High-throughput plant phenotyping (HTPP), initially developed for model plants in controlled environments, has recently transitioned to the more challenging context of field crop phenotyping. Despite difficulties from unstable environmental conditions, technological advancements have made efficient and precise field phenotyping feasible, enabling the assessment of crops in their actual growing environments using platforms like satellites, unmanned aerial vehicles (UAVs), and unmanned ground vehicles (UGVs). High-throughput field phenotyping (HTFP) is vital for diverse and structurally complex horticultural crops. These crops are critical for nutritional security but have received less attention in phenotyping research, creating a significant knowledge gap. This chapter addresses this gap by reviewing key high-throughput field phenotyping (HTFP) technologies and advanced imaging sensors—including RGB, thermal, and hyperspectral—applied to selected horticultural crops. Moreover, this chapter highlights practical, “fit-for-purpose” strategies powered by machine learning, from 3D yield estimation in apples to stress and disease detection in mangoes, tomatoes, grapes, and chili peppers. While significant challenges in cost, data standardization, and analysis remain, future advances in sensor fusion, AI-driven autonomy, and the deep integration of phenomic with genomic data are anticipated. This synergy is poised to accelerate the development of resilient, high-quality horticultural crops, ultimately strengthening global food systems.