Abstract: A crop prediction system performs various machine learning operations to predict crop production and to identify a set of farming operations that, if performed, optimize crop production. The crop prediction system uses crop prediction models trained using various machine learning operations based on geographic and agronomic information. Responsive to receiving a request from a grower, the crop prediction system can access information representation of a portion of land corresponding to the request, such as the location of the land and corresponding weather conditions and soil composition. The crop prediction system applies one or more crop prediction models to the access information to predict a crop production and identify an optimized set of farming operations for the grower to perform.

Abstract: Disclosed herein are methods and systems for registering an Augmented Reality (AR) scene to a 3D model of a certain area, comprising accumulating a geometrical dataset of a certain area based on visual data captured by imaging sensors during an AR session, obtaining a 3D model of the certain area, computing a similarity score for the geometrical dataset compared to the 3D model rotated in several candidate principal rotation angles based on parallelism of corresponding planes identified in the geometrical dataset and in the rotated 3D model, selecting a highest score principal rotation angle, computing a translation vector for each of a plurality of fine-tune rotation angles with respect to the selected principal rotation angle based on a distance between the corresponding planes, selecting a lowest distance fine-tune rotation angle, and registering the geometrical dataset to the 3D model according to the selected fine-tune rotation angle and its translation vector.

Abstract: Disclosed herein are methods and systems for registering an Augmented Reality (AR) scene to a 3D model of a certain area, comprising accumulating a geometrical dataset of a certain area based on visual data captured by imaging sensors during an AR session, obtaining a 3D model of the certain area, computing a similarity score for the geometrical dataset compared to the 3D model rotated in several candidate principal rotation angles based on parallelism of corresponding planes identified in the geometrical dataset and in the rotated 3D model, selecting a highest score principal rotation angle, computing a translation vector for each of a plurality of fine-tune rotation angles with respect to the selected principal rotation angle based on a distance between the corresponding planes, selecting a lowest distance fine-tune rotation angle, and registering the geometrical dataset to the 3D model according to the selected fine-tune rotation angle and its translation vector.