Subfield Moisture Mapping with Shallow-Water Flow Computation
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Current computer-based tools for monitoring soil nutrient levels in agricultural fields rely on low-granularity topographical maps, which fail to capture subfield-specific hydrologic differences and nutrient variations, leading to inadequate crop yield predictions.
Innovation Solution
A system and method for generating high-granularity subfield-based soil moisture models using advanced mathematical equations and high-resolution elevation data, incorporating overland flow and kinematic wave calculations to predict hydrologic fluxes and nutrient levels at the field-pixel level.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Area of stationary object
If zone-based topographical maps are used for monitoring soil nutrient levels, then the monitoring coverage is improved, but the measurement precision of subfield-specific hydrologic differences and nutrient levels deteriorates
Solution Approach 1:
The field is divided into multiple zones based on topographical characteristics, and each zone is further segmented into subfields with distinct hydrologic properties. This hierarchical segmentation allows comprehensive coverage while maintaining subfield-level precision for hydrologic monitoring and nutrient level assessment.
2Device complexity
If zone-based maps are used, then the device complexity is reduced, but the measurement precision of nitrogen washed out by water flow and nitrogen levels in individual subfields deteriorates
Solution Approach 1:
The monitoring system segments the field into zones and subfields hierarchically, enabling detailed nitrogen level tracking in each subfield while maintaining an overall zone-level structure for manageable complexity.
Solution Approach 2:
Different monitoring parameters are applied at different spatial scales: zone-level monitoring for general soil conditions and subfield-level monitoring for specific hydrologic fluxes and nitrogen levels. This local quality approach ensures precise measurement of nitrogen washed out by water flow in each subfield while keeping the overall system complexity manageable.
3Measurement precision
If high-granularity subfield-based models are generated, then the measurement precision of soil moisture and nutrient levels is improved, but the computational complexity and data processing requirements increase
Solution Approach 1:
The field is segmented into zones and subfields, allowing high-granularity modeling to be applied selectively to each subfield rather than the entire field at once. This reduces computational complexity by breaking down the large-scale problem into smaller, manageable sub-problems while maintaining high measurement precision for soil moisture and nutrient levels.
Solution Approach 2:
The system applies high-granularity modeling at the subfield level where it is most needed for capturing hydrologic differences, rather than uniformly across the entire field. This partial application of high-resolution modeling optimizes the balance between measurement precision and computational complexity.
Data Source
AI summary
Subfield moisture model improvement in generating overland flow modeling using shallow water calculations and kinematic wave calculations is disclosed. In an embodiment, a computer-implemented data processing method comprises: receiving precipitation data and infiltration data for an agricultural field; obtaining surface water depth data, surface water velocity data, and surface water discharge data for the same agricultural field; determining subfield geometry data for the agricultural field; executing a plurality of water calculations and wave calculations using the subfield geometry data to generate an overland flow model that includes moisture levels for the agricultural field; based on, at least in part, the overland flow model, generating and causing displaying a visual graphical image of the agricultural field comprising a plurality of color pixels having color values corresponding to the moisture levels determined for the agricultural field. Output of the overland flow model is provided to control computers of seeders, planters, fertilizer spreaders, harvesters, or combines to control seeding, planting, fertilizing or irrigation activities in the field.