Agricultural Field Row Mapping With Semantic GPS Localization
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Solution Overview
Problem
Current agricultural monitoring technologies, such as overhead imagery and GPS-based robots, lack precise data at the individual row or plant level, making it difficult to identify and address issues like pest infestations or crop degradation in a timely and efficient manner.
Innovation Solution
Generating a local mapping of agricultural fields using overhead vision data and GPS data to assign semantic identifiers to rows and plots, allowing for easy reference and notification of specific areas within the field, enabling more efficient agricultural operations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Area of stationary object
If overhead imagery (satellite or drone) is used to monitor agricultural fields, then coverage area is improved, but measurement precision at individual row or plant level deteriorates
Solution Approach 1:
The system segments the agricultural field into discrete rows and individual plants, creating a hierarchical structure where overhead imagery provides broad coverage while ground-based sensors provide detailed measurements for each segmented unit. This allows the system to maintain both large-area monitoring capability and precise row-level data collection.
2Ease of operation
If GPS sensors are used for rover localization, then ease of operation is improved, but loss of information deteriorates because operators cannot readily identify rover location based on GPS coordinates alone
Solution Approach 1:
The system introduces an intermediary layer that translates GPS coordinates into meaningful agricultural context. By matching rover location with the pre-created local mapping of rows and plots, the system converts abstract coordinates into identifiable field locations that operators can understand and act upon.
3Loss of information
If local mapping with semantic identifiers is created, then loss of information is reduced by enabling easy location identification, but device complexity increases due to additional mapping and coordination systems
Solution Approach 1:
The system performs preliminary action by creating the local mapping of rows and plots before agricultural operations begin. This pre-established semantic framework allows GPS coordinates to be quickly translated into meaningful locations during operations, reducing the need for complex real-time processing while maintaining accurate location identification.
4Productivity
If precise row-level data collection is implemented, then productivity is improved through targeted agricultural operations, but device complexity increases due to need for precise localization and mapping systems
Solution Approach 1:
The system merges overhead vision data with ground-based GPS and row identification data into a unified local mapping framework. This integration allows the system to leverage multiple data sources together, achieving precise row-level productivity improvements while distributing the complexity across different sensing modalities rather than relying on a single complex system.
Data Source
AI summary
Implementations are directed to assigning corresponding semantic identifiers to a plurality of rows of an agricultural field, generating a local mapping of the agricultural field that includes the plurality of rows of the agricultural field, and subsequently utilizing the local mapping in performance of one or more agricultural operations. In some implementations, the local mapping can be generated based on overhead vision data that captures at least a portion of the agricultural field. In these implementations, the local mapping can be generated based on GPS data associated with the portion of the agricultural field captured in the overhead vision data. In other implementations, the local mapping can be generated based on driving data generated during an episode of locomotion of a vehicle through the agricultural field. In these implementations, the local mapping can be generated based on GPS data associated with the vehicle traversing through the agricultural field.