Deere & Company

Alexander Ngai

Yueqi Li

Kangkang Wang

Zachary Beaver

Shuhao Fu

Implementations are described herein for performing depth estimation in the agricultural domain, including generating synthetic training data. In various implementations, one or more three-dimensional synthetic plants may be generated in in a three-dimensional space, wherein the one or more three-dimensional synthetic plants include homogenous and densely-distributed synthetic plant parts. The plurality of three-dimensional synthetic plants may be projected onto two-dimensional planes from first and second perspectives in the three-dimensional space to form a pair of synthetic stereoscopic images. The first and second synthetic stereoscopic images of the pair may be annotated to create a mapping between the individual synthetic plant parts across the first synthetic stereoscopic images. A feature matching machine learning model may be trained based on the mapping.

Sparse and/or dense depth estimation from stereoscopic imaging

Implementations are disclosed for dynamically allocating aspects of platform-independent machine-learning based agricultural state machines among edge and cloud computing resources. In various implementations, a GUI may include a working canvas on which graphical elements corresponding to platform-independent logical routines are manipulable to define a platform-independent agricultural state machine. Some of the platform-independent logical routines may include logical operations that process agricultural data using phenotyping machine learning model(s). Edge computing resource(s) available to a user for which the agricultural state machine is to be implemented may be identified. Constraint(s) imposed by the user on implementation of the agricultural state machine may be ascertained. Based on the edge computing resource(s) and constraint(s), logical operations of some platform-independent logical routines may be dynamically allocated to the edge computing resource(s), and logical operations of other platform-independent logical routines may be dynamically allocated to a cloud computing resource.

Dynamic allocation of platform-independent machine learning state machines between edge-based and cloud-based computing resources

Implementations are described herein for learning mappings between a domain specific language (DSL) and images, and leveraging those mappings for various purposes. In various implementations, a method for using a DSL to generate training data may include processing data indicative of ground truth image(s) depicting a real plant using a trained image-to-DSL machine learning (ML) model to generate a first expression in the DSL that describes structure of the real plant. The first expression may include a plurality of parameters, and may be processed to programmatically generate a plurality of synthetic DSL expressions. Each respective synthetic DSL expression may describe structure of a respective synthetic plant for which parameter(s) have been altered from the first expression. The synthetic DSL expressions may be processed using a renderer to create three-dimensional (3D) synthetic plant models. Two-dimensional (2D) synthetic images may be generated that depict the 3D synthetic plant models in an area.

Generating domain specific language expressions based on images

Implementations are described herein for reducing the time and costs associated with the crop scouting in a crop field. In various implementations, a method is implemented using one or more processors, and the method include: operating, based on a type and arrangement of a crop field, a robot to travel along a trajectory through the crop field using a first gait. The robot includes one or more vision sensors. The first gait includes a first repeating cycle of poses of the robot. The method can further include: synchronizing operation of one or more of the vision sensors with one or more poses of the first repeating cycle of poses of the multi-legged robot to capture one or more initial sequences of images depicting one or more points-of-interest of crops growing in the crop field.

Growing community of inventors

Irvine, CA, United States of America

Alexander Ngai