Empowered by geo-locating and sensor-based technologies, precision agriculture brings a data-intensive paradigm into farming. In this spirit, we investigate the role of outlier detection and visualization in decision-making for precision agriculture. We discuss two analysis tasks for visually monitoring fields that exhibit problematic crop growth compared to their neighbors, and for visualizing problematic areas inside a field. As a proof of concept, we analyze satellite imagery to case-study our tasks in the context of the Future Cropping project.