Remote Sensing Algorithms for Regenerative Agriculture Mapping
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Solution Overview
Problem
Current methods for tracking regenerative agricultural practices, such as cover crops and conservation tillage, are immature and lack the automation, scalability, and frequency needed to provide detailed and timely insights at various geographic scales.
Innovation Solution
A method utilizing satellite imagery to predict tillage practices, cover crop practices, and other regenerative agricultural practices by generating field-level zonal summary time series, identifying dormant periods, and applying decision tree classifiers to determine the adoption rates and timing of these practices.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If satellite imagery and automated algorithms are used to track regenerative practices, then the frequency, detail, and timeliness of monitoring improve, but the device complexity and data processing requirements increase
Solution Approach 1:
The patent segments the monitoring system into multiple components: satellite imagery acquisition, NDVI/NDTI index calculation, dormant period identification, tillage event detection, and adoption rate estimation. This segmentation allows each component to be optimized independently and processed through distributed computing systems, managing complexity while maintaining high monitoring frequency.
Solution Approach 2:
The patent introduces intermediate products (NDVI and NDTI time series) that serve as mediators between raw satellite imagery and final tillage practice predictions. These intermediate indices simplify the complex relationship between satellite data and agricultural practices, enabling automated detection while managing system complexity.
2Measurement precision
If field-level zonal summary time series are generated from satellite imagery, then the measurement precision of regenerative practice detection improves, but the loss of time and computational resources increase
Solution Approach 1:
The patent performs preliminary actions by generating NDVI and NDTI time series from satellite imagery before conducting tillage event detection. This preprocessing step organizes raw data into meaningful temporal patterns, improving detection accuracy while reducing the computational burden of subsequent analysis through efficient time series algorithms.
Solution Approach 2:
The patent transforms satellite imagery data into derived parameters (NDVI and NDTI indices) that change over time. This parameter transformation converts complex spectral data into simplified temporal patterns that are easier to analyze, improving measurement precision while reducing processing time through efficient time series analysis.
3Reliability
If dormant periods are identified and NDTI time series are analyzed to predict tillage events, then the reliability of tillage practice prediction improves, but the difficulty of detecting and measuring increases
Solution Approach 1:
The patent identifies dormant periods as preliminary conditions before conducting tillage event detection. By pre-defining the temporal windows when tillage is most likely to occur (based on NDVI thresholds and seasonal patterns), the system improves prediction reliability while reducing detection complexity through focused analysis of specific time periods rather than continuous monitoring.
Solution Approach 2:
The patent applies local quality by analyzing NDTI time series with different criteria during dormant periods versus active growing periods. The detection algorithm adjusts its sensitivity and thresholds based on the seasonal context, improving reliability by focusing detection efforts on periods when tillage events are most detectable while simplifying analysis during inactive periods.
Data Source
AI summary
This invention relates to methods for determining adoption and impact of regenerative farming practices. Embodiments of these methods, take satellite imagery and weather data as inputs, process those data according to methods of the present invention, and produce outputs which indicate whether a specific farming practice (for example, no-till or cover cropping) was adopted for a particular field or region for a particular season.