Abstract:

Spirulina is an algal microorganism that comes in four species. It is a green growth microorganism are widely used in water quality determination and monitoring. Profound learning and convolutional neural systems are turning into a broadly utilized strategy for picture classification in a number of domains. Moreover, Deep-Learning and Convolutional Neural Networks are yielding better outcomes and utilize strategy in a variety of picture classification problems. Computerized discovery of Spirulina is one of the most top research topics in the field. For this study, a broad spirulina picture dataset was specifically built. This paper presents the outcomes of a study which utilised Convolutional Neural Network technology in the spirulina discovery problem in order to ascertain whether it is appropriate to identify Spirulina. A comprehensive spirulina picture dataset was specifically devised utilizing an artificial picture augmentation technique on real pictures of spirulina gathered from waterways in Turkey. The dataset covers distinctive light conditions and it was computationally expanded from 60 original sample pictures to 1000 pictures. In this study, a customized Convolutional Neural Network configuration that settles the 4-class spirulina detection and identification problem from an extensive set of microscopic pictures are proposed. Outcomes are examined and compared with those of past investigations. We present a preliminary investigation that utilizes the Convolutional Neural Network in the problem of discovering spirulina.