Abstract: A method comprises identifying an area of worksite at which an operation is to be performed, identifying a historical rate of work metric, obtaining a model, identifying a number of work machines that are to perform the operation, identifying a crop type corresponding to the operation to be performed, identifying an ETC metric based on the area of the worksite, the historical rate of work metric, the number of work machines, the crop type, and the model. The method further comprises identifying, once the operation is initiated, a first weight metric, a second weight metric, a live estimate metric indicative of a first time until the work will be completed in the operation, and identifying an ETR metric, indicative of a second time until the work will be completed in the operation, based on the first weight metric, the ETC metric, the second weight metric, and the live estimate metric.

Abstract: A computer implemented method comprises obtaining an operational strategy input identifying an operational strategy, obtaining a first progress perspective selection input identifying a progress perspective selection, obtaining historical data, and obtaining forecast data. The computer implemented method further comprises generating a first output including first performance perspective data based on the historical data, the forecast data, the first operational strategy, and the first progress perspective selection. The computer implemented further comprises generating a control signal to control a user associated system based on the first output.

Abstract: A computer implemented method includes identifying an estimated time until full (ETF) metric indicative of an estimated time it will take until a tank of an agricultural harvester is full, at least to a threshold level, based at least on a yield flow rate metric, a tank capacity metric, a tank fill level metric, a crop moisture metric, an error correction metric, a crop type metric, a temperature metric, a humidity metric, one or more historical yield flow rate metrics and a model; and generating one or more control signals based on the ETF metric. The computer implemented method can further include identifying an estimated full location (EFL) metric, indicative of an estimated location at the worksite at which the tank of the agricultural harvester will be full, at least to the threshold level, based at least on a path metric, a travel speed metric, and the ETF metric.

Abstract: A computer implemented method comprises obtaining a query, obtaining querying source context data, and processing the query to identify an interrogative type corresponding to the query based on language in the query. The method further comprises generating, based on the identified interrogative type, a prompt, the prompt identifying a task for an analytics engine, providing the prompt to the analytics engine, executing, with one or more models, analytics on one or more items of the querying source context data, based on the prompt. The method also includes determining one or more features of an output to be generated based on the identified interrogative type, the output responsive to the query, generating the output based on the executed analytics and the determined one or more features, and controlling a user associated system based on the output.

Abstract: Systems, apparatus, articles of manufacture, and methods are disclosed to determine a boundary for vehicle operation in queried plot of land. An example apparatus includes circuitry to instantiate machine-readable instructions to: generate a first boundary based on a query for a boundary of a plot of land; compute a first probabilistic boundary for the first boundary based on an error of generation of the first boundary; compute a second probabilistic boundary for a second boundary based on an error of generation of the second boundary; and combine the first probabilistic boundary and a second probabilistic boundary to generate a soft boundary, the combination based on a first confidence score and a second confidence score.

Abstract: Systems, apparatus, articles of manufacture, and methods are disclosed to determine a boundary for a work plan. An example apparatus includes circuitry to instantiate the machine-readable instructions to: detect a first attribute and a second attribute based on a characteristic of a plot of land, the first attribute corresponds to an uncertain feature in the plot of land; determine a first machine operation based on the first attribute and a second machine operation based on the second attribute; determine a first boundary around a first region, the first region including a first area of the plot of land including the first attribute; determine a second boundary around a second region, the second region including a second area of the plot of land including the second attribute; and determine a work plan based on the first boundary, the second boundary, and a relevance between the first attribute and the second attribute.

Abstract: Systems, apparatus, articles of manufacture, and methods are disclosed to compress geospatial data. An example apparatus to compress geospatial data comprises circuitry to instantiate machine-readable instructions to: cause compression of first data and second data responsive to a query to compress data for a plot of land using a compression technique, the compression of the first data and the second data to generate a first compressed data set and a second compressed data set; extract features from the first compressed data set and the second compressed data set; and combine features from the first compressed data set and the second compressed data set to generate a set of geospatial features.

Abstract: During an initialization period of a machine learning model, an electronic data processor is configured to estimate an initial depletion estimate of time period until empty for a material in the tank or container of a machine based on summing initial weighted inputs to the machine learning model in accordance with an initial equation set being applicable to the initialization period that is defined by an initial sub-operation period. After the initialization period of the machine learning model, an electronic data processor is configured to estimate a revised depletion time, where the revised depletion time comprises a time duration until empty or near empty.

Abstract: A method comprises identifying an area of worksite at which an operation is to be performed, identifying a historical rate of work metric, obtaining a model, identifying a number of work machines that are to perform the operation, identifying a crop type corresponding to the operation to be performed, identifying an ETC metric based on the area of the worksite, the historical rate of work metric, the number of work machines, the crop type, and the model. The method further comprises identifying, once the operation is initiated, a first weight metric, a second weight metric, a live estimate metric indicative of a first time until the work will be completed in the operation, and identifying an ETR metric, indicative of a second time until the work will be completed in the operation, based on the first weight metric, the ETC metric, the second weight metric, and the live estimate metric.

Abstract: Novel tools and techniques for the machine discovery of aberrant states are provided. A system includes a plurality of network devices, and a decision system in communication with the plurality of network devices. Each of the plurality of network devices may be configured to generate a respective data stream. The decision system may include a processor and a non-transitory computer readable medium including instructions executable by the processor to obtain, via the plurality of network devices, one or more data streams. The decision system may build a historic model of a data stream, and determine a predicted value of the data stream at a future time, based on the historic model. The decision system may be configured to determine whether an anomaly has occurred based on a variation between a current value of the data stream and the predicted value of the data stream.

Abstract: Novel tools and techniques for the machine discovery of aberrant states are provided. A system includes a plurality of network devices, and a decision system in communication with the plurality of network devices. Each of the plurality of network devices may be configured to generate a respective data stream. The decision system may include a processor and a non-transitory computer readable medium including instructions executable by the processor to obtain, via the plurality of network devices, one or more data streams. The decision system may build a historic model of a data stream, and determine a predicted value of the data stream at a future time, based on the historic model. The decision system may be configured to determine whether an anomaly has occurred based on a variation between a current value of the data stream and the predicted value of the data stream.

Abstract: Novel tools and techniques for the machine discovery and rapid agglomeration of similar states are provided. A system includes a plurality of network devices, and a decision system. The plurality of network devices may be configured to generate a respective data stream. The decision system may include a processor and a non-transitory computer readable medium including instructions executable by the processor. The system may obtain, via the plurality of network devices, one or more data streams, determine an anomaly has occurred on a data stream of the one or more data streams, determine a set of data streams of the one or more streams sharing at least one of a set of centroids associated with the data stream, and determine, based on the set of data streams, the occurrence of an incident.

Abstract: Novel tools and techniques for the machine discovery and rapid agglomeration of similar states are provided. A system includes a plurality of network devices, and a decision system. The plurality of network devices may be configured to generate a respective data stream. The decision system may include a processor and a non-transitory computer readable medium including instructions executable by the processor. The system may obtain, via the plurality of network devices, one or more data streams, determine an anomaly has occurred on a data stream of the one or more data streams, determine a set of data streams of the one or more streams sharing at least one of a set of centroids associated with the data stream, and determine, based on the set of data streams, the occurrence of an incident.

Abstract: Novel tools and techniques for the machine discovery of aberrant states are provided. A system includes a plurality of network devices, and a decision system in communication with the plurality of network devices. Each of the plurality of network devices may be configured to generate a respective data stream. The decision system may include a processor and a non-transitory computer readable medium including instructions executable by the processor to obtain, via the plurality of network devices, one or more data streams. The decision system may build a historic model of a data stream, and determine a predicted value of the data stream at a future time, based on the historic model. The decision system may be configured to determine whether an anomaly has occurred based on a variation between a current value of the data stream and the predicted value of the data stream.