Digital Nutrient Model Assimilation for Soil Sampling
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Solution Overview
Problem
Current methods for determining nutrient content in soil are inefficient and inaccurate, often requiring frequent sampling and leading to inefficient nitrogen applications, which can result in wasted resources and environmental issues due to nitrogen leaching.
Innovation Solution
An agricultural intelligence computer system that uses limited soil samples to compute nutrient content values by assimilating data points, accounting for uncertainties in modeling and measurement, and generating improved estimates through a digital model that integrates field and external data.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If frequent soil sampling is performed to accurately determine nutrient content, then measurement precision improves, but loss of time and productivity worsen
Solution Approach 1:
The system performs preliminary nutrient content assessments using a digital model before actual soil sampling occurs. The model uses input data (weather, soil properties, crop information) to predict nutrient levels, allowing farmers to make informed decisions about whether sampling is necessary and when to sample, thereby reducing the frequency of sampling while maintaining accuracy.
Solution Approach 2:
A digital nutrient content model acts as an intermediary between direct measurement and decision-making. The model assimilates various input data and produces estimated nutrient content values that guide sampling decisions, reducing the need for frequent direct measurements while maintaining sufficient accuracy for nutrient management decisions.
2Reliability
If nitrogen application is increased to ensure adequate nutrient supply, then crop growth reliability improves, but loss of substance worsens due to nitrogen leaching
Solution Approach 1:
The system implements a feedback loop where actual soil sample measurements are compared with model predictions, and the model parameters are adjusted accordingly. This continuous refinement allows the system to accurately determine when nitrogen application is needed and at what rate, preventing both under-application (which would harm crop reliability) and over-application (which would cause leaching).
Solution Approach 2:
The system dynamically adjusts nitrogen application recommendations based on assimilated data from multiple sources including weather conditions, soil properties, crop growth stage, and actual soil sample measurements. This parameter optimization ensures nitrogen is applied at the right rate and time, maximizing crop uptake while minimizing leaching losses.
3Productivity
If digital nutrient content model is used to estimate nutrient levels, then productivity improves by reducing sampling frequency, but measurement precision worsens due to model errors
Solution Approach 1:
The system merges the digital model's estimated nutrient content with actual soil sample measurements through data assimilation. The model output and measurement data are combined to produce a refined estimate that leverages the strengths of both approaches: the model's ability to provide continuous estimates and the measurements' accuracy at specific points in time.
Solution Approach 2:
Actual soil sample measurements serve as feedback to continuously refine the digital model's parameters and predictions. This feedback mechanism allows the model to learn from real measurements and improve its accuracy over time, reducing the precision gap between model estimates and actual soil conditions while maintaining reduced sampling frequency.
Data Source
AI summary
In an embodiment, agricultural intelligence computer system stores a digital model of nutrient content in soil which includes a plurality of values and expressions that define transformations of or relationships between the values and produce estimates of nutrient content values in soil. The agricultural intelligence computer receives nutrient content measurement values for a particular field at a particular time. The agricultural intelligence computer system uses the digital model of nutrient content to compute a nutrient content value for the particular field at the particular time. The agricultural intelligence computer system identifies a modeling uncertainty corresponding to the computed nutrient content value and a measurement uncertainty corresponding to the received measurement values. Based on the identified uncertainties, the modeled nutrient content value, and the received measurement values, the agricultural intelligence computer system computes an assimilated nutrient content value.