CO2 Release Rate Detection for Stored Grain Pest Density
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Current methods for detecting stored grain pests, such as Cryptolestes ferrugineus, are time-consuming, labor-intensive, and lack accuracy, particularly in differentiating active, dead, or larvae pests, and are sensitive to environmental conditions.
Innovation Solution
A method based on constructing prediction models using CO2 release rates in relation to stored grain temperatures and water content to estimate the population density of Cryptolestes ferrugineus, allowing for early and accurate detection by measuring CO2 release rates and substituting values into established formulas.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual sampling method and trap method are used to monitor stored grain pests, then the monitoring can be performed, but the process is time-consuming and laborious
Solution Approach 1:
The patent replaces manual mechanical sampling and trap methods with an intelligent detection system that uses CO2 sensors to automatically monitor pest respiration. The system substitutes human labor with automated electronic detection, measuring CO2 release rates to calculate pest population density without manual intervention, thereby reducing both time and labor requirements while maintaining detection accuracy.
2Extent of automation
If image monitoring method is used, then active pests outside grains can be automatically identified, but it cannot differentiate borer pests, death-feigning pests, and larvae from each other
Solution Approach 1:
The patent shifts from visual parameter detection (image monitoring) to physiological parameter detection (CO2 release rate). By measuring the metabolic activity through CO2 emission, the system can identify and differentiate all pest types including borers, death-feigning pests, and larvae, regardless of their visual appearance or activity state, thereby achieving both automation and precise differentiation.
3Measurement precision
If infrared photoelectric technology is used, then detection can be performed, but it is sensitive to humidity and not easy to identify pests with similar body shapes
Solution Approach 1:
The patent replaces infrared photoelectric detection with CO2-based detection. Instead of relying on optical properties that are affected by humidity and visual appearance, the system measures the physiological output (CO2 respiration) of pests, which is independent of environmental humidity and pest morphology, thereby eliminating these sources of measurement error.
4Measurement precision
If acoustic detection method is used, then pest detection can be performed, but environmental noise must be removed to achieve accurate results
Solution Approach 1:
The patent replaces acoustic detection with CO2 detection. By measuring the concentration of CO2 gas released by pest respiration, the system avoids the problem of environmental noise interference that plagues acoustic methods. The CO2 measurement provides a direct physiological signal from pests without requiring complex noise filtering or signal processing.
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
This method provides efficient, accurate, and convenient detection of Cryptolestes ferrugineus population density, suitable for large-scale application, and can differentiate between active and inactive pests, reducing false positives and improving grain storage monitoring.
Implementation Method 1
a theoretical basis is provided for monitoring the pest-carrying grain situation using CO2 by establishing a monitoring model for a population density of the Cryptolestes ferrugineus
Data Source
AI summary
The present disclosure provides a method for detecting a population density of Cryptolestes ferrugineus based on a CO2 release rate, and belongs to the technical field of pest detection in stored grains. In the present disclosure, the method includes: constructing a prediction model of a population density of Cryptolestes ferrugineus based on a relationship between different stored grain temperatures, stored grain water contents, and CO2 release rates in the environment and the population density of the Cryptolestes ferrugineus; measuring the stored grain temperature and the CO2 release rate in the environment, substituting measured values into the prediction model of the population density of the Cryptolestes ferrugineus for calculation to obtain the population density of the Cryptolestes ferrugineus in a grain storage environment; and determining a pest-carrying grain grade. The method can eliminate an interference of dead pests and death-feigning pests, and can also detect borer pests.