The Climate Corporation

Sanjay Lamsal

Lijuan Xu

A computer system and computer-implemented techniques for determining and presenting improved seeding rate recommendations for planting seeds in a field are provided. In an embodiment, a computer-implemented method includes receiving digital data representing planting parameters including seed type information and planting row width, and retrieving a set of seeding models based upon the planting parameters, where each of the seeding models includes a regression model defining a relationship between plant yield and seeding rate on a specific field. The method also includes generating an empirical mixture model as a composite distribution of the set of seeding models, generating a seeding rate distribution for the planting parameters based upon the empirical mixture model, and calculating a seeding rate recommendation based on the seed rate distribution. The method then also includes planting plant seeds in the specific field consistent with the seeding rate recommendation.

Method for recommending seeding rate for corn seed using seed type and sowing row width

A computer system and computer-implemented techniques for determining and presenting improved seeding rate recommendations for sowing hybrid seeds in a field is provided. In an embodiment, determining and presenting seeding rate recommendations for a field may be accomplished using a server computer system that receives over a digital communication network, electronic digital data representing hybrid seed properties, including hybrid seed type, and sowing row width. Using digitally programmed seeding query logic, within the server computer system, receiving digital data representing planting parameters including hybrid seed type information and sowing row width. The seeding query logic then retrieves a set of one or more seeding models from an electronic digital seeding data repository based upon the planting parameters. Each of the seeding model retrieved contain a regression model for the hybrid seed type modeling a relationship between plant yield and seeding rate on a specific field. Using mixture model logic, within the server computer system, generating an empirical mixture model in digital computer memory that represents a composite distribution of the set of one or more seeding models. The mixture model logic then generates an optimal seeding rate distribution dataset in digital computer memory based upon the empirical mixture model, where the optimal seeding rate distribution dataset represents the optimal seeding rate across all measure fields. Using optimal seeding rate recommendation logic, within the server computer system, calculating and presenting on a digital display device an optimal seeding rate recommendation that is based upon the optimal seeding rate distribution dataset.

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San Francisco, CA, United States of America

Sanjay Lamsal