Modern agriculture is facing tremendous challenges in its sustainability, productivity, and quality for a rapidly growing human population, changing climate, and shortfall of arable land and water resources. Improved crops and advanced farming management are essential to tackling those challenges, but both encounter the same bottleneck in the evaluation of plant performance (plant phenotyping) and postharvest quality. Thus, it is paramount to utilize sensing, automation, and data analytics technologies to develop innovative solutions for high throughput plant phenotyping and postharvest quality assessment. This dissertation focused on the development of imaging-based approaches for the accurate, rapid, and nondestructive measurements of key plant phenotypic traits and postharvest quality properties.A ground mobile system was designed and implemented to integrate RTK-GPS, multi-view color, RGB-D, thermal, and hyperspectral imaging for field-based plant phenotyping. Computer control modules were developed for individual sensors and integrated into a LabVIEW program to control and synchronize sensors for field data collection. A number of image analysis methods were developed to extract phenotypic traits related to plant morphology, physiology, and development. Reflectance differences were studied between healthy and bruised blueberry tissues in the spectral range from 950 nm to 1650 nm, providing a basis for non-destructive detection of blueberry bruising. A new index, the bruise ratio index, was defined and calculated using a machine learning based approach to quantify bruise severity for individual berries. Experimental results showed that the developed systems and methodologies can accurately and rapidly extract key phenotypic traits (height, width, projected leaf area, volume, photosynthetic efficiency at the canopy level, germination rate, and flowering patterns) and postharvest quality properties (bruise ratio). Such extracted traits also demonstrated their usefulness for genetics/genomics studies and in farm management. Thus, these developed systems and methodologies can be effective and efficient tools for the evaluation of plant performance (plant phenotyping) and postharvest quality.