How Farm Machines Use Maps to Predictively Control Dividers

This patent describes an agricultural system that uses a geographic map of field characteristics to predictively control the end dividers on a harvesting machine, aiming for more efficient operation.

What it covers

This patent describes an agricultural system that obtains a map of a field. This map shows different values of a characteristic, like crop density or terrain, at various geographic locations (as described in the abstract). A control system then uses this map to generate a signal. This signal controls the operation of one or more end dividers on an agricultural harvester. For example, if the map shows a section of the field with very dense crop at the edge, the system could predictively adjust the end divider to ensure all crop is gathered efficiently, rather than reacting only when the harvester reaches that spot.

What it doesn't cover

• —Does not cover systems that control end dividers based solely on real-time sensor readings without using a pre-obtained geographic map.
• —Does not cover controlling components of an agricultural harvester other than the end dividers.
• —Does not cover systems that use a map for general navigation but not for specific predictive control of end dividers based on mapped characteristics.
• —Does not cover non-agricultural vehicles or machinery.

The clever bit

The novelty lies in using a pre-obtained map of field characteristics to *predictively* control the end dividers. Instead of reacting to conditions as the harvester encounters them, the system plans adjustments in advance based on known geographic data, leading to smoother and more optimized operation.

Why it matters

Precision agriculture aims to make farming more efficient and less wasteful. This patent contributes by offering a way to automate and optimize a specific part of the harvesting process: managing end dividers. By using maps to predictively adjust these dividers, farmers can potentially reduce crop loss, improve the quality of harvested material, and save time and fuel during operation.

Real-world examples

• 1.John Deere X9 Combine harvesters with precision agriculture modules
• 2.Case IH Axial-Flow combines integrated with field mapping software
• 3.AGCO Ideal combines using smart farming solutions
• 4.Any modern agricultural harvester equipped with GPS and field mapping capabilities