Mineral Earth Sciences LLC

X Development LLC

Nanzhu Wang

Yueqi Li

Elliott Grant

Cheng-en Guo

Ming Zheng

Chunfeng Wen

David Clifford

Aleksandra Deis

Jie Yang

Sergey Yaroshenko

Alan Eneev

Yujing Qian

Sicong Wang

Gaoxiang Chen

Techniques are disclosed that enable generating a unified data set by mapping a set of item description phrases, describing entries in a data set, to a set of canonical phrases. Various implementations include generating a similarity measure between each item description phrase and each canonical phrase by processing the corresponding item description phrase and the corresponding canonical phrase using a natural language processing model. Additional or alternative implementations include generating a bipartite graph based on the set of item description phrases, the set of canonical phrases, and the similarity measures. The mapping can be generated based on the bipartite graph.

Mapping data set(s) to canonical phrases using natural language processing model(s)

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.

Partitioning agricultural fields for annotation

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.

Generating a local mapping of an agricultural field for use in performance of agricultural operation(s)

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.

Analyzing crop fields based on agricultural management practices

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San Jose, CA, United States of America

Nanzhu Wang