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公开(公告)号：US20220005055A1
公开(公告)日：2022-01-06
申请号：US16918668
申请日：2020-07-01
Applicant: X Development LLC
Inventor： Nanzhu Wang , Chunfeng Wen , Yueqi Li
Abstract: Implementations are described herein for using machine learning to determine whether candidate crop fields are suitable for management by particular agricultural entities. In various implementations, a machine learning model may be applied to input data to generate output data. The input data may include a first plurality of data points corresponding to field-level agricultural management practices of an agricultural entity. The output data may be indicative of one or more predicted outcomes of the agricultural entity implementing the field-level agricultural management practices on one or more candidate crop fields not currently managed by the agricultural entity. Based on one or more of the predicted outcomes, one or more computing devices may be caused to provide a user associated with the agricultural entity with information about one or more of the candidate crop fields, and/or one or more parameter inputs of a graphical user interface may be prepopulated.
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公开(公告)号：US20220196433A1
公开(公告)日：2022-06-23
申请号：US17131098
申请日：2020-12-22
Applicant: X Development LLC
Inventor： Alan Eneev , Jie Yang , Yueqi Li , Yujing Qian , Nanzhu Wang , Sicong Wang , Sergey Yaroshenko
Abstract: Implementations are directed to assigning corresponding semantic identifiers to a plurality of rows of an agricultural field, generating a local mapping of the agricultural field that includes the plurality of rows of the agricultural field, and subsequently utilizing the local mapping in performance of one or more agricultural operations. In some implementations, the local mapping can be generated based on overhead vision data that captures at least a portion of the agricultural field. In these implementations, the local mapping can be generated based on GPS data associated with the portion of the agricultural field captured in the overhead vision data. In other implementations, the local mapping can be generated based on driving data generated during an episode of locomotion of a vehicle through the agricultural field. In these implementations, the local mapping can be generated based on GPS data associated with the vehicle traversing through the agricultural field.
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Patent Agency Ranking
公开(公告)号：US11321347B1
公开(公告)日：2022-05-03
申请号：US17075242
申请日：2020-10-20
Applicant: X Development LLC
Inventor： David Clifford , Ming Zheng , Elliott Grant , Nanzhu Wang , Cheng-en Guo , Aleksandra Deis
IPC: G06F16/29 , G06Q50/02 , G06F3/0484 , G06F16/906 , G06F16/28 , G06Q10/06 , G06F16/26 , G06F16/9038 , G06F16/904 , G06F16/248 , G06K9/62 , G06F3/04847 , G06T11/20
Abstract: Some implementations herein relate to a graphical user interface (GUI) that facilitates dynamically partitioning agricultural fields into clusters on an individual agricultural field-basis using agricultural features. A map of a geographic area containing a plurality of agricultural fields may be rendered as part of a GUI. The agricultural fields may be partitioned into a first set of clusters based on a first granularity value and agricultural features of individual agricultural fields. The individual agricultural fields may be visually annotated in the GUI to convey the first set of clusters of similar agricultural fields. Upon receipt of a second granularity value different from the first granularity value, the agricultural fields may be partitioned into a second set of clusters of similar agricultural fields. The map of the geographic area may be updated so that individual agricultural fields are visually annotated to convey the second set of clusters.
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公开(公告)号：US11295331B2
公开(公告)日：2022-04-05
申请号：US16918668
申请日：2020-07-01
Applicant: X Development LLC
Inventor： Nanzhu Wang , Chunfeng Wen , Yueqi Li
Abstract: Implementations are described herein for using machine learning to determine whether candidate crop fields are suitable for management by particular agricultural entities. In various implementations, a machine learning model may be applied to input data to generate output data. The input data may include a first plurality of data points corresponding to field-level agricultural management practices of an agricultural entity. The output data may be indicative of one or more predicted outcomes of the agricultural entity implementing the field-level agricultural management practices on one or more candidate crop fields not currently managed by the agricultural entity. Based on one or more of the predicted outcomes, one or more computing devices may be caused to provide a user associated with the agricultural entity with information about one or more of the candidate crop fields, and/or one or more parameter inputs of a graphical user interface may be prepopulated.
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公开(公告)号：US20220215037A1
公开(公告)日：2022-07-07
申请号：US17706317
申请日：2022-03-28
Applicant: X Development LLC
Inventor： David Clifford , Ming Zheng , Elliott Grant , Nanzhu Wang , Cheng-en Guo , Aleksandra Deis
IPC: G06F16/26 , G06F16/29 , G06F16/904 , G06F3/04847 , G06F16/9038 , G06F16/28 , G06Q50/02 , G06F16/248 , G06Q10/06 , G06F16/906
Abstract: Some implementations herein relate to a graphical user interface (GUI) that facilitates dynamically partitioning agricultural fields into clusters on an individual agricultural field-basis using agricultural features. A map of a geographic area containing a plurality of agricultural fields may be rendered as part of a GUI. The agricultural fields may be partitioned into a first set of clusters based on a first granularity value and agricultural features of individual agricultural fields. The individual agricultural fields may be visually annotated in the GUI to convey the first set of clusters of similar agricultural fields. Upon receipt of a second granularity value different from the first granularity value, the agricultural fields may be partitioned into a second set of clusters of similar agricultural fields. The map of the geographic area may be updated so that individual agricultural fields are visually annotated to convey the second set of clusters.
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公开(公告)号：US20220188854A1
公开(公告)日：2022-06-16
申请号：US17689218
申请日：2022-03-08
Applicant: X Development LLC
Inventor： Nanzhu Wang , Chunfeng Wen , Yueqi Li
Abstract: Implementations are described herein for using machine learning to determine whether candidate crop fields are suitable for management by particular agricultural entities. In various implementations, a machine learning model may be applied to input data to generate output data. The input data may include a first plurality of data points corresponding to field-level agricultural management practices of an agricultural entity. The output data may be indicative of one or more predicted outcomes of the agricultural entity implementing the field-level agricultural management practices on one or more candidate crop fields not currently managed by the agricultural entity. Based on one or more of the predicted outcomes, one or more computing devices may be caused to provide a user associated with the agricultural entity with information about one or more of the candidate crop fields, and/or one or more parameter inputs of a graphical user interface may be prepopulated.
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公开(公告)号：US20220122298A1
公开(公告)日：2022-04-21
申请号：US17075242
申请日：2020-10-20
Applicant: X Development LLC
Inventor： David Clifford , Ming Zheng , Elliott Grant , Nanzhu Wang , Cheng-en Guo , Aleksandra Deis
IPC: G06T11/00 , G06Q50/02 , G06Q30/02 , G06F16/901 , G06F3/0484
Abstract: Some implementations herein relate to a graphical user interface (GUI) that facilitates dynamically partitioning agricultural fields into clusters on an individual agricultural field-basis using agricultural features. A map of a geographic area containing a plurality of agricultural fields may be rendered as part of a GUI. The agricultural fields may be partitioned into a first set of clusters based on a first granularity value and agricultural features of individual agricultural fields. The individual agricultural fields may be visually annotated in the GUI to convey the first set of clusters of similar agricultural fields. Upon receipt of a second granularity value different from the first granularity value, the agricultural fields may be partitioned into a second set of clusters of similar agricultural fields. The map of the geographic area may be updated so that individual agricultural fields are visually annotated to convey the second set of clusters.
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