High-Resolution Fertility Prescription Visualization
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Solution Overview
Problem
Current agricultural tools lack the capability for fine-grained, pixel-level management of fields using satellite imagery, leading to inefficient fertilizer application and yield reduction due to outdated data and computational complexity, with a need for daily visualization of crop, water, and soil conditions.
Innovation Solution
A computer-implemented system that generates and updates fertility prescriptions continuously, using a gridded fertility model to interpolate data at high spatial resolution, allowing for real-time adaptation to in-season conditions and visualization of field metrics like nutrient status and crop stress.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If satellite imagery is used for field management, then spatial resolution of field data is improved, but computational complexity increases making regular display infeasible
Solution Approach 1:
The patent divides the field into discrete pixels or grid cells, allowing independent processing of each unit. This segmentation enables the system to handle high-resolution satellite imagery by processing individual pixels rather than treating the entire field as a single complex entity, making computational display feasible while maintaining high spatial resolution
Solution Approach 2:
The patent applies different data and models to different spatial locations within the field based on local characteristics. Each pixel or zone receives customized treatment with locally-relevant agronomic data, allowing the system to maintain high resolution without requiring uniform complex processing across the entire field
2Ease of operation
If fertilizer application is based on time-invariant zones, then management simplicity is improved, but application precision deteriorates leading to unnecessary costs
Solution Approach 1:
The patent transitions from static, time-invariant management zones to dynamic prescriptions that update continuously based on current satellite imagery and in-season conditions. Each pixel's fertilizer recommendation can change daily based on real-time crop health indicators, allowing precise application while maintaining automated simplicity through continuous model re-evaluation
3Ease of operation
If in-season fertilizer application is performed without real-time condition data, then operational simplicity is improved, but yield potential deteriorates due to inability to adapt to changing conditions
Solution Approach 1:
The patent implements a feedback loop where satellite imagery continuously monitors crop conditions, and the system automatically adjusts fertilizer prescriptions based on detected changes. This closed-loop approach maintains operational simplicity through automation while enabling real-time adaptation to in-season conditions, thereby maximizing yield potential without increasing operational complexity
4Quantity of substance
If satellite imagery is obtained only every few days, then data acquisition cost is reduced, but timeliness of field visualization deteriorates
Solution Approach 1:
The patent pre-processes and stores multiple layers of satellite imagery data at high resolution before they are needed for visualization. By having data ready in advance and maintaining a historical archive, the system can rapidly generate daily visualizations without waiting for new satellite passes, thus improving timeliness without increasing actual data acquisition frequency
Data Source
AI summary
Display of graphical maps of agricultural fields, coded with color or other indicators of values of data pertaining to agronomy at high resolution, and updated on a daily basis or on demand by recalculating agronomy models with the high-resolution data, is disclosed. Map displays may include multiple layers that relate to different agronomy metrics, and GUI widgets that are programmed to receive selection of values indicating different field properties or layers to display. In an embodiment, a computer-implemented data processing method providing an improvement in efficient calculation of digital data representing physical properties of agricultural fields, the method comprising receiving digital input specifying a request to display a map image of a specified agricultural field for a particular day; in response to receiving the input, calculating an interpolated digital image of the specified agricultural field with a plurality of different field properties, by: dividing a digital map of the specified field into a plurality of grids each having a same size and a same area; obtaining, from digital storage, a plurality of data for the different field properties and assigning the data as covariates; grouping the grids into a specified number of clusters based on values of the covariates; pseudo-randomly selecting a specified number of one or more sample values in each of the clusters; evaluating a digital fertility model using the sample values and storing a plurality of output values from the digital fertility model; interpolating a plurality of model values for the grids; generating and causing displaying a visual graphical image of the specified agricultural field including color pixels corresponding to each of the model values.