Invention Grant
- Patent Title: Measuring crop residue from imagery using a machine-learned semantic segmentation model
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Application No.: US16015627Application Date: 2018-06-22
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Publication No.: US10769771B2Publication Date: 2020-09-08
- Inventor: Luca Ferrari , John H. Posselius , James W. Henry , Taylor C. Bybee
- Applicant: CNH Industrial Canada, Ltd. , Autonomous Solutions, Inc.
- Applicant Address: CA Saskatoon, Saskatchewan US UT Mendon
- Assignee: CNH Industrial Canada, Ltd.,Autonomous Solutions, Inc.
- Current Assignee: CNH Industrial Canada, Ltd.,Autonomous Solutions, Inc.
- Current Assignee Address: CA Saskatoon, Saskatchewan US UT Mendon
- Agent Rebecca L. Henkel; Rickard K. DeMille
- Main IPC: G06T7/00
- IPC: G06T7/00 ; G06K9/00 ; G06K9/62 ; G06T7/11 ; A01B63/14 ; A01B76/00 ; A01B49/02

Abstract:
The present disclosure provides systems and methods that measure crop residue in a field from imagery of the field. In particular, the present subject matter is directed to systems and methods that include or otherwise leverage a machine-learned semantic segmentation model to determine a crop residue parameter value for a portion of a field based at least in part on imagery of such portion of the field captured by an imaging device. For example, the imaging device can be a camera positioned in a downward-facing direction and physically coupled to a work vehicle or an implement towed by the work vehicle through the field.
Public/Granted literature
- US20190392573A1 MEASURING CROP RESIDUE FROM IMAGERY USING A MACHINE-LEARNED SEMANTIC SEGMENTATION MODEL Public/Granted day:2019-12-26
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