公开(公告)号：US20220383042A1

公开(公告)日：2022-12-01

申请号：US17329528

申请日：2021-05-25

Applicant: X Development LLC

Inventor： Kangkang Wang ,  Hong Wu ,  Bodi Yuan ,  Zihao Li

IPC: G06K9/62

Abstract: Implementations are described herein for automatically labeling synthetic plant parts in synthetic training images, where the synthetic training images and corresponding labels can be used as training data for training machine learning models to detect, segment, and/or classify various parts of plants in digital images. In various implementations, a digital image may be obtained that captures an area. The synthetic training image may be generated to depict one or more three-dimensional synthetic plants in the area. In many implementations, a plant mask, identifying individual plants as a whole in the synthetic training image, as well as a part mask, uniquely identifying one or more parts of the synthetic plant models, can be overlaid on the synthetic training image to label the one or more parts of the synthetic plant models.

公开(公告)号：US20240144424A1

公开(公告)日：2024-05-02

申请号：US17976233

申请日：2022-10-28

Applicant: X Development LLC

Inventor： Hongxu Ma ,  Yuchi Ma ,  Hong Wu

IPC: G06T3/4053 ,  G06V10/44 ,  G06V20/10 ,  G06V20/13

CPC classification number: G06T3/4053 ,  G06V10/44 ,  G06V20/13 ,  G06V20/188

Abstract: Implementations are described herein for using one or more transformer networks to generate inferred image data based on processing image data capturing a particular geographic area during a particular time period, including first image data captured in a first spectral band and at a first spatial (and/or temporal) resolution and second image data captured in a second spectral band and at a second spatial (and/or temporal) resolution. The inferred image data can include second spectral information at the first spatial (and/or temporal) resolution, or vice versa. Thus, the spatial and/or temporal resolution of image data of a certain spectral band can be improved, allowing for more effective usage of satellite imagery in agricultural settings.

公开(公告)号：US20220358265A1

公开(公告)日：2022-11-10

申请号：US17307849

申请日：2021-05-04

Applicant: X Development LLC

Inventor： Kangkang Wang ,  Bodi Yuan ,  Zhiqiang Yuan ,  Hong Wu ,  Daniel Ribeiro Silva ,  Zihao Li

IPC: G06F30/27 ,  G06T17/00 ,  G06N3/04

Abstract: Implementations are described herein for realistic plant growth modeling and various applications thereof. In various implementations, a plurality of two-dimensional (2D) digital images that capture, over time, one or more of a particular type of plant based on one or more machine learning models to generate output, may be processed. The output may be analyzed to extract temporal features that capture change over time to one or more structural features of the particular type of plant. Based on the captured temporal features, a first parameter subspace of whole plant parameters may be learned, wherein the whole plant parameters are usable to generate a three-dimensional (3D) growth model that realistically simulates growth of the particular type of plant over time. Based on the first parameter subspace, one or more 3D growth models that simulate growth of the particular type of plant may be non-deterministically generated and used for various purposes.