Measurement Region Selection Using Yield Data for Fertilizer Sampling
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Solution Overview
Problem
Existing crop nutrient measurement devices struggle to provide a reliable representation of the entire agricultural field's nutrient content due to in-field variability, often requiring numerous measurements and lacking consideration of underlying factors, leading to inaccurate fertilizer recommendations that can cause yield and economic losses.
Innovation Solution
A computer-implemented method determines measurement regions within an agricultural field based on position-dependent yield data, using remote spectral data to identify areas with similar yields and nutrient content, allowing for precise fertilizer recommendations without extensive manual measurement.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If numerous manual measurements are taken to cover in-field variability, then measurement representativeness is improved, but time consumption and labor requirements increase
Solution Approach 1:
The agricultural field is segmented into multiple measurement regions based on position-dependent yield data. Instead of requiring uniform coverage of the entire field, the system identifies specific regions (e.g., high-yield, low-yield, average-yield zones) that are most representative of the field's variability. This segmentation allows targeted sampling that reduces the total number of measurements needed while maintaining statistical representativeness.
Solution Approach 2:
The system performs preliminary analysis using position-dependent yield data from prior crop seasons to determine measurement regions before actual nutrient measurements are taken. By pre-identifying which field regions are most representative based on historical yield patterns, the system enables planners to allocate measurement resources efficiently in advance, avoiding the need for extensive ad-hoc measurements.
2Ease of manufacture
If handheld devices are used for direct measurement, then measurement cost is reduced, but the ability to determine in-field spatial variability deteriorates
Solution Approach 1:
Position-dependent yield data serves as an intermediary that bridges the gap between low-cost handheld measurements and comprehensive field analysis. This intermediary data, derived from historical yield maps and spatial patterns, enables the system to interpret and contextualize handheld measurements, allowing them to represent broader field conditions rather than just local points.
Solution Approach 2:
The system uses position-dependent yield data from prior crop seasons as a proxy or copy of current field conditions. Historical yield spatial patterns serve as a template for understanding current nutrient distribution, allowing handheld measurements to be interpreted in the context of established spatial variability patterns without requiring exhaustive current field mapping.
3Productivity
If measurements are taken at representative locations, then measurement efficiency is improved, but the accuracy of fertilizer recommendations deteriorates
Solution Approach 1:
The system applies local quality by recognizing that different field regions have different nutrient requirements and variability characteristics. Instead of treating the entire field uniformly, the system identifies specific measurement regions (high-yield zones, low-yield zones, marginal areas) and allocates measurement efforts accordingly. This ensures that each measurement is strategically placed to capture local conditions that are critical for accurate fertilizer recommendations.
Data Source
AI summary
System and method for determining at least one first measurement region for carrying out at least one measurement with a crop nutrient measurement device for providing a fertilizer recommendation based on position dependent yield data. Different embodiments for determination of the measurement regions regarding specific locations and field conditions are disclosed.