Ag Leader Technology

David Wilson

Bill Cran

Mike Myers

Mitchell Freshour

Steve Schulteis

A J Steenhoek

Kolton Rottink

Luke Call

Adam Fett

Ryan Kool

Joe Holoubek

Caleb Huitt

Nate Mohling

The disclosure relates to the system and methods for generating a guidance path for automated navigation of large agricultural equipment. Vehicle path data from leading vehicles can be recorded, stored or inputted into the system for generation of guidance paths offset from the swaths traversed by the leading vehicle. Combines, planters sprayers and other vehicles utilizing guidance system can be used to record leading vehicle data for use by the following vehicle in real time or in subsequent cycles. Enterprise systems can manage multiple grain carts and/or combines or other vehicles to maximize efficiency.

Apparatus, systems and methods for automated navigation of agricultural equipment

A method for managing agricultural operations by predicting amount of crop or product remaining for a field includes performing agricultural operations on at least a portion of a field using an agricultural machine, sensing data associated with the agricultural operations using sensors associated with the agricultural machine, communicating the data associated with the agricultural operations to a computing device, analyzing the data associated with the agricultural operations performed using the computing device to determine an area prediction for a remaining portion of the field upon which the agricultural operations are to be performed, and using the area prediction for the remaining portion of the field by the computing device to determine a time associated with completing the agricultural operations for the field or an amount of material or other resources associated with completing the agricultural operations for the field.

Prediction of amount of crop or product remaining for field

A system, method, and apparatus for automatically gathering seed-specific data for an agricultural crop. Simulated seeds with contactless machine-readable data are co-mingled with actual seeds. Whether in stored form prior to planting, during planting, or after planting with the actual seed in the ground, appropriate readers can quickly and accurately read the seed-specific data for a variety of purposes. That can include simply confirming that the actual seed at least in proximity to a simulated seed is of a particular hybrid or variety. It could also include other seed-specific data such as time and date of planning, seed production company, seed-specific usage restrictions, etc. The data can be utilized by other systems. One example would be a precision agricultural system.

Automatic in field variety identification

Growing community of inventors

Ames, IA, United States of America

David Wilson