Geospatial Pest Pressure Prediction Using Machine Learning
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Solution Overview
Problem
Existing pest pressure prediction systems are inaccurate due to reliance on static logic and limited data visualization, leading to inefficiencies in monitoring and predicting future pest pressures, particularly at an individual farm level.
Innovation Solution
A pest pressure prediction computing device that integrates trap data, weather data, image data, and geospatial features, utilizing machine learning algorithms to identify correlations and generate accurate predictions of future pest pressures.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If static logic models (fixed phenology models and decision trees) are used for pest pressure prediction, then the system is simple to implement, but the prediction accuracy is limited
Solution Approach 1:
The patent transitions from static logic models to dynamic machine learning models that can adapt and learn from data. The system uses machine learning algorithms that continuously improve prediction accuracy by learning patterns from historical and real-time data, while maintaining manageable complexity through modular architecture and cloud-based processing.
Solution Approach 2:
The system changes from fixed parameter models to adaptive parameter models. Machine learning algorithms dynamically adjust parameters based on learned patterns from multiple data sources including weather data, trap data, and geospatial features, enabling accurate predictions without requiring complex manual model configuration.
2Measurement precision
If multiple data sources (trap data, weather data, image data, geospatial features) are integrated using machine learning, then prediction accuracy improves, but data processing complexity increases
Solution Approach 1:
The patent introduces a cloud-based machine learning platform as an intermediary that handles complex data processing. This intermediary receives diverse data sources (trap data, weather data, image data, geospatial features), processes them through machine learning algorithms, and returns simplified prediction results. This approach improves accuracy while keeping the complexity isolated in the cloud service rather than in the field deployment system.
3Loss of information
If individual farm-level monitoring is implemented, then localized pest pressure data is obtained, but visualization capabilities are limited and data collection time lag increases
Solution Approach 1:
The patent creates a multi-functional system that serves both individual farm-level monitoring and regional aggregation needs. The platform can visualize data at multiple scales (individual field, multiple fields, regional views) and performs multiple functions including real-time monitoring, historical analysis, and predictive modeling. This universal approach eliminates visualization limitations and reduces time lags by enabling parallel data collection and processing across multiple locations simultaneously.
Data Source
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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.