The global population is predicted to reach 9 billion by 2050, which requires the current food production to double to meet the global demand for food, feed, fiber, and bioenergy. This is a tall order and brings challenges to plant breeders to find genotypes with high yield and high-stress tolerance to adapt to the changing climate in the next 30 years. High-throughput phenotyping that uses modern imaging and sensing technologies to accelerate the breeding of specific crop genotypes is a promising way to solve the challenges. This dissertation focused on developing ground and aerial robotic systems for field-based high-throughput phenotyping and developing novel data processing methods to measure phenotypical traits. In this study, an unmanned aerial system that integrated color, multispectral, thermal cameras, and LiDAR sensor was developed to measure phenotypical traits at the plot level. A data processing pipeline was developed to extract phenotypical traits from the raw data, including canopy height, canopy cover, canopy volume, canopy vegetation index, and canopy temperature. The aerial system was also used to detect and count cotton blooms using the proposed novel bloom counting algorithm that uses Structure from Motion and Convolutional Neural Network. The unmanned aerial system and data processing methods can be effective and efficient tools for field-based high-throughput phenotyping. A modular agricultural robotic system (MARS) was developed, which consists of several modules. Different combinations of modules can form robot configurations for different purposes. The software was developed based on the Robot Operating System (ROS). The robot can auto navigate in the field, and several field tests showed the robot's usefulness in high throughput phenotyping. MARS robots can be easily adapted to different agricultural tasks and affordable and effective platforms for researchers and growers. The next generation Berry Impact Recording Device (BIRD Next) is an upgrade of the previous design. The sensor was designed to measure the mechanical impacts of small fruits and vegetables. The sensing range and frequency of the BIRD Next were significantly improved than the previous design. It integrates wireless communication (Bluetooth) and wireless charging, making the sensor waterproof and useable for produces whose processing involves water.