Computerized classification system for the identification of soil microorganisms

Kruk, Michał; Kozera, Ryszard; Osowski, S.; Trzciński, Paweł; Sas-Paszt, Lidia; Sumorok, Beata; Borkowski, Bolesław

doi:10.1063/1.4912894

Cited by 7 publications

(15 citation statements)

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“…With the development of technology, good results are achieved using computer-aided EM classification. In Kruk et al ( 2015 ), a system for automatic identification of different species of microorganisms in soil is proposed. The system first separates microorganisms from the background using the Otsu.…”

Section: Related Workmentioning

confidence: 99%

A Comparative Study of Deep Learning Classification Methods on a Small Environmental Microorganism Image Dataset (EMDS-6): From Convolutional Neural Networks to Visual Transformers

Zhao

Rahaman

et al. 2022

Front. Microbiol.

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In recent years, deep learning has made brilliant achievements in Environmental Microorganism (EM) image classification. However, image classification of small EM datasets has still not obtained good research results. Therefore, researchers need to spend a lot of time searching for models with good classification performance and suitable for the current equipment working environment. To provide reliable references for researchers, we conduct a series of comparison experiments on 21 deep learning models. The experiment includes direct classification, imbalanced training, and hyper-parameters tuning experiments. During the experiments, we find complementarities among the 21 models, which is the basis for feature fusion related experiments. We also find that the data augmentation method of geometric deformation is difficult to improve the performance of VTs (ViT, DeiT, BotNet, and T2T-ViT) series models. In terms of model performance, Xception has the best classification performance, the vision transformer (ViT) model consumes the least time for training, and the ShuffleNet-V2 model has the least number of parameters.

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Section: Related Workmentioning

confidence: 99%

A Comparative Study of Deep Learning Classification Methods on a Small Environmental Microorganism Image Dataset (EMDS-6): From Convolutional Neural Networks to Visual Transformers

Zhao

Rahaman

et al. 2022

Front. Microbiol.

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“…For instance, in the [5], the Fourier descriptors are used as features for the classification of TB-bacilli from sputum smear using multilayer perceptron neural network, and it achieves outstanding results of 92.5%. In [6], random forest (RF) and SVM are used for the classification of soil microorganism using potential colorimetric features and histogram features, where SVM achieves an accuracy of 98.19% and RF achieves 98.41%.…”

Section: Related Workmentioning

confidence: 99%

A new pairwise deep learning feature for environmental microorganism image analysis

Kulwa

Zhang

et al. 2022

Environ Sci Pollut Res

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Environmental microorganism (EM) offers a high-efficient, harmless, and low-cost solution to environmental pollution. They are used in sanitation, monitoring, and decomposition of environmental pollutants. However, this depends on the proper identification of suitable microorganisms. In order to fasten, low the cost, increase consistency and accuracy of identification, we propose the novel pairwise deep learning features to analyze microorganisms. The pairwise deep learning features technique combines the capability of handcrafted and deep learning features. In this technique we, leverage the Shi and Tomasi interest points by extracting deep learning features from patches which are centered at interest points' locations. Then, to increase the number of potential features that have intermediate spatial characteristics between nearby interest points, we use Delaunay triangulation theorem and straight-line geometric theorem to pair the nearby deep learning features. The potential of pairwise features is justified on the classification of EMs

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“…The identification of soil microorganisms is of great significance for agricultural production. In [69], a method based on digital image processing is introduced to identify soil microorganisms. As the workflow shown in Fig.…”

Section: Classification Tasksmentioning

confidence: 99%

“…In this method, the active contour method is applied to segment TB objects, and Fig. 24 The proposed system of recognition of the microorganism classes in [69].…”

Section: Classification Tasksmentioning

confidence: 99%

Applications of Artificial Neural Networks in Microorganism Image Analysis: A Comprehensive Review from Conventional Multilayer Perceptron to Popular Convolutional Neural Network and Potential Visual Transformer

Zhang¹,

Li²,

Yin³

et al. 2021

Preprint

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Microorganisms are widely distributed in the human daily living environment. They play an essential role in environmental pollution control, disease prevention and treatment, and food and drug production. The identification, counting, and detection are the basic steps for making full use of different microorganisms. However, the conventional analysis methods are expensive, laborious, and time-consuming. To overcome these limitations, artificial neural networks are applied for microorganism image analysis. We conduct this review to understand the development process of microorganism image analysis based on artificial neural networks. In this review, the background and motivation are introduced first. Then, the development of artificial neural networks and representative networks are introduced. After that, the papers related to microorganism image analysis based on classical and deep neural networks are reviewed from the perspectives of different tasks. In the end, the methodology analysis and potential direction are discussed.

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Computerized classification system for the identification of soil microorganisms

Cited by 7 publications

References 0 publications

A Comparative Study of Deep Learning Classification Methods on a Small Environmental Microorganism Image Dataset (EMDS-6): From Convolutional Neural Networks to Visual Transformers

A Comparative Study of Deep Learning Classification Methods on a Small Environmental Microorganism Image Dataset (EMDS-6): From Convolutional Neural Networks to Visual Transformers

A new pairwise deep learning feature for environmental microorganism image analysis

Applications of Artificial Neural Networks in Microorganism Image Analysis: A Comprehensive Review from Conventional Multilayer Perceptron to Popular Convolutional Neural Network and Potential Visual Transformer

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