Insect Detection System Using Sensor-Image Pairing for Automated Classification
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Solution Overview
Problem
Current technologies lack an efficient and automated method for detecting and classifying flying insects in a geographic area, such as agricultural fields or forests, without capturing or immobilizing them.
Innovation Solution
A method and apparatus that utilize an insect detection system comprising an insect sensor and one or more image sensors. The system acquires sensor data indicative of insect detection events and obtains digital images of detected insects, pairing the data to create classification datasets for machine-learning models.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Extent of automation
If traditional insect detection methods are used, then insects can be detected, but the system complexity increases and automation is reduced
Solution Approach 1:
The system divides insect detection into two independent modules: an insect sensor (acoustic/optical) for detecting insect presence and signatures, and image sensors (cameras) for visual identification. This segmentation allows each module to be optimized independently, reducing overall system complexity while maintaining high automation through specialized sensor functions.
Solution Approach 2:
The patent introduces an intermediary data pairing mechanism that correlates sensor data (acoustic/optical signatures) with image data. This intermediary layer enables automated classification by matching insect signatures from the insect sensor with visual characteristics from image sensors, achieving high automation without requiring a single complex integrated system.
2Productivity
If manual insect identification is used, then classification accuracy can be achieved, but productivity decreases
Solution Approach 1:
The system replaces manual mechanical inspection with automated sensor-based detection. Insect sensors capture acoustic and optical signatures automatically, and image sensors capture visual data, eliminating the need for manual observation while maintaining classification accuracy through automated pattern recognition algorithms.
Solution Approach 2:
The system implements feedback loops where sensor data and image data are continuously correlated and paired. This feedback mechanism allows the system to learn from detected patterns, improving classification accuracy over time while maintaining high detection speed through automated processing of sensor inputs.
3Object-affected harmful factors
If insect capture methods are used for identification, then detailed analysis is possible, but harmful effects increase
Solution Approach 1:
The system replaces physical capture and manipulation of insects with non-contact sensor-based detection. Acoustic sensors capture wing beat frequencies, optical sensors detect reflective patterns, and image sensors capture visual characteristics, all without touching or harming the insect, thereby eliminating harmful factors while preserving complete insect signature data.
Solution Approach 2:
The system creates digital copies of insect characteristics through sensor data and images rather than physical capture. By capturing acoustic signatures, optical patterns, and visual images, the system preserves complete information about the insect without needing to physically handle or immobilize it, avoiding harm while maintaining data integrity.
Data Source
AI summary
An insect detection system for detection of insects, the insect detection system comprising an insect sensor configured to acquire sensor data indicative of one or more insect detection events, each insect detection event being indicative of one or more detected insects in a probe volume of the insect sensor, the acquired sensor data being further indicative of at least one insect signature, and one or more image sensors each configured to obtain one or more digital images of at least part of the probe volume of the insect sensor, wherein the insect detection system is configured to create one or more classification datasets by pairing the acquired sensor data with the one or more images from each of the one or more image sensors.