Bitte verwenden Sie diesen Link, um diese Publikation zu zitieren, oder auf sie als Internetquelle zu verweisen: https://hdl.handle.net/10419/319502 
Erscheinungsjahr: 
2024
Quellenangabe: 
[Editor:] Rodino, Steliana [Editor:] Dragomir, Vili [Title:] Agrarian Economy and Rural Development - Trends and Challenges. International Symposium. 15th Edition [Publisher:] The Research Institute for Agricultural Economy and Rural Development (ICEADR) [Place:] Bucharest [Year:] 2024 [Pages:] 24-31
Verlag: 
The Research Institute for Agricultural Economy and Rural Development (ICEADR), Bucharest
Zusammenfassung: 
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 of precision 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
Schlagwörter: 
Smart agriculture
Artificial Intelligence
Machine Learning
Precision farming
Crop monitoring
IoT in agriculture
JEL: 
Q0
Dokumentart: 
Conference Paper

Datei(en):
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