IP Library › Granted Patent US 12,520,758
Granted Patent B2
US 12,520,758 · App. 18/672,543 · Granted Jan 13, 2026

Controlling an agricultural harvester based upon material other than grain (MOG) content characteristics

Inventors: Scott N. Clark (Bettendorf, IA); Duane M. Bomleny (Geneseo, IL); Justin C. Freehill (Fenton, IL); Scott E. Faulkner (Orion, IL); Nathan R. Vandike (Geneseo, IL); Nathan E. Krehbiel (Bettendorf, IA)
Assignee: Deere & Company
A01D41/127A01D41/12A01D41/1274G06V10/56G06V20/188G06V20/56
View Patent ↗
Loading inventors, assignments & file history…
Monitor This Case
Get email alerts when status or documents change.
Order Certified Copies
Most orders are placed with the USPTO same day — all within 24 business hours.
Order via The Patent Place →
Pre-filled with this patent's details
Quick Facts
Patent No.
US 12,520,758
App. No.
18/672,543
Granted
Jan 13, 2026
Kind
B2
Abstract

A content characteristic of material other than grain (MOG), such as MOG color or a leaf-to-stalk ratio in the MOG, are detected. A controllable subsystem on an agricultural harvester is controlled based upon the content characteristic of the MOG.

Claims (49)

1 . A computer implemented method, comprising:

detecting material other than grain (MOG) engaged by an agricultural harvester;

generating a detection signal indicative of the detected MOG;

identifying a content characteristic of the MOG based on the detection signal; and

generating a control signal to control a controllable subsystem on the agricultural harvester based on the content characteristic of the MOG.

2 . The computer implemented method of claim 1 identifying a content characteristic of the MOG comprises:

identifying a color of the MOG.

3 . The computer implemented method of claim 1 wherein the MOG includes leaf material and stalk material and wherein identifying a content characteristic comprises:

identifying a leaf-to-stalk ratio in the MOG based on the detection signal.

4 . The computer implemented method of claim 1 wherein detecting MOG comprises:

capturing an image of the MOG.

5 . The computer implemented method of claim 4 wherein generating a detection signal comprises:

generating an image signal indicative of the image of the MOG.

6 . The computer implemented method of claim 5 wherein identifying the content characteristic comprises:

processing the image of the MOG to identify the content characteristic of the MOG.

7 . The computer implemented method of claim 4 wherein capturing an image of the MOG comprises:

capturing an image of residue after the residue exits the agricultural harvester.

8 . The computer implemented method of claim 4 wherein capturing an image of the MOG comprises:

capturing an image of material being processed in the agricultural harvester.

9 . The computer implemented method of claim 4 wherein capturing an image of the MOG comprises:

capturing an image of crop prior to being engaged by the agricultural harvester.

10 . The computer implemented method of claim 1 wherein the agricultural harvester includes a crop processing component, and wherein detecting MOG comprises:

detecting a torque applied to the crop processing component.

11 . The computer implemented method of claim 1 wherein the agricultural harvester includes a set of deck plates and a deck plate actuator that controls a spacing corresponding to the deck plates and wherein generating a control signal comprises:

generating a control signal to control the deck plate actuator to control the spacing corresponding to the deck plates based on the content characteristic of the MOG.

12 . The computer implemented method of claim 1 wherein the agricultural harvester includes a header component actuator that drives a header component and wherein generating a control signal comprises:

generating a control signal to control the header component actuator to control the speed of the header component based on the content characteristic of the MOG.

13 . The computer implemented method of claim 1 wherein the agricultural harvester includes a display device and wherein generating a control signal comprises:

generating a display control signal to control the display device to display an indication of the content characteristic of the MOG.

14 . An agricultural system, comprising:

a sensor configured to detect material other than grain (MOG) engaged by an agricultural harvester and generate a detection signal indicative of the detected MOG;

a content characteristic processing system configured to identify a content characteristic of the MOG based on the detection signal;

a controllable subsystem; and

a control signal generator configured to generate a control signal to control the controllable subsystem on the agricultural harvester based on the content characteristic of the MOG.

15 . The agricultural system of claim 14 wherein the content characteristic processing system comprises:

a MOG color identification system configured to identify a color of the MOG.

16 . The agricultural system of claim 14 wherein the MOG includes leaf material and stalk material and wherein the content characteristic processing system comprises:

a leaf-to-stalk ratio identification system configured to identify a leaf-to-stalk ratio in the MOG based on the detection signal.

17 . The agricultural system of claim 14 wherein the sensor comprises:

an optical sensor configured to capture an image of the MOG and generate an image signal indicative of the image of the MOG.

18 . The agricultural system of claim 17 wherein the content characteristic processing system comprises:

an image processing system configured to process the image of the MOG to identify the content characteristic of the MOG.

19 . The agricultural system of claim 14 wherein the agricultural harvester includes a crop processing component and wherein the sensor comprises:

a torque sensor configured to detect a torque applied to the crop processing component to process the crop.

20 . An agricultural harvester, comprising:

a sensor configured to detect material other than grain (MOG) engaged by the agricultural harvester and generate a detection signal indicative of the detected MOG;

a content characteristic processing system configured to identify a content characteristic of the MOG based on the detection signal;

a controllable subsystem; and

a control signal generator configured to generate a control signal to control the controllable subsystem on the agricultural harvester based on the content characteristic of the MOG.

Assignments (1)
ASSIGNMENT OF ASSIGNOR'S INTEREST Recorded May 28, 2024
From: CLARK, SCOTT N.; BOMLENY, DUANE M.; FREEHILL, JUSTIN C.; FAULKNER, SCOTT E.; VANDIKE, NATHAN R.; KREHBIEL, NATHAN E.
To: DEERE & COMPANY
Reel/Frame 067541/0816 →
Continuity (1)
Related Publication 20250359511A1 · Nov 27, 2025
References Cited (38)
US 7721515B2 · Pollklas et al. · 2010 [cited by applicant]
US 9050890B2 · Buerkle et al. · 2015 [cited by applicant]
US 9740208B2 · Sugumaran · 2017 [cited by applicant]
US 9807933B2 · Boyd et al. · 2017 [cited by applicant]
US 9829883B1 · Lavoie et al. · 2017 [cited by applicant]
US 10049296B2 · Walker · 2018 [cited by applicant]
US 10255670B1 · Wu et al. · 2019 [cited by applicant]
US 10377197B2 · Fukatsu et al. · 2019 [cited by applicant]
US 10721859B2 · Wu et al. · 2020 [cited by applicant]
US 10754353B2 · Sporrer et al. · 2020 [cited by applicant]
US 10761544B2 · Anderson et al. · 2020 [cited by applicant]
US 11112262B2 · Anderson · 2021 [cited by applicant]
US 11308735B2 · Wagner et al. · 2022 [cited by applicant]
US 11324164B2 · Sorensen · 2022 [cited by applicant]
US 11470776B2 · Barther et al. · 2022 [cited by applicant]
US 11483972B2 · Dima et al. · 2022 [cited by applicant]
US 11499295B2 · Anderson · 2022 [cited by applicant]
US 11758844B2 · White et al. · 2023 [cited by applicant]
US 11805734B2 · Li et al. · 2023 [cited by applicant]
US 20060020402A1 · Bischoff et al. · 2006 [cited by applicant]
US 20190227554A1 · Cantrell et al. · 2019 [cited by applicant]
US 20200264154A1 · Saez et al. · 2020 [cited by applicant]
US 20210357664A1 · Kocer et al. · 2021 [cited by applicant]
US 20220232770A1 · Yanke et al. · 2022 [cited by applicant]
US 20220348322A1 · Zemenchik · 2022 [cited by applicant]
US 20230000015A1 · Herrmann et al. · 2023 [cited by applicant]
US 20230345873A1 · Goossens et al. · 2023 [cited by applicant]
CN 113287422A · 2021 [cited by applicant]
DE 102015224175B3 · 2017 [cited by applicant]
DE 102016202628A1 · 2017 [cited by applicant]
EP 2545761B1 · 2016 [cited by applicant]
EP 3401854A1 · 2018 [cited by applicant]
EP 3284334B1 · 2019 [cited by applicant]
EP 3552474B1 · 2021 [cited by applicant]
JP 2021058099A · 2021 [cited by applicant]
WO 2023021005A1 · 2023 [cited by applicant]
WO 2023047240A1 · 2023 [cited by applicant]
WO 2023187494A1 · 2023 [cited by applicant]