[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-120987-en":3,"doc-seo-120987-105":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},120987,4398048950312,"Violet","https://ap-avatar.wpscdn.com/avatar/400002538284de19e3c?_k=1778320343897328908",8,"Research & Report","Intelligent Agricultural Greenhouse Control System Based on Internet of Things and Machine Learning","This study conceptualizes and implements an intelligent agricultural greenhouse control system integrating the Internet of Things (IoT) and machine learning. By continuously monitoring key greenhouse environmental parameters and applying machine learning algorithms, the system regulates internal conditions to improve crop growth efficiency and yield while reducing resource waste. The work addresses shortcomings of traditional rule-based greenhouse management, emphasizing real-time sensing and precise control under food safety and production-efficiency pressures, with privacy concerns managed via encryption and controlled data policies.","arXiv :2402 .09488v2 [ ee ss . SY] 20 Mar 2025  \nIntelligent Agricultural Greenhouse Control System Based on Internet of Things and Machine Learning  \nCangqing Wang 1 ∗, Jiangchuan Gong1  \nAbstract  \nThis study endeavors to conceptualize and execute a sophisticated agricultural greenhouse control system grounded in the amalgamation of the Internet of Things (IoT) and machine learning. Through meticulous monitoring of intrinsic environmental parameters within the greenhouse and the integration of machine learning algorithms, the conditions within the greenhouse are aptly modulated. The envisaged outcome is an enhancement in crop growth efficiency and yield, accompanied by a reduction in resource wastage. In the backdrop of escalating global population figures and the escalating exigencies of climate change, agriculture confronts unprecedented challenges. Conventional agricultural paradigms have proven inadequate in addressing the imperatives of food safety and production efficiency. Against this backdrop, greenhouse agriculture emerges as a viable solution, proffering a controlled milieu for crop cultivation to augment yields, refine quality, and diminish reliance on natural resources  \n[1] . Nevertheless, greenhouse agriculture contends with a gamut of challenges. Traditional greenhouse management strategies, often grounded in experiential knowledge and predefined rules, lack targeted personalized regulation, thereby resulting in resource inefficiencies. The exigencies of real-time monitoring and precise control of the greenhouse’s internal environment gain paramount importance with the burgeoning scale of agriculture. To redress this challenge, the study introduces IoT technology and machine learning algorithms into greenhouse agriculture, aspiring to institute an intelligent agricultural greenhouse control system conducive to augmenting the efficiency and sustainability of agricultural production.  \nIndex Terms  \nInternet of Things(IoT), Machine Learning, RNN model, Agricultural greenhouse  \nI. INTRODUCTION  \nIn the formulation of the intelligent agricultural greenhouse control system, ethical considerations and system comprehensiveness demand meticulous attention. The real-time monitoring of greenhouse environmental parameters necessitates the collection and processing of copious sensitive data, thereby engendering privacy concerns for agricultural stakeholders, greenhouse equipment manufacturers, and other involved parties [2] . In response, the study employs secure data transmission and storage mechanisms that encrypt sensitive information, ensuring exclusive access to critical data by authorized personnel. Additionally, a well-defined data use policy delineates ownership and usage rights, complemented by data desensitization measures to mitigate potential privacy risks.  \nThe advent of IoT and machine learning may precipitate a paradigm shift in agricultural management, potentially engendering technological adaptation and employment challenges for practitioners. To mitigate this, the study proffers training and support initiatives, ensuring that agricultural practitioners are adept at harnessing new technologies. Concurrently, advocacy for open communication channels between practitioners, scientists, and governments is underscored to deliberate on the impact of technological integration on traditional agricultural practices.  \nPost-implementation of smart technologies, ensuring the sustainability and enduring maintenance of the system becomes a pertinent concern. To address this, the system is meticulously designed with modularity and upgradability, allowing for facile updates to hardware and software. Furthermore, a judicious maintenance plan and support system are instituted to uphold the system’s functionality over the long term.  \nThe introduction of novel technologies harbors the prospect of an uneven distribution of resources within society, potentially depriving certain regions or agricultural producers of the be","cbCaiulK5CEuj0AO","https://ap.wps.com/l/cbCaiulK5CEuj0AO","pdf",1133535,1,10,"English","en",105,"# Introduction\n## System objectives and expected outcomes\n## IoT and machine learning rationale\n## Data privacy, governance, and secure transmission\n## Adoption support and communication among stakeholders\n## System sustainability, modularity, and maintenance\n## Equity and accessibility in technology deployment","[{\"question\":\"What problem does the intelligent greenhouse control system target?\",\"answer\":\"It targets inefficiencies and lack of targeted personalized regulation in traditional greenhouse management, where rule-based approaches often lead to resource waste and inadequate real-time control.\"},{\"question\":\"How does the system use IoT and machine learning?\",\"answer\":\"It uses IoT to monitor greenhouse environmental parameters such as temperature, humidity, and light, then applies machine learning algorithms to modulate conditions in real time for adaptive crop management.\"},{\"question\":\"How are privacy and data access concerns handled?\",\"answer\":\"The study employs secure data transmission and storage with encryption, restricts access to authorized personnel, defines data usage ownership and rights, and uses data desensitization to reduce privacy risks.\"}]","Intelligent Agricultural Greenhouse Control System Based on Internet of Things and Machine Learning | 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problem does the intelligent greenhouse control system target?","Question",{"text":75,"@type":76},"It targets inefficiencies and lack of targeted personalized regulation in traditional greenhouse management, where rule-based approaches often lead to resource waste and inadequate real-time control.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"How does the system use IoT and machine learning?",{"text":80,"@type":76},"It uses IoT to monitor greenhouse environmental parameters such as temperature, humidity, and light, then applies machine learning algorithms to modulate conditions in real time for adaptive crop management.",{"name":82,"@type":73,"acceptedAnswer":83},"How are privacy and data access concerns handled?",{"text":84,"@type":76},"The study employs secure data transmission and storage with encryption, restricts access to authorized personnel, defines data usage ownership and rights, and uses data desensitization to reduce privacy 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