A preliminary study on automated freshwater algae recognition and classification system

A freshwater algae classification system based on machine learning with StyleGAN2-ADA augmentation for limited and imbalanced datasets

Automated algae classification using machine learning is a more efficient and effective solution compared to manual classification, which can be tedious and time-consuming. However, the practical application of such a classification approach is restricted by the scarcity of labeled freshwater algae datasets, especially for rarer algae. To overcome these challenges, this study proposes to generate artificial algal images with StyleGAN2-ADA and use both the generated and real images to train machine-learning-driven algae classification models. This approach significantly enhances the performance of classification models, particularly in their ability to identify rare algae. Overall, the proposed approach improves the F1-score of lightweight MobileNetV3 classification models covering all 20 freshwater algae covered in this research from 88.4% to 96.2%, while for the models that cover only the rarer algae, the experiments show an improvement from 80% to 96.5% in terms of F1-score. The results show that the approach enables the trained algae classification systems to effectively cover algae with limited image data.

Trends in digital image processing of isolated microalgae by incorporating classification algorithm

Identification of microalgae species is of importance due to the uprising of harmful algae blooms affecting both the aquatic habitat and human health. Despite this occurence, microalgae have been identified as a green biomass and alternative source due to its promising bioactive compounds accumulation that play a significant role in many industrial applications. Recently, microalgae species identification has been conducted through DNA analysis and various microscopy techniques such as light, scanning electron, transmission electron, and atomic force -microscopy. The aforementioned procedures have encouraged researchers to consider alternate ways due to limitations such as costly validation, requiring skilled taxonomists, prolonged analysis, and low accuracy. This review highlights the potential innovations in digital microscopy with the incorporation of both hardware and software that can produce a reliable recognition, detection, enumeration, and real-time acquisition of microalgae species. Several steps such as image acquisition, processing, feature extraction, and selection are discussed, for the purpose of generating high image quality by removing unwanted artifacts and noise from the background. These steps of identification of microalgae species is performed by reliable image classification through machine learning as well as deep learning algorithms such as artificial neural networks, support vector machines, and convolutional neural networks. Overall, this review provides comprehensive insights into numerous possibilities of microalgae image identification, image pre-processing, and machine learning techniques to address the challenges in developing a robust digital classification tool for the future.

Potential for Artificial Intelligence (AI) and Machine Learning (ML) Applications in Biodiversity Conservation, Managing Forests, and Related Services in India

The recent advancement in data science coupled with the revolution in digital and satellite technology has improved the potential for artificial intelligence (AI) applications in the forestry and wildlife sectors. India shares 7% of global forest cover and is the 8th most biodiverse region in the world. However, rapid expansion of developmental projects, agriculture, and urban areas threaten the country’s rich biodiversity. Therefore, the adoption of new technologies like AI in Indian forests and biodiversity sectors can help in effective monitoring, management, and conservation of biodiversity and forest resources. We conducted a systematic search of literature related to the application of artificial intelligence (AI) and machine learning algorithms (ML) in the forestry sector and biodiversity conservation across globe and in India (using ISI Web of Science and Google Scholar). Additionally, we also collected data on AI-based startups and non-profits in forest and wildlife sectors to understand the growth and adoption of AI technology in biodiversity conservation, forest management, and related services. Here, we first provide a global overview of AI research and application in forestry and biodiversity conservation. Next, we discuss adoption challenges of AI technologies in the Indian forestry and biodiversity sectors. Overall, we find that adoption of AI technology in Indian forestry and biodiversity sectors has been slow compared to developed, and to other developing countries. However, improving access to big data related to forest and biodiversity, cloud computing, and digital and satellite technology can help improve adoption of AI technology in India. We hope that this synthesis will motivate forest officials, scientists, and conservationists in India to explore AI technology for biodiversity conservation and forest management.

A state-of-the-art survey of object detection techniques in microorganism image analysis: from classical methods to deep learning approaches

Microorganisms play a vital role in human life. Therefore, microorganism detection is of great significance to human beings. However, the traditional manual microscopic detection methods have the disadvantages of long detection cycle, low detection accuracy in large orders, and great difficulty in detecting uncommon microorganisms. Therefore, it is meaningful to apply computer image analysis technology to the field of microorganism detection. Computer image analysis can realize high-precision and high-efficiency detection of microorganisms. In this review, first,we analyse the existing microorganism detection methods in chronological order, from traditional image processing and traditional machine learning to deep learning methods. Then, we analyze and summarize these existing methods and introduce some potential methods, including visual transformers. In the end, the future development direction and challenges of microorganism detection are discussed. In general, we have summarized 142 related technical papers from 1985 to the present. This review will help researchers have a more comprehensive understanding of the development process, research status, and future trends in the field of microorganism detection and provide a reference for researchers in other fields.

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

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 analysis of microorganisms is essential for making full use of different microorganisms. The conventional analysis methods are laborious and time-consuming. Therefore, the automatic image analysis based on artificial neural networks is introduced to optimize it. However, the automatic microorganism image analysis faces many challenges, such as the requirement of a robust algorithm caused by various application occasions, insignificant features and easy under-segmentation caused by the image characteristic, and various analysis tasks. Therefore, we conduct this review to comprehensively discuss the characteristics 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 presented. 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.

High-throughput method development for in-situ quantification of aquatic phototrophic biofilms

In the maritime field where biofouling has both economic and environmental impacts, in situ quantification methods of biofilm development are of outstanding importance. Indeed, it is challenging to temporally monitor biofilm formation due to the complexity of the marine ecosystem, common inaccessibility of sampling location and lack of standardized techniques. Here, an artificial polymeric surface was tested in situ and in vitro against natural phototrophic biofilms and monoculture biofilms using plate reader fluorescence. The suitability of the developed method was verified using fluorescence microscopy coupled with image analysis - a common quantification technique - demonstrating a strong correlation between the tested methods. The results indicated the efficiency of inherent chlorophyll fluorescence in quantifying undisturbed phototrophic biofilms in field and laboratory conditions using microplate reader. This work demonstrated that the suggested approach is promising for biofilm high-throughput testing, and therefore has the potential to be used in several research and industrial sectors for monitoring phototrophic biofilm development.

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

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.

Water monitoring by means of digital microscopy identification and classification of microalgae

Marine and freshwater microalgae belong to taxonomically and morphologically diverse groups of organisms spanning many phyla with thousands of species. These organisms play an important role as indicators of water ecosystem conditions since they react quickly and predictably to a broad range of environmental stressors, thus providing early signals of dangerous changes. Traditionally, microscopic analysis has been used to identify and enumerate different types of organisms present within a given environment at a given point in time. However, this approach is both time-consuming and labor intensive, as it relies on manual processing and classification of planktonic organisms present within collected water samples. Furthermore, it requires highly skilled specialists trained to recognize and distinguish one taxa from another on the basis of often subtle morphological differences. Given these restrictions, a considerable amount of effort has been recently funneled into automating different steps of both the sampling and classification processes, making it possible to generate previously unprecedented volumes of plankton image data and obtain an essential database to analyze the composition of plankton assemblages. In this review we report state-of-the-art methods used for automated plankton classification by means of digital microscopy. The computer-microscope system hardware and the image processing techniques used for recognition and classification of planktonic organisms (segmentation, shape feature extraction, pigment signature determination and neural network grouping) will be described. An introduction and overview of the topic, its current state and indications of future directions the field is expected to take will be provided, organizing the review for both experts and researchers new to the field.

How does the Internet of Things (IoT) help in microalgae biorefinery?

Microalgae biorefinery is a platform for the conversion of microalgal biomass into a variety of value-added products, such as biofuels, bio-based chemicals, biomaterials, and bioactive substances. Commercialization and industrialization of microalgae biorefinery heavily rely on the capability and efficiency of large-scale cultivation of microalgae. Thus, there is an urgent need for novel technologies that can be used to monitor, automatically control, and precisely predict microalgae production. In light of this, innovative applications of the Internet of things (IoT) technologies in microalgae biorefinery have attracted tremendous research efforts. IoT has potential applications in a microalgae biorefinery for the automatic control of microalgae cultivation, monitoring and manipulation of microalgal cultivation parameters, optimization of microalgae productivity, identification of toxic algae species, screening of target microalgae species, classification of microalgae species, and viability detection of microalgal cells. In this critical review, cutting-edge IoT technologies that could be adopted to microalgae biorefinery in the upstream and downstream processing are described comprehensively. The current advances of the integration of IoT with microalgae biorefinery are presented. What this review discussed includes automation, sensors, lab-on-chip, and machine learning, which are the main constituent elements and advanced technologies of IoT. Specifically, future research directions are discussed with special emphasis on the development of sensors, the application of microfluidic technology, robotized microalgae, high-throughput platforms, deep learning, and other innovative techniques. This review could contribute greatly to the novelty and relevance in the field of IoT-based microalgae biorefinery to develop smarter, safer, cleaner, greener, and economically efficient techniques for exhaustive energy recovery during the biorefinery process.

An Improvised Machine Learning Model Based on Mutual Information Feature Selection Approach for Microbes Classification

The accurate classification of microbes is critical in today's context for monitoring the ecological balance of a habitat. Hence, in this research work, a novel method to automate the process of identifying microorganisms has been implemented. To extract the bodies of microorganisms accurately, a generalized segmentation mechanism which consists of a combination of convolution filter (Kirsch) and a variance-based pixel clustering algorithm (Otsu) is proposed. With exhaustive corroboration, a set of twenty-five features were identified to map the characteristics and morphology for all kinds of microbes. Multiple techniques for feature selection were tested and it was found that mutual information (MI)-based models gave the best performance. Exhaustive hyperparameter tuning of multilayer layer perceptron (MLP), k-nearest neighbors (KNN), quadratic discriminant analysis (QDA), logistic regression (LR), and support vector machine (SVM) was done. It was found that SVM radial required further improvisation to attain a maximum possible level of accuracy. Comparative analysis between SVM and improvised SVM (ISVM) through a 10-fold cross validation method ultimately showed that ISVM resulted in a 2% higher performance in terms of accuracy (98.2%), precision (98.2%), recall (98.1%), and F1 score (98.1%).

A Cost-Effective In Situ Zooplankton Monitoring System Based on Novel Illumination Optimization

A cost-effective and low-power-consumption underwater microscopic imaging system was developed to capture high-resolution zooplankton images in real-time. In this work, dark-field imaging was adopted to reduce backscattering and background noise. To produce an accurate illumination, a novel illumination optimization scheme for the light-emitting diode (LED) array was proposed and applied to design a lighting system for the underwater optical imaging of zooplankton. A multiple objective genetic algorithm was utilized to find the best location of the LED array, which resulted in the specific illumination level and most homogeneous irradiance in the target area. The zooplankton imaging system developed with the optimal configuration of LEDs was tested with Daphnia magna under laboratory conditions. The maximal field of view was 16 mm × 13 mm and the optical resolution was 15 μm. The experimental results showed that the imaging system developed could capture high-resolution and high-definition images of Daphnia. Subsequently, Daphnia individuals were accurately segmented and their geometrical characters were measured by using a classical image processing algorithm. This work provides a cost-effective zooplankton measuring system based on an optimization illumination configuration of an LED array, which has a great potential for minimizing the investment and operating costs associated with long-term in situ monitoring of the physiological state and population conditions of zooplankton.

Region Growing for Segmenting Green Microalgae Images

We describe a specialized methodology for segmenting 2D microscopy digital images of freshwater green microalgae. The goal is to obtain representative algae shapes to extract morphological features to be employed in a posterior step of taxonomical classification of the species. The proposed methodology relies on the seeded region growing principle and on a fine-tuned filtering preprocessing stage to smooth the input image. A contrast enhancement process then takes place to highlight algae regions on a binary pre-segmentation image. This binary image is also employed to determine where to place the seed points and to estimate the statistical probability distributions that characterize the target regions, i.e., the algae areas and the background, respectively. These preliminary stages produce the required information to set the homogeneity criterion for region growing. We evaluate the proposed methodology by comparing its resulting segmentations with a set of corresponding ground-truth segmentations (provided by an expert biologist) and also with segmentations obtained with existing strategies. The experimental results show that our solution achieves highly accurate segmentation rates with greater efficiency, as compared with the performance of standard segmentation approaches and with an alternative previous solution, based on level-sets, also specialized to handle this particular problem.

Automatic plankton image classification combining multiple view features via multiple kernel learning

BACKGROUND

Plankton, including phytoplankton and zooplankton, are the main source of food for organisms in the ocean and form the base of marine food chain. As the fundamental components of marine ecosystems, plankton is very sensitive to environment changes, and the study of plankton abundance and distribution is crucial, in order to understand environment changes and protect marine ecosystems. This study was carried out to develop an extensive applicable plankton classification system with high accuracy for the increasing number of various imaging devices. Literature shows that most plankton image classification systems were limited to only one specific imaging device and a relatively narrow taxonomic scope. The real practical system for automatic plankton classification is even non-existent and this study is partly to fill this gap.

RESULTS

Inspired by the analysis of literature and development of technology, we focused on the requirements of practical application and proposed an automatic system for plankton image classification combining multiple view features via multiple kernel learning (MKL). For one thing, in order to describe the biomorphic characteristics of plankton more completely and comprehensively, we combined general features with robust features, especially by adding features like Inner-Distance Shape Context for morphological representation. For another, we divided all the features into different types from multiple views and feed them to multiple classifiers instead of only one by combining different kernel matrices computed from different types of features optimally via multiple kernel learning. Moreover, we also applied feature selection method to choose the optimal feature subsets from redundant features for satisfying different datasets from different imaging devices. We implemented our proposed classification system on three different datasets across more than 20 categories from phytoplankton to zooplankton. The experimental results validated that our system outperforms state-of-the-art plankton image classification systems in terms of accuracy and robustness.

CONCLUSIONS

This study demonstrated automatic plankton image classification system combining multiple view features using multiple kernel learning. The results indicated that multiple view features combined by NLMKL using three kernel functions (linear, polynomial and Gaussian kernel functions) can describe and use information of features better so that achieve a higher classification accuracy.

A Multi-Platform Optical Sensor for In Vivo and In Vitro Algae Classification

Differentiation among major algal groups is important for the ecological and biogeochemical characterization of water bodies, and for practical management of water resources. It helps to discern the taxonomic groups that are beneficial to aquatic life from the organisms causing harmful algal blooms. An LED-induced fluorescence (LEDIF) instrument capable of fluorescence, absorbance, and scattering measurements; is used for in vivo and in vitro identification and quantification of four algal groups found in freshwater and marine environments. Aqueous solutions of individual and mixed dissolved biological pigments relevant to different algal groups were measured to demonstrate the LEDIF’s capabilities in measuring extracted pigments. Different genera of algae were cultivated and the cell counts of the samples were quantified with a hemacytometer and/or cellometer. Dry weight of different algae cells was also measured to determine the cell counts-to-dry weight correlations. Finally, in vivo measurements of different genus of algae at different cell concentrations and mixed algal group in the presence of humic acid were performed with the LEDIF. A field sample from a local reservoir was measured with the LEDIF and the results were verified using hemacytometer, cellometer, and microscope. The results demonstrated the LEDIF’s capabilities in classifying and quantifying different groups of live algae.

Automated identification of copepods using digital image processing and artificial neural network

Background

Copepods are planktonic organisms that play a major role in the marine food chain. Studying the community structure and abundance of copepods in relation to the environment is essential to evaluate their contribution to mangrove trophodynamics and coastal fisheries. The routine identification of copepods can be very technical, requiring taxonomic expertise, experience and much effort which can be very time-consuming. Hence, there is an urgent need to introduce novel methods and approaches to automate identification and classification of copepod specimens. This study aims to apply digital image processing and machine learning methods to build an automated identification and classification technique.

Results

We developed an automated technique to extract morphological features of copepods' specimen from captured images using digital image processing techniques. An Artificial Neural Network (ANN) was used to classify the copepod specimens from species Acartia spinicauda, Bestiolina similis, Oithona aruensis, Oithona dissimilis, Oithona simplex, Parvocalanus crassirostris, Tortanus barbatus and Tortanus forcipatus based on the extracted features. 60% of the dataset was used for a two-layer feed-forward network training and the remaining 40% was used as testing dataset for system evaluation. Our approach demonstrated an overall classification accuracy of 93.13% (100% for A. spinicauda, B. similis and O. aruensis, 95% for T. barbatus, 90% for O. dissimilis and P. crassirostris, 85% for O. similis and T. forcipatus).

Conclusions

The methods presented in this study enable fast classification of copepods to the species level. Future studies should include more classes in the model, improving the selection of features, and reducing the time to capture the copepod images.

Water monitoring: automated and real time identification and classification of algae using digital microscopy

Microalgae are unicellular photoautotrophs that grow in any habitat from fresh and saline water bodies, to hot springs and ice. Microalgae can be used as indicators to monitor water ecosystem conditions. These organisms react quickly and predictably to a broad range of environmental stressors, thus providing early signals of a changing environment. When grown extensively, microalgae may produce harmful effects on marine or freshwater ecology and fishery resources. Rapid and accurate recognition and classification of microalgae is one of the most important issues in water resource management. In this paper, a methodology for automatic and real time identification and enumeration of microalgae by means of image analysis is presented. The methodology is based on segmentation, shape feature extraction, pigment signature determination and neural network grouping; it attained 98.6% accuracy from a set of 53,869 images of 23 different microalgae representing the major algal phyla. In our opinion this methodology partly overcomes the lack of automated identification systems and is on the forefront of developing a computer-based image processing technique to automatically detect, recognize, identify and enumerate microalgae genera and species from all the divisions. This methodology could be useful for an appropriate and effective water resource management.

Automatic identification of algal community from microscopic images

A good understanding of the population dynamics of algal communities is crucial in several ecological and pollution studies of freshwater and oceanic systems. This paper reviews the subsequent introduction to the automatic identification of the algal communities using image processing techniques from microscope images. The diverse techniques of image preprocessing, segmentation, feature extraction and recognition are considered one by one and their parameters are summarized. Automatic identification and classification of algal community are very difficult due to various factors such as change in size and shape with climatic changes, various growth periods, and the presence of other microbes. Therefore, the significance, uniqueness, and various approaches are discussed and the analyses in image processing methods are evaluated. Algal identification and associated problems in water organisms have been projected as challenges in image processing application. Various image processing approaches based on textures, shapes, and an object boundary, as well as some segmentation methods like, edge detection and color segmentations, are highlighted. Finally, artificial neural networks and some machine learning algorithms were used to classify and identifying the algae. Further, some of the benefits and drawbacks of schemes are examined.

InCoB2012 Conference: from biological data to knowledge to technological breakthroughs

Ten years ago when Asia-Pacific Bioinformatics Network held the first International Conference on Bioinformatics (InCoB) in Bangkok its theme was North-South Networking. At that time InCoB aimed to provide biologists and bioinformatics researchers in the Asia-Pacific region a forum to meet, interact with, and disseminate knowledge about the burgeoning field of bioinformatics. Meanwhile InCoB has evolved into a major regional bioinformatics conference that attracts not only talented and established scientists from the region but increasingly also from East Asia, North America and Europe. Since 2006 InCoB yielded 114 articles in BMC Bioinformatics supplement issues that have been cited nearly 1,000 times to date. In part, these developments reflect the success of bioinformatics education and continuous efforts to integrate and utilize bioinformatics in biotechnology and biosciences in the Asia-Pacific region. A cross-section of research leading from biological data to knowledge and to technological applications, the InCoB2012 theme, is introduced in this editorial. Other highlights included sessions organized by the Pan-Asian Pacific Genome Initiative and a Machine Learning in Immunology competition. InCoB2013 is scheduled for September 18-21, 2013 at Suzhou, China.