Crop Yield Optimization via Nutrient Segmentation
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Solution Overview
Problem
Current methods for optimizing crop nutritional conditions to enhance yields are limited, as they often focus on best-performing plants and do not account for variations across all yield ranges, leading to suboptimal fertilization strategies for low-yielding plants and increased costs due to unnecessary fertilization.
Innovation Solution
The use of quantile regression and artificial neural network modeling to analyze nutrient-yield relationships across all yield ranges, allowing for the determination of optimal nutrient concentration levels for each nutrient element, which can be used to develop tailored fertilization strategies for individual plants or groups, thereby improving overall crop production and reducing fertilizer costs.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If fertilization strategies are optimized for best-performing plants only, then individual plant yields for high-yielding areas are improved, but overall crop production remains suboptimal and fertilizer costs increase due to unnecessary fertilization
Solution Approach 1:
The patent segments the crop population into different yield categories (e.g., low-yielding, medium-yielding, high-yielding plants) and develops separate nutrient-yield function models for each segment. This allows tailored fertilization strategies for each group rather than applying a uniform approach, optimizing fertilizer use for each yield category while reducing unnecessary fertilization for plants that cannot achieve higher yields due to environmental constraints
Solution Approach 2:
The patent applies local quality by determining specific nutrient concentration requirements for each yield category and individual plant based on their inherent potential. Each plant or group receives a customized nutrient recommendation based on its specific yield category, environmental conditions, and nutrient-yield function, rather than a blanket fertilization approach, thereby optimizing fertilizer application locally for each plant's potential
2Ease of operation
If uniform fertilization strategies are applied to all plants, then implementation is simplified, but fertilizer costs increase and environmental safety deteriorates due to unnecessary fertilization
Solution Approach 1:
The patent changes the parameter of nutrient concentration recommendations based on the yield category and individual plant characteristics. By using quantile regression or neural network models to determine optimal nutrient concentrations for different yield categories, the system adjusts fertilization parameters dynamically rather than applying uniform rates, reducing excess fertilizer application and associated environmental harm while maintaining operational feasibility through systematic categorization
3Device complexity
If nutrient-yield relationships are analyzed only for best-performing plants, then model development is simplified, but the applicability to all yield ranges is limited and overall crop production optimization is compromised
Solution Approach 1:
The patent segments the data analysis process by creating separate nutrient-yield function models for different yield categories (e.g., 10th percentile, 50th percentile, 90th percentile plants). This segmentation allows each model to be trained on appropriate data ranges while collectively covering the full spectrum of plant performance, enabling optimization across all yield ranges rather than only for top performers
Solution Approach 2:
The patent extends the analysis from a single-dimensional approach (only best-performing plants) to a multi-dimensional framework by incorporating multiple yield categories and percentiles. This dimensional expansion allows the system to capture the full complexity of nutrient-yield relationships across different plant performance levels, environmental conditions, and yield potentials, thereby optimizing overall crop production
Data Source
AI summary
Systems, apparatuses, and methods as described herein relate to analyzing plant nutrient-yield relationships and identifying optimum plant fertilization strategies to improve individual plant yields and overall plant yields. Nutrient data and yield data for plants in all yield ranges, rather than nutrient data and yield data for only the best-performing plants, is categorized based on the yield data and then analyzed to determine the yield as a function of nutrient condition for each nutrient element for the plants in each yield category. The nutrient-yield function can be modeled using a polynomial equation of various orders that can be used for determining the nutritional condition to improve crop production for plants in each yield category.