Automated System for Identifying and Recognizing Rotifer Contamination in Spirulina

The blue green microalgae Spirulina platensis is an important source of nutrients. An important obstacle for the production of Spirulina is Rotifer. In this paper we present a method for automated identification and recognition of rotifer contamination in Spirulina using image processing techniques. Getting the expert's advice is an essential one to improve the production. Our primary objective is to provide high nutrients Spirulina free from contaminations and secondary objective is to solve the quality issue by identifying rotifer contaminations at the earliest and giving suggestions through the mobile phones. Using microscopic examination for identifying rotifier in live culture is time consuming and manmade process. There is a chance for man made error and our new technique is machine made and the errors to be minimized. Existing system need qualified persons for operation and our proposed system can be operated by anyone. We examine lab scale Spirulina culture through both existing and new techniques for recognizing the rotifer. Our proposed tool for identifying contaminations at beginning stage is easy to eradicate the contaminations from live culture. It will be helpful to biologist to take necessary actions against contaminations of the same before further multiplications of contaminations. According to the Survey of Sun food Super Food, in 2020 the expected world wide Spirulina production is about 220 thousand tons. Hence Spirulina can be a solution for solving the world nutrition problems due to its very high growth rate and high nutritional value. Our research-automatic identification of contamination in algae through the processing of mobile phone images is an easiest way for improving and evaluating the growth of Spirulina for effective cultivation of algae.