Automatic identification of Scenedesmus polymorphic microalgae from microscopic images

Giraldo-Zuluaga, Jhony-Heriberto; Salazar, Augusto; Díez, German; Gomez, Alexander; Martínez, Tatiana; Vargas, Jonathan; Peñuela, Mariana

doi:10.1007/s10044-017-0662-3

Cited by 32 publications

(12 citation statements)

References 27 publications

(26 reference statements)

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“…The effectiveness of each algorithm was verified by testing in a real-world environment. Jhony−Heriberto Giraldo −Zuluaga et al utilized a digital microscope to take images of the microalgae and obtained the microalgae species through the image process (Giraldo-Zuluaga et al, 2016). Images were characterized by statistical features, which were derived from the calculation and analysis of texture features.…”

Section: Microalgae Detection and Classification With Machine Learningmentioning

confidence: 99%

Machine learning for microalgae detection and utilization

Ning

Zhou

2022

Front. Mar. Sci.

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Microalgae are essential parts of marine ecology, and they play a key role in species balance. Microalgae also have significant economic value. However, microalgae are too tiny, and there are many different kinds of microalgae in a single drop of seawater. It is challenging to identify microalgae species and monitor microalgae changes. Machine learning techniques have achieved massive success in object recognition and classification, and have attracted a wide range of attention. Many researchers have introduced machine learning algorithms into microalgae applications, and similarly significant effects are gained. The paper summarizes recent advances based on various machine learning algorithms in microalgae applications, such as microalgae classification, bioenergy generation from microalgae, environment purification with microalgae, and microalgae growth monitor. Finally, we prospect development of machine learning algorithms in microalgae treatment in the future.

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Section: Microalgae Detection and Classification With Machine Learningmentioning

confidence: 99%

Machine learning for microalgae detection and utilization

Ning

Zhou

2022

Front. Mar. Sci.

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“…Experimental results predicted better classification accuracy for CNN. Giraldo_Zuluaga et al [ 79 ] proposed an automatic identification approach for Scenedesmus algae in microscopic images. The dataset consisted of Scenedesmus coenobia images belonging to four classes; coenobia with one cell, coenobia with 2 cells, coenobia with 4 cells and coenobia with 8 cells.…”

Section: In Microorganisms Image Recognitionmentioning

confidence: 99%

“…= 93.60% Imbalanced dataset Qiu et al [ 76 ] Image segmentation of Chaetoceros SVM, threshold method, grey Surface direction angle model Pixel-level features – C = 2 – Chaetoceros contour was not completely filled in segmentation results Correa et al [ 77 ] Classification of microalgae species using imbalanced dataset – Feature extraction using FlowCam SVM, K-NN, ANN, naïve bayes C = 19 TI = 24,302 Kappa = 0.981 F1-score = 0.982 Limited features were extracted using Flowcam Medina et al [ 78 ] Detection of algae in underwater pipeline – Shape, Texture and deep features CNN and MLP (ANN) C = 2 TI = 41,992 Acc. = 99.4% Incomplete details about quantity of dataset classes Giraldo-Zuluaga et al [ 79 ] Identification of Scenedesmus Microalgae in microscopic images Threshold method Shape and Texture features SVM, ANN C = 4 TI = 1680 Acc. = 98.63% High processing time Dannemiller et al [ 80 ] Segmentation of algae microscopic images Non-uniform background Subtraction, SVM Texture features – C = 2 Tr.…”

Section: In Microorganisms Image Recognitionmentioning

confidence: 99%

Machine Learning and Deep Learning Based Computational Approaches in Automatic Microorganisms Image Recognition: Methodologies, Challenges, and Developments

Rani

Kotwal

Manhas

et al. 2021

Arch Computat Methods Eng

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Microorganisms or microbes comprise majority of the diversity on earth and are extremely important to human life. They are also integral to processes in the ecosystem. The process of their recognition is highly tedious, but very much essential in microbiology to carry out different experimentation. To overcome certain challenges, machine learning techniques assist microbiologists in automating the entire process. This paper presents a systematic review of research done using machine learning (ML) and deep leaning techniques in image recognition of different microorganisms. This review investigates certain research questions to analyze the studies concerning image pre-processing, feature extraction, classification techniques, evaluation measures, methodological limitations and technical development over a period of time. In addition to this, this paper also addresses the certain challenges faced by researchers in this field. Total of 100 research publications in the chronological order of their appearance have been considered for the time period 1995–2021. This review will be extremely beneficial to the researchers due to the detailed analysis of different methodologies and comprehensive overview of effectiveness of different ML techniques being applied in microorganism image recognition field.

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“…Early work on the applications of machine learning in algae classification can date back from 1992 [4], in which, a feedforward neural network was trained using OPA (Optical Plankton Analyser) features of 8 algae, achieving an average classification accuracy of over 90%, without graphic features. Giraldo-Zuluaga et al [8] proposed to use the segmentation algorithm to filter, orient, and subsequently extract the micro-algae profiles from microscopic images for micro-algae identification, which reported an accuracy of 98.6% with Support Vector Machine (SVM) and 97.3% with Artificial Neural Network (ANN) with 2 hidden layers, respectively. Li et al features or extracted features from image processing, which lack generality in different environments.…”

Section: Related Workmentioning

confidence: 99%

Multi-Target Deep Learning for Algal Detection and Classification

Qian

Zhao

Liu³

et al. 2020

2020 42nd Annual International Conference of the IEEE Engineering in Medicine &Amp; Biology Society (EMBC)

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Water quality has a direct impact on industry, agriculture, and public health. Algae species are common indicators of water quality. It is because algal communities are sensitive to changes in their habitats, giving valuable knowledge on variations in water quality. However, water quality analysis requires professional inspection of algal detection and classification under microscopes, which is very time-consuming and tedious. In this paper, we propose a novel multi-target deep learning framework for algal detection and classification. Extensive experiments were carried out on a large-scale colored microscopic algal dataset. Experimental results demonstrate that the proposed method leads to the promising performance on algal detection, class identification and genus identification.Clinical relevanceTo the best of our knowledge, we are the first in the AI community to apply deep learning to detect and classify algae from large-scale colored microscopic images. The method can be easily extended and implemented for detection and classification of biological cells and tissues.

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Automatic identification of Scenedesmus polymorphic microalgae from microscopic images

Cited by 32 publications

References 27 publications

Machine learning for microalgae detection and utilization

Machine learning for microalgae detection and utilization

Machine Learning and Deep Learning Based Computational Approaches in Automatic Microorganisms Image Recognition: Methodologies, Challenges, and Developments

Multi-Target Deep Learning for Algal Detection and Classification

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