Local Field Mapping With Semantic Row IDs for Crop Issue Tracking
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Solution Overview
Problem
Agricultural monitoring technologies lack precise data at the individual row or plant level, and GPS-based localization of robots is inadequate for identifying issues in crops, leading to inefficiencies in addressing localized crop conditions.
Innovation Solution
Generate a local mapping of agricultural fields using overhead vision data and GPS data to identify and assign semantic identifiers to rows and plots, enabling efficient tracking and notification of crop events.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Area of stationary object
If overhead imagery (satellite or drone) is used for monitoring agricultural variations, 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 plots, creating a hierarchical structure where overhead imagery provides plot-level context while ground-based rovers provide row-level detail. This segmentation allows the system to allocate measurement resources efficiently across different spatial scales.
Solution Approach 2:
The patent implements a nested monitoring structure where satellite/drone imagery nests within ground-based rover data, which in turn nests within individual plant-level measurements. Each layer provides context for the layers below while being informed by them, creating a multi-scale monitoring system that optimizes both coverage and precision.
2Measurement precision
If GPS sensors are used for rover localization, then location tracking is improved, but ease of operation for identifying specific crop issues deteriorates
Solution Approach 1:
The patent introduces row identifiers and plot identifiers as intermediary elements between GPS coordinates and crop issues. Instead of directly mapping GPS coordinates to problems, the system uses these semantic identifiers as intermediaries that make the data meaningful to operators, translating precise location data into actionable information.
Solution Approach 2:
The system creates a simplified conceptual copy of the field layout with labeled rows and plots that mirrors the physical GPS-coordinated environment. This abstract representation serves as an easier-to-interpret interface for operators, allowing them to identify issues using familiar row/plot references rather than raw coordinate data.
3Ease of operation
If semantic identifiers are assigned to rows and plots, then ease of operation is improved, but device complexity increases
Solution Approach 1:
The system performs preliminary mapping and assigns semantic identifiers to rows and plots before actual crop monitoring begins. This advance preparation creates a ready-to-use reference framework that simplifies subsequent operations without adding complexity during critical monitoring and response phases.
Solution Approach 2:
The row and plot identifier system serves multiple functions simultaneously: it provides location tracking, enables issue identification, facilitates communication between system components, and supports data organization. This multi-functionality justifies the added complexity by delivering substantial operational benefits across multiple aspects of the 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.