Crop Yield Prediction via Historical Vector Matching
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Solution Overview
Problem
Current crop yield prediction techniques often fail to provide accurate, long-range predictions due to their focus on specific characteristics rather than future conditions, leading to difficulties for businesses in the food production supply chain that require lead time to adjust to yield changes.
Innovation Solution
A method and system that generate vectors representing current and historical growing conditions, allowing for comparison and prediction of crop yields by determining the closest matching historical vectors, which are then used to adjust farm equipment for optimal yield management.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If sensor-based monitoring of specific crop characteristics is used to predict yield, then measurement precision is improved, but prediction accuracy deteriorates because future conditions are not considered
Solution Approach 1:
The system performs preliminary actions by collecting and storing historical weather data, soil data, and crop characteristic data before yield prediction is needed. This historical data is then used in conjunction with current sensor measurements to make more accurate predictions that account for both past patterns and present conditions.
Solution Approach 2:
The system merges multiple data sources including sensor-based crop characteristic measurements, historical weather data, soil data, and preliminary yield data into a unified prediction model. This combination allows the system to maintain measurement precision while improving overall prediction accuracy by considering both specific characteristics and broader environmental factors.
2Loss of information
If daily crop monitoring is performed to update yield predictions, then information freshness is improved, but prediction stability deteriorates causing drastic changes when conditions change
Solution Approach 1:
The system implements feedback mechanisms where current crop characteristic measurements and weather data are continuously compared against historical patterns. This feedback allows the system to update yield predictions in a controlled manner, maintaining information freshness while filtering out noise and preventing drastic unpredictable changes through pattern recognition and comparison.
Solution Approach 2:
The system changes parameters by weighting different data sources dynamically - giving more weight to stable historical patterns when conditions are normal, and adjusting weights when significant changes are detected. This allows the system to maintain stability while still responding to genuine changes in crop conditions.
3Reliability
If complex machine learning techniques and neural networks are used for yield prediction, then prediction accuracy is improved, but device complexity increases making implementation difficult in farm systems
Solution Approach 1:
The system segments the yield prediction task into manageable components: data collection from sensors, historical data retrieval, pattern matching algorithms, and prediction generation. Each component can be independently implemented and optimized, reducing overall system complexity while maintaining prediction accuracy through modular architecture.
Solution Approach 2:
The system uses an intermediary approach by implementing a layered architecture where complex machine learning models can be used in the backend for high-accuracy predictions, while simpler interfaces and algorithms provide predictions to farm operators. This intermediary layer translates complex model outputs into actionable insights without requiring the entire system to be complex.
Data Source
AI summary
A method of predicting crop yield includes generating, via a processor, a plurality of vectors representative of growing conditions for a current time period and a plurality of vectors representative of growing conditions for a previous time period. The processor compares the plurality of vectors for the current time to the vectors of the previous time periods for corresponding growing conditions and determines which previous vectors are closest to the current vectors. The plurality of previous time periods are each associated with crop yields. Thus, the processor can determine a crop yield for the current time period for a selected crop producing field and crop type based on crop yields for the closest previous time periods.