Unmanned aerial vehicle-based systems and methods for agricultural landscape modeling

• US 10,901,420 B2
• Filed: 11/03/2017
• Issued: 01/26/2021
• Est. Priority Date: 11/04/2016
• Status: Active Grant

• Current Assignee: Intel Corporation
• Original Assignee: Intel Corporation
• Inventors: Achtelik, Markus, Stumpf, Jan, Gurdan, Daniel, Jaeger, Bastian
• Primary Examiner(s): Ismail, Mahmoud S
• Application Number:
  US15/803,141
• Publication Number:
  US 20180129879A1
• Time in Patent Office: 1,180 Days
• Field of Search: 701 3
• CPC Class Codes: A01B 79/02 combined with other agricultural processing, e.g. fertilising, planting
• CPC Class Codes: B64C 39/024 of the remote controlled vehicle type, i.e. RPV
• CPC Class Codes: B64D 47/08 Arrangements of cameras
• CPC Class Codes: B64U 2101/30 for imaging, photography or videography
• CPC Class Codes: B64U 2201/20 Remote controls
• CPC Class Codes: G01C 11/02 Picture taking arrangements specially adapted for photogrammetry or photographic surveying, e.g. controlling overlapping of pictures
• CPC Class Codes: G01C 11/06 by comparison of two or more pictures of the same area
• CPC Class Codes: G01C 5/00 Measuring height; Measuring distances transverse to line of sight; Levelling between separated points; Surveyors' levels G01C3/20, G01C3/30 take precedence
• CPC Class Codes: G05D 1/0094 involving pointing a payload, e.g. camera, weapon, sensor, towards a fixed or moving target
• CPC Class Codes: G05D 1/104 involving a plurality of aircrafts, e.g. formation flying traffic control systems for aircraft G08G5/00
• CPC Class Codes: G06T 17/05 Geographic models
• CPC Class Codes: G06V 20/188 Vegetation

Abstract

Unmanned aerial vehicle-based systems and methods for agricultural landscape modeling are disclosed herein. An example unmanned aerial vehicle includes a communicator to receive an instruction to request the unmanned aerial vehicle to fly over an area of interest. The instruction is from a vehicle in the area of interest. The unmanned aerial vehicle is to fly over the area of interest. The example unmanned aerial vehicle includes a camera to generate image data for the area of interest. The example unmanned aerial vehicle includes a data generator to generate a vegetation landscape model of the area of interest based on the image data. The communicator is to communicate the vegetation landscape model to the vehicle.

Claims

1. An unmanned aerial vehicle comprising:

a communicator to receive an instruction to request the unmanned aerial vehicle to fly over an area of interest, the instruction from a vehicle in the area of interest;

a camera including a first sensor to generate image data for the area of interest and a second sensor to generate depth data indicative of a height of the unmanned aerial vehicle relative to the area of interest; and

a data generator to;

align the image data and the depth data to generate aligned data, the aligned data including coordinate data based on the depth data and corresponding spectral channel values based on the image data; and

generate a vegetation landscape model of the area of interest based on the aligned data, the communicator to communicate the vegetation landscape model to the vehicle.

2. The unmanned aerial vehicle as defined in claim 1, wherein the image data is first image data and the camera includes a third sensor, the third sensor to generate second image data, the data generator to align the first image data, the second image data, and the depth data to generate the aligned data.

3. The unmanned aerial vehicle as defined in claim 2, further including an image data processor to perform vignette correction of at least one of the first image data or the second image data.

4. The unmanned aerial vehicle as defined in claim 2, wherein the first sensor includes a red-green-blue sensor and the second sensor includes a near-infrared sensor.

5. The unmanned aerial vehicle as defined in claim 1, wherein the data generator is to determine a plurality of vegetation indices for the area of interest based on the image data, the data generator to generate the vegetation landscape model based on the vegetation indices.

6. The unmanned aerial vehicle as defined in claim 5, wherein the data generator is to determine respective ones of the vegetation indices based on respective pixel values of the image data.

7. The unmanned aerial vehicle as defined in claim 6, wherein the data generator is to correlate the pixel values to respective spectral energy levels, the data generator to determine the vegetation indices based on the spectral energy levels.

8. The unmanned aerial vehicle as defined in claim 1, wherein the vegetation landscape model indicates an amount of vegetation in the area of interest.

9. The unmanned aerial vehicle as defined in claim 1, wherein the area of interest includes a first portion and a second portion, the image data is first image data, the first sensor is to generate the first image data for the first portion, the communicator is to access second image data for the second portion, and the data generator is to generate the vegetation landscape model based on the first image data for the first portion and the second image data for the second portion.

10. An unmanned aerial vehicle comprising:

means for generating image data for an area of interest in response to receiving an instruction to fly over the area of interest from a vehicle;

means for generating depth data indicative of a height of the unmanned aerial vehicle relative to the area of interest;

means for generating a vegetation landscape model of the area of interest, the vegetation landscape model generating means to;

align the image data and the depth data to generate aligned data, the aligned data including coordinate data based on the depth data and corresponding spectral channel values based on the image data; and

generate the vegetation landscape model based on the aligned data; and

means for communicating the vegetation landscape model to the vehicle.

11. The unmanned aerial vehicle as defined in claim 10, wherein the means for generating the image data includes:

a first sensor to detect first image data and a second sensor to detect second image data; and

,

means for correlating the first image data and the second image data.

12. The unmanned aerial vehicle as defined in claim 11, wherein the first sensor includes a red-green-blue sensor and the second sensor includes a near-infrared sensor.

13. The unmanned aerial vehicle as defined in claim 10, wherein the means for generating the vegetation landscape model is to determine vegetation indices for the area of interest based on the image data.

14. At least one non-transitory computer readable storage medium, comprising instructions that, when executed, cause at least one unmanned aerial vehicle to at least:

generate image data for an area of interest while flying over the area of interest;

generate depth data indicative of a height of the at least one unmanned aerial vehicle relative to the area of interest;

align the image data and the depth data to generate aligned data, wherein the aligned data includes coordinate data based on the depth data and corresponding spectral channel values based on the image data;

generate a vegetation landscape model of the area of interest based on the aligned data; and

communicate the vegetation landscape model in substantially real-time to a vehicle to service the area of interest.

15. The at least one non-transitory computer readable storage medium as defined in claim 14, wherein the image data includes first image data and second image data and the instructions, when executed, cause the at least one unmanned aerial vehicle to align the first image data, the second image data, and the depth data to generate the aligned data.

16. The at least one non-transitory computer readable storage medium as defined in claim 15, wherein the first image data is red-green-blue image data and the second image data is near-infrared image data.

17. The at least one non-transitory computer readable storage medium as defined in claim 15, wherein the instructions, when executed, cause the at least one unmanned aerial vehicle to perform vignette correction of at least one of the first image data or the second image data.

18. The at least one non-transitory computer readable storage medium as defined in claim 14, wherein the instructions, when executed, cause the at least one unmanned aerial vehicle to determine a plurality of vegetation indices for the area of interest based on the image data and to generate the vegetation landscape model based on the vegetation indices.

19. The at least one non-transitory computer readable storage medium as defined in claim 18, wherein the instructions, when executed, cause the at least one unmanned aerial vehicle to calculate respective ones of the vegetation indices based on respective pixel values of the image data.