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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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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：US20230074663A1
公开(公告)日：2023-03-09
申请号：US17467888
申请日：2021-09-07
Applicant: X Development LLC
Inventor： Zhiqiang Yuan , Elliott Grant
Abstract: Implementations are described herein for auditing performance of large-scale tasks. In various implementations, one or more ground-level vision sensors may capture a first set of one or more images that depict an agricultural plot prior to an agricultural task being performed in the agricultural plot, and a second set of one or more images that depict the agricultural plot subsequent to the agricultural task being performed in the agricultural plot. The first and second sets of images may be processed in situ using edge computing device(s) based on a machine learning model to generate respective pluralities of pre-task and post-task inferences about the agricultural plot. Performance of the agricultural task may include comparing the pre-task inferences to the post-task inferences to generate operational metric(s) about the performance of the agricultural task in the agricultural plot. The operational metric(s) may be presented at one or more output devices.
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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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公开(公告)号：US20220217894A1
公开(公告)日：2022-07-14
申请号：US17147048
申请日：2021-01-12
Applicant: X Development LLC
Inventor： Cheng-en Guo , Jie Yang , Zhiqiang Yuan , Elliott Grant
Abstract: Implementations are described herein for predicting soil organic carbon (“SOC”) content for agricultural fields detected in digital imagery. In various implementations, one or more digital images depicting portion(s) of one or more agricultural fields may be processed. The one or more digital images may have been acquired by a vision sensor carried through the field(s) by a ground-based vehicle. Based on the processing, one or more agricultural inferences indicating agricultural practices or conditions predicted to affect SOC content may be determined. Based on the agricultural inferences, one or more predicted SOC measurements for the field(s) may be determined.
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Patent Agency Ranking
公开(公告)号：US20220129673A1
公开(公告)日：2022-04-28
申请号：US17077651
申请日：2020-10-22
Applicant: X Development LLC
Inventor： Sergey Yaroshenko , Zhiqiang Yuan
Abstract: Implementations are disclosed for selectively operating edge-based sensors and/or computational resources under circumstances dictated by observation of targeted plant trait(s) to generate targeted agricultural inferences. In various implementations, triage data may be acquired at a first level of detail from a sensor of an edge computing node carried through an agricultural field. The triage data may be locally processed at the edge using machine learning model(s) to detect targeted plant trait(s) exhibited by plant(s) in the field. Based on the detected plant trait(s), a region of interest (ROI) may be established in the field. Targeted inference data may be acquired at a second, greater level of detail from the sensor while the sensor is carried through the ROI. The targeted inference data may be locally processed at the edge using one or more of the machine learning models to make a targeted inference about plants within the ROI.
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公开(公告)号：US20220121919A1
公开(公告)日：2022-04-21
申请号：US17072941
申请日：2020-10-16
Applicant: X Development LLC
Inventor： Zhiqiang Yuan , Theodore Monyak
Abstract: Techniques are disclosed that enable generating a predicted yield for a cereal grain crop based on one or more traits extracted from image(s) of the cereal grain crop. Various implementations include determining a heading trait value based on the number of identified spikelets, where the spikelets are identified by processing the image(s) of the cereal grain crop using a spikelet detection model. Additional or alternative implementations include generating a predicted cereal grain crop yield based on one or more additional or alternative trait values such as one or more heading values, one or more projected leaf area values, one or more stand spacing values, one or more wheat rust values, one or more maturity detection values, one or more intercropping phenotyping values extracted cereal grains intercropped with other crops, one or more additional or alternative trait output values, and/or combinations thereof.
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公开(公告)号：US10949972B2
公开(公告)日：2021-03-16
申请号：US16236743
申请日：2018-12-31
Applicant: X Development LLC
Inventor： Cheng-en Guo , Wilson Zhao , Jie Yang , Zhiqiang Yuan , Elliott Grant
IPC: G06T7/00 , G06T5/50 , G06T7/143 , A01D41/127 , G06K9/00 , 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：US20210053229A1
公开(公告)日：2021-02-25
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：US11606896B2
公开(公告)日：2023-03-21
申请号：US17147048
申请日：2021-01-12
Applicant: X Development LLC
Inventor： Cheng-en Guo , Jie Yang , Zhiqiang Yuan , Elliott Grant
Abstract: Implementations are described herein for predicting soil organic carbon (“SOC”) content for agricultural fields detected in digital imagery. In various implementations, one or more digital images depicting portion(s) of one or more agricultural fields may be processed. The one or more digital images may have been acquired by a vision sensor carried through the field(s) by a ground-based vehicle. Based on the processing, one or more agricultural inferences indicating agricultural practices or conditions predicted to affect SOC content may be determined. Based on the agricultural inferences, one or more predicted SOC measurements for the field(s) may be determined.
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