View source for 18476934. MACHINE CONTROL USING A PREDICTIVE MAP simplified abstract (Deere & Company)

=MACHINE CONTROL USING A PREDICTIVE MAP=


==Organization Name==

[[:Category:Deere & Company|Deere & Company]]


[[Category:Deere & Company]]

==Inventor(s)==

[[:Category:Nathan R. Vandike of Geneseo IL (US)|Nathan R. Vandike of Geneseo IL (US)]][[Category:Nathan R. Vandike of Geneseo IL (US)]]

[[:Category:Bhanu Kiran Reddy Palla of Bettendorf IA (US)|Bhanu Kiran Reddy Palla of Bettendorf IA (US)]][[Category:Bhanu Kiran Reddy Palla of Bettendorf IA (US)]]

[[:Category:Noel W. Anderson of Fargo ND (US)|Noel W. Anderson of Fargo ND (US)]][[Category:Noel W. Anderson of Fargo ND (US)]]

[[:Category:Colin D. Engel of Bettendorf IA (US)|Colin D. Engel of Bettendorf IA (US)]][[Category:Colin D. Engel of Bettendorf IA (US)]]

==MACHINE CONTROL USING A PREDICTIVE MAP - A simplified explanation of the abstract==


This abstract first appeared for US patent application 18476934 titled 'MACHINE CONTROL USING A PREDICTIVE MAP

==Simplified Explanation==

The abstract describes a patent application for a system that obtains information maps mapping agricultural characteristic values in a field, predicts future values based on sensor data, and outputs predictive maps for automated machine control.

* Agricultural work machine obtains information maps of agricultural characteristic values in a field.
* In-situ sensor on the machine senses agricultural characteristics as it moves through the field.
* Predictive map generator uses information maps and sensor data to predict future values in the field.
* Predictive maps can be used for automated machine control.

== Potential Applications ==
This technology could be applied in precision agriculture, crop management, and yield optimization.

== Problems Solved ==
This innovation helps farmers make data-driven decisions, optimize resource allocation, and improve overall crop yield.

== Benefits ==
The system can increase efficiency, reduce waste, and enhance sustainability in agriculture practices.

== Potential Commercial Applications ==
Potential commercial applications include precision farming equipment, agricultural machinery, and software for data analysis and prediction.

== Possible Prior Art ==
One possible prior art could be similar systems used in the field of precision agriculture or remote sensing technologies.

== Unanswered Questions ==

=== How does the system handle variability in soil types and conditions within the same field? ===
The system may use machine learning algorithms to adapt to different soil types and conditions, but the specific methods are not detailed in the abstract.

=== What types of agricultural characteristics can the system sense and predict? ===
The abstract mentions "agricultural characteristic values" but does not specify the exact parameters or variables that the system can handle.


==Original Abstract Submitted==

One or more information maps are obtained by an agricultural work machine. The one or more information maps map one or more agricultural characteristic values at different geographic locations of a field. An in-situ sensor on the agricultural work machine senses an agricultural characteristic as the agricultural work machine moves through the field. A predictive map generator generates a predictive map that predicts a predictive agricultural characteristic at different locations in the field based on a relationship between the values in the one or more information maps and the agricultural characteristic sensed by the in-situ sensor. The predictive map can be output and used in automated machine control.



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