Long-term monitoring of horticultural crops is necessary to realize the forecasting of phenotypes and enable good management decisions. Autonomously, learning 3D models of crops is difficult due to their complex structure, occlusions that naturally occur, and deformation, or growth over time. Typically, multiple viewpoints are required to obtain relevant information of a crop, e.g., the position and shape of flowers, fruits, and the stem; furthermore, the sensing has to be carried out frequently to improve the phenotype forecast. Therefore, we will develop active sensing strategies to measure crops over their life cycle. We will provide methods to enable efficient sensing of relevant features and improve state-of-the-art next-best-view planners. We will make viewpoint planning efficient by considering the required regions of interest, which contain the relevant features for decision making. Furthermore, we will exploit prior information to speed up repeated coverage over time. We will use information about previously perceived crop parts and newly gained knowledge about significant features to speed up the coverage of relevant parts. Finally, we will optimize the sequence of the necessary viewpoints by trading off the cost of sensor data acquisition and the resulting reduced forecast uncertainty. To summarize, project IP3 will provide methods for efficiently obtaining relevant sensor data of horticultural crops and enable efficient decision making for necessary management actions in the other IPs.