Traditional phenotyping methods, though reliable, are often time-consuming, subjective, and difficult to scale, particularly when large breeding populations or field-scale evaluations are involved. In recent years, computer vision-based object detection has emerged as a transformative approach for high-throughput plant phenotyping. By identifying and localizing specific plant traits such as panicles, fruits, or diseased leaf regions in digital images, object detection allows non-invasive, high-throughput, and consistent evaluation of crop performance. This chapter explores the application of object detection in plant phenotyping, highlighting how its integration into trait discovery workflows can accelerate data-driven decision-making in plant breeding and crop management. A practical case study is presented based on PanicleDet—a deep learning-based model developed for identifying developmental stages of rice panicles in field conditions. This system exemplifies how object detection can be customized to support the dynamic nature of trait assessment, enabling timely agronomic interventions and improving the precision of phenotyping. The chapter is particularly relevant for plant scientists and computer vision researchers working towards scalable, automated trait analysis in diverse agricultural environments.