Automated Pest Analysis System Using Hyperspectral Imaging
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Solution Overview
Problem
Current methods for analyzing pest infestations in agricultural crops, such as soybean cyst nematodes, are slow, cumbersome, and resource-intensive, requiring manual counting and lacking accuracy.
Innovation Solution
An automated system that separates pests from plants, illuminates the samples to produce emitted light, and compares the light to a model to discriminate and quantify pest presence using a hyperspectral imaging system.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual counting of pests is used, then accuracy of analysis can be maintained, but productivity is extremely low and resource intensive
Solution Approach 1:
The patent replaces manual mechanical counting with an automated imaging and analysis system that uses digital cameras, image processing algorithms, and computer vision to detect, count, and analyze pests. This substitution maintains measurement precision through automated image analysis while dramatically increasing productivity by processing multiple samples simultaneously without human intervention.
Solution Approach 2:
The system creates digital copies (images) of pests and plant samples, then analyzes these copies through software algorithms. This allows the original samples to be examined repeatedly without physical manipulation, maintaining accuracy while enabling rapid automated processing and comparison across multiple samples.
2Productivity
If manual counting of pests is used, then resource consumption is high, but device complexity remains low
Solution Approach 1:
The imaging system serves multiple functions: capturing pest images, counting pests, analyzing pest characteristics, and generating reports. By consolidating these functions into a single automated platform, the system increases productivity while managing device complexity through integrated design rather than separate specialized devices.
Solution Approach 2:
The system performs self-analysis through automated image processing and recognition algorithms that independently identify and count pests without requiring expert manual intervention. This self-service capability increases productivity while reducing the need for complex manual操作流程 and specialized human resources.
3Measurement precision
If automated imaging system is implemented, then productivity and accuracy are improved, but device complexity increases
Solution Approach 1:
The system segments the analysis process into distinct modules: image acquisition, image preprocessing, pest detection, counting, and reporting. Each module handles a specific task, which improves overall accuracy through specialized processing while managing complexity by dividing the system into manageable, independently optimized components.
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
The system enables rapid and accurate analysis of pest infestations, improving efficiency and accuracy in determining pest presence and resistance in crops.
Implementation Method 1
illuminating the sample to produce light of mixed wavelengths emitted from at least one discrete spatial sample point of the sample
Implementation Method 2
an imaging device adjacent the light source for receiving light from the illuminated sample and creating an image of the sample
Data Source
AI summary
Methods and assemblies are provided for evaluating plants for presence of pests. Methods may include separating pests from a plant to produce a sample of pests for analysis, illuminating the sample to produce emitted light from the sample, and comparing the emitted light from the sample to a model to discriminate pests within the sample. Assemblies may include a separating unit operable to separate pests from a plant to produce a sample comprising pests, a light source for illuminating at least part of the sample, and an imaging device adjacent the light source for receiving light from the illuminated sample and creating an image of the sample.