Historical Yield Map Clustering for Agricultural Management Zones
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Solution Overview
Problem
Existing agricultural management systems fail to effectively delineate management zones within fields based on historical yield data, leading to inefficient crop management practices.
Innovation Solution
A computer system is employed to process historical yield data, applying techniques such as empirical cumulative density transformation, spatial smoothing, and clustering to identify contiguous regions with similar yield-limiting factors, enabling uniform management practices.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If traditional uniform management practices are applied across the entire field, then operational simplicity is maintained, but crop productivity and yield are reduced due to ignoring spatial variability in yield-limiting factors
Solution Approach 1:
The patent divides the agricultural field into multiple management zones based on historical yield data and spatial analysis. Each zone is identified by grouping locations with similar yield patterns and limiting factors, transforming a single uniform management approach into targeted zone-specific management strategies that improve productivity without excessive complexity
Solution Approach 2:
The patent applies local quality by tailoring management practices to specific zones within the field rather than applying uniform treatment everywhere. Each management zone receives customized recommendations for seeding, irrigation, and nitrogen application based on its unique yield characteristics and limiting factors, optimizing productivity for each local area
2Measurement precision
If management zones are delineated using detailed spatial analysis and clustering algorithms, then management precision is improved, but computational complexity and data processing requirements increase
Solution Approach 1:
The patent performs preliminary actions by using historical yield data from previous growing seasons to pre-identify potential management zones before current season operations begin. This advance analysis allows the system to establish zone boundaries and characteristics in advance, reducing the need for complex real-time processing during active field operations
Solution Approach 2:
The patent creates simplified representations or copies of the complex spatial data through clustering algorithms that group locations with similar yield patterns. These clustered zones serve as simplified models that capture the essential spatial variability without requiring processing of every individual data point, reducing computational complexity while maintaining delineation precision
3Reliability
If historical yield data from multiple years is analyzed, then the reliability of management zone identification is improved, but data processing time and computational resources increase
Solution Approach 1:
The patent performs preliminary aggregation and analysis of historical yield data from multiple years to establish stable zone boundaries and characteristics before current season operations. By pre-processing multi-year data to identify consistent spatial patterns and limiting factors, the system improves reliability of zone identification while reducing the need for time-consuming analysis during active decision-making periods
Solution Approach 2:
The patent maintains continuity by using established management zones and their identified limiting factors as a foundation for ongoing management decisions across multiple seasons. Once zones are delineated using historical data, the same zone framework can be reused and refined over time, avoiding repeated full-scale analysis while maintaining reliable zone identification
Data Source
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AI summary
In an embodiment, a method comprises: receiving digital yield data representing yields of crops that have been harvested from an agricultural field; applying an empirical cumulative density function to the digital yield data to generate transformed digital yield data; smoothing the transformed digital yield data to result in generating and storing smooth transformed digital yield data; determining a first count value for a plurality of management classes; generating a plurality of first management zones for the agricultural field by clustering the smooth transformed digital yield data and using the first count value; generating a set of first merged management zones by merging one or more small management zones, of the plurality of first management zones, with their respective similar neighboring large zones; storing the set of first merged management zones and the first count value in a set of management zone metrics.