High-throughput phenotyping has become an important tool in modern wheat breeding, enabling rapid and precise measurement of plant traits. This chapter discusses the integration of advanced technologies, such as unmanned aerial vehicles, imaging sensors, and deep learning algorithms, in the phenotyping process. Key imaging techniques, including RGB cameras, multispectral, fluorescence, thermal, and 3D imaging, are explored for their ability to capture diverse phenotypic traits in wheat. The chapter also highlights the potential of satellite imagery and hyperspectral sensors to provide large-scale, high-resolution data. The incorporation of deep learning models further enhances the analysis of these complex datasets, facilitating improved prediction accuracy and trait selection. The convergence of these technologies offers promising advancements in wheat breeding by improving the speed and efficiency of phenotypic assessments.