Drone-based mosquito amelioration based on risk analysis and pattern classifiers

• US 9,856,020 B1
• Filed: 07/27/2016
• Issued: 01/02/2018
• Est. Priority Date: 07/27/2016
• Status: Expired due to Fees

• Current Assignee: International Business Machines Corporation
• Original Assignee: International Business Machines Corporation
• Inventors: Gordon, Michael S., Kozloski, James R., Pickover, Clifford A., Weisz, Justin D.
• Primary Examiner(s): SOOFI, YAZAN A
• Application Number:
  US15/220,500
• Time in Patent Office: 524 Days
• Field of Search: 701 3
• CPC Class Codes: B64C 39/024 of the remote controlled vehicle type, i.e. RPV
• CPC Class Codes: B64D 1/18 by spraying, e.g. insecticides spraying apparatus per se B05B
• CPC Class Codes: B64D 47/08 Arrangements of cameras
• CPC Class Codes: B64U 10/10 Rotorcrafts
• CPC Class Codes: B64U 2101/00 UAVs specially adapted for particular uses or applications
• CPC Class Codes: B64U 2101/30 for imaging, photography or videography
• CPC Class Codes: B64U 2101/45 for releasing liquids or powders in-flight, e.g. crop-dusting
• CPC Class Codes: B64U 2201/10 autonomous, i.e. by navigating independently from ground or air stations, e.g. by using inertial navigation systems [INS]
• CPC Class Codes: B64U 2201/104 using satellite radio beacon positioning systems, e.g. GPS
• CPC Class Codes: B64U 2201/20 Remote controls
• CPC Class Codes: B64U 30/20 Rotors; Rotor supports
• CPC Class Codes: B64U 50/19 using electrically powered motors
• CPC Class Codes: G06F 18/24 Classification techniques
• CPC Class Codes: G06F 18/2413 based on distances to training or reference patterns
• CPC Class Codes: G06N 3/08 Learning methods
• CPC Class Codes: G06V 10/764 using classification, e.g. of video objects
• CPC Class Codes: G06V 10/82 using neural networks
• CPC Class Codes: G06V 20/13 Satellite images
• CPC Class Codes: G06V 20/17 taken from planes or by drones

Abstract

A method, system, and/or computer program product ameliorates mosquito populations. A flying drone is deployed over an area. Sensor readings that identify a presence of water in the area are received, and one or more processors determine a confidence level L that the water in the area is stagnant water. The processor(s) also determine a risk R of mosquito larvae being present in the stagnant water based on the confidence level L. The flying drone is then directed to perform an amelioration action against the mosquito larvae based on the values of L and R.

Claims

1. A flying drone-based method of mosquito amelioration, the flying drone-based method comprising:

deploying a flying drone over an area;

receiving, by one or more processors, sensor readings that identify a presence of water in the area;

determining, by one or processors, a confidence level L that the water in the area is stagnant water;

determining, by one or more processors, a risk R of mosquito larvae being present in the stagnant water based on the confidence level L; and

directing, by one or more processors, the flying drone to perform an amelioration action against the mosquito larvae based on values of L and R.

2. The flying drone-based method of claim 1, further comprising:

determining, by one or more processors, the confidence level L based on image classification of images of the water produced by the flying drone, wherein the image classification is performed by image pattern classifications performed by a deep neural network.

3. The flying drone-based method of claim 1, further comprising:

determining, by one or more processors, the risk R by matching past incidents of stagnant water to a presence of mosquito larvae as identified by a deep neural network.

4. The flying drone-based method of claim 1, wherein the amelioration action is from a group consisting of deploying an insecticide from the flying drone, deploying an insect repellant from the flying drone, deploying an insect trap from the flying drone, deploying natural biologic mosquito enemies from the flying drone, deploying artificial breeding facilities that are lethal to the mosquito larvae from the flying drone, activating mosquito lasers from the flying drone, and activating an electrical mosquito eliminator on the flying drone.

5. The flying drone-based method of claim 1, wherein the flying drone comprises a mechanical agitator, and wherein the amelioration action comprises the flying drone inserting the mechanical agitator into the stagnant water to agitate the stagnant water.

6. The flying drone-based method of claim 1, wherein the confidence level L is determined by economic conditions associated with the area.

7. The flying drone-based method of claim 1, further comprising:

capturing, by the flying drone, a sample of the water;

examining, by an on-board testing device on the flying drone, the sample to determine a level of mosquito larvae presence in the water; and

adjusting, by one or more processors, the risk R based on the level of mosquito larvae present in the water.

8. A computer program product for ameliorating mosquitos by a flying drone, the computer program product comprising a non-transitory computer readable storage medium having program code embodied therewith, the program code readable and executable by a processor to perform a method comprising:

deploying a flying drone over an area;

receiving sensor readings that identify a presence of water in the area;

determining a confidence level L that the water in the area is stagnant water;

determining a risk R of mosquito larvae being present in the stagnant water based on the confidence level L; and

directing the flying drone to perform an amelioration action against the mosquito larvae based on values of L and R.

9. The computer program product of claim 8, wherein the method further comprises:

determining, by one or more processors, the confidence level L based on image classification of images of the water produced by the flying drone, wherein the image classification is performed by image pattern classifications performed by a deep neural network.

10. The computer program product of claim 8, wherein the method further comprises:

determining the risk R by matching past incidents of stagnant water to a presence of mosquito larvae as identified by a deep neural network.

11. The computer program product of claim 8, wherein the amelioration action is from a group consisting of deploying an insecticide from the flying drone, deploying an insect repellant from the flying drone, deploying an insect trap from the flying drone, deploying natural biologic mosquito enemies from the flying drone, deploying artificial breeding facilities that are lethal to the mosquito larvae from the flying drone, activating mosquito lasers from the flying drone, and activating an electrical mosquito eliminator on the flying drone.

12. The computer program product of claim 8, wherein the flying drone comprises a mechanical agitator, and wherein the amelioration action comprises the flying drone inserting the mechanical agitator into the stagnant water to agitate the stagnant water.

13. The computer program product of claim 8, wherein the confidence level L is determined by economic conditions associated with the area.

14. The computer program product of claim 8, wherein the method further comprises:

capturing, by the flying drone, a sample of the water;

examining, by an on-board testing device on the flying drone, the sample to determine a level of mosquito larvae presence in the water; and

adjusting the risk R based on the level of mosquito larvae present in the water.

15. A computer system comprising:

one or more processors;

one or more computer readable memories; and

one or more non-transitory computer readable storage mediums, wherein program instructions are stored on at least one of the one or more non-transitory storage mediums for execution by at least one of the one or more processors via at least one of the one or more computer readable memories to perform a method comprising;

deploying a flying drone over an area;

receiving sensor readings that identify a presence of water in the area;

determining a confidence level L that the water in the area is stagnant water;

determining a risk R of mosquito larvae being present in the stagnant water based on the confidence level L; and

directing the flying drone to perform an amelioration action against the mosquito larvae based on values of L and R.

16. The computer system of claim 15, wherein the method further comprises:

determining, by one or more processors, the confidence level L based on image classification of images of the water produced by the flying drone, wherein the image classification is performed by image pattern classifications performed by a deep neural network.

17. The computer system of claim 15, wherein the method further comprises:

determining the risk R by matching past incidents of stagnant water to a presence of mosquito larvae as identified by a deep neural network.

18. The computer system of claim 15, wherein the flying drone comprises a mechanical agitator, and wherein the amelioration action comprises the flying drone inserting the mechanical agitator into the stagnant water to agitate the stagnant water.

19. The computer system of claim 15, wherein the confidence level L is determined by economic conditions associated with the area.

20. The computer system of claim 15, wherein the method further comprises:

capturing, by the flying drone, a sample of the water;

examining, by an on-board testing device on the flying drone, the sample to determine a level of mosquito larvae presence in the water; and

adjusting the risk R based on the level of mosquito larvae present in the water.