Soil Analysis System Using Crop-Specific Nutrient Thresholds
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Solution Overview
Problem
Current soil analysis methods lack consistency and accuracy in providing recommendations for increasing crop yield, as they do not effectively account for the specific nutrient availability and crop type, leading to inefficient fertilizer application and potential nutrient imbalances.
Innovation Solution
A system and method that utilize processors to analyze soil samples by receiving nutrient measurements and estimates, comparing them to threshold values specific to each crop type, and generating recommendations for foliar or soil fertilizers to optimize nutrient availability and crop yield, incorporating data from sensor systems and various soil tests like the Albrecht test.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If general soil analysis methods are used, then the analysis process is simple, but the accuracy and consistency of nutrient availability assessment deteriorates
Solution Approach 1:
The patent applies local quality by tailoring threshold values and analysis parameters to specific crop types. Different crops have different nutrient requirements and soil conditions, so the system uses crop-specific threshold values (e.g., corn vs. soybean thresholds) to provide accurate nutrient availability assessments for each local agricultural context, thereby improving measurement precision while maintaining operational simplicity through automated crop-type-based parameter selection.
Solution Approach 2:
The system changes parameters dynamically based on crop type by selecting different threshold values from predefined sets. When a crop type is identified, the system automatically switches to corresponding crop-specific threshold parameters for total nutrient measurements and available nutrient estimates, enabling accurate assessment across different agricultural contexts without complicating the user interface or analysis process.
2Ease of operation
If fertilizer application is not tailored to crop needs, then the application process is straightforward, but nutrient deficiencies and excesses increase
Solution Approach 1:
The patent implements feedback by continuously comparing measured total nutrient values and estimated available nutrient values against crop-specific threshold values. This feedback mechanism identifies nutrient deficiencies (when measurements fall below thresholds) and generates targeted fertilizer recommendations, ensuring reliable nutrient balance while keeping the user interface simple through automated decision-making.
Solution Approach 2:
The system performs self-service by automatically generating fertilizer recommendations based on the comparison between measured values and crop-specific thresholds. The analysis system independently determines nutrient deficiencies and excesses, then provides tailored fertilizer application guidance without requiring manual intervention, thereby maintaining ease of operation while improving nutrient balance accuracy.
3Measurement precision
If crop-specific threshold values are used, then nutrient availability assessment accuracy improves, but system complexity increases
Solution Approach 1:
The patent applies preliminary action by pre-establishing and storing crop-specific threshold values in a database before actual analysis. Common crop thresholds (corn, soybean, wheat, etc.) are predetermined and stored, so during analysis the system simply retrieves the appropriate thresholds based on crop type identification. This preliminary preparation enables high measurement precision without increasing operational complexity, as the complex threshold management is performed in advance rather than in real-time.
Solution Approach 2:
The system achieves universality by creating a single integrated platform that handles multiple crop types through a unified interface. The same user-friendly system accommodates various crops (corn, soybean, wheat, cotton, etc.) by automatically selecting appropriate crop-specific thresholds from a shared database, thereby providing accurate crop-specific analysis without requiring separate systems for each crop type and avoiding increased user-facing complexity.
Data Source
AI summary
The present disclosure describes a system, method, and non-transitory computer readable medium for analyzing soil samples. Accordingly, soil sample units may be obtained and provided to a server that generates raw data. The raw data is sent to a database, where it is downloaded. The raw data is subsequently organized into a sub-report for each nutrient or variable contained in the raw data. An average for each nutrient in the raw data and a number of additional factors related to the raw data may be calculated. The average and additional factors are used to determine bulk recommendations by comparing target data to an exchangeable measured value. Additionally, the factors are also used to determine challenges and solutions by comparing the average data to the target data for each nutrient. The system compares the raw data to the measured values and mathematically adjusts the compared values to compute an optimal treatment algorithm.