Abstract: Implementations are described herein for leveraging private and public agricultural knowledge graphs to generate agricultural inferences for growers, automatically based on agricultural events and/or on demand. In various implementations, public data source(s) may be identified using a public agricultural knowledge graph. These public source(s) may contain public data usable to respond to an agricultural query seeking agricultural inference(s) about a subject agricultural field managed by an agricultural entity. Public data retrieved from the public data source(s) may be encoded into public embedding(s) and passed to a private computing system controlled by the agricultural entity. The private computing system may identify, using a private agricultural knowledge graph, private data source(s) containing private data that can respond to the agricultural query, and encode that private data into private embedding(s).

Abstract: Implementations are described herein for obtaining a sequence of high-elevation images capturing a particular geographic area during a particular time period in a plurality of spectral bands; applying the sequence of high-elevation images as input to upstream machine learning model(s) to generate remote sensing embeddings indicating terrain feature(s) of the particular geographic area; applying agricultural data obtained from a local agricultural knowledge graph as input to additional upstream machine learning model(s) to generate agricultural knowledge embedding(s); inferring a natural-language description of a status of the particular geographic area based on generating: an aggregate representative embedding that semantically represents a plurality of agricultural conditions of the particular geographic area, and a natural-language description of one or more of the plurality of agricultural conditions of the particular area using a large language model; and causing a user device associated with an agricultu

Abstract: Implementations are described herein for leveraging an agricultural knowledge graph to generate messages automatically. In various implementations, an agricultural event may trigger proactive performance of one or more of the following operations. Nodes of the agricultural knowledge graph may be identified as related to the agricultural event, including field node(s) representing subject agricultural field(s) to which the agricultural event is relevant and other node(s) connected to one or more of the field nodes by edge(s). Machine learning model(s) may be accessed based on the identified nodes and/or the edges that connect the identified nodes. Data relevant to the subject agricultural field(s) may be retrieved from data source(s) controlled by an agricultural entity and processed based on the machine learning model(s) to generate inference(s) about the subject agricultural field(s).

Abstract: Implementations are described herein for integrating phenotyping machine learning (ML) models with an agricultural knowledge graph (AKG) to facilitate streamlined and flexible phenotypic inferences. In various implementations, the AKG may be traversed based on input including image(s) of crops growing in an agricultural plot until a destination node is reached. The AKG may include a phenotypic taxonomy of nodes representing a taxonomic hierarchy of organisms, with the destination node corresponding to a level of the phenotypic taxonomy that is commensurate in scope with the input. The phenotypic taxonomy of nodes may provide access to a corresponding taxonomy of phenotyping ML models, each trained to generate phenotypic inference(s) at a specificity that corresponds to a level of the phenotypic taxonomy. The crop image(s) may be processed using the phenotyping ML model that is accessible via the destination node to generate phenotypic inference(s) about the agricultural plot.

Abstract: Techniques are disclosed herein that enable generating a relationship embedding indicating a relationship between one or more agricultural attributes of a table of agricultural data with one or more nodes in an agricultural knowledge graph. Various implementations include processing a table of agricultural data with rows of agricultural records and columns of agricultural attributes. Additional or alternative implementations include processing the table of agricultural data using an embedding model portion of the mapping model to generate an embedding space representation of each of the agricultural attributes. Various implementations can include selecting a node corresponding to a given agricultural attribute based on a distance between the embedding space representation of the given agricultural attribute and the embedding space representations of one or more nodes.

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.

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.

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.

Abstract: A system for managing color consistency in a display including a plurality of panels configured to display a source image includes: a processor; and a memory, and the memory stores instructions that, when executed by the processor, cause the processor to: select a first panel among the plurality of panels as a master panel; capture a first image of the first panel and a second image of a second panel among the plurality of panels; identify a first comparative area within the first image and a second comparative area within the second image; compare the first and second comparative areas to perform a comparison; adjust data of the source image based on the comparison, and the data is adjusted by remapping colors of the source image; and transmit the adjusted data to the second panel.

Abstract: A system for managing color consistency in a display including a plurality of panels configured to display a source image includes: a processor; and a memory, and the memory stores instructions that, when executed by the processor, cause the processor to: select a first panel among the plurality of panels as a master panel; capture a first image of the first panel and a second image of a second panel among the plurality of panels; identify a first comparative area within the first image and a second comparative area within the second image; compare the first and second comparative areas to perform a comparison; adjust data of the source image based on the comparison, and the data is adjusted by remapping colors of the source image; and transmit the adjusted data to the second panel.