Plant Localization via Invariant Anchor Point Mapping
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Solution Overview
Problem
Existing plant localization techniques in precision agriculture are computationally expensive, error-prone, and time-consuming, especially when dealing with large crop fields, making it challenging to accurately identify and target individual plants for agricultural tasks.
Innovation Solution
The method involves processing high-elevation images of multiple resolutions by aligning lower-resolution images based on invariant anchor points to generate mappings between pixels, allowing for the localization of individual plants within higher-resolution images, which can be used for precise agricultural tasks without the need for computationally expensive stitching or blending.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If multiple high-elevation images are blended or fused to create a global image for plant localization, then measurement precision is improved, but computational cost and time consumption increase significantly
Solution Approach 1:
The patent divides the image processing task into two segments: (1) process only lower-resolution images to identify invariant anchor points and establish coordinate mappings, and (2) apply these mappings directly to higher-resolution images for plant localization. This segmentation avoids the computationally expensive step of blending multiple high-resolution images while maintaining localization accuracy.
Solution Approach 2:
The patent extracts only the essential information (invariant anchor points and coordinate mappings) from the lower-resolution images, rather than processing or blending the entire high-resolution image data. This extraction approach retains the necessary localization information while eliminating redundant computational steps.
2Measurement precision
If multiple high-elevation images are blended or fused to create a global image for plant localization, then measurement precision is improved, but computational resources and complexity increase
Solution Approach 1:
The processing workflow is segmented into distinct stages: lower-resolution image analysis for anchor point identification, coordinate mapping generation, and higher-resolution image localization. This segmentation simplifies the overall system complexity by breaking down the complex blending operation into manageable, independent steps.
Solution Approach 2:
The patent extracts only the critical transformation information (coordinate mappings based on invariant anchor points) from the lower-resolution images. This extraction eliminates the need for complex image blending algorithms while preserving the essential spatial relationships needed for accurate plant localization.
3Productivity
If sparse sampling is used to extrapolate crop yields and disease diagnoses for entire plots, then productivity is improved, but measurement precision deteriorates
Solution Approach 1:
Instead of using sparse sampling to infer plot-level conditions (bottom-up approach), the patent inverts the approach by using high-resolution imagery processed through efficient coordinate mapping to achieve precise plant-level localization. This top-down approach maintains productivity while improving measurement precision by enabling individual plant identification rather than relying on extrapolation from sparse samples.
Data Source
AI summary
Implementations are described herein for localizing individual plants using high-elevation images at multiple different resolutions. A first set of high-elevation images that capture the plurality of plants at a first resolution may be analyzed to classify a set of pixels as invariant anchor points. High-elevation images of the first set may be aligned with each other based on the invariant anchor points that are common among at least some of the first set of high-elevation images. A mapping may be generated between pixels of the aligned high-elevation images of the first set and spatially-corresponding pixels of a second set of higher-resolution high-elevation images. Based at least in part on the mapping, individual plant(s) of the plurality of plants may be localized within one or more of the second set of high-elevation images for performance of one or more agricultural tasks.