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: Implementations are described herein for performing joint optimization of multi-task learning of dense predictions (MT-DP) and hardware-aware neural architecture search (NAS). In various implementations, a set of tasks to be performed using a resource-constrained edge computing system may be determined. Based on a base multi-task dense-prediction (MT-DP) architecture template, the set of tasks, and a plurality of hardware-based constraints of a target edge computing system, a network architecture search (NAS) may be used to sample candidate MT-DP architecture(s) from a search space of neural network architecture components. Each sampled candidate MT-DP architecture may include a distinct assembly of sampled neural network architecture components applied to the base MT-DP architecture template. Image data may be processed using the candidate MT-DP architecture(s) to determine performance metrics. These performance metrics may be used to jointly train the MT-DP architecture(s) and/or the NAS.

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.

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.

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.

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.