Pest Pressure Prediction Using Geospatial ML
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Solution Overview
Problem
Current systems for predicting pest pressure are inaccurate due to reliance on static logic and limited data visualization, often focused at individual farm levels with significant time lags, and fail to capture the complexity of pest pressure dynamics.
Innovation Solution
A network-based system using a pest pressure prediction computing device that receives trap data, weather data, and image data, identifies geospatial features, and applies machine learning algorithms to correlate pest pressure with these factors, generating accurate predictions for geographic locations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If static logic and limited data sources are used for pest pressure prediction, then the system is simple to implement, but the prediction accuracy deteriorates
Solution Approach 1:
The patent combines multiple data sources including trap data, weather data, image data, and geospatial features into a unified machine learning model. This integration of diverse data types and analytical methods resolves the contradiction by achieving high prediction accuracy through comprehensive data analysis while managing system complexity through automated processing pipelines.
Solution Approach 2:
The system dynamically adjusts prediction parameters by incorporating real-time weather conditions, seasonal variations, and geospatial characteristics. This allows the model to adapt to changing environmental factors, improving prediction accuracy without requiring a completely complex system redesign, as the changes are implemented through configurable model parameters.
2Loss of time
If individual farm-level monitoring is used, then the system is easy to deploy, but data visualization and prediction timeliness deteriorate
Solution Approach 1:
The system is designed to operate at multiple scales simultaneously, functioning effectively at both individual farm level and regional level. The platform accepts data from multiple traps across different locations and provides unified visualization, enabling timely predictions without sacrificing deployment simplicity. The modular architecture allows the same system to serve both localized and broader agricultural regions.
3Measurement precision
If comprehensive data collection from multiple sources is implemented, then prediction accuracy improves, but data processing complexity increases
Solution Approach 1:
The machine learning model automatically processes and integrates data from multiple sources without requiring manual intervention. The system self-adjusts by learning patterns from historical data and adapting to new information, reducing the need for complex manual data processing workflows while maintaining high prediction accuracy through automated feature extraction and model training.
Solution Approach 2:
The patent replaces manual data processing and analysis with automated machine learning algorithms. Instead of relying on complex mechanical or manual systems to process diverse data types, the system uses computational models to automatically integrate trap counts, weather patterns, image recognition results, and geospatial information, simplifying the overall processing complexity while improving accuracy.
Data Source
AI summary
System and methods for predicting future pest pressures are provided. A pest pressure prediction computing device includes a memory and a processor communicatively coupled to the memory. The processor is programmed to receive trap data for a plurality of pest traps in a geographic location, receive weather data for the geographic location, receive image data for the geographic location, identify at least one geospatial feature within or proximate to the geographic location, apply a machine learning algorithm to the trap data, the weather data, the image data, and the at least one identified geospatial feature to identify a correlation between pest pressure and the at least one geospatial feature, and generate predicted future pest pressures for the geographic location based at least on the identified correlation between pest pressure and the at least one geospatial feature.