Effective agriculture and environment monitoring

Patent metadata

Field	Value
Publication number	US-11585960-B2
Application number	US-201916702861-A
Country	US
Kind code	B2
Filing date	Dec 4, 2019
Priority date	Dec 4, 2019
Publication date	Feb 21, 2023
Grant date	Feb 21, 2023

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

What is claimed is:

1. 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 a future occlusion of the remote inspection platform; and
in response to the predicted future occlusion:
determining whether to invoke a local inspection platform to complete the inspection plan; and
generating a deployment plan for the local inspection platform, wherein the deployment plan is based at least in part on predicted conditions of the predicted future occlusion, respective capabilities of the of the local inspection platform, and the monitoring requirements of the inspection plan.

2. The method of claim 1 , wherein the local inspection platform comprises a plurality of autonomous vehicles having varying respective capabilities.

3. The method of claim 2 , further comprising:
calculating a number of selected autonomous vehicles of the quality factor required to satisfy the inspection plan.

4. The method of claim 3 , wherein the respective capabilities are 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.

5. The method of claim 2 , further comprising:
pre-staging the plurality of autonomous vehicles having varying respective capabilities in an area of interest of the inspection plan; and
pre-loading one or more of the plurality of autonomous vehicles with data, states of computation, and code segments from a base station.

6. The method of claim 5 , further comprising:
monitoring the area of interest during the occlusion; and
reporting measurements to the base station during the occlusion.

7. The method of claim 6 , 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.

8. The method of claim 2 , 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.

9. The method of claim 8 , 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.

10. The method of claim 8 , 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.

11. The method of claim 8 , further comprising applying a selection function to compute a quality factor for the autonomous vehicle.

12. The method of claim 8 , the deployment plan is further based at least in part on a rate of monitoring desired for each sub-area, an acceptable delay in receiving data from each sub-area, and the respective capabilities of each of the plurality of autonomous vehicles.

13. The method of claim 2 , wherein the remote inspection platform comprises a satellite and wherein the autonomous vehicle comprises an aerial autonomous vehicle.

14. The method of claim 1 , wherein the received prediction comprises a cloud density and a cloud duration over the area of interest.

15. The method of claim 1 , wherein the predicted future occlusion is predicted to interrupt the inspection plan for the remote inspection platform.

16. 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 a future occlusion of the remote inspection platform; and
in response to the predicted future occlusion, automatically deploy one or more of a plurality of local inspection platforms based upon predicted conditions of the predicted future occlusion, respective capabilities of the one or more the plurality of local inspection platforms, and monitoring requirements for the inspection plan.

17. The base controller of claim 16 , wherein the remote inspection platform comprises a satellite and wherein the plurality of local inspection platforms comprises a plurality of aerial autonomous vehicles.

18. The base controller of claim 17 , 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.

19. The base controller of claim 16 , wherein the received prediction comprises a cloud density and a cloud duration condition over the area of interest.

20. A computer program product effective agriculture monitoring, the computer program product comprising:
one or more non-transitory computer readable storage media, and program instructions collectively stored on the one or more non-transitory 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 a future occlusion of the remote inspection platform; and
program instructions to, in response to the predicted future occlusion:
determine whether to invoke a local inspection platform to complete the inspection plan; and
generate a deployment plan for the local inspection platform, wherein the deployment plan is based at least in part on predicted conditions of the predicted future occlusion, respective capabilities of the of the local inspection platform, and the monitoring requirements of the inspection plan.

Assignees

• IBM

Inventors

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

Classifications

• B64U2201/10

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

• B64U2201/20

  Remote controls · CPC title

• B64U2101/40

  for agriculture or forestry operations · CPC title

• B64U2201/104

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

• G01W1/10 Primary

  Devices for predicting weather conditions (computers per se G06; display devices G09) · CPC title