Chlorella Algae Image Analysis Using Artificial Neural Network and Deep Learning

Lee

et al. 2019

Water

An excessive increase in algae often has various undesirable effects on drinking water supply systems, thus proper management is necessary. Algal monitoring and classification is one of the fundamental steps in the management of algal blooms. Conventional microscopic methods have been most widely used for algal classification, but such approaches are time-consuming and labor-intensive. Thus, the development of alternative methods for rapid, but reliable algal classification is essential where an advanced machine learning technique, known as deep learning, is considered to provide a possible approach for rapid algal classification. In recent years, one of the deep learning techniques, namely the convolutional neural network (CNN), has been increasingly used for image classification in various fields, including algal classification. However, previous studies on algal classification have used CNNs that were arbitrarily chosen, and did not explore possible CNNs fitting algal image data. In this paper, neural architecture search (NAS), an automatic approach for the design of artificial neural networks (ANN), is used to find a best CNN model for the classification of eight algal genera in watersheds experiencing algal blooms, including three cyanobacteria (Microcystis sp., Oscillatoria sp., and Anabaena sp.), three diatoms (Fragilaria sp., Synedra sp., and two green algae (Staurastrum sp. and Pediastrum sp.). The developed CNN model effectively classified the algal genus with an F1-score of 0.95 for the eight genera. The results indicate that the CNN models developed from NAS can outperform conventional CNN development approaches, and would be an effective tool for rapid operational responses to algal bloom events. In addition, we introduce a generic framework that provides a guideline for the development of the machine learning models for algal image analysis. Finally, we present the experimental results from the real-world environments using the framework and NAS.

Section: Cnn Modelmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Algal Morphological Identification in Watersheds for Drinking Water Supply Using Neural Architecture Search for Convolutional Neural Network

Lee

et al. 2019

Water

Arch Computat Methods Eng

“…The training dataset included 100 algae regions and 100 background regions. Lakshmi et al [ 81 ] proposed a ML based approach for algal image classification. Firstly, 400 chlorella algae images were acquired and pre-processed to remove noise using median filter.…”

Section: In Microorganisms Image Recognitionmentioning

confidence: 99%

“…= 98.63% High processing time Dannemiller et al [ 80 ] Segmentation of algae microscopic images Non-uniform background Subtraction, SVM Texture features – C = 2 Tr. = 200 – Less description about performance evaluation Lakshmi et al [ 81 ] Classification of Chlorella algae images – Texture features, deep features ANN, CNN TI = 400 Te. = 220 Acc.…”

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

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.

Concurrency and Computation

Detection of algal tiny objects based on morphological features

Yuan,

Peng,

Shi

et al. 2024

SummaryThe dense and toxic blooms formed by cyanobacteria in aquatic environments pose significant threats to public health and aquatic ecosystems. Timely monitoring and prevention of cyanobacterial blooms in freshwater bodies are thus imperative. Although object detection methods have been applied in the field of algae identification, existing research faces several challenges. A primary issue is the overly idealistic setting of training sets, which are disconnected from the actual water quality environments, impeding practical algae identification and water quality monitoring. In this paper, we collect 2024 microscopic images of algae from a reservoir in Southern China, forming a comprehensive and diverse dataset for object detection. Addressing the aforementioned challenges, we propose an attention‐based strategy for the detection of tiny algal objects, which effectively samples and extracts features from algal targets. Our model uniquely leverages the morphable characteristics of algae, enhancing the accuracy and efficiency of identification. The training and improvement results of this model presented in our study are expected to aid in establishing a future system for real‐time algae monitoring and water quality assessment.