Soil Property Model Predicting Nutrient Levels
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Precision agronomists face high costs and labor intensiveness in dividing agricultural fields into management zones for soil sampling due to the need for extensive sampling across large zones, which is time-consuming and costly.
Innovation Solution
A method and system using a soil test model that leverages statistical relationships between nutrient levels and field-specific characteristics from known regions to predict nutrient levels in unknown regions within the same agricultural field, employing machine learning algorithms and actual soil test results from one zone to enhance predictions in other zones.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If soil sampling is conducted extensively across large agricultural zones to obtain accurate nutrient levels, then measurement precision is improved, but loss of time and loss of substance increase due to labor intensiveness and cost
Solution Approach 1:
The patent creates virtual soil test values by copying and transferring actual soil test results from proximate regions to target regions. Instead of physically sampling every zone, the system replicates nutrient level data from nearby sampled regions and uses machine learning models to adapt these copied values to specific management zones, significantly reducing field sampling requirements while maintaining prediction accuracy
Solution Approach 2:
The patent introduces machine learning models and proximate region soil test data as intermediaries between actual soil samples and target management zones. The model acts as a mediator that translates limited actual soil test results into predicted nutrient levels for unsampled regions, using field-specific characteristics and spatial relationships to bridge the gap between sampled and unsampled areas
2Measurement precision
If soil sampling is conducted extensively across large agricultural zones to obtain accurate nutrient levels, then measurement precision is improved, but cost increases due to labor intensiveness
Solution Approach 1:
The system copies actual soil test results from proximate regions and applies them to target management zones through virtual soil testing. This copying approach eliminates the need for expensive and labor-intensive physical sampling in every zone, while the machine learning model ensures the copied data is appropriately adapted to local conditions, maintaining accuracy without the associated costs
Solution Approach 2:
The system enables self-service by allowing the machine learning model to automatically generate virtual soil test values using available data from proximate regions and field characteristics. This automated process eliminates the need for manual soil sampling, laboratory analysis, and agronomist interpretation for every zone, significantly reducing labor costs and enabling the system to serve itself through algorithmic prediction
3Loss of energy
If the number of soil samples is reduced to lower costs, then loss of substance decreases, but measurement precision deteriorates
Solution Approach 1:
The machine learning model serves as an intermediary that enhances the value of limited soil samples. By using the model to process actual soil test results from proximate regions and combine them with field-specific characteristics, the system extracts maximum information from minimal sampling, maintaining prediction accuracy despite reduced sample numbers
Solution Approach 2:
The system copies actual soil test results from proximate regions and uses machine learning to adapt these copied values to target zones. This copying approach allows the system to extend the utility of each actual soil sample across multiple management zones, effectively multiplying the information value of each sample while maintaining prediction reliability
4Manufacturing precision
If management zones are divided into many small regions for precise fertilizer prescriptions, then manufacturing precision is improved, but device complexity increases due to extensive sampling requirements
Solution Approach 1:
The system copies actual soil test results from proximate regions and applies them to multiple management zones through virtual soil testing. This approach enables precise fertilizer prescriptions for numerous small zones without requiring physical sampling in each zone, maintaining manufacturing precision while eliminating the complexity of coordinating extensive field sampling across many small regions
Solution Approach 2:
The machine learning model serves multiple functions simultaneously: it predicts nutrient levels for unsampled zones, adapts proximate region data to local conditions, and generates virtual soil test values for fertilizer prescription. This multi-functionality allows the system to handle complex management zone divisions without proportionally increasing sampling complexity, as the single model performs multiple analytical tasks
Data Source
AI summary
In an agricultural field having first regions with current soil test values known from actual tests and second regions having unknown soil test values, nutrient levels are predicted using a soil test model which defines a statistical relationship between (i) nutrient levels for a given region of a training field for a given season and (ii) field specific characteristics for the given region in a previous growing season and nutrient levels in the given region or proximate regions from the given growing season. Acquired known field specific characteristics and current soil test values from the first regions are then applied to the soil test model to calculate the predicted nutrient level the second regions. This can reduce the cost of soil sampling by using actual soil test results from one management zone as a predictor when modeling other zones' properties.