Easy Identification Of Freshwater Algae

Easy Identification Of Freshwater Algae

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The isolation, cultivation and identification of microalgae indigenous to an environment is primarily of ecological, biotechnological and commercial interest1. BBM, a traditional chemically defined medium was used to isolate microalgae in our study. We were able to successfully grow and isolate 17 different microalgal genera in this medium. We are aware that several genera may not be easily cultivable in this medium, hence we do not claim that this study is representative of the total microalgal biodiversity in Singapore.

While some microalgae were easy to identify based on cellular morphology, coccoid forms were generally difficult to distinguish based on microscopy. In this regard, species identification using LSU rDNA sequencing was a powerful technique. One of the greatest challenges was to standardize a method that would allow for extraction of DNA from different types of algae. This is probably because cell wall compositions of microalgae vary widely and may include cellulose, pectins, hemicelluloses, arabinogalactan proteins (AGPs), extensin, lignin, β-mannans, β-xylans, complex sulfated polysaccharides and glycoproteins12. Eventually, an in-house modification of the technique by Martin-Laurent et al14 led to a freeze-sonication based extraction method that was useful in extracting all the isolates we chose to study.

Red algae (Rhodophyceae) in freshwater aquaria usually belong to the very wide-spread staghorn and black beard algae (BBA). Their colour is not red by nature, they are usually grayish. However, they turn reddish when you immerse them in alcohol. This test makes red algae very easy to identify.

From a biological point of view, these slimy, blue-green coats are no algae at all, but cyanobacteria (blue bacteria). They grow as a film on plants and decoration and are easy to identify thanks to their strong, foul smell. You can often find them in the substrate on the aquarium glass (as you can see in the photo). For more information about blue-green algae please go to this article.

One of the most obnoxious green algae belongs to the genus Cladophora (reticulated algae). It is easy to diagnose as it branches visibly, which leads to a bushy growth habit. It is harder than other filamentous algae and is not as attractive a food source for algivores than other algae species. For further information please go to this article.

9780646314082T Entwisle, J Sonneman and S Lewis1997Hardback248 pages13.2cm x 21cmFor the first time, a guide to the conspicuous algae found in the streams and lakes of Australia. Spectacular photographs and simple illustrated keys allow easy identification. Over 300 colour photographs, supplemented by elegant hand-drawings, display the beauty and diversity of more than 120 genera of freshwater algae. The easy-to-read text and computer-generated identification icons cover 96 genera in detail. This is an essential reference for naturists, students, water-managers, scientists, in fact anyone interested in the extraordinary but until now hidden world of freshwater algae.

Acorn Naturalists marine algae rubber stamps are custom made from images of marine algae as photographed in the field and then precisely replicated on durable rubber stamps. These algae stamps provide creative additions to fish printing and other art projects. Each rubber stamp measures 3 inches by 4 inches, has the algae etched into the top of the stamp for easy identification, and is mounted onto an easy-to-grip wooden base. Five beautiful marine algae stamps are available, with discount pricing for the set.

Freshwater algae can be used as indicators to monitor freshwater ecosystem condition. Algae react quickly and predictably to a broad range of pollutants. Thus they provide early signals of worsening environment. This study was carried out to develop a computer-based image processing technique to automatically detect, recognize, and identify algae genera from the divisions Bacillariophyta, Chlorophyta and Cyanobacteria in Putrajaya Lake. Literature shows that most automated analyses and identification of algae images were limited to only one type of algae. Automated identification system for tropical freshwater algae is even non-existent and this study is partly to fill this gap.

The development of the automated freshwater algae detection system involved image preprocessing, segmentation, feature extraction and classification by using Artificial neural networks (ANN). Image preprocessing was used to improve contrast and remove noise. Image segmentation using canny edge detection algorithm was then carried out on binary image to detect the algae and its boundaries. Feature extraction process was applied to extract specific feature parameters from algae image to obtain some shape and texture features of selected algae such as shape, area, perimeter, minor and major axes, and finally Fourier spectrum with principal component analysis (PCA) was applied to extract some of algae feature texture. Artificial neural network (ANN) is used to classify algae images based on the extracted features. Feed-forward multilayer perceptron network was initialized with back propagation error algorithm, and trained with extracted database features of algae image samples. System's accuracy rate was obtained by comparing the results between the manual and automated classifying methods. The developed system was able to identify 93 images of selected freshwater algae genera from a total of 100 tested images which yielded accuracy rate of 93%.

This study demonstrated application of automated algae recognition of five genera of freshwater algae. The result indicated that MLP is sufficient, and can be used for classification of freshwater algae. However for future studies, application of support vector machine (SVM) and radial basis function (RBF) should be considered for better classifying as the number of algae species studied increases.

However, most efforts for automated analysis and identification of algae images were limited to some specific type of algae division only. This is because of the difficulties in implementation of an application that can detect all types of algae division due to the variation found in algae shapes, properties, and colours. So far, only a few or limited studies exist on automated identification of tropical freshwater algae [22].

Therefore, this study is an early attempt to devise an automated recognition and classification system for several common algae. A combination of image processing with ANN approaches used to automatic detection and recognition of some selected freshwater algae genera. These algae were from the divisions of Bacillariophyta(Navicula), Chlorophyta (Scenedesmus) and Cyanobacteria (Chroococcus, Microcystis and Oscillatoria) found in tropical Putrajaya Lake. Although this lake is a mesotrophic lake, there is a need to monitor changes in its water quality as socio-economic developments take place in surrounding areas. Automated recognition and classification system for algae will be one of the several tools to be developed for monitoring algae diversity of and hence, water quality changes, the lake. This study is also an extension of previous studies by other workers who focused on certain algal taxa only.

Putrajaya Lake is a man-made freshwater lake. The lake, which covers an area of 650 ha, is located at the new capital city of Malaysia known as Putrajaya. The lake was constructed to provide a landscape feature and varied recreational activities for the city population as well as creating wildlife habitats [23]. Putrajaya Lake is warm polymictic, oligotrophic to mesotrophic, and is located at the south of the densely inhabited Klang Valley, Malaysia. Major inflows from upstream outside surrounding areas contain certain level of pollutants. Nutrient loading at the lake are mainly come from non-point sources. These include the use of agrochemicals, fertilizer, land clearing, and soil leveling at the surrounding areas. Freshwater algae images used in this work have been captured from water samples collected from different locations at Putrajaya Lake, Malaysia. Water samples were analyzed and examined by using electronic microscope Manufactured by Thermo fisher scientific company model(MTC#B1-220ASA), and freshwater algae images were transferred to digital storage devices by using a Dino-Eye Eyepiece camera Manufactured by Dutech scientific company model (AM423X) which attached to the microscope lens, and connected with personal computer via USB port for image acquisition.

Image acquisition was performed using attached camera assisted with computer software (DinoCapture 2.0), and captured image resolution was 1280 × 1024 pixels. Manual identification of algae species were carried out based on their taxonomic characteristic by Aishah [24]. The data set included three genera of Cyanobacteria, one genera of Chlorophyta, and one genus of Bacillariophyta as shown in Figure 1. 100 image samples collected to be used for each selected algae genus. The algae image samples are then classified into two groups, training group which contains 40 images for each algae genus, and testing group which contains 60 images for each algae genus. The operating system platform used in this work was Intel CORE i5 CPU, 4 GB RAM, Windows 7 professional (64 bit). Image processing and other related approaches were performed using computer software MATLAB 7.0.

Matlab 7.0 was used to develop the automated freshwater algae detection and classification prototype. Matlab 7.0 has the ability to integrate technical computing environment which is suitable for algorithm design and development. It is considered as a high-level programming language which includes a lot of functions that support image processing and classification methods. The development process of the automated prototype involves image preprocessing, segmentation, feature extraction and classification. The system architecture is shown in Figure 2. 781b155fdc