Fiducial Crop Markings for Plant-Level Robot Localization
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Solution Overview
Problem
Agricultural technologies face challenges in accurately localizing individual plants and collecting granular data due to the limitations of conventional position coordinate sensors, such as GPS, which can be inaccurate and unreliable, especially under canopies or in dense crop environments, hindering efficient agricultural operations and yield optimization.
Innovation Solution
Incorporating fiducial markings on agricultural surfaces like synthetic mulch, which can be detected by mobile computing devices and autonomous agricultural devices, allowing for precise localization and data collection at the individual plant level, even when GPS data is unreliable, by correlating sensor data with stored plant information.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If GPS sensors are used for robot localization in agricultural areas, then the system is simple and easy to operate, but the localization precision is insufficient at the individual plant level
Solution Approach 1:
The patent introduces fiducial markings as an intermediary element between the GPS system and the plants. These markings serve as intermediate reference points that the robot can detect with high precision using vision sensors, bridging the gap between coarse GPS localization and fine-grained plant-level positioning requirements
Solution Approach 2:
The patent replaces reliance on satellite-based GPS mechanical/systematic positioning with an optical/vision-based detection system that reads fiducial markings. This substitution enables higher precision localization by using visual pattern recognition instead of satellite signal triangulation
2Reliability
If GPS is used for localization, then the apparatus is simple, but it cannot operate reliably under canopies or in dense crop environments
Solution Approach 1:
The fiducial markings act as intermediary reference points that are detectable regardless of satellite signal availability. By placing these markings on synthetic mulch or ground surfaces, the system creates a localized reference framework that works reliably under canopies where GPS signals are blocked
Solution Approach 2:
The fiducial markings are pre-installed on the agricultural ground cover before planting or robot operation. This preliminary placement ensures that the reference points are already in position and can be immediately used for localization when the robot enters the field, regardless of weather or canopy conditions
3Measurement precision
If satellite imaging is used for agricultural monitoring, then the coverage area is large, but the data precision at individual plant level is insufficient
Solution Approach 1:
The patent segments the agricultural field into discrete zones marked by fiducial markings, each associated with specific plants. This segmentation allows the system to collect and manage data at the individual plant level while still covering large agricultural areas through systematic placement of markings across the entire field
Solution Approach 2:
The patent applies local quality by placing fiducial markings at specific locations on the ground that correspond to individual plants or plant groups. This creates localized high-precision reference points distributed throughout the field, enabling detailed plant-level monitoring while maintaining overall field coverage
Data Source
AI summary
Implementations set forth herein relate to using fiducial markings on one or more localized portions of an agricultural apparatus in order to generate local and regional data that can be correlated for planning and executing agricultural maintenance. An array of fiducial markings can be disposed onto plastic mulch that surrounds individual crops, in order that each fiducial marking of the array can operate as a signature for each individual crop. Crop data, such as health and yield, corresponding to a particular crop can then be stored in association with a corresponding fiducial marking, thereby allowing the certain data for the particular crop to be tracked and analyzed. Furthermore, autonomous agricultural devices can rely on the crop data, over other sources of data, such as GPS satellites, thereby allowing the autonomous agricultural devices to be more reliable.