Mosquito Sex Sorting Using Imaging and Robotic Separation
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Solution Overview
Problem
Current mosquito Sterile Insect Technique (SIT) methods face challenges in scaling up due to labor-intensive sex sorting and handling processes, particularly in distinguishing and separating male and female mosquitoes, which is costly and inefficient for large-scale operations.
Innovation Solution
A method and apparatus for mechanical sex-sorting of mosquitoes using imaging and a robot arm to classify and separate mosquitoes based on their stationary phase, employing a trained neural network to identify and sort male and female mosquitoes, either by storing or removing them, thereby reducing manual labor and increasing efficiency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual sex sorting of mosquitoes is performed, then sorting accuracy can be maintained, but labor intensity and operational costs increase significantly
Solution Approach 1:
The patent replaces manual mechanical sorting with an automated imaging and classification system. A camera captures images of mosquitoes, and a trained neural network automatically classifies them by sex, eliminating the need for manual inspection while maintaining high sorting accuracy.
Solution Approach 2:
The patent creates visual copies (images) of the mosquitoes through imaging, allowing the neural network to analyze and classify sexual characteristics from these copies rather than requiring direct manual examination of the actual insects.
2Manufacturing precision
If manual loading of pupa into release boxes is performed, then quantity control can be achieved, but productivity decreases for large scale operations
Solution Approach 1:
The patent replaces manual counting and loading operations with an automated system that uses imaging to detect and count mosquitoes, then mechanically transfers them using a robot arm, achieving both quantity control and high productivity simultaneously.
Solution Approach 2:
The system performs self-counting and self-sorting through the neural network classification and automated robot arm operation, eliminating the need for human operators to manually count and transfer each mosquito.
3Measurement precision
If sorting is performed on adult mosquitoes, then classification accuracy improves, but mosquitoes may move or fly away during the process
Solution Approach 1:
The patent performs classification imaging before the mosquitoes become fully active, capturing images during the emergence phase or when mosquitoes are temporarily stationary, ensuring both accurate classification and process completion.
Solution Approach 2:
The system adapts to the dynamic nature of mosquitoes by using rapid imaging techniques that can capture clear images even when mosquitoes are in motion, and by timing the sorting process to occur when mosquitoes are naturally less active.
4Productivity
If scaling up mosquito SIT operations is attempted, then disease control coverage increases, but operational costs become prohibitive
Solution Approach 1:
The patent replaces labor-intensive manual operations with automated imaging and robot arm systems, dramatically increasing productivity while reducing operational costs associated with human labor for large-scale mosquito sorting and release operations.
Data Source
AI summary
Method and apparatus for mechanical sex-sorting of mosquitoes by extracting a class of mosquitoes from unsorted mosquitoes comprises obtaining unsorted mosquitoes, obtaining images of individual mosquitoes in a stationary phase, electronically classifying the individuals from the images into male mosquitoes and/or female mosquitoes, and possibly also unclassified objects; obtaining co-ordinates of individuals of at least one of the male mosquito and female mosquito classifications, and using a robot arm to reach an individual identified by the obtained coordinates to store or remove the individuals, thereby to provide sex-sorted mosquitoes.