Smart Grain Trap Imaging for Real-Time Insect Counting
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Solution Overview
Problem
Current methods for detecting insect infestation in stored grains are not capable of real-time monitoring, are inaccurate, time-consuming, and require trained personnel, leading to potential financial losses and reduced grain quality.
Innovation Solution
A smart trap system with a perforated chamber, collection chamber, and imaging system that captures and processes images to count insects accurately, using image analysis algorithms to determine insect counts in real-time.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional manual inspection methods are used to detect insect infestation, then personnel can identify insects, but the process is time-consuming and requires trained personnel
Solution Approach 1:
The patent replaces manual mechanical inspection with an automated imaging system that uses cameras to capture images of insects in traps. The system processes these images through algorithms to automatically count and detect insects, eliminating the need for trained personnel to manually inspect and count insects, thereby reducing both time consumption and dependency on human expertise.
Solution Approach 2:
The system creates visual copies (images) of the insects through the imaging system and camera. These image copies are then processed and analyzed to count insects, replacing the need for direct physical inspection. This allows for rapid analysis of multiple traps simultaneously without requiring personnel to physically handle or visually examine each trap.
2Reliability
If traditional inspection methods are used, then insect infestation can be detected, but intervention is delayed and financial losses increase
Solution Approach 1:
The system enables continuous monitoring by automatically capturing images from multiple traps at regular intervals and processing them in real-time. This continuous operation allows for immediate detection of infestation events as they occur, eliminating the delays associated with periodic manual inspections and enabling timely intervention to prevent financial losses.
Solution Approach 2:
The system provides immediate feedback by processing images and generating insect count data in real-time. This feedback loop allows operators to quickly respond to detected infestations, taking corrective action before the problem escalates and causes significant damage to stored grain.
3Measurement precision
If manual counting methods are used, then insect counts can be obtained, but labor costs increase and accuracy decreases
Solution Approach 1:
The system replaces manual counting operations with automated image processing algorithms that analyze captured images and count insects. This substitution eliminates human error in counting, provides consistent and repeatable results, and requires minimal operational input once the system is deployed, thereby improving both accuracy and ease of operation.
4Measurement precision
If comprehensive image processing is performed on entire images, then accurate insect detection is achieved, but processing time increases
Solution Approach 1:
The system extracts and processes only the relevant portions of images containing traps and insects, rather than analyzing entire large-scale images. This extraction approach focuses computational resources on the critical areas where insects are located, maintaining high detection accuracy while significantly reducing processing time and computational load.
Solution Approach 2:
The image processing is divided into segmented steps: initial image capture, identification of trap regions, extraction of insect-containing areas, and final counting. This segmentation allows each processing stage to be optimized independently, improving overall processing efficiency while maintaining accuracy at each step.
Data Source
AI summary
A system for real-time monitoring of insects includes a smart trap and an image processor. The smart trap includes a chamber with perforations sized to admit insects into an interior of the smart trap; a collection chamber located within the interior of the smart trap; and an imaging system for capturing images that include the collection chamber. The image processor is configured to receive images captured by the smart trap and to determine a count of insects within the collection chamber based on image analysis of the received image. Image analysis includes identifying a region within the received image corresponding with a boundary of the collection chamber, cropping the received image to the identified region to generate a cropped image, modifying at least one characteristic of the cropped image to generate a modified, cropped image, and determining a count of insects based on the modified, cropped image.