Detection of Rice Fungal Spores Based on Micro- Hyperspectral and Microfluidic Techniques

Abstract

As rice is one of the world’s most important food crops, protecting it from fungal diseases is very important for agricultural production. At present, it is difficult to diagnose rice fungal diseases at an early stage using relevant technologies, and there are a lack of rapid detection methods. This study proposes a microfluidic chip-based method combined with microscopic hyperspectral detection of rice fungal disease spores. First, a microfluidic chip with a dual inlet and three-stage structure was designed to separate and enrich Magnaporthe grisea spores and Ustilaginoidea virens spores in air. Then, the microscopic hyperspectral instrument was used to collect the hyperspectral data of the fungal disease spores in the enrichment area, and the competitive adaptive reweighting algorithm (CARS) was used to screen the characteristic bands of the spectral data collected from the spores of the two fungal diseases. Finally, the support vector machine (SVM) and convolutional neural network (CNN) were used to build the full-band classification model and the CARS filtered characteristic wavelength classification model, respectively. The results showed that the actual enrichment efficiency of the microfluidic chip designed in this study on Magnaporthe grisea spores and Ustilaginoidea virens spores was 82.67% and 80.70%, respectively. In the established model, the CARS-CNN classification model is the best for the classification of Magnaporthe grisea spores and Ustilaginoidea virens spores, and its F1-core index can reach 0.960 and 0.949, respectively. This study can effectively isolate and enrich Magnaporthe grisea spores and Ustilaginoidea virens spores, providing new methods and ideas for early detection of rice fungal disease spores.

Recent Advances in Microfluidics for the Early Detection of Plant Diseases in Vegetables, Fruits, and Grains Caused by Bacteria, Fungi, and Viruses

“…This method achieved a maximum clearance rate of 98% for fungal spores. Moreover, microfluidics with a double entrance and three-stage structure demonstrated the ability to diagnose rice fungal diseases by isolating and enriching Magnaporthe grisea spores and Ustilaginoidea virens spores in the air with 82.67% and 80.70% efficiency, respectively . A microfluidics chip comprising a half-wave pretreatment channel, inertial impactor, and low-pressure collection chamber facilitated separation based on spores of different sizes .…”

Ensuring food safety: Microfluidic‐based approaches for the detection of food contaminants

“…In stark contrast to bacteria, fungi diseases can often be identified by their unique morphological differences. A recent study developed a microfluidic chip to be combined with microscopic hyperspectral detection of rice fungal disease spores 76 . The chip can separate Magnaporthe grisea and Ustilaginoidea virens spores using a series of separation channels and enrichment areas in a PDMS chip that creates a sparse two‐phase flow with a low Reynolds number.…”

Hyperspectral imaging for quantifying Magnaporthe oryzae sporulation on rice genotypes

“…Segmentation of leaf disc images enabled the differentiation of pixels representing downy mildew sporulation from other parts of the leaf. Zhang et al [39] used a microfluidic chip-based method combined with microscopic HSI to rapidly detect and quantify fungal spores on rice leaves. These studies suggest that HSI is an effective and objective method for assessing fungal sporulation and studying the dynamics of host-pathogen interactions.…”

“…Incorporating a detailed examination of M. oryzae sporulation into the selection process under controlled conditions may help to identify small positive effects related to blast resistance [11,14,41]. Accurate estimation of the quantity of inoculum during epidemics demands high precision in data collection and analysis [39,40]. The characterization and quantification of sporulation using the entire spectrum in the VIS and NIR range may offer insights into the spatial dynamics of pathogen reproduction and disease spread as well as the mechanisms of disease resistance in host plants [37].…”