Measuring crop residue from imagery using a machine-learned classification model in combination with principal components analysis
US10817755B2 · kind B2 · utility
Assignees
Inventors
Key dates
| Filing date | Jun 22, 2018 |
| Grant date | Oct 27, 2020 |
| Priority date | — |
| Expiry date | Jan 11, 2039 |
Classification
- Technology area (CPC G)Physics
- CPC primaryG06V20/10
- WIPO fieldComputer technology
- WIPO sectorElectrical 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 crop residue classification 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. Furthermore, principal components analysis, such as projecting image patches onto Eigen-images, can be performed to reduce the dimensionality of the feature vector provided to the classification model.
Source: USPTO / EPO open patent data. Objective bibliographic and citation counts.