Leaf Spatial Analysis via Natural Leaf Coordinate System
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Solution Overview
Problem
Current hyperspectral imaging systems for plant phenotyping lack effective spatial analysis methodologies, relying on averaged spectrum calculations that disregard the spatial distribution of color patterns on plant leaves, which are crucial for identifying nutrient stresses and predicting nitrogen content.
Innovation Solution
A novel imaging system and methodology that utilize the Natural Leaf Coordinate System (NLCS) to remap and encode leaf pixels, enabling the calculation of a nitrogen index (NLCS-N) based on spectral and spatial information, distinguishing nitrogen-sufficient from nitrogen-deficient plants with improved accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If traditional averaged spectrum calculation is used for image analysis, then the processing is simple and fast, but the spatial distribution information of color patterns on plant leaves is lost
Solution Approach 1:
The patent divides the leaf image into multiple spatial regions (e.g., vein regions, interveinal regions, different quadrants) and calculates spectral features for each region separately. This segmentation preserves spatial distribution information while maintaining computational efficiency by processing discrete regions rather than every pixel individually.
Solution Approach 2:
The patent applies different analysis methods to different spatial regions of the leaf. For example, vein regions are analyzed separately from interveinal regions, and different spectral indices are calculated for different leaf zones. This local quality approach preserves spatial information by treating each region with appropriate analysis while keeping processing manageable.
2Measurement precision
If spatial analysis methodology is implemented to capture color distribution patterns, then the prediction accuracy of nitrogen content improves, but the device complexity and processing requirements increase
Solution Approach 1:
The patent transitions from traditional 1D spectral analysis to 2D spatial-spectral analysis by incorporating spatial coordinates into the analysis framework. This dimensional expansion captures color distribution patterns across the leaf surface, improving nitrogen prediction accuracy while using computational approaches that manage the increased complexity.
Solution Approach 2:
The patent performs preliminary spatial segmentation and region identification before detailed spectral analysis. By pre-defining regions of interest (veins, interveinal areas, leaf zones) and preparing spatial masks in advance, the system reduces the complexity of subsequent analysis while preserving spatial distribution information for accurate nitrogen content prediction.
3Reliability
If multiple spatial regions of the leaf are analyzed separately, then the nitrogen index prediction robustness improves, but the processing time and computational load increase
Solution Approach 1:
The patent segments the leaf into distinct spatial regions (vein regions, interveinal regions, quadrants) and calculates nitrogen-related spectral indices for each region separately. This segmentation improves prediction robustness by capturing spatial variability in nitrogen distribution while using efficient region-based processing to minimize computational overhead compared to pixel-by-pixel analysis.
Solution Approach 2:
The patent combines results from multiple spatial region analyses into a composite nitrogen index or classification. By merging information from different leaf zones and regions through aggregation or integration methods, the system achieves robust nitrogen prediction that accounts for spatial heterogeneity while completing processing in a single integrated workflow.
Data Source
AI summary
A method for spatial analysis of leaf images is disclosed which includes acquiring one or more leaf images of plants, generating 3-dimensional (3D) images from the acquired one or more images from spectral heatmaps, identifying a stem and a plurality of veins, identifying i) one or more of valleys and ridges as regions between two consecutive veins from the plurality of veins, or ii) one or more of peaks and valleys as regions between two consecutive veins from the plurality of veins in the one or more generated 3D images, calculating average slopes in the generated 3D images between i) one or more of peak-to-valley, or ii) ridge-to-valley for the veins, calculating a nitrogen index based on the calculated average slopes, if the calculated average slope is between two thresholds, communicate to a user to add a predetermined chemical to the plants.