Phytoplankton, also known as microalgae is an important bio-indicator for water quality. Water quality is of great concern to administrative authorities as it not only affects human health but also have other negative environmental impact. Conventional laboratory analyses of water quality which includes classification and analyses of phytoplankton are time consuming and requires special attention to quality control. Schulze et.al., have developed an automated system for the analysis of phytoplankton.
Phytoplankton communities give more information on changes in water quality than mere nutrient concentrations. The amount of phytoplankton species in the water bodies informs about the health of water ways. Species of plankton present in the water bodies are also important. Surplus availability of nutrients makes phytoplankton growth out of control and produces toxic compounds which have harmful effects on human health and other high order consumers. Detailed directives and guidelines are available with the concern local regulatory and governing authorities to analyse the presence of phytoplankton so as to monitor the quality of water. In order to manage water quality we need a broader understanding aboutplanktons and their interaction with environment. The Ecology of Freshwater Phytoplankton by C.S.Reynolds and Ecology of Harmful Algae edited by Granéli & Turner may be the suitable reference books for detail study on this subject. Plankton: A Guide to their Ecology and Monitoring for Water Quality by Lain Macleod Suthers may be used as a practical guide for monitoring water quality with respect to phytoplankton. It involves lot of practical works and one to one classification of phytoplankton. Conventionally it is carried manually with microscope and hand. These manual methods are non-reproducible and take lot of time to come-up with final solution. Automation of phytoplankton analysis can overcome this problem.
Schulze et.al. have developed a novel system and named it 'PlanktoVision'. The basic principle behind this is using automated microscopy and image analysis software for the automated identification of phytoplankton for monitoring freshwater quality. They developed 'PlanktoVision' specifically to improve water quality analysis. They have adopted some of the features already available with similar system named 'PLASA (PLAnkton Structure Analysis)'. Additionally they have used a new method, where different focal levels are integrated into one image during the microscopy (Quick Full Focus images). The image analysis software was programmed as a plug-in for ImageJ, which is a free and open source project written in Java. This allows the use of the software on almost any operating system without costs. Since the code for the plug-in is also licensed as free software anyone can adapt and expand the system.
|Figure Schulze et.al., 2013|
Major protocols involved with 'PlanktoVision' are (Figure) 1. Automated image acquisition 2. Image processing. Image processing includes i) Adaptation of ImageJ ii) Segmentation iii) Selected features & classification. Strains preservation and fixation followed by sedimentation are the prerequisite to automated acquisition of images. For the automated classification they examined the use of neural networks. Neural networks consist of artificial neurons, resembling the properties of biological neurons. These neurons are equations connected in different layers and allow complex classification tasks. Complete methods and other details can be accessed from the original article published in the journal 'BMC Bioinformatics'. The ImageJ plugins and other instructions to download the software along with the data set supporting the results of Schulze et.al. are available on https://github.com/KatjaSchulze/PlanktoVision. Once the softwares are downloaded and installed the usage part of the plugin can be found under the sub menu 'Plugins>PlanktoVision' and is divided into the five parts PVsettings, PVsegment, PVtraining, PVtest & PVanalysis.
Though the current version have some limitations, further specification, modification and elaboration will make 'PlanktoVision' one of the useful as well as open resource for phytoplankton analysis.