3D Grain Pest Egg Detection Using Pest Hole Structure Features
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Solution Overview
Problem
Current methods for detecting pest eggs in grains are inefficient and inaccurate, relying on human experience, and existing technologies fail to detect pest eggs effectively due to their concealment and strong vitality, posing a significant threat to grain storage safety.
Innovation Solution
A method and device utilizing three-dimensional reconstruction and image processing to identify pest holes and eggs based on biological and geometric features, including image acquisition, three-dimensional reconstruction, segmentation, pest hole area identification, and determination of alive pest eggs through geometric and physical features.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If high-resolution X-ray two-dimensional imaging and image processing technology is used to identify larval, pupal and adult stages of pests, then detection capability for later pest stages is improved, but detection capability for pest eggs remains insufficient
Solution Approach 1:
The patent transitions from two-dimensional X-ray imaging to three-dimensional reconstruction technology. By acquiring multi-angle projection images and reconstructing them into 3D volume data, the system achieves comprehensive spatial coverage that enables detection of pest eggs at any orientation and depth within grains, resolving the limitation of 2D imaging for egg detection
2Loss of time
If early detection of pests is attempted using current methods, then intervention time is improved, but detection accuracy deteriorates due to reliance on human experience
Solution Approach 1:
The patent replaces human visual inspection and experience-based judgment with automated image processing algorithms and machine learning models. The system uses 3D reconstruction, segmentation, and feature extraction algorithms to objectively identify pest eggs based on their geometric and density characteristics, eliminating subjectivity and achieving consistent high accuracy across different detection scenarios
Solution Approach 2:
The patent performs detection at the egg stage, which is the earliest possible time before larvae hatch and begin feeding. By detecting eggs through their distinct 3D geometric features and density characteristics in the reconstructed volume, the system enables preventive action before actual grain damage occurs, maximizing the benefit of early detection
3Loss of substance
If pest eggs are not detected early, then economic losses increase, but implementing current detection methods fails to provide rapid detection capability
Solution Approach 1:
The patent implements a continuous automated detection workflow where 3D reconstruction, segmentation, and pest egg identification occur in an integrated pipeline without manual intervention. The system processes the entire grain batch through rapid scanning and automated analysis, providing continuous detection capability that maintains high speed while ensuring no eggs are missed
Solution Approach 2:
The patent utilizes changes in X-ray attenuation parameters to differentiate pest eggs from grain structures. By analyzing density variations and geometric parameters in the 3D reconstructed data, the system rapidly identifies eggs based on their unique physical properties, enabling fast detection without requiring time-consuming manual examination
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 and accurate detection of pest eggs within grains, improving detection efficiency and safety by identifying pest holes and determining the presence and vitality of pest eggs, thereby reducing long-term grain loss and ensuring storage safety.
Implementation Method 1
With the help of high-resolution X-ray two-dimensional imaging and image processing technology
Implementation Method 2
three-dimensionally reconstructing on the scanned image of the grain pile to obtain a three-dimensional image of a multi-grain pile
Data Source
AI summary
A method, device, system and computer-readable medium for rapidly detecting a pest egg in a grain based on a pest egg structure feature and a pest hole structure feature is disclosed. The detecting method includes acquiring a gray digital image of a grain pile and three-dimensionally reconstructing it to obtain a three-dimensional image of a multi-grain pile; segmenting the three-dimensional image of the multi-grain pile to obtain one or more grains-contained unit images; identifying pest hole areas in each of one or more grains-contained unit images based on a biological feature of a pest; determining whether the pest hole area is a pest egg area based on the pest hole structure feature; and determining whether an alive pest egg exists in the pest egg area based on a geometric feature and a physical feature of the pest egg.