A rapid detection method of early spore viability based on <scp>AC</scp> impedance measurement

Yang, Ning; Hu, Jiaqi; Xu, Zhou; Wang, Aiying; Yu, Junjie; Tao, Xinyi

doi:10.1111/jfpe.13520

Cited by 3 publications

(2 citation statements)

References 35 publications

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“…Specifically, the conidia of M. oryzae under the 10 × 40 field were produced with isolates collected from 10 Chinese provinces including Anhui, Chongqing, Guangdong, Hubei, Hunan, Jiangsu, Jiangxi, Liaoning, Sichuan and Yunnan in the past five years, while the conidia of M. oryzae under 10 × 20 field were collected from fresh diseased rice leaf in Liaoning Province in 2019 and 2020. 1 M. oryzae; 2 Fusarium spp., 3 Alternaria spp., 4 Botrytis spp., 5 rice pollen.…”

Section: Dataset Constructionmentioning

confidence: 99%

“…Rice blast is an important epidemic disease caused by Magnaporthe oryzae. The disease is mainly transmitted through airborne conidia and can spread rapidly under suitable environmental conditions, leading to large-scale disease epidemics and often causing 10-30% yield loss of rice annually [3,4]. Monitoring the quantity of airborne spores in the field is important for assessing the occurrence and prevalence of rice blast.…”

Section: Introductionmentioning

confidence: 99%

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Automatic Detection of Rice Blast Fungus Spores by Deep Learning-Based Object Detection: Models, Benchmarks and Quantitative Analysis

Zhou,

Lai,

Huang

et al. 2024

Agriculture

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The severity of rice blast and its impacts on rice yield are closely related to the inoculum quantity of Magnaporthe oryzae, and automatic detection of the pathogen spores in microscopic images can provide a rapid and effective way to quantify pathogen inoculum. Traditional spore detection methods mostly rely on manual feature extraction and shallow machine learning models, and are mostly designed for the indoor counting of a single spore class, which cannot handle the interference of impurity particles in the field. This study achieved automatic detection of rice blast fungus spores in the mixture with other fungal spores and rice pollens commonly encountered under field conditions by using deep learning based object detection techniques. First, 8959 microscopic images of a single spore class and 1450 microscopic images of mixed spore classes, including the rice blast fungus spores and four common impurity particles, were collected and labelled to form the benchmark dataset. Then, Faster R-CNN, Cascade R-CNN and YOLOv3 were used as the main detection frameworks, and multiple convolutional neural networks were used as the backbone networks in training of nine object detection algorithms. The results showed that the detection performance of YOLOv3_DarkNet53 is superior to the other eight algorithms, and achieved 98.0% mean average precision (intersection over union > 0.5) and an average speed of 36.4 frames per second. This study demonstrated the enormous application potential of deep object detection algorithms in automatic detection and quantification of rice blast fungus spores.

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Section: Dataset Constructionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Automatic Detection of Rice Blast Fungus Spores by Deep Learning-Based Object Detection: Models, Benchmarks and Quantitative Analysis

Zhou,

Lai,

Huang

et al. 2024

Agriculture

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Auto-balancing bridge based wide impedance spectrum measurement with consideration of Op-Amp input impedance

Cai,

Wang,

et al. 2023

Review of Scientific Instruments

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In an auto-balancing bridge for high impedance measurements, an operational amplifier (Op-Amp) is used to follow the intermediate potential. However, the input impedance of the Op-Amp introduces significant effects in high impedance measurements. This paper proposes a two-step excitation method (TSEM) and an incremental iterative method (IIM). The TSEM determines the magnitude of the Op-Amp input impedance and the initial value of the device under test. The IIM utilizes the TSEM results as initial conditions to quickly bring the bridge to equilibrium. To overcome the distortion issues associated with small amplitude excitation signals generated by the DAC under low resolution conditions, a programmable gain amplifier is designed. Additionally, a half-cycle difference algorithm is developed prior to the three-parameter sine fit to mitigate low-frequency direct-current drift caused by power frequency, thus improving measurement accuracy. Experimental results demonstrate that when the reference impedance is set to 1 MΩ, impedance measurements ranging from 1 kΩ to 100 MΩ can be achieved within the frequency range of 1 to 100 kHz. The precision evaluation reveals a relative standard deviation (RSD) of the modulus better than 0.384% and a standard deviation (SD) of the phase angle better than 3.49 mrad; especially for the impedance under test of 1 MΩ, the RSD is better than 0.006% and the SD is better than 0.1 mrad.

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Classification Methods for Airborne Disease Spores from Greenhouse Crops Based on Multifeature Fusion

Liu

et al. 2020

Applied Sciences

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Airborne fungal spores have always played an important role in the spread of fungal crop diseases, causing great concern. The traditional microscopic spore classification method mainly relies on naked eye observations and classification by professional and technical personnel in a laboratory. Due to the large number of spores captured, this method is labor-intensive, time-consuming, and inefficient, and sometimes leads to huge errors. Thus, an alternative method is required. In this study, a method was proposed to identify airborne disease spores from greenhouse crops using digital image processing. First, in an indoor simulation, images of airborne disease spores from three greenhouse crops were collected using portable volumetric spore traps. Then, a series of image preprocessing methods were used to identify the spores, including mean filtering, Gaussian filtering, OTSU (maximum between-class variance) method binarization, morphological operations, and mask operations. After image preprocessing, 90 features of the spores were extracted, including color, shape, and texture features. Based on these features, logistics regression (LR), K nearest neighbor (KNN), random forest (RF), and support vector machine (SVM) classification models were built. The test results showed that the average accuracy rates for the 3 classes of disease spores using the SVM model, LR model, KNN model, and RF model were 94.36%, 90.13%, 89.37%, and 89.23%, respectively. The harmonic average of the accuracy and the recall rate value (F value) were higher for the SVM model and its overall average value reached 91.68%, which was 2.03, 3.59, and 3.96 percentage points higher than the LR model, KNN model, and RF model, respectively. Therefore, this method can effectively identify 3 classes of diseases spores and this study can provide a reference for the identification of greenhouse disease spores.

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A rapid detection method of early spore viability based on AC impedance measurement

Cited by 3 publications

References 35 publications

Automatic Detection of Rice Blast Fungus Spores by Deep Learning-Based Object Detection: Models, Benchmarks and Quantitative Analysis

Automatic Detection of Rice Blast Fungus Spores by Deep Learning-Based Object Detection: Models, Benchmarks and Quantitative Analysis

Auto-balancing bridge based wide impedance spectrum measurement with consideration of Op-Amp input impedance

Classification Methods for Airborne Disease Spores from Greenhouse Crops Based on Multifeature Fusion

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