Predicting Soil-Active Biological Product Performance

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Solution Overview

Problem

Existing methods for evaluating and predicting the performance of soil-active agricultural biological products are hindered by the complexity and variability of soil temperature and moisture conditions, which are difficult to estimate from above-ground weather data, leading to inefficient and costly field testing and limited understanding of below-surface conditions.

Innovation Solution

A system and method combining customized field modeling with machine learning techniques to correlate above-ground weather data with soil moisture and temperature at different depths, generating predictive models for the performance of bio-pesticides, bio-stimulants, and other soil-active products, enabling precise recommendations for agricultural applications.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Measurement precision

If field testing is conducted to evaluate soil-active agricultural biological products, then performance prediction accuracy is improved, but cost and time requirements increase significantly

Engineering Contradiction:

Improveperformance prediction accuracyVSAvoidtime for field testing

Core Design Contradiction:

Measurement precisionVSLoss of time

Solution Approach 1:

The system performs preliminary modeling and prediction using above-ground weather data and environmental variables before conducting actual field testing. By pre-assessing product performance through computational models that simulate soil conditions and biological product interactions, the system identifies promising products and conditions, thereby reducing the scope and duration of required field testing while maintaining prediction accuracy

Inventive Principle:

Principle #10Preliminary action

Solution Approach 2:

The system introduces an intermediary computational modeling layer that correlates above-ground weather data with below-ground soil conditions. This intermediary model acts as a bridge, translating easily obtainable above-ground environmental data into predictions of below-ground product performance, reducing direct dependence on extensive field testing

Inventive Principle:

Principle #24Intermediary (Mediator)

2Measurement precision

If field testing is conducted to evaluate soil-active agricultural biological products, then performance prediction accuracy is improved, but cost increases significantly

Engineering Contradiction:

Improveperformance prediction accuracyVSAvoidcost of field testing

Core Design Contradiction:

Measurement precisionVSLoss of energy

Solution Approach 1:

The system creates computational copies and simulations of field testing conditions using environmental models. Instead of conducting numerous physical field tests, the system uses virtual models that replicate soil environments, weather patterns, and product interactions, providing accurate performance predictions at a fraction of the cost of actual field testing

Inventive Principle:

Principle #26Copying

Solution Approach 2:

The system changes the parameters being measured from direct below-ground product performance to above-ground environmental variables that correlate with product performance. By measuring and modeling environmental parameters (temperature, moisture, pH) that influence product activity, the system achieves performance prediction without the high cost of direct field testing

Inventive Principle:

Principle #35Parameter changes

3Loss of information

If direct measurement of soil moisture and temperature is performed, then below-surface environmental understanding is improved, but measurement complexity and disruption increase

Engineering Contradiction:

Improvebelow-surface environmental understandingVSAvoidmeasurement system complexity

Core Design Contradiction:

Loss of informationVSDevice complexity

Solution Approach 1:

The system uses above-ground weather data and environmental measurements as intermediaries to infer below-surface soil conditions. Rather than directly measuring difficult-to-access soil parameters, the system correlates easily obtainable above-ground data (air temperature, precipitation, humidity) with modeled below-ground conditions, providing comprehensive environmental understanding without complex subsurface instrumentation

Inventive Principle:

Principle #24Intermediary (Mediator)

Solution Approach 2:

The system replaces physical mechanical measurement devices in the soil with computational modeling approaches. Instead of inserting sensors and physical probes into the soil, the system uses mathematical models and algorithms to estimate below-ground environmental conditions based on above-ground observations, eliminating the need for complex physical measurement infrastructure

Inventive Principle:

Principle #28Mechanics substitution (Replace mechanical system)

Data Source

PatentUS11195109B2Modeling and prediction of below-ground performance of agricultural biological products in precision agriculture

Publication Date: 2021.12.07 DTN LLC

AI summary

A below-ground agricultural biological performance modeling approach in precision agriculture combines customized field modeling with machine learning techniques for environmental matching of variables to describe a below-surface soil state, to understand and predict the performance of soil-active agricultural biological products such as bio-pesticides, bio-stimulants, plant growth regulators, and other biologically-derives soil adjuvants. The modeling approach characterizes the influence of environmental relationships on the performance of such soil-active agricultural biological products to develop a suite of predictive models to provide notifications, advisories, and recommendations for appropriate products for individual fields.