Measuring crop residue from imagery using a machine-learned semantic segmentation model
US10769771B2 · kind B2 · utility
1Cited by
7References
20Claims
0Family size
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Key dates
| Filing date | Jun 22, 2018 |
| Grant date | Sep 8, 2020 |
| Priority date | — |
| Expiry date | Mar 5, 2039 |
Classification
- Technology area (CPC G)Physics
- CPC primaryG06T2207/30188
- WIPO fieldOther special machines
- WIPO sectorMechanical engineering
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.
Source: USPTO / EPO open patent data. Objective bibliographic and citation counts.