Akademik Veri Yönetim Sistemi
ARTIFICIAL INTELLIGENCE, SMART TECHNOLOGIES AND CLIMATE CHALLENGES IN MODERN AGRICULTURE, Assoc. Prof. Dr. Tefide KIZILDENİZ, Editör, BZT TURAN PUBLISHING HOUSE, Delaware, ss.85-99, 2026
This study comprehensively examines the role and applications of data fusion in agriculture, focusing on the integration of satellite, unmanned aerial vehicle (UAV), and ground-based sensor data. The rapid digitalization of agricultural systems has led to the generation of large volumes of heterogeneous data characterized by differences in spatial, temporal, and spectral resolutions. In this context, data fusion emerges as a critical approach to combine multisource data and enhance the accuracy, consistency, and reliability of agricultural analyses. The study presents the fundamental concepts of data fusion and categorizes fusion techniques into low-level, feature-level, and decision-level approaches. Furthermore, it evaluates major data sources used in agriculture, including satellite imagery for large-scale monitoring, UAV data for high- resolution observations, and IoT-based sensors for real-time environmental measurements. Various data fusion methods are discussed, including statistical approaches, machine learning-based techniques, and deep learning-based models, highlighting their advantages, limitations, and application potentials. The findings indicate that multi-source data fusion significantly improves applications such as crop monitoring, stress detection, yield prediction, and resource optimization within precision agriculture. Additionally, the integration of advanced artificial intelligence techniques enhances the capability of analyzing complex and high-dimensional datasets. However, challenges such as data standardization, interoperability, computational requirements, and user adaptation remain critical barriers. In conclusion, data fusion plays a pivotal role in advancing smart and sustainable agricultural systems by enabling more efficient resource management and informed decision-making processes. Future developments in artificial intelligence, IoT, and cloud-based systems are expected to further expand the applicability and impact of data fusion in Agriculture 4.0 and beyond.