Monitoring plants using leaf feature detection is a challenging perception task because different leaves, even from the same plant, may have very different shapes, sizes and deformations. In addition, leaves may be occluded by other leaves making it hard to determine some of their characteristics. In this paper we use a Time-of-Flight (ToF) camera mounted on a robot arm to acquire the depth information needed for plant leaf detection. Under a Next Best View (NBV) paradigm, we propose a criterion to compute a new camera position that offers a better view of a target leaf. The proposed criterion exploits some typical errors of the ToF camera, which are common to other 3D sensing devices as well. This approach is also useful when more than one leaf is segmented as the same region, since moving the camera following the same NBV criterion helps to disambiguate this situation.