Mosquito Imaging Tray for Dual-Sided Species Identification
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Solution Overview
Problem
Existing methods for mosquito species identification, such as visual inspection and DNA analysis, are time-consuming, costly, and prone to errors, while computer vision-based approaches lack reliable commercial implementations due to the absence of comprehensive image datasets and technical challenges.
Innovation Solution
A computer vision system with an imaging apparatus and tray design for capturing dual-sided images of mosquitoes, utilizing internal lighting and a tray with orientation markings, coupled with deep convolutional neural networks for species identification, enabling rapid and accurate classification.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of manufacture
If visual inspection by trained experts is used for mosquito species identification, then identification can be performed without specialized equipment, but the process is time-consuming and error-prone
Solution Approach 1:
The patent replaces manual visual inspection by experts with an automated imaging system using digital photography and computer vision algorithms. The system captures high-resolution images of mosquitoes and uses machine learning models to automatically identify species, replacing the mechanical process of human visual examination with an automated digital system that provides both speed and accuracy.
Solution Approach 2:
The patent creates digital copies (images) of mosquitoes for analysis instead of requiring direct visual inspection. By capturing detailed images and analyzing them through computer vision, the system preserves the ability to examine morphological features while enabling automated processing and eliminating the need for continuous expert presence.
2Measurement precision
If DNA barcoding is used for mosquito species identification, then identification accuracy is improved, but the cost per specimen and time requirements increase significantly
Solution Approach 1:
The patent replaces DNA barcoding with an imaging-based computer vision system. Instead of using molecular biology techniques to identify species, the system uses digital images and machine learning algorithms to achieve accurate species identification, thereby eliminating the time-consuming and expensive DNA analysis process while maintaining high accuracy.
Solution Approach 2:
The patent changes the identification parameter from molecular (DNA sequences) to visual (morphological features in images). By shifting from genetic analysis to image-based analysis, the system achieves comparable accuracy with dramatically reduced cost and time per specimen.
3Productivity
If computer vision methods are applied for mosquito identification, then identification speed and throughput are improved, but the lack of comprehensive image datasets and technical complexity reduce reliability
Solution Approach 1:
The patent performs preliminary actions by creating a comprehensive, standardized image dataset of mosquitoes across multiple species and conditions before deploying the identification system. The dataset includes images from various angles, lighting conditions, and life stages, which are used to train and validate the computer vision models, ensuring reliable performance in real-world applications.
Solution Approach 2:
The patent standardizes imaging parameters (lighting, resolution, angle, background) to ensure consistent image quality across all specimens. By controlling these parameters and using standardized protocols, the system produces reliable, reproducible results that enhance the trustworthiness of automated identification.
4Measurement precision
If high-resolution imaging is used to capture fine morphological details for species differentiation, then identification accuracy is improved, but the complexity and cost of the imaging system increase
Solution Approach 1:
The patent extracts only the essential imaging components needed to capture fine morphological details, rather than using a full-featured complex microscope system. The system uses a standardized digital camera with controlled lighting and positioning to capture the necessary details, eliminating unnecessary complexity while maintaining the resolution needed for species differentiation.
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
Facilitates rapid and accurate identification of mosquito species with high throughput, reducing human error and costs, and providing comprehensive species, sex, life stage, and population origin analysis.
Implementation Method 1
light sources to illuminate the optical field of view of the imaging device
Data Source
AI summary
An apparatus and system for generating digital images of specimens and identifying the species of an invertebrate, such as a mosquito, are disclosed. The apparatus includes an imaging device capable of capturing images, light sources to illuminate the optical field of view of the imaging device, a housing enclosing the imaging device and the light source, and an opening in the housing where a specimen tray can be inserted. The specimen tray can include a lid and multiple wells separated by transparent walls, each well capable of holding a specimen. The wells are fully enclosed when the lid is placed on the tray and the tray includes markings on both sides indicating the tray orientation, as the tray is transparent on the lid and base of the tray to enable imaging from two sides of the tray. The housing and tray also includes detents and indents for locking the tray into the correct placement when inserted into the slot. The imaging device is capable of capturing high quality images of two distinct viewpoints of all specimens in the wells.