公开(公告)号：US20230196754A1

公开(公告)日：2023-06-22

申请号：US17558928

申请日：2021-12-22

Applicant: X Development LLC

Inventor： Daniel Ribeiro Silva ,  Jinmeng Rao

IPC: G06V10/84 ,  G06V10/75 ,  G06V10/82 ,  G06V20/68

CPC classification number: G06V10/84 ,  G06V10/82 ,  G06V10/757 ,  G06V20/68

Abstract: Implementations are described herein for tracking objects with changing appearances across temporally-disparate images. In various implementations, a first probability distribution over a plurality of classes may be determined for a first biological object depicted in a first image captured at a first point in time. The classes may represent stages of growth of biological objects. Additional probability distribution(s) over the plurality of classes may be determined for candidate biological object(s) depicted in a second image captured at a second point in time subsequent to the first point in time. The candidate biological object(s) may potentially match the first biological object depicted in the first image. Based on a time interval between the first and second points in time, the first probability distribution may be compared to the probability distribution(s) of the candidate biological object(s) depicted in the second image to match one of the candidate biological object(s) depicted in the second image to the first biological object depicted in the first image.

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.

公开(公告)号：US20230196762A1

公开(公告)日：2023-06-22

申请号：US17559631

申请日：2021-12-22

Applicant: X Development LLC

Inventor： Daniel Ribeiro Silva

IPC: G06V20/10 ,  G06V10/764

CPC classification number: G06V20/188 ,  G06V10/764

Abstract: Implementations are described herein for reducing the time and costs associated with the collection and processing of information for observing and evaluating crop growth. In various implementations, a temporal sequence of images depicting a growth of a crop over a time interval may be processed using a machine learning model. Based on the processing, a crop trajectory of image embeddings may be generated that represent the growth of the crop over the time interval. The crop trajectory of image embeddings may be compared with one or more reference crop trajectories of image embeddings. Each of the one or more reference crop trajectories may include a plurality of image embeddings that represent growth of the same type of crop as the crop trajectory of image embeddings over a respective time interval. Data associated with the comparing may be provided as output.

公开(公告)号：US20230102576A1

公开(公告)日：2023-03-30

申请号：US17485928

申请日：2021-09-27

Applicant: X Development LLC

Inventor： Sergey Yaroshenko ,  Gabriella Levine ,  Elliott Grant ,  Daniel Ribeiro Silva ,  Linda Kanu ,  Francis Ebong

IPC: A01B79/00 ,  A01B63/00 ,  G06K9/00 ,  G06N3/02

Abstract: Implementations are disclosed for adaptively adjusting various parameters of equipment in unpredictable terrain, such as agricultural fields. In various implementations, edge computing device(s) may obtain a first image captured by vision sensor(s) transported across an agricultural field by a vehicle. The first image may depict plant(s) growing in the agricultural area. The edge computing device(s) may process the first image based on a machine learning model to generate agricultural inference(s) about the plant(s) growing in the agricultural area. The edge computing device(s) may determine a quality metric for the agricultural inference(s). While the vehicle continues to travel across the agricultural field, and based on the quality metric: the edge computing device(s) may trigger one or more hardware adjustments to one or more of the vision sensors, or one or more adjustments in an operation of the vehicle.