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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：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：US11532080B2
公开(公告)日：2022-12-20
申请号：US16950037
申请日：2020-11-17
Applicant: X Development LLC
Inventor： Zhiqiang Yuan , Bodi Yuan , Ming Zheng
Abstract: Implementations are described herein for normalizing counts of plant-parts-of-interest detected in digital imagery to account for differences in spatial dimensions of plants, particularly plant heights. In various implementations, one or more digital images depicting a top of a first plant may be processed. The one or more digital images may have been acquired by a vision sensor carried over top of the first plant by a ground-based vehicle. Based on the processing: a distance of the vision sensor to the first plant may be estimated, and a count of visible plant-parts-of-interest that were captured within a field of view of the vision sensor may be determined. Based on the estimated distance, the count of visible plant-parts-of-interest may be normalized with another count of visible plant-parts-of-interest determined from one or more digital images capturing a second plant.
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公开(公告)号：US20220156917A1
公开(公告)日：2022-05-19
申请号：US16950037
申请日：2020-11-17
Applicant: X Development LLC
Inventor： Zhiqiang Yuan , Bodi Yuan , Ming Zheng
Abstract: Implementations are described herein for normalizing counts of plant-parts-of-interest detected in digital imagery to account for differences in spatial dimensions of plants, particularly plant heights. In various implementations, one or more digital images depicting a top of a first plant may be processed. The one or more digital images may have been acquired by a vision sensor carried over top of the first plant by a ground-based vehicle. Based on the processing: a distance of the vision sensor to the first plant may be estimated, and a count of visible plant-parts-of-interest that were captured within a field of view of the vision sensor may be determined. Based on the estimated distance, the count of visible plant-parts-of-interest may be normalized with another count of visible plant-parts-of-interest determined from one or more digital images capturing a second plant.
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Patent Agency Ranking
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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公开(公告)号：US20220036070A1
公开(公告)日：2022-02-03
申请号：US16943247
申请日：2020-07-30
Applicant: X Development LLC
Inventor： Bodi Yuan , Zhiqiang Yuan , Ming Zheng
Abstract: Techniques are described herein for using artificial intelligence to predict crop yields based on observational crop data. A method includes: obtaining a first digital image of at least one plant; segmenting the first digital image of the at least one plant to identify at least one seedpod in the first digital image; for each of the at least one seedpod in the first digital image: determining a color of the seedpod; determining a number of seeds in the seedpod; inferring, using one or more machine learning models, a moisture content of the seedpod based on the color of the seedpod; and estimating, based on the moisture content of the seedpod and the number of seeds in the seedpod, a weight of the seedpod; and predicting a crop yield based on the moisture content and the weight of each of the at least one seedpod.
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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：US20230072361A1
公开(公告)日：2023-03-09
申请号：US17986358
申请日：2022-11-14
Applicant: X Development LLC
Inventor： Zhiqiang Yuan , Bodi Yuan , Ming Zheng
Abstract: Implementations are described herein for normalizing counts of plant-parts-of-interest detected in digital imagery to account for differences in spatial dimensions of plants, particularly plant heights. In various implementations, one or more digital images depicting a top of a first plant may be processed. The one or more digital images may have been acquired by a vision sensor carried over top of the first plant by a ground-based vehicle. Based on the processing: a distance of the vision sensor to the first plant may be estimated, and a count of visible plant-parts-of-interest that were captured within a field of view of the vision sensor may be determined. Based on the estimated distance, the count of visible plant-parts-of-interest may be normalized with another count of visible plant-parts-of-interest determined from one or more digital images capturing a second plant.
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