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公开(公告)号：US20230045607A1
公开(公告)日：2023-02-09
申请号：US17964425
申请日：2022-10-12
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：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：US11256915B2
公开(公告)日：2022-02-22
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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4.发明申请公开(公告)号：US20190191630A1
公开(公告)日：2019-06-27
申请号：US15854607
申请日：2017-12-26
Applicant: X Development LLC
Inventor： William Regan , Matthew Bitterman , David Brown , Elliott Grant , Zhiqiang Yuan
CPC classification number: A01G7/00 , A01G22/00 , B64C39/024 , B64C2201/127 , G05D1/0094 , G05D2201/0201 , G06K9/66 , G06T7/0004 , G06T11/60 , G06T2207/30188 , G09B5/06 , G09B19/003
Abstract: Systems and Methods for Augmented-Human Field Inspection Tools for Automated Phenotyping Systems and Agronomy Tools. In one embodiment, a method for plant phenotyping, includes: acquiring a first set of observations about plants in a field by a trainer. The trainer carries a sensor configured to collect observations about the plant, and the first set of observations includes ground truth data. The method also includes processing the first set of observations about the plants by a trait extraction model to generate instructions for a trainee; and acquiring a second set of observations about the plants by a trainee while the trainee follows the instructions.
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公开(公告)号：US20230368257A1
公开(公告)日：2023-11-16
申请号：US17743096
申请日：2022-05-12
Applicant: X Development LLC
Inventor： Elliott Grant , Zhiqiang Yuan
CPC classification number: G06Q30/0601 , G06N20/00 , H04L9/3213 , H04L9/50
Abstract: Implementations set forth herein relate to utilizing S2 cell values to characterize arbitrary portions of land parcels and storing the S2 cell values in association with a non-fungible token (NFT) that is stored on a blockchain network, or other peer-to-peer (P2P) network. The S2 cell values can be generated by iteratively using bounding shapes that are selected to extend over at least a portion of a respective parcel of land, and each bounding shape can be represented by one or more single dimensional values. When a generated bounding shape extends outside of a boundary of a parcel of land, subcells of the bounding shape can be generated to define further bounding shapes. A land NFT for the list of cell values for the bounding shapes can be stored at a blockchain address for an authenticated owner of the parcel of land.
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Patent Agency Ranking
6.发明公开公开(公告)号：US20230169764A1
公开(公告)日：2023-06-01
申请号：US17540037
申请日：2021-12-01
Applicant: X Development LLC
Inventor： Zhiqiang Yuan
CPC classification number: G06V20/188 , G06V10/774 , G06V20/13 , G06T11/00 , G06V10/803 , G06V20/17 , G06T7/0012 , G06N3/0454 , G06T2207/20081 , G06T2207/10032 , G06T2207/30188 , G06T2200/24 , G06T2207/30252 , G06T2207/20084
Abstract: Implementations are described herein for conditioning a generator machine learning model to generate synthetic ground-level data that is biased towards a given agricultural area based on high-elevation images. In various implementations, a plurality of ground-level images may be accessed that depict crops within a specific agricultural area. A first set of high-elevation image(s) may also be accessed that depict the specific agricultural area. The ground-level images and the first set of high-elevation image(s) may be used to condition an air-to-ground generator machine learning model to generate synthetic ground-level data from high-elevation imagery depicting the specific agricultural area. A second set of high-elevation image(s) that depict a specific sub-region of the specific agricultural area may then be accessed and processed using the air-to-ground generator machine learning model to generate synthetic ground-level data that infers one or more conditions of the specific sub-region of the specific agricultural area.
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公开(公告)号：US20230140138A1
公开(公告)日：2023-05-04
申请号：US18089337
申请日：2022-12-27
Applicant: X Development LLC
Inventor： Cheng-en Guo , Wilson Zhao , Jie Yang , Zhiqiang Yuan , Elliott Grant
IPC: G06T3/40 , G06T7/00 , A01D41/127 , G06T5/50 , G06T7/143 , G06N3/08 , G06Q10/04 , G06Q50/02 , G06V20/10 , G06N3/047 , G06V10/82 , G06V20/13
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：US11562486B2
公开(公告)日：2023-01-24
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 , G06V20/13 , A01D41/127 , G06T5/50 , G06T7/143 , G06N3/04 , G06N3/08 , G06Q10/04 , G06Q50/02 , G06V20/10
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：US20220405962A1
公开(公告)日：2022-12-22
申请号：US17354147
申请日：2021-06-22
Applicant: X Development LLC
Inventor： Zhiqiang Yuan , Jie Yang
Abstract: Implementations are described herein for localizing individual plants using high-elevation images at multiple different resolutions. A first set of high-elevation images that capture the plurality of plants at a first resolution may be analyzed to classify a set of pixels as invariant anchor points. High-elevation images of the first set may be aligned with each other based on the invariant anchor points that are common among at least some of the first set of high-elevation images. A mapping may be generated between pixels of the aligned high-elevation images of the first set and spatially-corresponding pixels of a second set of higher-resolution high-elevation images. Based at least in part on the mapping, individual plant(s) of the plurality of plants may be localized within one or more of the second set of high-elevation images for performance of one or more agricultural tasks.
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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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