To accelerate the monitoring and counting of biodiversity of various species, there is a need for automating the process of computing biodiversity. The calculations of the alpha and beta biodiversity indexes are fundamental for the analysis of ecological and biodiversity studies. Sukhna and Dhanas lakes, India are critical for the maintenance of the health of the residents and aquatic life thriving in them. Both lakes are prone to pollution. Due to these factors, there is a need for building digitized infrastructure for monitoring the health of these lakes. Hence in this research work, an automated algorithm has been devised for the computation of biodiversity of microorganisms. The work focuses on the surface water of both these lakes. The automation of biodiversity computation is done with image processing algorithms and is applied to the primary data collected. From this study, it is apparent that the counting of microorganisms using image processing algorithms is an easier and efficient way for biodiversity studies as compared to the manual process of estimating the population of microbes. The results show that the species richness of Dhanas Lake is more as compared to Sukhna Lake. The dissimilarity between the two lakes is five species as per the primary data collected. This shows that the biodiversity of Dhanas Lake is better than the Sukhna Lake but it is prone to harmful algal blooms. This may be attributed to the fact that Dhanas Lake may have multiple sources of pollution that need to be identified.
Biodiversity Index, Biodiversity Monitoring, Image Processing, Computational Environment Sciences, Algae, Fungi
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