Crop Yield Trend Modeling via Data Decomposition
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Solution Overview
Problem
Existing methods for collecting crop yield data from agricultural fields are prone to errors and inaccuracies, often missing critical interrelations between neighboring fields and failing to account for local microclimate and irrigation practices, leading to incomplete and unreliable yield measurements.
Innovation Solution
A computer-implemented system that models crop yield trends by incorporating inter-year variability, local weather conditions, and interrelations between adjacent fields, using statistical models to correct and complete yield data, and employing techniques like singular value decomposition and conditional autoregressive approaches to forecast and reconstruct yield data.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional survey processes are used to collect yield data, then data collection is simple and quick, but the measurement precision and reliability are low due to errors and missing critical data
Solution Approach 1:
The patent introduces computational models and algorithms as intermediaries between raw survey data and final yield estimates. These models act as mediators that process, correct, and complete the imperfect survey data by incorporating multiple influencing factors, thereby improving measurement precision without requiring a complete redesign of the data collection process
Solution Approach 2:
The patent combines multiple data sources and modeling approaches to create a composite yield estimation system. By integrating traditional survey data with additional factors such as weather conditions, soil characteristics, and irrigation practices through weighted combinations and statistical models, the system achieves higher reliability than any single data source alone
2Measurement precision
If county-level survey reports are used, then data collection is efficient, but the measurement precision is insufficient due to missing field-specific microclimate and irrigation information
Solution Approach 1:
The patent applies local quality by incorporating field-specific characteristics such as microclimate conditions, soil properties, and irrigation practices into the yield estimation model. Each field is analyzed with its unique attributes rather than applying uniform county-level averages, thereby capturing local variations that affect yield and reducing information loss
Solution Approach 2:
The patent performs preliminary data processing and model training before actual yield estimation. By pre-processing survey data, identifying missing information patterns, and training computational models in advance, the system prepares to fill information gaps efficiently when conducting actual yield measurements, reducing the impact of missing field-specific data
3Reliability
If simple survey methods are used, then the ease of operation is high, but the reliability of yield data is low due to unavoidable errors at all levels
Solution Approach 1:
The patent implements feedback mechanisms where model predictions are continuously refined based on comparison with actual yield data. The system uses observed yields to update and improve its computational models, creating a closed-loop system that increases reliability over time while maintaining operational simplicity through automated iterative improvement
Data Source
AI summary
A method and system for modeling trends in crop yields is provided. In an embodiment, the method comprises receiving, over a computer network, electronic digital data comprising yield data representing crop yields harvested from a plurality of agricultural fields and at a plurality of time points; in response to receiving input specifying a request to generate one or more particular yield data; determining one or more factors that impact yields of crops that were harvested from the plurality of agricultural fields; decomposing the yield data into decomposed yield data that identifies one or more data dependencies according to the one or more factors; generating, based on the decomposed yield data, the one or more particular yield data; generating forecasted yield data or reconstructing the yield data by incorporating the one or more particular yield data into the yield data.