[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-122128-en":3,"doc-seo-122128-105":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":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},122128,687197207639,"Asher","https://ap-avatar.wpscdn.com/davatar_a8503ba1806abce46bf441b54a3ca4cd",8,"Research & Report","Machine Learning techniques for Hydroponic Cultures - Research study","Hydroponics enables plant cultivation without soil, using controlled conditions to support optimal growth. This work applies machine learning to leverage large datasets while benefiting from the ability to control hydroponic inputs, aiming to reduce resource use and improve production under climate-change constraints. Four models—random forest, support vector machine, extreme gradient boosting, and a neural network—were evaluated in two scenarios: using all dataset features versus using only features measurable during cultivation. Extreme gradient boosting achieved the strongest performance in the second scenario, reaching low error metrics (MAE, MBE, RMSE).","Plini L., Mascolo D. -Machine Learning techniques for Hydroponic Cultures 445  \nMachine Learning techniques for Hydroponic Cultures  \nLeonardo Plini*1 , Davide Mascolo1  \n1 Sapien~~z~~a University ofRome  \n[plini.2000543@studenti.uniromal.it](plini.2000543@studenti.uniromal.it); [mascolo.200199l@studenti.uniromal.it](mascolo.200199l@studenti.uniromal.it)  \nAbstract:Hydroponics is an innovative agricultural technique that enables the cultivation of plants without the use of soil, providing controlled conditions for optimal plant growth. In recent years, machine leaming (ML) techniques have gained prominence in various domains, including agriculture, due to their ability to analyze large datasets and derive valuable insights: the combination of ML and the opportunity to control all the inputs in an hydroponic cultivation represents an invaluable chance to reduce resource requirements and increase the production inline with the constraints imposed by climate change. In this work, we tested four different machine learning models, namely, random forest (RF), support vector machine (SVM), extreme gradient boosting (XGB) and a neural network. These models were tested in two different scenarios considering two sets of variables. The first scenario is done considering all the features of the dataset while the second scenario is characterized only by the features that can be measured during the cultivation. The best result is obtained in the second scenario with extreme gradient boosting (XGB) that achieved a value of 8.37 for mean absolute error (MAE), 8.20 for mean bias error (MBE) and 13.16 for root mean square error (RMSE) .  \nKeywords: Hydroponic Cultures, Machine Leaming, innovative agric~~u~~ltural techniques.  \n• [Corresponding Author: plini.2000543@studenti.uniromal.it](Corresponding Author: plini.2000543@studenti.uniromal.it)  \n446  \n1. Introduction  \nThe climate change represents a urgent and complex problem that can impact human life in different ways, but the main challenge is related to food supply due to land and water scarcity. According to the Organisation for Economie Cooperation and Development (OECD), farming accounts for around 70% of water used in the world and contributes to water pollution from excess nutrients, pesticides and other pollutants.  \nFor this reason, sustainable management of water in agriculture becomes a criticai challenge to address water scarcity and a global focus for every country. In this context, technology has made important steps proposing new frameworks that improve the current usage of resources such as hydroponic, aeroponic and aquaponic systems. All these apparatuses require careful implementations, and several modelling decisions must be taken into consideration to reach the best performance in terms of production.  \nIn this landscape, Machine Leaming techniques are acquiring a centrai role. Infact, we can leverage these methodologies to clarify the effect of each component in a controlled environment.  \nThe purpose of this paper is to analyse the data of a tornato crop in an hydroponic setup to highlight the possible benefits of this procedure and the plausible extension to other cultivations.  \n2. Literature R~~e~~view and Related works  \nTechnology has always played a centrai role when it comes to agricultural applications and it has driven huge changes during the history. However, only in the recent years has been possible to leverage internet ~~ap~~plications and huge compute resources to gain relevant insights. Impedovo e at. [Impedovo et al., 2018] showed how to manage heterogeneous information and data coming from real dataset that collect physical, bi  \nological and sensory values. Signore et al [Signore et al., 2018] designed and implemented an experiment in \"la Noria\" F~~arm~~ of the Institute of Science of Food Production of the National Research Council using the Nutrient Film Technique (NFT) for an hybrid variety of cherry tornato. The data were public and accessible  \nth","cbCaikYKGGZfZzJZ","https://ap.wps.com/l/cbCaikYKGGZfZzJZ","pdf",674765,1,12,"English","en",105,"# Introduction\n## Climate change and sustainable water management\n## Role of technology and hydroponic systems\n# Literature Review and Related Works\n## Prior studies using machine learning in hydroponics\n## Public datasets and nutrient management\n# Proposed Methodology\n## Data preprocessing and time-window aggregation\n## Correlation analysis and scenario design","[{\"question\":\"What problem does the study address in hydroponic cultivation?\",\"answer\":\"The study targets sustainability challenges driven by climate change, especially food supply and resource constraints, by improving decision-making through data analysis in controlled hydroponic environments.\"},{\"question\":\"Which machine learning models were tested?\",\"answer\":\"The research evaluated random forest (RF), support vector machine (SVM), extreme gradient boosting (XGB), and a neural network.\"},{\"question\":\"How did the two evaluation scenarios differ, and what were the results?\",\"answer\":\"One scenario used all dataset features, while the other used only features measurable during cultivation. The best performance came from the second scenario, where XGB achieved MAE 8.37, MBE 8.20, and RMSE 13.16.\"}]","Machine Learning techniques for Hydroponic Cultures - Research study | PDF",1785808956,30,{"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},"machine-learning-techniques-for-hydroponic-cultures-research-study","",{"@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/machine-learning-techniques-for-hydroponic-cultures-research-study/122128/",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":20},"InteractionCounter",{"@type":67},"ViewAction",{"@type":69,"mainEntity":70},"FAQPage",[71,77,81],{"name":72,"@type":73,"acceptedAnswer":74},"What problem does the study address in hydroponic cultivation?","Question",{"text":75,"@type":76},"The study targets sustainability challenges driven by climate change, especially food supply and resource constraints, by improving decision-making through data analysis in controlled hydroponic environments.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"Which machine learning models were tested?",{"text":80,"@type":76},"The research evaluated random forest (RF), support vector machine (SVM), extreme gradient boosting (XGB), and a neural network.",{"name":82,"@type":73,"acceptedAnswer":83},"How did the two evaluation scenarios differ, and what were the results?",{"text":84,"@type":76},"One scenario used all dataset features, while the other used only features measurable during cultivation. The best performance came from the second scenario, where XGB achieved MAE 8.37, MBE 8.20, and RMSE 13.16.","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},[93,97,101,105,110,115,120,122,127,130,134],{"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":29,"slug":121},"research-report",{"id":123,"doc_module":4,"doc_module_name":46,"category_name":124,"show_sort_weight":125,"slug":126},9,"Religion & Spirituality",20,"religion-spirituality",{"id":125,"doc_module":4,"doc_module_name":46,"category_name":128,"show_sort_weight":125,"slug":129},"World Cup","world-cup",{"id":131,"doc_module":4,"doc_module_name":46,"category_name":132,"show_sort_weight":131,"slug":133},10,"Lifestyle","lifestyle",{"id":135,"doc_module":4,"doc_module_name":46,"category_name":136,"show_sort_weight":106,"slug":137},19,"General","general"]