Biological and agricultural environments can be dynamic, unstructured, and uncertain, posing challenges for data collection at the necessary spatial and temporal scales to enable meaningful analysis and decision-making. Small robotic systems, often coupled with computer vision applications, can overcome some of these challenges by enabling autonomous or human-assisted remote and in situ data collection. This talk will present a suite of technologies that leverage ground, surface, and aerial robotics, mechatronics, and computer vision to broaden sensing capabilities across different types of agricultural environments. Demonstrative case studies will be presented, including robot-assisted hydrologic assessment, high-throughput phenotyping, and aerial manipulation, with a focus on their value in precision agriculture applications. This talk will illustrate how the strategic, user-focused design of automated systems to overcome unique data collection challenges can enable better understanding and decision-making in agricultural and biological environments.