Pest Detection Using Machine Learning and Sensor Fusion
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Solution Overview
Problem
Current pest detection methods are low-tech and inefficient, requiring manual inspection of traps and often leading to undetected pests due to nocturnal activity and lack of direct observation.
Innovation Solution
A pest detection system using a computing device and sensors like motion, imaging, audio, and structured light sensors, combined with machine learning algorithms to identify pests and send alerts, with a user interface displaying detection locations and pest activity on a building floor plan.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If manual inspection of traps is used for pest detection, then device complexity is reduced, but detection reliability deteriorates due to nocturnal pest activity and lack of direct observation
Solution Approach 1:
The patent replaces manual mechanical inspection with automated electronic detection systems including motion sensors, imaging sensors, audio transducers, and machine learning algorithms. These electronic systems continuously monitor for pest presence without human intervention, significantly improving detection reliability for nocturnal pests while the complexity is managed through integrated circuit boards and centralized processing.
Solution Approach 2:
The pest detection system operates autonomously by automatically detecting pests through multiple sensors, processing data through machine learning algorithms, and generating alerts without requiring manual trap inspection. The system serves itself by continuously monitoring and self-reporting pest presence, eliminating the need for human observers and improving reliability for nocturnal detection.
2Measurement precision
If multiple sensors and machine learning algorithms are deployed for automated pest detection, then detection precision is improved, but energy consumption increases
Solution Approach 1:
The system employs periodic sensing and processing cycles rather than continuous operation. Motion sensors trigger imaging sensors only when movement is detected, and machine learning processing occurs in discrete batches. This periodic operation maintains high detection precision through multiple sensor types while significantly reducing overall energy consumption compared to continuous monitoring.
Solution Approach 2:
The system dynamically adjusts its operational mode based on detected conditions. When no pest activity is detected, sensors operate in low-power modes or remain dormant. Upon detecting motion or other pest indicators, the system activates additional sensors and processing power. This dynamic operation maintains detection precision when needed while minimizing energy consumption during normal periods.
Data Source
AI summary
In some implementations, a computing device may receive a notification from a pest detector. In response, the computing device may initiate execution of a software application and display a user interface. The user interface may enable playback of the notification, such as displaying a photograph of a pest or playing back audio of noise made by the pest. The user interface may display a floor plan of a building along with a location of the pest detector and additional locations of additional pest detectors located in the building superimposed on the floor plan. The user interface may enable selecting a particular building of multiple buildings, a particular floor of multiple floors, or a particular room of multiple rooms. The user interface may display where pests have been detected superimposed on the floor plan.