CLIMATE LLC

A connector is provided for use with a communication apparatus to facilitate communication of data, via the communication apparatus, between a vehicle or agricultural implement and a computing device. The connector includes a first portion first portion configured to couple the connector to the vehicle or agricultural implement, and a second portion configured to couple the connector to the communication apparatus. The second portion of the connector includes a substrate having at least one circuit trace defined along a surface of the substrate and configured to conductively transfer electronic signals between the connector and the communication apparatus.

Systems and methods for monitoring compliance of an agricultural program are disclosed. In one embodiment, a computer-implemented method may include receiving a ruleset corresponding to the agricultural program, receiving first activity and/or location data from at least one of one or more agricultural implements, through a communication interface communicatively coupled with the at least one processor and configured to enable communication between the at least one processor and the one or more agricultural implements, comparing the first activity and/or location data against the ruleset to determine whether the at least one of the one or more agricultural implements is in compliance with the agricultural program, and transmitting a command to the at least one of the one or more agricultural implements to generate an alert when the at least one of the one or more agricultural implements is determined to be out of compliance.

A system for generating a seed recommendation is disclosed. The system includes a processor, a display, and a memory. The processor may be configured to retrieve genetic data having a first dimensionality; generate embeddings corresponding to the genetic data, the embeddings having a second dimensionality lower than the first dimensionality; categorize the embeddings into one or more clusters, such that genetically similar seed products are assigned to the same cluster based on the embeddings of the genetically similar seed products; using agronomy data, assign additional seed products to the one or more clusters; generate a recommendation to a grower to plant a first seed categorized in a first cluster, when the grower has previously planted a second seed in the first cluster; and cause the display to display the generated recommendation to the grower.

Systems and methods are provided to generate marketing content insights. In one embodiment, a disclosed method includes receiving input data including one or more advertising assets and one or more metrics corresponding to the one or more advertising assets; using a computer vision model, identifying one or more constituent elements of the one or more advertising assets; appending the one or more constituent elements and/or descriptions of the one or more constituent elements to the input data to generate a structured data set; training a generative AI model using the structured data set; training a prediction model using the structured data set; based on the trained generative AI model and a prompt, generating an additional advertising asset not included in the one or more advertising assets; and based on the trained prediction model, generating at least one predicted metric of the additional advertising asset.

A computer-implemented method for generating an improved map of field anomalies using digital images and machine learning models is disclosed. In an embodiment, a method comprises: obtaining a shapefile that defines boundaries of an agricultural plot and boundaries of the field containing the plot; obtaining a plurality of plot images within the field from one or more image capturing devices that are located within the boundaries of the field; calibrating and pre-processing the plurality of plot images to create a plot map of the agricultural plot at a plot level; based on the plot map of the agricultural plot, generating a plot grid; based on the plot grid and the plot map, generating a plurality of plot tiles; based on the plurality of plot tiles, generating, using a first machine learning model and a plurality of first image classifiers corresponding to one or more first anomalies, a set of classified plot images that depicts at least one anomaly; based on the set of classified plot images, generating a plot anomaly map for the agricultural plot; transmitting the plot anomaly map to one or more controllers that control one or more agricultural machines or database systems to perform agricultural functions on the agricultural plot.

Systems and methods are provided for managing hybrid seeds for planting. One example computer-implemented method includes receiving a first dataset of hybrid seeds for planting on one or more target fields, and selecting a subset of the hybrid seeds based at least on environmental classification data for the hybrid seeds, location data for the one or more target fields, and one or more properties of the plants grown from the hybrid seeds. The method also includes generating a representative yield value for each hybrid seed in the subset of hybrid seeds based on historical yield data for the seeds and generating risk values for the subset of hybrid seeds based on yield variability of the hybrid seeds over time as indicated in the historical yield data. The method further includes generating a second dataset of hybrid seeds for planting based on the risk values and the representative yield values.

Systems and methods are provided for defining field regions within agricultural fields. An example computer-implemented method includes identifying a field, retrieving data for the identified field, and displaying a graphical display including the identified field and at least some of the retrieved data. The method also includes receiving, via the graphical display, an input to create a field region within the identified field based on passes of an agricultural apparatus in the identified field and retrieving pass data for the passes of the agricultural apparatus in the field. The method further includes displaying, on the graphical display, the pass data, defining the field region based on the pass data, so that a boundary of the field region corresponds to a start point and an end point of each of the passes of the agricultural apparatus, and displaying, on the graphical display, field performance data constrained to the field region.

A computer-implemented method is provided for facilitating agricultural operations in an agricultural field. In one example embodiment, the method includes identifying, based on spatial sampling of soil spectrum data for an agricultural field, ground sampling locations within the field to obtain physical soil samples representative of soil makeup for the field. The method also includes generating a soil model particular to the field by correlating soil properties for soil included in the soil samples obtained from the identified ground sampling locations to particular soil spectral bands included in the soil spectrum data for the field at the corresponding ground sampling locations. The method then further includes compiling, using the soil model, a soil map of the entire field visually illustrating particular seeds and/or populations of seeds to plant at different locations across the field and/or particular nutrient applications to apply at different locations across the field.

A drive unit adapted for use in farm equipment includes a first transceiver and a processing module. The processing module is configured to receive first data from the farm equipment, including sensor data and/or controller data, and integrate the received first data with additional second data indicating an agricultural prescription for a geographic region to form agricultural data. The processing module is also configured to apply one or more spatial filter constraints and temporal filter constraints to the agricultural data and then compute, from the filtered agricultural data, a difference between actual execution of one or more parameters of the agricultural prescription and expected execution of the one or more parameters. The processing module is further configured to, when the difference exceeds a predefined stored threshold, autonomously generate and transmit to a remote computing device, a settings alert to thereby trigger generation of an updated agricultural prescription.