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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
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.
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公开(公告)号：US11604947B2
公开(公告)日：2023-03-14
申请号：US16947984
申请日：2020-08-26
Applicant: X Development LLC
Inventor： Kangkang Wang , Bodi Yuan , Lianghao Li , Zhiqiang Yuan
IPC: G06K9/62 , G06V30/194
Abstract: Implementations are described herein for automatically generating quasi-realistic synthetic training images that are usable as training data for training machine learning models to perceive various types of plant traits in digital images. In various implementations, multiple labeled simulated images may be generated, each depicting simulated and labeled instance(s) of a plant having a targeted plant trait. In some implementations, the generating may include stochastically selecting features of the simulated instances of plants from a collection of plant assets associated with the targeted plant trait. The collection of plant assets may be obtained from ground truth digital image(s). In some implementations, the ground truth digital image(s) may depict real-life instances of plants having the target plant trait. The plurality of labeled simulated images may be processed using a trained generator model to generate a plurality of quasi-realistic synthetic training images, each depicting quasi-realistic and labeled instance(s) of the targeted plant trait.
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公开(公告)号：US11544920B2
公开(公告)日：2023-01-03
申请号：US17463360
申请日：2021-08-31
Applicant: X Development LLC
Inventor： Lianghao Li , Kangkang Wang , Zhiqiang Yuan
Abstract: Implementations are described herein for automatically generating synthetic training images that are usable as training data for training machine learning models to detect, segment, and/or classify various types of plants in digital images. In various implementations, a digital image may be obtained that captures an area. The digital image may depict the area under a lighting condition that existed in the area when a camera captured the digital image. Based at least in part on an agricultural history of the area, a plurality of three-dimensional synthetic plants may be generated. The synthetic training image may then be generated to depict the plurality of three-dimensional synthetic plants in the area. In some implementations, the generating may include graphically incorporating the plurality of three-dimensional synthetic plants with the digital image based on the lighting condition.
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公开(公告)号：US20220391752A1
公开(公告)日：2022-12-08
申请号：US17342196
申请日：2021-06-08
Applicant: X Development LLC
Inventor： Elliott Grant , Kangkang Wang , Bodi Yuan , Zhiqiang Yuan
Abstract: Implementations are described herein for automatically generating labeled synthetic images that are usable as training data for training machine learning models to make an agricultural prediction based on digital images. A method includes: generating a plurality of simulated images, each simulated image depicting one or more simulated instances of a plant; for each of the plurality of simulated images, labeling the simulated image with at least one ground truth label that identifies an attribute of the one or more simulated instances of the plant depicted in the simulated image, the attribute describing both a visible portion and an occluded portion of the one or more simulated instances of the plant depicted in the simulated image; and training a machine learning model to make an agricultural prediction using the labeled plurality of simulated images.
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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.
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Patent Agency Ranking
公开(公告)号：US11113525B1
公开(公告)日：2021-09-07
申请号：US16877138
申请日：2020-05-18
Applicant: X Development LLC
Inventor： Lianghao Li , Kangkang Wang , Zhiqiang Yuan
Abstract: Implementations are described herein for automatically generating synthetic training images that are usable as training data for training machine learning models to detect, segment, and/or classify various types of plants in digital images. In various implementations, a digital image may be obtained that captures an area. The digital image may depict the area under a lighting condition that existed in the area when a camera captured the digital image. Based at least in part on an agricultural history of the area, a plurality of three-dimensional synthetic plants may be generated. The synthetic training image may then be generated to depict the plurality of three-dimensional synthetic plants in the area. In some implementations, the generating may include graphically incorporating the plurality of three-dimensional synthetic plants with the digital image based on the lighting condition.
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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.
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公开(公告)号：US20220319005A1
公开(公告)日：2022-10-06
申请号：US17843343
申请日：2022-06-17
Applicant: X Development LLC
Inventor： Lianghao Li , Kangkang Wang , Zhiqiang Yuan
Abstract: Implementations are described herein for automatically generating synthetic training images that are usable, for instance, as training data for training machine learning models to detect and/or classify various types of plant diseases at various stages in digital images. In various implementations, one or more environmental features associated with an agricultural area may be retrieved. One or more synthetic plant models may be generated to visually simulate one or more stages of a progressive plant disease, taking into account the one or more environmental features associated with the agricultural area. The one or more synthetic plant models may be graphically incorporated into a synthetic training image that depicts the agricultural area.
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公开(公告)号：US20220067451A1
公开(公告)日：2022-03-03
申请号：US16947984
申请日：2020-08-26
Applicant: X Development LLC
Inventor： Kangkang Wang , Bodi Yuan , Lianghao Li , Zhiqiang Yuan
Abstract: Implementations are described herein for automatically generating quasi-realistic synthetic training images that are usable as training data for training machine learning models to perceive various types of plant traits in digital images. In various implementations, multiple labeled simulated images may be generated, each depicting simulated and labeled instance(s) of a plant having a targeted plant trait. In some implementations, the generating may include stochastically selecting features of the simulated instances of plants from a collection of plant assets associated with the targeted plant trait. The collection of plant assets may be obtained from ground truth digital image(s). In some implementations, the ground truth digital image(s) may depict real-life instances of plants having the target plant trait. The plurality of labeled simulated images may be processed using a trained generator model to generate a plurality of quasi-realistic synthetic training images, each depicting quasi-realistic and labeled instance(s) of the targeted plant trait.
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公开(公告)号：US20210397836A1
公开(公告)日：2021-12-23
申请号：US17463360
申请日：2021-08-31
Applicant: X Development LLC
Inventor： Lianghao Li , Kangkang Wang , Zhiqiang Yuan
Abstract: Implementations are described herein for automatically generating synthetic training images that are usable as training data for training machine learning models to detect, segment, and/or classify various types of plants in digital images. In various implementations, a digital image may be obtained that captures an area. The digital image may depict the area under a lighting condition that existed in the area when a camera captured the digital image. Based at least in part on an agricultural history of the area, a plurality of three-dimensional synthetic plants may be generated. The synthetic training image may then be generated to depict the plurality of three-dimensional synthetic plants in the area. In some implementations, the generating may include graphically incorporating the plurality of three-dimensional synthetic plants with the digital image based on the lighting condition.
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