-
公开(公告)号：US20230186529A1
公开(公告)日：2023-06-15
申请号：US17548169
申请日：2021-12-10
Applicant: X Development LLC
Inventor： Zhiqiang Yuan , Elliott Grant
CPC classification number: G06T11/001 , G06T7/60 , G06T7/90 , G06T2207/10024 , G06T2207/10116 , G06T2207/20081 , G06T2207/20084 , G06T2207/30188
Abstract: Implementations are described herein for colorizing an X-ray image and predicting one or more phenotypic traits about a plant based on the colorized X-ray image. In various implementations, an X-ray image that depicts a plant with a canopy of the plant partially occluding a part-of-interest is obtained, where the part-of-interest is visible through the canopy in the X-ray image. The X-ray images is colorized to predict one or more phenotypic traits of the part-of-interest. The colorization includes processing the X-ray image based on a machine learning model to generate a colorized version of the X-ray image, and predicting the one or more phenotypic traits based on one or more visual features of the colorized version of the X-ray image.
-
公开(公告)号：US20230102495A1
公开(公告)日：2023-03-30
申请号：US17485903
申请日：2021-09-27
Applicant: X Development LLC
Inventor： Zhiqiang Yuan , Rhishikesh Pethe , Francis Ebong
Abstract: Implementations are disclosed for adaptively reallocating computing resources of resource-constrained devices between tasks performed in situ by those resource-constrained devices. In various implementations, while the resource-constrained device is transported through an agricultural area, computing resource usage of the resource-constrained device ma may be monitored. Additionally, phenotypic output generated by one or more phenotypic tasks performed onboard the resource-constrained device may be monitored. Based on the monitored computing resource usage and the monitored phenotypic output, a state may be generated and processed based on a policy model to generate a probability distribution over a plurality of candidate reallocation actions. Based on the probability distribution, candidate reallocation action(s) may be selected and performed to reallocate at least some computing resources between a first phenotypic task of the one or more phenotypic tasks and a different task while the resource-constrained device is transported through the agricultural area.
-
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.
-
公开(公告)号：US20220398415A1
公开(公告)日：2022-12-15
申请号：US17344328
申请日：2021-06-10
Applicant: X Development LLC
Inventor： Zhiqiang Yuan
Abstract: Implementations are described herein for localizing individual plants by aligning high-elevation images using invariant anchor points while disregarding variant feature points, such as deformable plants. High-elevation images that capture the plurality of plants at a resolution at which wind-triggered deformation of individual plants is perceptible between the high-elevation images may be obtained. First regions of the high-elevation images that depict the plurality of plants may be classified as variant features that are unusable as invariant anchor points. Second regions of the high-elevation images that are disjoint from the first set of regions may be classified as invariant anchor points. The high-elevation images may be aligned based on invariant anchor point(s) that are common among at least some of the high-elevation images. Based on the aligned high-elevation images, individual plant(s) may be localized within one of the high-elevation images for performance of one or more agricultural tasks.
-
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.
-
Patent Agency Ranking
公开(公告)号：US20220219329A1
公开(公告)日：2022-07-14
申请号：US17683696
申请日：2022-03-01
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.
-
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.
-
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.
-
公开(公告)号：US20210256702A1
公开(公告)日：2021-08-19
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 , G06K9/00 , G06T5/50 , G06T7/143 , G06N3/04 , G06N3/08 , G06Q10/04 , G06Q50/02
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.
-
公开(公告)号：US20210011694A1
公开(公告)日：2021-01-14
申请号：US16506161
申请日：2019-07-09
Applicant: X Development LLC
Inventor： Bin Ni , Zhiqiang Yuan , Qianyu Zhang
Abstract: Techniques are described herein for translating source code in one programming language to source code in another programming language using machine learning. In various implementations, one or more components of one or more generative adversarial networks, such as a generator machine learning model, may be trained to generate “synthetically-naturalistic” source code that can be used as a translation of source code in an unfamiliar language. In some implementations, a discriminator machine learning model may be employed to aid in training the generator machine learning model, e.g., by being trained to discriminate between human-generated (“genuine”) and machine-generated (“synthetic”) source code.
-
-
-
-
-
-
-
-
-
Search Results
41
records
(took 0.024 seconds)
