66},"DigitalDocument",{"name":9,"@type":58},"Person",{"url":42,"name":60,"@type":61},"DocShare","Organization","application/pdf","2026-08-23","2026-08-05",true,{"@type":67,"interactionType":68,"userInteractionCount":20},"InteractionCounter",{"@type":69},"ViewAction",{"@type":71,"mainEntity":72},"FAQPage",[73,79,83],{"name":74,"@type":75,"acceptedAnswer":76},"What optimization goal is used for the hydroponic lettuce system?","Question",{"text":77,"@type":78},"The gain in fresh matter mass is used as the optimization goal.","Answer",{"name":80,"@type":75,"acceptedAnswer":81},"Which machine learning approach is selected in the study?",{"text":82,"@type":78},"An artificial neural network (ANN) combined with a brute-force algorithm is selected for the optimization.",{"name":84,"@type":75,"acceptedAnswer":85},"How are the optimal parameters validated?",{"text":86,"@type":78},"Validation plantings are conducted to test whether the found irrigation time off, electric conductivity, and pH combination produces the predicted production value.","https://schema.org",{"og:url":53,"og:type":89,"og:title":13,"og:site_name":60,"og:description":14},"article",{"robots":91,"canonical":53},"index,follow",{"doc_id":7,"site_id":25},{"code":4,"msg":5,"data":94},[95,99,103,107,112,116,121,124,129,132,136],{"id":21,"doc_module":4,"doc_module_name":47,"category_name":96,"show_sort_weight":97,"slug":98},"Story & Novel",90,"story-novel",{"id":48,"doc_module":4,"doc_module_name":47,"category_name":100,"show_sort_weight":101,"slug":102},"Literature",80,"literature",{"id":54,"doc_module":4,"doc_module_name":47,"category_name":104,"show_sort_weight":105,"slug":106},"Exam",70,"exam",{"id":108,"doc_module":4,"doc_module_name":47,"category_name":109,"show_sort_weight":110,"slug":111},5,"Comic",60,"comic",{"id":20,"doc_module":4,"doc_module_name":47,"category_name":113,"show_sort_weight":114,"slug":115},"Technology",50,"technology",{"id":117,"doc_module":4,"doc_module_name":47,"category_name":118,"show_sort_weight":119,"slug":120},7,"Healthcare",40,"healthcare",{"id":11,"doc_module":4,"doc_module_name":47,"category_name":12,"show_sort_weight":122,"slug":123},30,"research-report",{"id":125,"doc_module":4,"doc_module_name":47,"category_name":126,"show_sort_weight":127,"slug":128},9,"Religion & Spirituality",20,"religion-spirituality",{"id":127,"doc_module":4,"doc_module_name":47,"category_name":130,"show_sort_weight":127,"slug":131},"World Cup","world-cup",{"id":133,"doc_module":4,"doc_module_name":47,"category_name":134,"show_sort_weight":133,"slug":135},10,"Lifestyle","lifestyle",{"id":137,"doc_module":4,"doc_module_name":47,"category_name":138,"show_sort_weight":108,"slug":139},19,"General","general"]