Variable-Grid Soil Interpolation Across Agricultural Boundaries
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Solution Overview
Problem
Conventional soil data interpolation techniques fail to account for environmental variability and distinguish between agricultural and non-agricultural points, leading to inaccurate data sets and inefficient agricultural modeling.
Innovation Solution
A method and system that generate a variable grid with boundary information, assign soil point data values to respective grid indices, and interpolate only compatible data points within the grid, avoiding interpolation across boundaries.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional interpolation techniques are used to process all soil data points uniformly, then the processing is simple and fast, but the accuracy of the interpolated data decreases due to inclusion of non-agricultural points and environmental variability
Solution Approach 1:
The patent divides the agricultural field into a variable grid system with multiple zones, where each zone can be independently processed. Non-agricultural points are segmented and excluded from interpolation, while agricultural points are processed separately. This segmentation allows the system to maintain high accuracy by treating different data types differently, while managing complexity through systematic organization of the grid structure.
Solution Approach 2:
The patent applies different interpolation treatments to different locations within the field. Agricultural points receive full interpolation processing, while non-agricultural points are excluded. The variable grid allows each local area to be processed according to its specific characteristics, improving overall data accuracy without requiring uniform complex processing across the entire field.
2Loss of information
If basic interpolation without environmental variability consideration is used, then the processing is straightforward, but the data set lacks environmental variability information needed for accurate agricultural modeling
Solution Approach 1:
The patent performs preliminary classification of data points into agricultural and non-agricultural categories before interpolation. Environmental variability information is captured and preserved in the variable grid structure during this preliminary stage, allowing the system to maintain complete environmental data while preparing for accurate interpolation processing in subsequent steps.
3Measurement precision
If non-agricultural points are included in interpolation, then more data points are available for processing, but the interpolated values become wildly skewed due to extreme variability in non-agricultural areas
Solution Approach 1:
The patent extracts and removes non-agricultural points from the interpolation process while retaining agricultural points. The variable grid system identifies and separates non-agricultural data points, excluding them from interpolation calculations. This extraction ensures that only relevant agricultural data points contribute to interpolated values, preventing skewing while maintaining an adequate quantity of valid data points for accurate interpolation.
Data Source
AI summary
A method includes generating a field geospatial map layer; receiving soil data; assigning the soil data and type to a variable grid; generating interpolated soil data; and storing the interpolated soil data values in spatial data files. A computing system includes a processor; and a memory having stored thereon instructions that, when executed by the one or more processors, cause the computing system to: generate a field geospatial map layer; receive soil data; assign the soil data and type to a variable grid; generate interpolated soil data; and store the soil data in spatial data files. A non-transitory computer readable medium includes program instructions that when executed by a computer, cause the computer to: generate a field geospatial map layer; receive soil data; assign the soil data and type to a variable grid; generate interpolated soil data; and store the soil data in spatial data files.