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公开(公告)号：US11553634B2
公开(公告)日：2023-01-17
申请号：US16589833
申请日：2019-10-01
Applicant: X Development LLC
Inventor： Elliott Grant , Hongxiao Liu , Zhiqiang Yuan , Sergey Yaroshenko , Benoit Schillings , Matt VanCleave
Abstract: Implementations are described herein for analyzing vision data depicting undesirable plants such as weeds to detect various attribute(s). The detected attribute(s) of a particular undesirable plant may then be used to select, from a plurality of available candidate remediation techniques, the most suitable remediation technique to eradicate or otherwise eliminate the undesirable plants.
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公开(公告)号：US11501443B2
公开(公告)日：2022-11-15
申请号：US17110141
申请日：2020-12-02
Applicant: X Development LLC
Inventor： Jie Yang , Cheng-en Guo , Zhiqiang Yuan , Elliott Grant , Hongxu Ma
IPC: G06T7/00 , A01D41/127 , G06T5/50 , G06T7/143 , G06N3/04 , G06N3/08 , G06Q10/04 , G06Q50/02 , G06V20/13 , G06V20/10
Abstract: Implementations relate to detecting/replacing transient obstructions from high-elevation digital images, and/or to fusing data from high-elevation digital images having different spatial, temporal, and/or spectral resolutions. In various implementations, first and second temporal sequences of high-elevation digital images capturing a geographic area may be obtained. These temporal sequences may have different spatial, temporal, and/or spectral resolutions (or frequencies). A mapping may be generated of the pixels of the high-elevation digital images of the second temporal sequence to respective sub-pixels of the first temporal sequence. A point in time at which a synthetic high-elevation digital image of the geographic area may be selected. The synthetic high-elevation digital image may be generated for the point in time based on the mapping and other data described herein.
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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：US11285612B2
公开(公告)日：2022-03-29
申请号：US16545441
申请日：2019-08-20
Applicant: X Development LLC
Inventor： Zhiqiang Yuan , Elliott Grant
Abstract: Implementations are described herein for coordinating semi-autonomous robots to perform agricultural tasks on a plurality of plants with minimal human intervention. In various implementations, a plurality of robots may be deployed to perform a respective plurality of agricultural tasks. Each agricultural task may be associated with a respective plant of a plurality of plants, and each plant may have been previously designated as a target for one of the agricultural tasks. It may be determined that a given robot has reached an individual plant associated with the respective agricultural task that was assigned to the given robot. Based at least in part on that determination, a manual control interface may be provided at output component(s) of a computing device in network communication with the given robot. The manual control interface may be operable to manually control the given robot to perform the respective agricultural task.
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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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Patent Agency Ranking
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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公开(公告)号：US20210383535A1
公开(公告)日：2021-12-09
申请号：US16895759
申请日：2020-06-08
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：US20210150717A1
公开(公告)日：2021-05-20
申请号：US17160928
申请日：2021-01-28
Applicant: X Development LLC
Inventor： Cheng-en Guo , Wilson Zhao , Jie Yang , Zhiqiang Yuan , Elliott Grant
IPC: G06T7/00 , A01D41/127 , G06K9/00 , G06T5/50 , G06T7/143 , G06N3/04 , G06N3/08 , G06Q10/04 , G06Q50/02
Abstract: Implementations relate to diagnosis of crop yield predictions and/or crop yields at the field- and pixel-level. In various implementations, a first temporal sequence of high-elevation digital images may be obtained that captures a geographic area over a given time interval through a crop cycle of a first type of crop. Ground truth operational data generated through the given time interval and that influences a final crop yield of the first geographic area after the crop cycle may also be obtained. Based on these data, a ground truth-based crop yield prediction may be generated for the first geographic area at the crop cycle's end. Recommended operational change(s) may be identified based on distinct hypothetical crop yield prediction(s) for the first geographic area. Each distinct hypothetical crop yield prediction may be generated based on hypothetical operational data that includes altered data point(s) of the ground truth operational data.
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公开(公告)号：US20210092891A1
公开(公告)日：2021-04-01
申请号：US16589833
申请日：2019-10-01
Applicant: X Development LLC
Inventor： Elliott Grant , Hongxiao Liu , Zhiqiang Yuan , Sergey Yaroshenko , Benoit Schillings , Matt VanCleave
Abstract: Implementations are described herein for analyzing vision data depicting undesirable plants such as weeds to detect various attribute(s). The detected attribute(s) of a particular undesirable plant may then be used to select, from a plurality of available candidate remediation techniques, the most suitable remediation technique to eradicate or otherwise eliminate the undesirable plants.
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公开(公告)号：US20210056307A1
公开(公告)日：2021-02-25
申请号：US16545396
申请日：2019-08-20
Applicant: X Development LLC
Inventor： Yueqi Li , Hongxiao Liu , Zhiqiang Yuan
Abstract: Implementations are described herein for utilizing various image processing techniques to facilitate tracking and/or counting of plant-parts-of-interest among crops. In various implementations, a sequence of digital images of a plant captured by a vision sensor while the vision sensor is moved relative to the plant may be obtained. A first digital image and a second digital image of the sequence may be analyzed to determine one or more constituent similarity scores between plant-parts-of-interest across the first and second digital images. The constituent similarity scores may be used, e.g., collectively as a composite similarity score, to determine whether a depiction of a plant-part-of-interest in the first digital images matches a depiction of a plant-part-of-interest in the second digital image.
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