Pebrine diagnosis using quantitative phase imaging and machine learning

Prasobhkumar, P.P.; Venukumar, Aravind; Francis, C.R.; Gorthi, Sai Siva

doi:10.1002/jbio.202100044

Cited by 5 publications

(1 citation statement)

References 37 publications

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“…By observing the light and dark areas that appear on the screen, the distribution of light-intensity can reflect the imaging information of the object [25]. Polarization imaging technology can detect the polarization information of the object surface, and compared with ordinary optical images, the brightness and contrast of the object and the background in the polarization image are relatively enhanced [26,27]. Therefore, this study combined the characteristics of diffraction imaging and polarization imaging to build a fungal spore diffraction-polarization fingerprint image acquisition system (Figure 1).…”

Section: Diffraction-polarization Fingerprint Image Acquisition Systemmentioning

confidence: 99%

Detection Method of Fungal Spores Based on Fingerprint Characteristics of Diffraction–Polarization Images

Wang,

Zhang,

Taha

et al. 2023

JoF

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The most significant aspect of promoting greenhouse productivity is the timely monitoring of disease spores and applying proactive control measures. This paper introduces a method to classify spores of airborne disease in greenhouse crops by using fingerprint characteristics of diffraction–polarized images and machine learning. Initially, a diffraction–polarization imaging system was established, and the diffraction fingerprint images of disease spores were taken in polarization directions of 0°, 45°, 90° and 135°. Subsequently, the diffraction–polarization images were processed, wherein the fingerprint features of the spore diffraction–polarization images were extracted. Finally, a support vector machine (SVM) classification algorithm was used to classify the disease spores. The study’s results indicate that the diffraction–polarization imaging system can capture images of disease spores. Different spores all have their own unique diffraction–polarization fingerprint characteristics. The identification rates of tomato gray mold spores, cucumber downy mold spores and cucumber powdery mildew spores were 96.02%, 94.94% and 96.57%, respectively. The average identification rate of spores was 95.85%. This study can provide a research basis for the identification and classification of disease spores.

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Section: Diffraction-polarization Fingerprint Image Acquisition Systemmentioning

confidence: 99%

Detection Method of Fungal Spores Based on Fingerprint Characteristics of Diffraction–Polarization Images

Wang,

Zhang,

Taha

et al. 2023

JoF

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A Review of Silk Farming Automation Using Artificial Intelligence, Machine Learning, and Cloud-Based Solutions

Raju,

Sarkar,

Canamedi

et al. 2024

Lecture Notes in Networks and Systems

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Maximum Gradient Autofocus Technology of Microsporidia Images Based on Color Feature

Xiong

Bai³

et al. 2023

Int. J. Patt. Recogn. Artif. Intell.

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There are many impurities in the microscopic images of extracted microsporidia samples of Bombyx mori pebrine, and Bombyx mori pebrine with elliptical symmetric shape has certain fluidity and obvious stratification. Traditional focusing methods cannot accurately locate the main regions of microsporidia images, and the focusing effect is poor. On this basis, an automatic focusing method combining the microsporidia image features and the evaluation and determination of maximum gradient direction is proposed. First, the HSV color space with stable color information is used to extract the suspected positions of microsporidia targets, so that the interference of some impurities under complex backgrounds is removed and the redundancy of image content calculation is reduced. Then, combined with the light green features of Bombyx mori pebrine, the G-component gray image of microsporidia in the RGB color space is used to extract the significant gradient region. A dynamic focus window is constructed to accurately locate the target region and reduce the influence of microsporidia flow on the focus evaluation function and the bimodal interference caused by impurities. Finally, the maximum second-order difference is obtained through the four-dimensional gradient distribution, and the focus sharpness evaluation function is formulated to adapt to the microsporidia shape and improve the sensitivity of the focus function. The experiments show that under the dynamic window of microsporidia color gradient of different samples, the sharpness ratio and the highest sensitivity factor of the focus evaluation function proposed in this paper can reach 0.06341 and 0.95, respectively. It can meet the accurate and sensitive autofocus of microscopic images of color microsporidia samples under complex backgrounds.

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Pebrine diagnosis using quantitative phase imaging and machine learning

Cited by 5 publications

References 37 publications

Detection Method of Fungal Spores Based on Fingerprint Characteristics of Diffraction–Polarization Images

Detection Method of Fungal Spores Based on Fingerprint Characteristics of Diffraction–Polarization Images

A Review of Silk Farming Automation Using Artificial Intelligence, Machine Learning, and Cloud-Based Solutions

Maximum Gradient Autofocus Technology of Microsporidia Images Based on Color Feature

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