Image selection for machine control
A vegetation index characteristic is assigned to an image of vegetation at a worksite. The vegetation index characteristic is indicative of how a vegetation index value varies across the corresponding image. The image is selected for predictive map generation based upon the vegetation index characteristic. The predictive map is provided to a harvester control system which generates control signals that are applied to controllable subsystems of the harvester, based upon the predictive map and the location of the harvester.
1. A method of controlling a work machine, comprising:
receiving a plurality of images of spectral response at a worksite;
identifying a set of vegetation index values based on the spectral response;
identifying a vegetation index characteristic, corresponding to each image, indicative of how the set of vegetation index metric values varies across the corresponding image;
selecting an image, from the plurality of images, based on the vegetation index characteristic;
generating, from the selected image, a predictive map; and
controlling a controllable subsystem of the work machine based on a location of the work machine and the predictive map.
2. The method of claim 1 , wherein identifying the vegetation index characteristic comprises:
identifying a magnitude of a range of the vegetation index values, a vegetation index value distribution and a vegetation index value variability metric.
3. The method of claim 2 , wherein selecting an image comprises:
selecting a set of images, from the plurality of images, based on the vegetation index characteristics corresponding to the images, in the set of images.
4. The method of claim 3 , wherein generating a predictive map further comprises:
generating a predicted yield map based on the selected set of images.
5. The method of claim 1 , wherein identifying the vegetation index characteristic comprises:
calculating a set of imagery spectral values in the spectral response for a corresponding image; and
identifying a variability of the imagery spectral values across the set of metric values.
6. The method of claim 3 , wherein selecting the set of images comprises:
selecting the set of images that have vegetation index characteristics that show more variation than the vegetation index characteristics of non-selected images.
7. The method of claim 3 , wherein selecting the set of images comprises:
selecting the set of images that have vegetation index characteristics that meet a threshold vegetation index characteristic value.
8. The method of claim 4 , wherein selecting the set of images comprises:
selecting the set of images based on a vegetation distribution represented in the images that inhibits spectral saturation and that reflects a predefined level of plant growth.
9. The method of claim 1 , wherein controlling the controllable subsystem comprises controlling a machine actuator.
10. The method of claim 1 , wherein controlling the controllable subsystem comprises controlling a propulsion subsystem.
11. The method of claim 1 , wherein controlling the controllable subsystem comprises controlling a steering subsystem.
12. The method of claim 1 , wherein controlling the controllable subsystems comprises:
controlling a crop processing subsystem.
13. A work machine, comprising:
a communication system configured to receive a plurality of images of vegetation at a worksite;
a controllable subsystem;
an image selector configured to generate a vegetation index characteristic that includes a variability, distribution, and magnitude metric, corresponding to each image, indicative of how a vegetation index value varies across each image, and to select an image based on the vegetation index characteristic;
a processor configured to generate a predictive map based on the selected image; and
subsystem control logic configured to control the controllable subsystem of the work machine based on a location of the work machine and the predictive map.
14. The work machine of claim 13 , wherein the image selection comprises:
variability identifier logic configured to identify a set of vegetation index metric values for a corresponding image and identify a variability of the vegetation index characteristic across the set of vegetation index metric values.
15. The work machine of claim 14 , wherein the processor is configured to generate a predictive yield map based on the selected set of images.
16. The work machine of claim 14 , wherein variability identifier logic is configured to identify a set of leaf area index metric values for a corresponding image and identify a variability of the leaf area metric values across the set of leaf area index metric values.
17. The work machine of claim 14 , wherein variability identifier logic is configured to identify a set of normalized difference vegetation index metric values for a corresponding image and identify a variability of the normalized difference vegetation index metric value across the set of normalized difference vegetation index metric values.
18. The work machine of claim 13 , wherein the image selector is configured to select a set of images that have vegetation index characteristics that indicate more vegetation variability than the vegetation index characteristics of non-selected images.
19. The work machine of claim 13 , wherein the image selector is configured to select a set of images that have vegetation index characteristics that meet a threshold vegetation index characteristic value.
20. An image selection system, comprising:
a communication system configured to receive a plurality of images of vegetation at a worksite;
an image selector configured to generate a vegetative index variability metric, corresponding to each image, indicative of how a vegetation index value varies across each image, and to select an image based on the vegetation index variability metric; and
a processor configured to generate at least one predictive map based on the selected images.