Camera-Based Insect Mortality Assessment With Siamese Networks
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Solution Overview
Problem
Existing image-based methods for differentiating between living and dead insect specimens are time-consuming and prone to errors due to sensitivity to lighting and positional changes, hindering efficient insecticide discovery and development.
Innovation Solution
A system utilizing a Siamese neural network (SNN) with convolutional neural networks (CNNs) analyzes time-series images of insect specimens to determine vitality status, employing a trained model robust to lighting and positional variations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If manual image-based methods are used to differentiate between living and dead insect specimens, then the system is simple to implement, but the process is time-consuming and error-prone
Solution Approach 1:
The patent replaces manual visual inspection with an automated image-based system using cameras and computer vision algorithms. The system captures images of insect specimens and automatically analyzes them to determine vitality status, eliminating the need for manual examination while maintaining simplicity through software-based processing
Solution Approach 2:
The system creates digital copies (images) of insect specimens and analyzes these copies to determine mortality status. By working with image data rather than direct physical examination, the system enables automated processing while maintaining the ability to assess vitality through visual characteristics captured in the images
2Measurement precision
If automated image-based methods are used, then productivity increases, but measurement precision decreases due to sensitivity to lighting and positional changes
Solution Approach 1:
The system captures multiple images at different time points and analyzes changes in parameters such as movement, posture, and behavior over time. By monitoring temporal dynamics rather than relying on single static images, the system becomes less sensitive to lighting and positional variations while maintaining high accuracy in vitality assessment
Solution Approach 2:
The system continuously monitors insect specimens over time and uses feedback from sequential images to update vitality assessments. This temporal feedback mechanism allows the system to distinguish between transient variations (caused by lighting or positioning) and genuine changes in vitality status, improving measurement precision
Data Source
AI summary
A system is provided for insect mortality assessment. In one example, the system includes one or more cameras and a removable plate located a predetermined distance from the cameras. The removable plate has a well in which an insect specimen is disposed. A memory stores instructions that, when executed by a processor, cause the processor to transmit one or more signals to the one or more cameras to capture a plurality of time-series images of the insect specimen and pre-process the time-series images of the insect specimen. The instructions further cause the system to analyze, using a trained Siamese neural network, the time-series images of the insect specimen to determine vitality status of the insect specimen, and store results indicating the vitality status of the insect specimen in the memory.