Remote Sensing Seed Purity Estimation With Field Stress Scoring
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Solution Overview
Problem
Existing methods for estimating genetic purity of hybrid seeds are costly, time-consuming, and limited to known genetic markers, making large-scale genetic purity estimation and cross-pollination detection challenging.
Innovation Solution
A processor-implemented method using remote sensing and Geographic Information Systems (GIS) for large-scale genetic purity estimation and cross-pollination detection, incorporating spatial, temporal, and mechanical isolation considerations, along with field suitability and stress assessment, to generate Non-Fungible Tokens (NFTs) for seed packets.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional methods like DNA Fingerprinting, Genotyping-by-Sequencing, or Grow-Out Tests are used for genetic purity estimation, then measurement precision is improved, but cost and time consumption increase significantly
Solution Approach 1:
The patent replaces traditional mechanical/biological laboratory methods (DNA Fingerprinting, Genotyping-by-Sequencing, Grow-Out Tests) with an optical/electronic remote sensing system using satellites, drones, or ground-based sensors to capture spectral data. This substitution enables non-invasive, rapid assessment of crop phenology and genetic purity without physical sample collection and laboratory analysis, dramatically reducing time while maintaining accuracy through advanced image processing and machine learning algorithms
Solution Approach 2:
The system transforms the measurement approach by changing from direct genetic analysis (DNA-level parameters) to indirect phenological parameter measurement through spectral reflectance. By monitoring vegetation indices, phenological stage timing, and growth patterns that differ between hybrid and purebred crops, the system estimates genetic purity through observable phenotypic parameters rather than direct genetic markers, enabling faster large-scale assessment
2Measurement precision
If traditional genetic testing methods are applied to large-scale seed plots, then measurement precision is improved, but device complexity and operational difficulty increase
Solution Approach 1:
The patent replaces complex laboratory equipment and specialized testing infrastructure with relatively simple remote sensing platforms (satellites, drones, or ground-based sensors) combined with automated image processing systems. This substitution simplifies the overall system while enabling large-scale deployment, as the sensing platforms can cover extensive areas without requiring complex sample collection, transportation, and laboratory processing infrastructure
Solution Approach 2:
The system incorporates automated phenological stage classification and genetic purity estimation algorithms that process remote sensing data independently without requiring constant human intervention or specialized domain expertise for each measurement. The machine learning models automatically identify phenological stages, detect off-type plants, and calculate genetic purity percentages, making the system easier to operate at scale while maintaining precision
3Manufacturing precision
If buffer zones and isolation distances are increased to prevent cross-pollination, then seed purity is improved, but area occupied by seed plots decreases
Solution Approach 1:
The system uses remote sensing to continuously monitor phenological stages of crops in and around seed plots, providing real-time feedback on flowering timing and cross-pollination risk. This feedback enables dynamic adjustment of management practices, such as temporary mechanical barriers or targeted pesticide application, rather than requiring permanent large buffer zones. The feedback loop allows precision management that maintains purity while minimizing land loss
Solution Approach 2:
The system transforms the approach from static spatial isolation (fixed buffer zones) to dynamic monitoring and management based on actual phenological parameters. By measuring and comparing phenological stage timing between seed plots and neighboring crops, the system identifies actual cross-pollination risks and applies targeted interventions only when and where needed, rather than universally increasing isolation distances across all seed plots
4Measurement precision
If detailed phenological monitoring and multiple isolation assessments are performed, then measurement precision is improved, but loss of time and computational resources increase
Solution Approach 1:
The system performs preliminary classification of phenological stages using remote sensing data before conducting detailed cross-pollination risk assessment. By first identifying which crop plots are in flowering stage and their expected flowering periods in advance, the system pre-filters the data that requires intensive processing, reducing the overall computational burden and time required for complete genetic purity estimation while maintaining accuracy
Data Source
AI summary
Current approaches for cross pollination contamination detection mainly rely on field surveys that are generally costly and consumes lot of time. The genetic purity of seed estimation techniques needs specialized equipment which again are associated with high cost and longer time. Present disclosure provides method and system for genetic purity estimation and cross-pollination detection in agricultural fields. The system receives remote sensing, weather, historical and soil health data of region of interest. The system then uses data to calculate contamination score representing contamination due to cross pollination. Thereafter, system uses data to determine field suitability and stress score. Further, system detects deviations in crop characteristics indicative of off-types in and around region of interest based on data. Finally, system estimates genetic purity of seeds coming from crop plotted in region of interest based on contamination, field suitability and field stress score, and off-types to determine genetic purity of seed score.