Agricultural Inspection Switching Under Cloud Occlusion
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Solution Overview
Problem
Remote agricultural inspection platforms face challenges due to cloud occlusion, which can lead to delayed detection of crop issues such as parasites, drought, or damage, resulting in potential crop loss.
Innovation Solution
A system and method that utilize local inspection platforms, such as autonomous vehicles, to supplement remote inspection when cloud occlusion occurs, optimizing their deployment based on predicted cloud conditions, enabling effective monitoring and reporting, and seamlessly transitioning back to remote inspection when conditions improve.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Area of stationary object
If remote inspection platforms are used for agricultural monitoring, then the coverage area is large and resource usage is efficient, but cloud occlusion causes inspection interruptions and data gaps
Solution Approach 1:
The system segments the inspection coverage into multiple zones, with remote platforms covering broad areas and local platforms providing focused monitoring in specific regions. This segmentation allows the system to maintain continuous monitoring by switching between remote and local platforms based on cloud occlusion conditions in different areas.
Solution Approach 2:
Local inspection platforms serve as intermediary components between the remote inspection platforms and the ground crops. When remote platforms are occluded by clouds, local platforms act as intermediaries to continue data collection, ensuring inspection continuity without direct remote observation.
2Reliability
If local inspection platforms are deployed to ensure continuous monitoring, then inspection reliability is improved, but resource usage and system complexity increase
Solution Approach 1:
The system dynamically adjusts the deployment and operation of local inspection platforms based on real-time cloud occlusion predictions and actual inspection needs. Local platforms are activated only when and where needed, transforming a static system into a dynamic one that adapts to changing conditions, thereby maintaining reliability without permanent complexity.
Solution Approach 2:
The system changes operational parameters such as the number of active local platforms, their deployment locations, and inspection frequencies based on cloud occlusion conditions and crop monitoring requirements. This parameter adjustment allows the system to maintain inspection continuity while optimizing resource usage and managing complexity.
3Loss of information
If local inspection platforms are used during cloud occlusion, then data continuity is maintained, but operational costs and resource consumption increase
Solution Approach 1:
The system performs preliminary actions by predicting cloud occlusion events in advance and proactively deploying local inspection platforms before actual occlusion occurs. This preliminary deployment ensures data continuity is maintained without waiting for complete data loss, optimizing the timing of resource consumption.
Solution Approach 2:
The system applies partial action by deploying local inspection platforms only in specific areas and for specific durations necessary to maintain data continuity, rather than deploying them universally and continuously. This selective deployment reduces overall resource consumption while ensuring data continuity where and when needed.
Data Source
AI summary
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.