Effective agriculture and environment monitoring

Patent metadata

Field	Value
Publication number	US-2021173119-A1
Application number	US-201916702861-A
Country	US
Kind code	A1
Filing date	Dec 4, 2019
Priority date	Dec 4, 2019
Publication date	Jun 10, 2021
Grant date	—

Abstract

A computer-implemented method for effective agriculture and environment monitoring. The method may comprise measuring a desired variable over an area of interest using a remote inspection platform according to an inspection plan, predicting an occlusion of the remote inspection platform, and in response to the predicted occlusion, determining whether to invoke a local inspection platform to complete the inspection plan. The occlusion in some embodiments interrupts the inspection plan for the remote inspection platform.

First claim

1 - 18 . (canceled)

19 . A method for agriculture monitoring, comprising:
measuring a specified variable over an area of interest using a remote inspection platform according to an inspection plan; receiving a prediction of an occlusion of the remote inspection platform; and in response to the predicted occlusion, determining whether to invoke a local inspection platform to complete the inspection plan.

20 . The method of claim 19 , wherein the local inspection platform comprises an autonomous vehicle.

21 . The method of claim 19 , wherein the received prediction comprises a cloud density and a cloud duration over the area of interest.

22 . The method of claim 20 , wherein the autonomous vehicle is associated with a quality factor; and further comprising:
calculating a number of selected autonomous vehicles of the quality factor required to satisfy the inspection plan.

23 . The method of claim 22 , wherein the quality factor is chosen from the group consisting of a high-flying autonomous vehicle having a high camera resolution and a low-flying autonomous vehicle having a ground condition sensor.

24 . The method of claim 20 , further comprising pre-loading the autonomous vehicle with data, states of computation, and code segments from a base station.

25 . The method of claim 24 , further comprising:
monitoring the area of interest during the occlusion; and reporting measurements to the base station during the occlusion.

26 . The method of claim 25 , further comprising:
determining whether the occlusion is sufficiently reduced for the remote inspection platform to restart with sufficient accuracy; and return measurement from the autonomous vehicle to the remote inspection platform; and return the autonomous vehicles to a home location.

27 . The method of claim 20 , further comprising:
dividing the area of interest into sub-areas; calculating a number of autonomous vehicles needed to satisfy the inspection plan for each sub-area; and assigning the calculated number of autonomous vehicles to each sub-area.

28 . The method of claim 27 , further comprising, responsive to multiple autonomous vehicles being assigned to a sub-area:
dividing the sub-area into sub-sub-areas; assigning an autonomous vehicle to each sub-sub-area.

29 . The method of claim 27 , wherein calculating the number of autonomous vehicles needed to satisfy the inspection plan for each sub-area comprises:
calculating a monitoring rate needed for a time period; and comparing a mapping speed and a quality of each autonomous vehicle to the calculated monitoring rate to determine a coverage amount for the autonomous vehicle.

30 . The method of claim 27 , further comprising applying a selection function to compute a quality factor for the autonomous vehicle.

31 . The method of claim 20 , further comprising dynamically allocating the autonomous vehicle based upon conditions of the occlusion, capabilities of the autonomous vehicle, and monitoring requirements for the inspection plan.

32 . The method of claim 31 , wherein the remote inspection platform comprises a satellite and wherein the autonomous vehicle comprises an aerial autonomous vehicle.

33 . The method of claim 19 , wherein the occlusion is predicted to interrupt the inspection plan for the remote inspection platform.

34 . A base controller for a multi-platform inspection system, comprising a processor coupled to a memory, the memory containing instructions that, when executed on the processor:
initiate an inspection plan using data from a remote inspection platform over an area of interest; receive a prediction of an occlusion of the remote inspection platform; and in response to the predicted occlusion, automatically deploy local inspection platforms based upon conditions of the occlusion, capabilities of the local inspection platform, and monitoring requirements for the inspection plan.

35 . The base controller of claim 34 , wherein the remote inspection platform comprises a satellite and wherein the local inspection platform comprises an aerial autonomous vehicle.

36 . The base controller of claim 35 , further comprising instructions that, when executed on the processor:
divide the area of interest into sub-areas; calculate a number of aerial autonomous vehicles needed to satisfy the inspection plan for each sub-area; and assign the calculated number of autonomous vehicles to each sub-area.

37 . The base controller of claim 34 , wherein the received prediction comprises a cloud density and a cloud duration condition over the area of interest.

38 . A computer program product effective agriculture monitoring, the computer program product comprising:
one or more computer readable storage media, and program instructions collectively stored on the one or more computer readable storage media, the program instructions comprising: program instructions to measure a desired variable over an area of interest using a remote inspection platform according to an inspection plan; program instructions to receive a prediction of an occlusion of the remote inspection platform; and program instructions to, in response to the predicted occlusion, determine whether to invoke a local inspection platform to complete the inspection plan.

Assignees

• IBM

Inventors

• Singh Jitendra
• Tewari Mukul
• Sastri Vinayak
• Nagar Seema
• Dey Kuntal

Classifications

• B64U2101/35

  for science, e.g. meteorology · CPC title

• B64U2201/104

  using satellite radio beacon positioning systems, e.g. GPS · CPC title

• B64U2101/30

  for imaging, photography or videography · CPC title

• B64U2201/20

  Remote controls · CPC title

• B64U2201/10

  autonomous, i.e. by navigating independently from ground or air stations, e.g. by using inertial navigation systems [INS] · CPC title