公开(公告)号：US20230171303A1

公开(公告)日：2023-06-01

申请号：US17540015

申请日：2021-12-01

Applicant: X Development LLC

Inventor： Yueqi Li ,  Alexander Ngai

IPC: H04L67/10 ,  G06N3/12 ,  G06Q50/02 ,  G06F9/50 ,  H04L67/12

CPC classification number: H04L67/10 ,  G06N3/126 ,  G06Q50/02 ,  G06F9/5027 ,  H04L67/12

Abstract: 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.

公开(公告)号：US20230133026A1

公开(公告)日：2023-05-04

申请号：US17667287

申请日：2022-02-08

Applicant: X Development LLC

Inventor： Kangkang Wang ,  Alexander Ngai ,  Zachary Beaver

IPC: H04N13/363 ,  H04N13/178 ,  G06N20/00 ,  H04N13/183 ,  G06V10/74 ,  G06V10/82 ,  H04N13/275

Abstract: 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.