Smart Mosquito Trap With AI Species Classification
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Solution Overview
Problem
Current entomological classification systems are cumbersome and inefficient for identifying specific mosquito species, particularly in situations requiring rapid detection of foreign mosquitoes, and there is a lack of digital advancements in mosquito traps for automated classification.
Innovation Solution
A smart mosquito trap equipped with imaging devices, passive infrared sensors, and AI algorithms to capture and analyze mosquito anatomies, behaviors, and movements for automated genus and species classification using convolutional neural networks.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual inspection and classification methods are used, then experts can identify mosquito species through microscopes, but the process is time-consuming and cannot keep pace with rapid disease outbreak response or border surveillance needs
Solution Approach 1:
The patent replaces the mechanical manual inspection process with an automated image processing system. A camera captures images of trapped mosquitoes, and computer algorithms automatically analyze morphological features to identify species, eliminating the need for manual microscope examination while maintaining identification accuracy
Solution Approach 2:
The system creates digital copies (images) of mosquitoes and analyzes these copies instead of examining the actual specimens manually. This allows multiple analyses to be performed simultaneously on the same specimen without requiring physical manipulation or time-consuming manual observation
2Adaptability or versatility
If traditional entomological classification systems are used, then comprehensive species identification is possible, but the systems are cumbersome and not adaptable for specialized situations such as foreign mosquito detection
Solution Approach 1:
The patent segments the classification task into distinct automated steps: image capture, feature extraction, and algorithmic identification. This breakdown allows the system to handle specialized situations by adjusting which features are analyzed or which algorithms are applied, without requiring a complete redesign of the entire classification system
Solution Approach 2:
The system is designed to be dynamic and adaptable by allowing configuration of analysis parameters and algorithms based on the specific surveillance needs. For example, the system can be tuned to prioritize detection of particular species or to adjust the level of detail in analysis based on whether the situation is routine monitoring or investigating a potential outbreak
3Quantity of substance
If hundreds of mosquitoes are trapped for surveillance, then comprehensive data collection is achieved, but manual identification of each specimen becomes impossibly cumbersome
Solution Approach 1:
The system enables self-service automated identification where the mosquitoes essentially identify themselves through their captured images. The algorithm automatically processes each image, extracts relevant features, and generates identification results without human intervention, allowing high-volume processing of trapped specimens
Solution Approach 2:
The automated system enables continuous processing of mosquito images as they are captured, maintaining a steady throughput of identifications. Multiple images can be analyzed simultaneously and continuously without the interruptions inherent in manual inspection, maximizing the identification throughput for large numbers of trapped mosquitoes
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
Enables rapid, automated identification of mosquito species without human intervention, facilitating timely public health responses and global data sharing for mosquito surveillance.
Implementation Method 1
passive infrared sensors at the entrance of the trap to sense wing-beat frequencies and size of the insect
Implementation Method 2
A smart mosquito trap equipped with imaging devices, passive infrared sensors, and AI algorithms using mask region-based convolutional neural networks to classify mosquitoes
Data Source
AI summary
An insect trap includes a combination of one or more components used to classify the insect according to a genus and species. The trap includes an imaging device, a digital microphone, and passive infrared sensors at the entrance of the trap to sense wing-beat frequencies and size of the insect (to identify entry of a mosquito). A lamb-skin membrane, filled with an insect attractant such as carbon dioxide mixed with gas air inside, mimics human skin so that the insect can rest on the membrane and even pierce the membrane as if a blood meal is available. An imaging device such as a passive infrared sensor or a camera gathers image data of the insect. The insect may be a mosquito.