Algae is an aquatic organism of an enormous and diverse group, which has the ability to conduct photosynthesis. The various sorts of microalgae play trivial roles in marine and fresh water environment. Microalgae are of various sizes and shapes, ranging from unicellular to multicellular forms. These algae were from the division of Anabaena, Oscillatoria, Microcystis Scenedesmus, Pediastrum and Cosmarium found in fresh water lake. In very high density these microalgae may discolor the water, outcompete, and become poisonous to other life forms. This is technically termed as harmful algal blooms. It is one of the most serious water pollution problems. Today, humans in many ways to use microalgae’s for example, as fertilizers, soil conditioners, and livestock feed. A hybrid method is apply to automatic detection and recognition of some selected freshwater algae genera by combining the image processing technique with ANN approaches. Thus, analysis and prediction of algae is significant, which can be achieved using machine learning processing.

Recently, diatoms, a type of algae microorganism with numerous species, are relatively helpful for water quality determination, and is treated as an important topic in applied biology nowadays. Simultaneously, deep learning (DL) also becomes an important model applied for various image classification problems. This study introduces a new Inception model for diatom image classification. The presented model involves two main stages namely segmentation and classification. Here, a deep learning based Inception model is employed for classification purposes. To further improve the classifier efficiency, edge detection based segmentation model is also applied where the segmented input is provided as an input to the classifier stage. An experimental validation takes place on diverse set of diatom dataset with various preprocessing models. The results pointed out that the presented DL model shows extraordinary classification performance with a classifier accuracy of 99%.

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Diatoms act an essential contributor to the fundamental creation in aquatic ecosystem, which is positioned at the foundation of the food chain. Presently, the diatoms appear as a most important topic over the globe in studies interrelated to weather changes, and in the design of functions which enables to model of those variations. In addition, it is an efficient indicator of ecological conditions and is widely employed in water quality assessment. In a similar way, deep learning model is a widely employed technique for classifying images among diverse applications. In this paper, an optimal segmentation and classification model for diatom images particularly species images. Here, edge detection based segmentation model is employed for segmenting the images and then Inception model is utilized for classifying images. A detailed simulation process takes place on the benchmark diatom images. An overall accuracy of 99 is attained by the presented model on the applied set of test images. The outcome is compared to the state of art classification models and the results exhibited the superior performance of the presented model.