The rapidly growing world population challenges farmers to meet the rising food demand. Monitoring crop phenotypes, or the physical plant traits, is useful in tracking plant development, maintaining plant health, and increasing yield. However, phenotyping efforts are traditionally manual and become tedious for large scale farms. Thus, it is imperative to develop autonomous solutions to monitor plants accurately, remotely, and timely. To meet this objective, computer vision techniques have been used by researchers to perform automatic plant phenotyping on video and image data collected from either indoor, controlled environments or from the field. Furthermore, these methods have focused on using traditional pixel-based processing, machine learning, and deep learning for plant phenotyping. In this study, various modern computer vision techniques are implemented to automatically phenotype plants for agriculture applications, thereby reducing manual labor while accurately detecting important traits to help increase yield.