[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-119445-en":3,"doc-seo-119445-105":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},119445,1374391974468,"Eden","https://ap-avatar.wpscdn.com/davatar_29158cc5080c5b710cf443261637dec0",8,"Research & Report","Artificial intelligence and machine learning approaches for smart agriculture - Conference Paper","Artificial Intelligence and Machine Learning integration in agriculture enables a shift from traditional farming to efficient, data-driven, sustainable practices. Smart agriculture applies AI-driven predictive analytics, image processing, and IoT sensors to improve crop monitoring, irrigation management, pest detection, and yield prediction. Machine learning supports decisions by learning from datasets covering soil health, weather, and plant diseases. The review discusses AI/ML applications, benefits, and challenges, including progress in deep learning, computer vision, and automation, while addressing barriers such as data availability, model accuracy, and implementation costs, and outlining future research directions for food security and sustainability.","Sekar, S.; Rajesh, S.; Sekar, S. D.  \nConference Paper  \nArtificial intelligence and machine learning approaches for smart agriculture  \nProvided in Cooperation with:  \nThe Research Institute for Agriculture Economy and Rural Development (ICEADR), Bucharest  \nSuggested Citation: Sekar, S.; Rajesh, S.; Sekar, S. D. (2024) : Artificial intelligence and machine learning approaches for smart agriculture, In: Rodino, Steliana Dragomir, Vili (Ed.): Agrarian Economy and Rural Development-Trends and Challenges. International Symposium. 15th Edition, The Research Institute for Agricultural Economy and Rural Development (ICEADR), Bucharest, pp. 24-31  \nThis Version is available at:  \n[https://hdl.handle.net/10419/319502](https://hdl.handle.net/10419/319502)  \nStandard-Nutzungsbedingungen:  \nDie Dokumente auf EconStor dürfen zu eigenen wissenschaftlichen Zwecken und zum Privatgebrauch gespeichert und kopiert werden.  \nSie dürfen die Dokumente nicht für öffentliche oder kommerzielle Zwecke vervielfältigen, öffentlich ausstellen, öffentlich zugänglich machen, vertreiben oder anderweitig nutzen.  \nSofern die Verfasser die Dokumente unter Open-Content-Lizenzen (insbesondere CC-Lizenzen) zur Verfügung gestellt haben sollten, gelten abweichend von diesen Nutzungsbedingungen die in der dort genannten Lizenz gewährten Nutzungsrechte.  \nTerms of use:  \nDocuments in EconStor maybe saved and copied foryour personal and scholarly purposes.  \nYou are not to copy documents for public or commercial purposes, to exhibit the documents publicly, to make them publicly available on the internet, or to distribute or otherwise use the documents in public.  \nIf the documents have been made available under an Open Content Licence (especially Creative Commons Licences), you may exercise further usage rights as specified in the indicated licence.  \nARTIFICIAL INTELLIGENCE AND MACHINE LEARNING APPROACHES FOR SMART AGRICULTURE  \nS. Sekar1, a), S. Rajesh 2, b), and S. D. Sekar2, c)  \n1 Department of Mechanical Engineering, Rajalakshmi Engineering College, Chennai, India.  \n2 Department of Mechanical Engineering, R.M.K. Engineering College, Tiruvallur, India.  \na) Corresponding author: [sekar.s@rajalakshmi.edu.in](sekar.s@rajalakshmi.edu.in);b) [rjs.mech@rmkec.ac.in](rjs.mech@rmkec.ac.in);c)  \n[sds.mech@rmkec.ac.in](sds.mech@rmkec.ac.in)  \nAbstract: The integration of Artificial Intelligence (AI) and Machine Learning (ML) in agriculture has transformed traditional farming into a more efficient, data-driven, and sustainable practice. Smart agriculture leverages AI-driven techniques such as predictive analytics, image processing, and Internet of Things (IoT) sensors to optimize crop monitoring, irrigation management, pest detection, and yield prediction. Machine learning models enhance decision-making by analyzing vast datasets related to soil health, weather conditions, and plant diseases. This review explores various AI and ML approaches in smart agriculture, highlighting their applications, benefits, and challenges. It also discusses advancements in deep learning, computer vision, and automation technologies that are shaping the future ofprecision farming. Despite the significant progress, issues related to data availability, model accuracy, and implementation costs remain barriers to widespread adoption. The study concludes with future research directions and the potential of AI-driven smart agriculture to enhance global food security and sustainability.  \nKey words: Smart agriculture, Artificial Intelligence, Machine Learning, Precision farming, Crop monitoring, IoT in agriculture  \nJEL Clasiffication: Q0  \nINTRODUCTION  \nAgriculture plays a fundamental role in ensuring food security, economic stability, and sustainable development worldwide. However, traditional farming practices face significant challenges, including climate variability, soil degradation, water scarcity, pest infestations, and labor shortages. Additionally, the increasing global population deman","cbCaio0VHhsfN8Yo","https://ap.wps.com/l/cbCaio0VHhsfN8Yo","pdf",954743,1,9,"English","en",105,"# Introduction\n## Crop Monitoring and Disease Detection\n## Precision Irrigation and Water Management","[{\"question\":\"What roles do AI and ML play in smart agriculture?\",\"answer\":\"They support data-driven decision-making and enable automation and precision farming. Applications include monitoring, prediction, and optimization of key agricultural processes.\"},{\"question\":\"Which AI technologies are used to improve crop monitoring and early disease detection?\",\"answer\":\"Computer vision and deep learning analyze images from drones, satellites, or on-field sensors. The goal is early identification of diseases, pests, and nutrient deficiencies.\"},{\"question\":\"What challenges limit widespread adoption of AI and ML in agriculture?\",\"answer\":\"Barriers include data availability, model accuracy, and implementation costs. These factors affect real-world deployment and scalability.\"}]","Artificial intelligence and machine learning approaches for smart agriculture - Conference Paper | PDF",1785724320,23,{"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},"artificial-intelligence-and-machine-learning-approaches-for-smart-agriculture-conference-paper","",{"@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/artificial-intelligence-and-machine-learning-approaches-for-smart-agriculture-conference-paper/119445/",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":4},"InteractionCounter",{"@type":67},"ViewAction",{"@type":69,"mainEntity":70},"FAQPage",[71,77,81],{"name":72,"@type":73,"acceptedAnswer":74},"What roles do AI and ML play in smart agriculture?","Question",{"text":75,"@type":76},"They support data-driven decision-making and enable automation and precision farming. 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