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

{
  "publication_number": "US10081426B2",
  "country": "US",
  "kind": "B2",
  "title": "Drone-based mosquito amelioration based on risk analysis and pattern classifiers",
  "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 flying drone is then directed to perform an amelioration action against the mosquito larvae based a value of the determined confidence level L.",
  "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; and directing, by one or more processors, the flying drone to perform an amelioration action against potential mosquito larvae in the stagnant water based on a value of the determined confidence level L exceeding a predefined value.",
    "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, a risk R of mosquito larvae being present in the stagnant water 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 mosquitoes 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; and directing the flying drone to perform an amelioration action against potential mosquito larvae in the stagnant water based on a value of the determined confidence level L exceeding a predefined value.",
    "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 a risk R of mosquito larvae being present in the stagnant water 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; and directing the flying drone to perform an amelioration action against potential mosquito larvae in the stagnant water based on a value of the determined confidence level L exceeding a predefined value.",
    "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 a risk R of mosquito larvae being present in the stagnant water 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."
  ],
  "cpc": [
    "B64D 1/18",
    "B64C 2201/108",
    "B64C 2201/12",
    "B64C 2201/141",
    "B64C 2201/146",
    "B64C 39/024",
    "B64U 2101/30",
    "B64U 2101/45",
    "B64U 2201/10",
    "B64U 2201/104",
    "B64U 2201/20",
    "B64U 50/19",
    "G05D 1/101",
    "G06F 18/24",
    "G06F 18/2413",
    "G06K 9/6267",
    "G06K 9/66",
    "G06N 3/0464",
    "G06N 3/08",
    "G06N 3/09",
    "G06V 10/764",
    "G06V 10/82",
    "G06V 20/13",
    "G06V 20/17"
  ],
  "assignees": [
    "IBM"
  ],
  "filing_date": "2017-11-03",
  "publication_date": "2018-09-25",
  "grant_date": "2018-09-25",
  "priority_date": "2016-07-27",
  "application_number": "US-201715802579-A",
  "family_id": "60805037",
  "citations": [
    "CN102897323A",
    "CN202863767U",
    "CN202863768U",
    "US2009205363A1",
    "US2012196304A1",
    "US2013047497A1",
    "US2014237951A1",
    "US8967029B1",
    "US9600997B1"
  ]
}