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A Detection Method for Crop Fungal Spores Based on Microfluidic Separation Enrichment and AC Impedance
Xiaodong Zhang1,2, Boxue Guo1,2, Yafei Wang1,2
1School of Agricultural Engineering, Jiangsu University, Zhenjiang 212013, China.
Abstract:
The timely monitoring of airborne crop fungal spores is important for maintaining food security. In this study, a method based on microfluidic separation and enrichment and AC impedance characteristics was proposed to detect spores of fungal pathogens that cause diseases on crops. Firstly, a microfluidic chip with tertiary structure was designed for the direct separation and enrichment of Ustilaginoidea virens spores, Magnaporthe grisea spores, and Aspergillus niger spores from the air. Then, the impedance characteristics of fungal spores were measured by impedance analyzer in the enrichment area of a microfluidic chip. The impedance characteristics of fungal spores were analyzed, and four impedance characteristics were extracted: absolute value of impedance (abs), real part of impedance (real), imaginary part of impedance (imag), and impedance phase (phase). Finally, based on the impedance characteristics of extracted fungal spores, K-proximity (KNN), random forest (RF), and support vector machine (SVM) classification models were established to classify the three fungal spores. The results showed that the microfluidic chip designed in this study could well collect the spores of three fungal diseases, and the collection rate was up to 97. The average accuracy of KNN model, RF model, and SVM model for the detection of three disease spores was 93.33, 96.44 and 97.78, respectively. The F1-Score of KNN model, RF model, and SVM model was 90, 94.65, and 96.18, respectively. The accuracy, precision, recall, and F1-Score of the SVM model were all the highest, at 97.78, 96.67, 96.69, and 96.18, respectively. Therefore, the detection method of crop fungal spores based on microfluidic separation, enrichment, and impedance characteristics proposed in this study can be used for the detection of airborne crop fungal spores, providing a basis for the subsequent detection of crop fungal spores.
Insights
A new method uses microfluidic chips and AC impedance to detect airborne fungal spores, crucial for crop disease monitoring and food security. This approach achieved high accuracy in identifying three common fungal pathogens.
Area of Science:
- Agricultural Science
- Biotechnology
- Sensor Technology
Background:
- Airborne fungal spores pose a significant threat to crop health and global food security.
- Accurate and timely detection of these spores is essential for effective disease management.
Purpose of the Study:
- To develop and validate a novel method for the direct separation, enrichment, and detection of airborne crop fungal spores.
- To establish a classification system for identifying specific fungal pathogens using microfluidic and impedance analysis.
Main Methods:
- Designed a microfluidic chip with a tertiary structure for separating and enriching spores of *Ustilaginoidea virens*, *Magnaporthe grisea*, and *Aspergillus niger*.
- Measured and analyzed AC impedance characteristics (absolute value, real part, imaginary part, phase) of enriched fungal spores.
- Developed and compared K-nearest neighbors (KNN), random forest (RF), and support vector machine (SVM) classification models for spore identification.
Main Results:
- The microfluidic chip demonstrated a high spore collection rate of up to 97%.
- The SVM model achieved the highest classification accuracy (97.78%) and F1-Score (96.18%) among the tested models.
- The method effectively distinguished between the three targeted fungal spore types.
Conclusions:
- The proposed method integrating microfluidic separation, enrichment, and AC impedance analysis is effective for detecting airborne crop fungal spores.
- This technology provides a robust foundation for real-time monitoring and management of crop diseases.
- The high accuracy and efficiency of the SVM model highlight its potential for practical application in agriculture.
