A computer-implemented method executed by one or more processors, the method comprising: receiving image data representative of images of a cultivar (130; 502); providing a skeletonized baseline multi-dimensional data model of the cultivar (130; 502), the cultivar (130; 502) growing over time, wherein the skeletonized baseline multi-dimensional data model (130; 502) is generated at least partially from the images and wherein the skeletonized baseline multi-dimensional data model is skeletonized by selecting keypoints, and providing a line between the keypoints, wherein the keypoints are provided as an endpoint and/or an intersection of lines of the skeletonized baseline multi-dimensional data model; determining an encoding based on the skeletonized baseline multi-dimensional data model, and a skeletonized target multi-dimensional model, the encoding defining a string of symbols, and being based on an alphabet and a set of rules, wherein the skeletonized target multi-dimensional data model is skeletonized by selecting keypoints, and providing a line between the keypoints, wherein the keypoints are provided as an endpoint and/or an intersection of lines of the skeletonized target multi-dimensional data model; providing an expected multi-dimensional model based on the encoding, and a modified set of rules, the modified set of rules being based on the set of rules, the expected multi-dimensional model representing the cultivar (130; 502) after a period of time; selecting a set of actions by determining multiple predicted multi-dimensional models for each set of actions in a plurality of sets of actions, and, for each predicted multi-dimensional model, providing a predicted yield that can be used to determine impact with respect an expected yield, the set of actions being selected based on a respective impact and at least one action in the set of actions comprising pruning a feature of the cultivar (130; 502); and providing the set of actions as output for executing one or more actions of the set of actions on the cultivar (130; 502). receiving image data representative of images of a cultivar (130; 502); providing a skeletonized baseline multi-dimensional data model of the cultivar (130; 502), the cultivar (130; 502) growing over time, wherein the skeletonized baseline multi-dimensional data model (130; 502) is generated at least partially from the images and wherein the skeletonized baseline multi-dimensional data model is skeletonized by selecting keypoints, and providing a line between the keypoints, wherein the keypoints are provided as an endpoint and/or an intersection of lines of the skeletonized baseline multi-dimensional data model; determining an encoding based on the skeletonized baseline multi-dimensional data model, and a skeletonized target multi-dimensional model, the encoding defining a string of symbols, and being based on an alphabet and a set of rules, wherein the skeletonized target multi-dimensional data model is skeletonized by selecting keypoints, and providing a line between the keypoints, wherein the keypoints are provided as an endpoint and/or an intersection of lines of the skeletonized target multi-dimensional data model; providing an expected multi-dimensional model based on the encoding, and a modified set of rules, the modified set of rules being based on the set of rules, the expected multi-dimensional model representing the cultivar (130; 502) after a period of time; selecting a set of actions by determining multiple predicted multi-dimensional models for each set of actions in a plurality of sets of actions, and, for each predicted multi-dimensional model, providing a predicted yield that can be used to determine impact with respect an expected yield, the set of actions being selected based on a respective impact and at least one action in the set of actions comprising pruning a feature of the cultivar (130; 502); and providing the set of actions as output for executing one or more actions of the set of actions on the cultivar (130; 502).