The Future of Farming: Leveraging AI, Machine Learning, and Smart Systems for Optimal Agricultural Practices

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dc.authorid0000-0001-8035-0278
dc.contributor.authorÇakmakçı, Muhammet Fatih
dc.contributor.authorGünay, Faruk Baturalp
dc.contributor.authorid410449
dc.date.accessioned2025-04-16T07:43:00Z
dc.date.available2025-04-16T07:43:00Z
dc.date.issued2024-10
dc.departmentFakülteler, Meslek Yüksekokulu, Bilgisayar Programcılığı Bölümü
dc.description.abstractIn smart agriculture (SA), key applications of intelligent technologies include pest management, weed control, monitoring agricultural products, storage management, disease management and control, weather forecasting and monitoring, irrigation management, yield prediction, soil composition and management, and machinery management. Managing the agricultural production supply chain, measuring soil variability, improving agricultural production and management, reducing resource usage, monitoring water consumption, enhancing agricultural processes, identifying agricultural risks and hazards, and optimizing decision-making are crucial application areas of agricultural technologies.The use of digital technologies in agriculture and livestock is rapidly increasing. Optical, mechanical, electrochemical sensors, air flow, and location tracking technologies provide early warnings for diseases and pests, optimizing harvest processes. Smart monitoring systems enhance agricultural efficiency, while digital technologies improve productivity, sustainability, and effectiveness. Smart greenhouses, irrigation, and fertilization systems support agricultural sustainability by monitoring environmental and plant parameters.In livestock management, environmental and body sensors improve animal health and living conditions. Machine learning algorithms are effective in detecting mating behaviors and diseases in livestock. Precision livestock systems monitor health and welfare parameters, increasing productivity and protecting animal health. Artificial intelligence (AI) and machine learning (ML) applications are effective in analyzing soil data, plant phenotyping, and carbon stock estimation. Smart irrigation systems contribute to water conservation and increased efficiency. Additionally, smart harvesting systems help achieve sustainable production with lower costs and increased productivity. These technologies enhance the sustainability of agriculture and livestock by strengthening the capacity to manage productivity and environmental impacts. This article will discuss the topics that mentioned above.
dc.identifier.citationÇakmakçı, M.F. & Günay, F.B. (2024). The Future of Farming: Leveraging AI, Machine Learning, and Smart Systems for Optimal Agricultural Practices. M. Şeker, B. Polat, A. Çay, M. Güven (Ed.), International Balkan Agriculture Congress içinde (196. ss.). Çanakkale: ÇOMU Publication.
dc.identifier.endpage196
dc.identifier.isbn9786258278514
dc.identifier.startpage196
dc.identifier.urihttps://dspace.mudanya.edu.tr/handle/20.500.14362/311
dc.institutionauthorÇakmakçı, Muhammet Fatih
dc.language.isoen
dc.publisherÇOMU Publication
dc.relation.journalInternational Balkan Agriculture Congress
dc.relation.publicationcategoryKonferans Ögesi- Uluslararası- Kurum Öğretim Elemanı
dc.rightsinfo:eu-repo/semantics/openAccess
dc.subjectSmart agriculture
dc.subjectintelligent technologies
dc.subjectmachine learning
dc.subjectdata mining
dc.titleThe Future of Farming: Leveraging AI, Machine Learning, and Smart Systems for Optimal Agricultural Practices
dc.typeSunum
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