The Potato Operation is a project belonging to the domain of agricultural robotics. It aims to automate pulp sampling of potatoes in order to detect viral diseases. The results of the analysis of these samples are used for the certification of potatoes marked out as seed. The difficulty of this problem lies in the high variability of natural objects, such as their shape, colour or texture. This paper presents two computer vision approaches that have been implemented and tested, as well as the robotic apparatus required for the complete installation. The first computer vision approach, the so-called 3-D image analysis, is based on a combination of classical image analysis methods with active vision. The second approach, the so-called thermography, combines analysis of thermal images of potatoes with active vision. Other methods are also briefly described, which could lead to possible improvements of the results; they rely on alternative lighting schemes, such as fluorometry or the use of multiple sources. Finally, results of the different methods are presented and the long-term goals of the project are discussed.