A Smart Irrigation System Using the IoT and Advanced Machine Learning Model
A Systematic Literature Review
DOI:
https://doi.org/10.22399/ijcesen.526Keywords:
Smart Irrigation Systems, IoT in Agriculture, Machine Learning Models, PRISMA methodologyAbstract
The rapid advancement of IoT (Internet of Things) technologies and sophisticated machine learning models is driving innovation in irrigation systems, laying the foundation for more effective and eco-friendly smart agricultural procedures. This systematic literature review strives to uncover the advancements and challenges in the advancement and implementation of IoT-based smart irrigation systems integrated with advanced machine learning techniques. By analyzing 43 relevant studies published between 2017 and 2024, the research focuses on the ability of these technologies have evolved to meet the challenges of modern agriculture irrigation system. Predictive analytics, anomaly detection, and adaptive control—that enhance irrigation precision and decision-making processes. Employing the PRISMA methodology, this review uncovers the strengths and limitations of current systems, highlighting significant achievements in real-time data utilization and system responsiveness. However, it also brings attention to unresolved issues, including the complexities of data integration, network reliability, and the scalability of IoT-based frameworks. Additionally, the study identifies crucial gaps in standardization and the need for flexible solutions that can adapt to diverse environmental conditions. By offering a comprehensive analysis, this review provides key insights for advancing smart irrigation technologies, emphasizing the importance of continued research in overcoming the existing barriers to wider adoption and effectiveness in various agricultural settings.
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