Pest Pressure Heat Maps Using Machine Learning
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Solution Overview
Problem
Current pest pressure monitoring systems are inaccurate in predicting future pest pressures due to reliance on static logic and limited data visualization, often focused at individual farm levels with significant time lags and inadequate integration of multiple data types.
Innovation Solution
A network-based system using a heat map generation computing device that receives trap, weather, and image data, applies machine learning algorithms to generate predicted future pest pressure values, and displays dynamic time-lapse heat maps on mobile devices, integrating multiple data types for accurate and real-time monitoring.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If static logic and limited data types are used for pest pressure prediction, then system complexity is reduced, but prediction accuracy deteriorates
Solution Approach 1:
The patent combines multiple data types (trap data, weather data, image data) and integrates them through machine learning algorithms to create a comprehensive pest pressure prediction system. This merging of diverse data sources and processing methods resolves the contradiction by achieving high prediction accuracy through integrated multi-source data analysis while managing system complexity through unified architectural design.
Solution Approach 2:
The system transforms static logic into dynamic machine learning models that can adapt and learn from data. By changing from fixed prediction rules to adaptive algorithms that process multiple data types, the system achieves superior prediction accuracy while the modular implementation keeps complexity manageable.
2Loss of information
If individual farm level monitoring is implemented, then data collection scope is reduced, but visualization quality and timeliness deteriorate
Solution Approach 1:
The system is designed to operate at multiple scales simultaneously - it can provide detailed farm-level monitoring while also aggregating data for regional or landscape-level visualization. The heat map technology and machine learning models work effectively whether applied to a single farm or multiple farms, making the system universally applicable across different spatial scopes without sacrificing visualization quality.
3Loss of time
If trap inspection frequency is increased, then data timeliness is improved, but time and resource consumption increase
Solution Approach 1:
The system employs automated trap inspection using image data and machine learning algorithms that can process and analyze trap contents without human intervention. The automated image recognition and pest identification enable continuous monitoring with minimal human time investment, achieving high data timeliness while reducing resource consumption compared to manual inspection methods.
4Reliability
If multiple data types are integrated, then prediction comprehensiveness is improved, but data processing complexity increases
Solution Approach 1:
The system uses machine learning algorithms as intermediaries to process and integrate multiple data types. These algorithms serve as mediators that automatically synthesize trap data, weather data, and image data into coherent pest pressure predictions. The intermediary processing layer manages the complexity of multi-source data integration while delivering comprehensive and reliable prediction results.
Data Source
AI summary
Systems and methods for generating and displaying heat maps are provided. A heat map generation computing device includes a memory and a processor. The processor is programmed to receive trap data for a plurality of pest traps in a geographic location, the trap data including current and historical pest pressure values at each of the plurality of pest traps, receive weather data for the geographic location, receive image data for the geographic location, apply a machine learning algorithm to generate predicted future pest pressure values at each of the plurality of pest traps, generate a first heat map for a first point in time and a second heat map for a second point in time, and transmit the first and second heat maps to a mobile computing device to cause a user interface on the mobile computing device to display a time lapse heat map.